| paper_id | francis2019stakesscalesskepticism |
|---|---|
| short_label | Francis et al. (2019) |
| citation | Francis, K. B., Beaman, C. P., & Hansen, N. (2019). Stakes, Scales, and Skepticism. Ergo, 6(16). |
| doi | 10.3998/ergo.12405314.0006.016 |
| year | 2019 |
| published | Yes |
| publication_language | English |
| publication_language_other | — |
| research_objective | Investigate the scalarity of stakes effects on knowledge judgments by varying stakes magnitude across multiple scenarios using evidence-fixed and evidence-seeking paradigms, and replicate Sripada & Stanley (2012). |
| data_available_online | Yes |
| data_url | https://researchdata.reading.ac.uk/205/ |
| notes | Effect sizes were recovered from the public raw data; analysis/effect_sizes.qmd is the source of truth for computed d and v values. For evidence-seeking effects, YAML stores raw low-minus-high d values; downstream meta-analysis reverses evidence-seeking effects programmatically. Study 1 negative-polarity evidence-fixed effects are reverse-coded to align with knowledge-attribution direction. |
| study_id | 1 |
|---|---|
| label | Experiment 1: Evidence-fixed design (multiple scenarios; 4 stakes levels; polarity manipulation) |
| objective | Test whether varying stakes magnitude (4 levels) affects agreement with knowledge attributions/denials across six scenarios, and whether this interacts with prompt polarity ('knows' vs 'doesn't know'). |
| study_language | English |
| study_language_other | — |
| design | — |
| design_other | 2 (polarity: know vs doesn't know; between-subjects) × 4 (stakes scale: 1–4; within-subjects) mixed design; six scenarios presented in a randomized block design. |
| manipulated_factors | Prompt polarity: 'knows' vs 'doesn't know' |
| paradigm | Agreement with knowledge claim |
| paradigm_other | — |
| notes | Effects are scenario-by-polarity lowest-vs-highest stakes contrasts recovered from raw data. Study 1 negative-polarity evidence-fixed effects reverse-code agreement with “doesn’t know” to knowledge-attribution direction before computing d. |
| n_final | 97 |
|---|---|
| recruitment | mTurk |
| recruitment_other | — |
| compensation | money |
| compensation_other | $1.75 |
| characteristics | MTurk sample: 44 females, 52 males, 1 non-binary; ages 20–71; positive polarity N=55, negative polarity N=42. |
| mean_age | 39.64 |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
| label | Likert 7-point |
|---|---|
| points | 7 |
| anchors | 1 = strongly disagree; 7 = strongly agree |
| direction | Higher numbers indicate stronger agreement with the prompt sentence (note: control Prompt 2 uses a reversed Likert scale). |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
| knowledge_question_text | To what extent do you agree or disagree with the following claim: Subject x knows that P / doesn't know that P (scenario-specific). |
|---|---|
| knowledge_question_first | — |
| additional_question_text | — |
paramedic · Paramedic GPS/wrong-turn scenario; stakes scaled by severity of consequences (lives).| scenario_code | paramedic |
|---|---|
| scenario_type | Paramedic GPS/wrong-turn scenario; stakes scaled by severity of consequences (lives). |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
vaccine · Vaccine checklist scenario; stakes scaled by number/severity of harms (lives).| scenario_code | vaccine |
|---|---|
| scenario_type | Vaccine checklist scenario; stakes scaled by number/severity of harms (lives). |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
mountaineering · Mountaineering rope-inspection scenario; stakes scaled by injury severity (physical injury).| scenario_code | mountaineering |
|---|---|
| scenario_type | Mountaineering rope-inspection scenario; stakes scaled by injury severity (physical injury). |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
game_show · Game show trivia scenario; stakes scaled by money at stake.| scenario_code | game_show |
|---|---|
| scenario_type | Game show trivia scenario; stakes scaled by money at stake. |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
introduction · Guest-speaker name introduction scenario; stakes scaled by embarrassment/reputation.| scenario_code | introduction |
|---|---|
| scenario_type | Guest-speaker name introduction scenario; stakes scaled by embarrassment/reputation. |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
arson · Arson/sprinkler system scenario; stakes scaled by value of possessions (including baby).| scenario_code | arson |
|---|---|
| scenario_type | Arson/sprinkler system scenario; stakes scaled by value of possessions (including baby). |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
s1_e1 · Experiment 1: Evidence-fixed design -- Paramedic -- positive polarity · Within-Subjects · d=-0.121513595094 · v=0.011522200282| effect_id | s1_e1 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Paramedic -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Paramedic; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. |
| metric | SMD |
|---|---|
| d | -0.121513595094 |
| v | 0.011522200282 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Paramedic Low vs Paramedic 3. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos. | Provenance
Paramedic: lives... Megan... paramedic... accident... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Megan is familiar with the surrounding area... traveling on the right route to get to the accident. | ||||||
awareness | Yes | The stakes are conveyed to the protagonist within the vignette (she is told the consequences). | Provenance
Over the radio, Megan is told that there is one person at the scene of the accident... | ||||||
evidence | First Person | Evidence comes from the agent’s own navigation resources/knowledge (familiarity + checking GPS). | Provenance
Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary... | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Subject x knows..."). | Provenance
Paramedic +Subject x... knows that she will make it to the accident without taking a wrong turn. | ||||||
evidence_reliability | Medium | Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium. | Provenance
Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 55 | 5.50909090909 | 1.34539994429 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low column: Paramedic Low. | ||||||
stakes4 | Highest stakes | 55 | 5.67272727273 | 1.3479002251 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high column: Paramedic 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e2 · Experiment 1: Evidence-fixed design -- Vaccine -- positive polarity · Within-Subjects · d=-0.0802223158773 · v=0.0153833192653| effect_id | s1_e2 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Vaccine -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Vaccine; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. |
| metric | SMD |
|---|---|
| d | -0.0802223158773 |
| v | 0.0153833192653 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Vaccine Low vs Vaccine 3. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos. | Provenance
Vaccine: lives... medical researcher... vaccine... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Elaine has done this before, and she has a check list that specifies all of the steps... | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (assistant informs her). | Provenance
Elaine's assistant has informed her that there is one human research participant... | ||||||
evidence | First Person | Evidence is based on the agent’s own checking/procedure-following (consulting a checklist). | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of the steps correctly. | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Elaine knows..."). | Provenance
Elaine knows that she is making the vaccine correctly | ||||||
evidence_reliability | High | The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability is coded High. | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of the steps correctly. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 55 | 5.98181818182 | 1.14650691864 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low column: Vaccine Low. | ||||||
stakes4 | Highest stakes | 55 | 6.07272727273 | 1.11976428496 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high column: Vaccine 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e3 · Experiment 1: Evidence-fixed design -- Mountaineering -- positive polarity · Within-Subjects · d=-0.112815214964 · v=0.0146515699543| effect_id | s1_e3 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Mountaineering -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Mountaineering; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. |
| metric | SMD |
|---|---|
| d | -0.112815214964 |
| v | 0.0146515699543 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Mountaineering Low vs Mountaineering 3. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos. | Provenance
Mountaineering: personal injury... mountain climbing expedition... inspect the rope... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Visibility is reducing, making the climb increasingly dangerous... | ||||||
awareness | Yes | The stakes are described within the scenario (agent is in the situation and consequences are described). | Provenance
Visibility is reducing... the drop... If not tied together securely... injuries... | ||||||
evidence | First Person | Evidence comes from the agent’s own inspection/checking (first-person). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Subject x knows..."). | Provenance
Mountaineering +Subject x... knows that the rope is tied securely. | ||||||
evidence_reliability | High | The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is coded High. | Provenance
How many times does S need to inspect the rope before she knows that it is tied securely? |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 55 | 5.83636363636 | 1.21355975243 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low column: Mountaineering Low. | ||||||
stakes4 | Highest stakes | 55 | 5.96363636364 | 1.03572548135 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high column: Mountaineering 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e4 · Experiment 1: Evidence-fixed design -- Game show -- positive polarity · Within-Subjects · d=0.0385435142968 · v=0.00648211042106| effect_id | s1_e4 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Game show -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Game show; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. |
| metric | SMD |
|---|---|
| d | 0.0385435142968 |
| v | 0.00648211042106 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: GameShow low vs GameShow 3. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos. | Provenance
Game show: finance... "What is the capital of Tanzania?" ... "Dodoma" | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Emma has recently read a list of the most obscure world capitals and the city "Dodoma" pops into her head. | ||||||
awareness | Yes | Stakes are described as part of the protagonist’s situation (game show winnings/losses). | Provenance
As this is the final round of the game show, $1,000,000 is at stake... | ||||||
evidence | First Person | Evidence is from the agent’s own memory (first-person evidence). | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Subject x knows..."). | Provenance
Game show +Subject x... knows that the capital of Tanzania is Dodoma. | ||||||
evidence_reliability | Medium | The evidence is memory of a recently read list without an additional check, so reliability is coded Medium. | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 55 | 5.27272727273 | 1.45874813726 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low column: GameShow low. | ||||||
stakes4 | Highest stakes | 55 | 5.21818181818 | 1.37019745929 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high column: GameShow 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e5 · Experiment 1: Evidence-fixed design -- Introduction -- positive polarity · Within-Subjects · d=-0.129102557916 · v=0.0126670930616| effect_id | s1_e5 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Introduction -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Introduction; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. |
| metric | SMD |
|---|---|
| d | -0.129102557916 |
| v | 0.0126670930616 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Intro Low vs Intro 3. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos. | Provenance
Introduction: reputation... introduce a guest speaker... "Dr. Woodbridge" | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. | ||||||
awareness | Yes | Stakes are described within the scenario as consequences of misnaming the speaker. | Provenance
If Nicole introduces the guest speaker by the wrong name... it will reflect very badly... | ||||||
evidence | External | Evidence is from an external written source (notebook with the name). | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Subject x knows..."). | Provenance
Introduction +Subject x... knows that the guest speakers name is "Dr. Woodbridge". | ||||||
evidence_reliability | Medium | The evidence is a notebook record made earlier in the day, an external written record but not an official or independently verified source, so reliability is coded Medium. | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 55 | 5.98181818182 | 1.22460740634 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low column: Intro Low. | ||||||
stakes4 | Highest stakes | 55 | 6.12727272727 | 1.01934157134 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high column: Intro 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e6 · Experiment 1: Evidence-fixed design -- Possessions/Arson -- positive polarity · Within-Subjects · d=0.0164528734539 · v=0.0159562267938| effect_id | s1_e6 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Possessions/Arson -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Possessions/Arson; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. |
| metric | SMD |
|---|---|
| d | 0.0164528734539 |
| v | 0.0159562267938 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Personal Val Low vs Personal Val 3. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos. | Provenance
Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist. | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Only a functioning sprinkler system can stop a fire set by an arsonist. | ||||||
awareness | Yes | The risk/stakes are described within the scenario context. | Provenance
Natalie is living in an area where there have been a series of fires set by arsonists recently. | ||||||
evidence | First Person | Evidence comes from the agent’s own prior checking/inspection (first-person). | Provenance
A week ago, Natalie checked that the sprinklers were working... | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Subject x knows..."). | Provenance
Arson +Subject x... knows that the sprinklers are working in the x room. | ||||||
evidence_reliability | Medium | A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is coded Medium for current harmonization. | Provenance
A week ago, Natalie checked that the sprinklers were working... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 55 | 5.90909090909 | 1.00503781526 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low column: Personal Val Low. | ||||||
stakes4 | Highest stakes | 55 | 5.89090909091 | 1.19679707232 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high column: Personal Val 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e7 · Experiment 1: Evidence-fixed design -- Paramedic -- negative polarity · Within-Subjects · d=-0.149740006799 · v=0.0210717079245| effect_id | s1_e7 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Paramedic -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Paramedic; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; negative_polarity_reverse_coded |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| metric | SMD |
|---|---|
| d | -0.149740006799 |
| v | 0.0210717079245 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Paramedic Low vs Paramedic 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded knowledge-attribution scores (8 - raw agreement with denial). |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos. | Provenance
Paramedic: lives... Megan... paramedic... accident... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Megan is familiar with the surrounding area... traveling on the right route to get to the accident. | ||||||
awareness | Yes | The stakes are conveyed to the protagonist within the vignette (she is told the consequences). | Provenance
Over the radio, Megan is told that there is one person at the scene of the accident... | ||||||
evidence | First Person | Evidence comes from the agent’s own navigation resources/knowledge (familiarity + checking GPS). | Provenance
Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary... | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Subject x knows..."). | Provenance
Paramedic +Subject x... knows that she will make it to the accident without taking a wrong turn. | ||||||
evidence_reliability | Medium | Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium. | Provenance
Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 42 | 5.42857142857 | 1.54829342941 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low column: Paramedic Low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. | ||||||
stakes4 | Highest stakes | 42 | 5.64285714286 | 1.30330590108 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high column: Paramedic 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e8 · Experiment 1: Evidence-fixed design -- Vaccine -- negative polarity · Within-Subjects · d=0.0484386042375 · v=0.0103158241577| effect_id | s1_e8 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Vaccine -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Vaccine; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; negative_polarity_reverse_coded |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| metric | SMD |
|---|---|
| d | 0.0484386042375 |
| v | 0.0103158241577 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Vaccine Low vs Vaccine 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded knowledge-attribution scores (8 - raw agreement with denial). |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos. | Provenance
Vaccine: lives... medical researcher... vaccine... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Elaine has done this before, and she has a check list that specifies all of the steps... | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (assistant informs her). | Provenance
Elaine's assistant has informed her that there is one human research participant... | ||||||
evidence | First Person | Evidence is based on the agent’s own checking/procedure-following (consulting a checklist). | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of the steps correctly. | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Elaine knows..."). | Provenance
Elaine knows that she is making the vaccine correctly | ||||||
evidence_reliability | High | The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability is coded High. | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of the steps correctly. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 42 | 5.61904761905 | 1.43054451431 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low column: Vaccine Low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. | ||||||
stakes4 | Highest stakes | 42 | 5.54761904762 | 1.51741726905 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high column: Vaccine 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e9 · Experiment 1: Evidence-fixed design -- Mountaineering -- negative polarity · Within-Subjects · d=0.0406284919837 · v=0.0125311746321| effect_id | s1_e9 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Mountaineering -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Mountaineering; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; negative_polarity_reverse_coded |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| metric | SMD |
|---|---|
| d | 0.0406284919837 |
| v | 0.0125311746321 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Mountaineering Low vs Mountaineering 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded knowledge-attribution scores (8 - raw agreement with denial). |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos. | Provenance
Mountaineering: personal injury... mountain climbing expedition... inspect the rope... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Visibility is reducing, making the climb increasingly dangerous... | ||||||
awareness | Yes | The stakes are described within the scenario (agent is in the situation and consequences are described). | Provenance
Visibility is reducing... the drop... If not tied together securely... injuries... | ||||||
evidence | First Person | Evidence comes from the agent’s own inspection/checking (first-person). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Subject x knows..."). | Provenance
Mountaineering +Subject x... knows that the rope is tied securely. | ||||||
evidence_reliability | High | The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is coded High. | Provenance
How many times does S need to inspect the rope before she knows that it is tied securely? |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 42 | 5.40476190476 | 1.79510885341 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low column: Mountaineering Low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. | ||||||
stakes4 | Highest stakes | 42 | 5.33333333333 | 1.72027602247 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high column: Mountaineering 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e10 · Experiment 1: Evidence-fixed design -- Game show -- negative polarity · Within-Subjects · d=0.131502174867 · v=0.0104410020413| effect_id | s1_e10 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Game show -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Game show; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; negative_polarity_reverse_coded |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| metric | SMD |
|---|---|
| d | 0.131502174867 |
| v | 0.0104410020413 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: GameShow low vs GameShow 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded knowledge-attribution scores (8 - raw agreement with denial). |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos. | Provenance
Game show: finance... "What is the capital of Tanzania?" ... "Dodoma" | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Emma has recently read a list of the most obscure world capitals and the city "Dodoma" pops into her head. | ||||||
awareness | Yes | Stakes are described as part of the protagonist’s situation (game show winnings/losses). | Provenance
As this is the final round of the game show, $1,000,000 is at stake... | ||||||
evidence | First Person | Evidence is from the agent’s own memory (first-person evidence). | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Subject x knows..."). | Provenance
Game show +Subject x... knows that the capital of Tanzania is Dodoma. | ||||||
evidence_reliability | Medium | The evidence is memory of a recently read list without an additional check, so reliability is coded Medium. | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 42 | 5.33333333333 | 1.60284300262 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low column: GameShow low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. | ||||||
stakes4 | Highest stakes | 42 | 5.11904761905 | 1.65577159012 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high column: GameShow 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e11 · Experiment 1: Evidence-fixed design -- Introduction -- negative polarity · Within-Subjects · d=0.166792146986 · v=0.0187820334615| effect_id | s1_e11 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Introduction -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Introduction; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; negative_polarity_reverse_coded |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| metric | SMD |
|---|---|
| d | 0.166792146986 |
| v | 0.0187820334615 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Intro Low vs Intro 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded knowledge-attribution scores (8 - raw agreement with denial). |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos. | Provenance
Introduction: reputation... introduce a guest speaker... "Dr. Woodbridge" | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. | ||||||
awareness | Yes | Stakes are described within the scenario as consequences of misnaming the speaker. | Provenance
If Nicole introduces the guest speaker by the wrong name... it will reflect very badly... | ||||||
evidence | External | Evidence is from an external written source (notebook with the name). | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Subject x knows..."). | Provenance
Introduction +Subject x... knows that the guest speakers name is "Dr. Woodbridge". | ||||||
evidence_reliability | Medium | The evidence is a notebook record made earlier in the day, an external written record but not an official or independently verified source, so reliability is coded Medium. | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 42 | 5.97619047619 | 1.19935135392 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low column: Intro Low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. | ||||||
stakes4 | Highest stakes | 42 | 5.7380952381 | 1.62389960956 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high column: Intro 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s1_e12 · Experiment 1: Evidence-fixed design -- Possessions/Arson -- negative polarity · Within-Subjects · d=-0.0170691726829 · v=0.0217680938563| effect_id | s1_e12 |
|---|---|
| subgroup | Experiment 1: Evidence-fixed design -- Possessions/Arson -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Possessions/Arson; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; negative_polarity_reverse_coded |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| metric | SMD |
|---|---|
| d | -0.0170691726829 |
| v | 0.0217680938563 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses. Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing d. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Personal Val Low vs Personal Val 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded knowledge-attribution scores (8 - raw agreement with denial). |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos. | Provenance
Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist. | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
Only a functioning sprinkler system can stop a fire set by an arsonist. | ||||||
awareness | Yes | The risk/stakes are described within the scenario context. | Provenance
Natalie is living in an area where there have been a series of fires set by arsonists recently. | ||||||
evidence | First Person | Evidence comes from the agent’s own prior checking/inspection (first-person). | Provenance
A week ago, Natalie checked that the sprinklers were working... | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ("Subject x knows..."). | Provenance
Arson +Subject x... knows that the sprinklers are working in the x room. | ||||||
evidence_reliability | Medium | A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is coded Medium for current harmonization. | Provenance
A week ago, Natalie checked that the sprinklers were working... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 42 | 5.78571428571 | 1.3710546819 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low column: Personal Val Low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. | ||||||
stakes4 | Highest stakes | 42 | 5.80952380952 | 1.41831392923 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high column: Personal Val 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
| study_id | 2 |
|---|---|
| label | Appendix II: Registered replication of Sripada & Stanley (2012) (pine nuts) |
| objective | Replicate Sripada & Stanley’s evidence-fixed design across three vignette pairs (basic; implicit/explicit; ignorant), measuring (i) evidence strength and (ii) agreement with a knowledge attribution. |
| study_language | English |
| study_language_other | — |
| design | Between-Subjects |
| design_other | 3 vignette pairs × 2 stakes (low vs high) between-subjects; each participant reads one vignette and answers Q1 (evidence strength) then Q2 (knowledge agreement). |
| manipulated_factors | Vignette pair type: Basic vs Implicit/Explicit vs Ignorant |
| paradigm | Agreement with knowledge claim |
| paradigm_other | — |
| notes | — |
| n_final | 367 |
|---|---|
| recruitment | mTurk |
| recruitment_other | — |
| compensation | money |
| compensation_other | $0.50 |
| characteristics | MTurk sample: 183 females, 246 males, 1 non-binary; ages 20–67; 63 suspicious/VPS responses removed; assigned to six conditions (N=58–68 per condition). |
| mean_age | 35.99 |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
| label | Likert 7-point |
|---|---|
| points | 7 |
| anchors | Q1: 1 = Very weak evidence; 7 = Very strong evidence. Q2: 1 = Strongly agree; 7 = Strongly disagree. |
| direction | Higher numbers indicate stronger evidence (Q1). For Q2, anchors are Strongly agree (1) to Strongly disagree (7) (paper reports effects in terms of 'levels of agreement'). |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
| knowledge_question_text | "Hannah knows her noodles are not topped with pine nuts." |
|---|---|
| knowledge_question_first | — |
| additional_question_text | What is the strength of Hannah's evidence that her noodles are not topped with pine nuts? |
peanuts · Pine-nuts/noodles vignette (Sripada & Stanley 2012 replication).| scenario_code | peanuts |
|---|---|
| scenario_type | Pine-nuts/noodles vignette (Sripada & Stanley 2012 replication). |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
s2_e1 · Basic — Evidence strength (Q1) · Between-Subjects · d=0.565873199091 · v=0.0332179460976| effect_id | s2_e1 |
|---|---|
| subgroup | Basic — Evidence strength (Q1) |
| subgroup_desc | Strength of evidence rating (7-point) |
| design | Between-Subjects |
| design_other | — |
| quality_flags | raw_data_recovered |
| notes | Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes. |
| metric | SMD |
|---|---|
| d | 0.565873199091 |
| v | 0.0332179460976 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd. |
| scenario | peanuts |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | — |
| evidence_reliability | High |
| group_high | Basic_high |
|---|---|
| group_low | Basic_low |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | — |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | peanuts | This is the Sripada & Stanley-style pine-nuts restaurant vignette (coded as peanuts-style scenario). | Provenance
What is the strength of Hannah's evidence that her noodles are not topped with pine nuts? | ||||||
skeptical_pressure | No | Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong. | Provenance
Hannah notes that the menu says her dish does not contain pine nuts. | ||||||
awareness | Yes | Coded to match the original Sripada & Stanley extraction; the Basic vignette protagonist is aware of the allergy stakes. | Provenance
Hannah is very much aware of this, and has known this for a very long time. | ||||||
evidence | External | Coded to match the original Sripada & Stanley extraction; evidence is from an external written source (the menu). | Provenance
Hannah notes that the menu says her dish does not contain pine nuts. | ||||||
attribution_person | — | DV is evidence-strength (not a knowledge attribution), so self/other knowledge-ascription coding is not applicable. | Provenance
What is the strength of Hannah's evidence... | ||||||
evidence_reliability | High | Coded to match the original Sripada & Stanley extraction; menu. | Provenance
Sarah says, 'The noodles may be topped with pine nuts.' Hannah notes that the menu says her dish does not contain pine nuts. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Basic_low | Basic low stakes | 58 | 3.93103448276 | 1.82441597447 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column: Basic Low__Strength_Evidence. | ||||||
Basic_high | Basic high stakes | 68 | 2.89705882353 | 1.82960484994 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column: Basic High__Strength_Evidence. |
| test | t |
|---|---|
| t | 3.17 |
| f | — |
| chi2 | — |
| z | — |
| df1 | 124 |
| df2 | — |
| p | 0.002 |
| reported_d | 0.57 |
| reported_r | — |
| notes | Direction reported: strength of evidence higher in low-stakes scenario. |
| page | 12 |
|---|---|
| table_ref | Figure 2 |
| tei_id | — |
s2_e2 · Basic — Knowledge attribution (Q2) · Between-Subjects · d=0.458627340663 · v=0.0327819403839| effect_id | s2_e2 |
|---|---|
| subgroup | Basic — Knowledge attribution (Q2) |
| subgroup_desc | Agreement with 'Hannah knows...' reverse-coded to agreement |
| design | Between-Subjects |
| design_other | — |
| quality_flags | raw_data_recovered; q2_reverse_coded_to_agreement |
| notes | Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes. |
| metric | SMD |
|---|---|
| d | 0.458627340663 |
| v | 0.0327819403839 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; Q2 was reverse-coded to agreement before computing low minus high. |
| scenario | peanuts |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | Basic_high |
|---|---|
| group_low | Basic_low |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | — |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | peanuts | This is the Sripada & Stanley-style pine-nuts restaurant vignette (coded as peanuts-style scenario). | Provenance
"Hannah knows her noodles are not topped with pine nuts." | ||||||
skeptical_pressure | No | Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong. | Provenance
Hannah notes that the menu says her dish does not contain pine nuts. | ||||||
awareness | Yes | Coded to match the original Sripada & Stanley extraction; the Basic vignette protagonist is aware of the allergy stakes. | Provenance
Hannah is very much aware of this, and has known this for a very long time. | ||||||
evidence | External | Coded to match the original Sripada & Stanley extraction; evidence is from an external written source (the menu). | Provenance
Hannah notes that the menu says her dish does not contain pine nuts. | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ('Hannah knows...'). | Provenance
"Hannah knows her noodles are not topped with pine nuts." | ||||||
evidence_reliability | High | Coded to match the original Sripada & Stanley extraction; menu. | Provenance
Sarah says, 'The noodles may be topped with pine nuts.' Hannah notes that the menu says her dish does not contain pine nuts. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Basic_low | Basic low stakes | 58 | 3.72413793103 | 1.84289154423 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column: Basic Low__Know_Prompt. | ||||||
Basic_high | Basic high stakes | 68 | 2.91176470588 | 1.70806073258 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column: Basic High__Know_Prompt. |
| test | t |
|---|---|
| t | -2.57 |
| f | — |
| chi2 | — |
| z | — |
| df1 | 124 |
| df2 | — |
| p | 0.011 |
| reported_d | 0.46 |
| reported_r | — |
| notes | Direction reported: levels of agreement higher in low-stakes scenario. |
| page | 12 |
|---|---|
| table_ref | Figure 2 |
| tei_id | — |
s2_e3 · Implicit/Explicit — Evidence strength (Q1) · Between-Subjects · d=0.0995353183474 · v=0.0336764491585| effect_id | s2_e3 |
|---|---|
| subgroup | Implicit/Explicit — Evidence strength (Q1) |
| subgroup_desc | Strength of evidence rating (7-point) |
| design | Between-Subjects |
| design_other | — |
| quality_flags | raw_data_recovered |
| notes | Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes. |
| metric | SMD |
|---|---|
| d | 0.0995353183474 |
| v | 0.0336764491585 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd. |
| scenario | peanuts |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | — |
| evidence_reliability | High |
| group_high | ImplicitExplicit_high |
|---|---|
| group_low | ImplicitExplicit_low |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | — |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | peanuts | Same pine-nuts/noodles vignette family; coded as peanuts-style scenario. | Provenance
What is the strength of Hannah's evidence that her noodles are not topped with pine nuts? | ||||||
skeptical_pressure | No | Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong. | Provenance
Hannah notes that the menu says her dish does not contain pine nuts. | ||||||
awareness | Yes | Coded to match the original Sripada & Stanley extraction; no ignorance manipulation is present. | Provenance
Implicit Low Stakes: "Hannah likes the taste of most foods and is not a very picky eater." | ||||||
evidence | External | Coded to match the original Sripada & Stanley extraction; evidence is from an external written source (the menu). | Provenance
Hannah notes that the menu says her dish does not contain pine nuts. | ||||||
attribution_person | — | DV is evidence-strength (not a knowledge attribution), so self/other knowledge-ascription coding is not applicable. | Provenance
What is the strength of Hannah's evidence... | ||||||
evidence_reliability | High | Coded to match the original Sripada & Stanley extraction; menu. | Provenance
Sarah says, 'The noodles may be topped with pine nuts.' Hannah notes that the menu says her dish does not contain pine nuts. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
ImplicitExplicit_low | Implicit/Explicit low stakes | 58 | 3.62068965517 | 1.94510258789 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column: Implicit Low__Strength_Evidence. | ||||||
ImplicitExplicit_high | Implicit/Explicit high stakes | 61 | 3.4262295082 | 1.96179353648 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column: Explicit High__Strength_Evidence. |
| test | t |
|---|---|
| t | 0.54 |
| f | — |
| chi2 | — |
| z | — |
| df1 | 117 |
| df2 | — |
| p | 0.588 |
| reported_d | — |
| reported_r | — |
| notes | Effect not significant per Figure 2 caption. |
| page | 12 |
|---|---|
| table_ref | Figure 2 |
| tei_id | — |
s2_e4 · Implicit/Explicit — Knowledge attribution (Q2) · Between-Subjects · d=0.270934142758 · v=0.0339432476044| effect_id | s2_e4 |
|---|---|
| subgroup | Implicit/Explicit — Knowledge attribution (Q2) |
| subgroup_desc | Agreement with 'Hannah knows...' reverse-coded to agreement |
| design | Between-Subjects |
| design_other | — |
| quality_flags | raw_data_recovered; q2_reverse_coded_to_agreement |
| notes | Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes. |
| metric | SMD |
|---|---|
| d | 0.270934142758 |
| v | 0.0339432476044 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; Q2 was reverse-coded to agreement before computing low minus high. |
| scenario | peanuts |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | ImplicitExplicit_high |
|---|---|
| group_low | ImplicitExplicit_low |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | — |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | peanuts | Same pine-nuts/noodles vignette family; coded as peanuts-style scenario. | Provenance
"Hannah knows her noodles are not topped with pine nuts." | ||||||
skeptical_pressure | No | Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong. | Provenance
Hannah notes that the menu says her dish does not contain pine nuts. | ||||||
awareness | Yes | Coded to match the original Sripada & Stanley extraction; no ignorance manipulation is present. | Provenance
Implicit Low Stakes: "Hannah likes the taste of most foods and is not a very picky eater." | ||||||
evidence | External | Coded to match the original Sripada & Stanley extraction; evidence is from an external written source (the menu). | Provenance
Hannah notes that the menu says her dish does not contain pine nuts. | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ('Hannah knows...'). | Provenance
"Hannah knows her noodles are not topped with pine nuts." | ||||||
evidence_reliability | High | Coded to match the original Sripada & Stanley extraction; menu. | Provenance
Sarah says, 'The noodles may be topped with pine nuts.' Hannah notes that the menu says her dish does not contain pine nuts. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
ImplicitExplicit_low | Implicit/Explicit low stakes | 58 | 3.79310344828 | 1.92635116117 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column: Implicit Low__Know_Prompt. | ||||||
ImplicitExplicit_high | Implicit/Explicit high stakes | 61 | 3.27868852459 | 1.87199668394 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column: Explicit High__Know_Prompt. |
| test | t |
|---|---|
| t | -1.48 |
| f | — |
| chi2 | — |
| z | — |
| df1 | 117 |
| df2 | — |
| p | 0.142 |
| reported_d | — |
| reported_r | — |
| notes | Effect not significant per Figure 2 caption. |
| page | 12 |
|---|---|
| table_ref | Figure 2 |
| tei_id | — |
s2_e5 · Ignorant — Evidence strength (Q1) · Between-Subjects · d=0.119302333567 · v=0.0328540310837| effect_id | s2_e5 |
|---|---|
| subgroup | Ignorant — Evidence strength (Q1) |
| subgroup_desc | Strength of evidence rating (7-point) |
| design | Between-Subjects |
| design_other | — |
| quality_flags | raw_data_recovered |
| notes | Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes. |
| metric | SMD |
|---|---|
| d | 0.119302333567 |
| v | 0.0328540310837 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd. |
| scenario | peanuts |
|---|---|
| skeptical_pressure | No |
| awareness | No |
| evidence | External |
| attribution_person | — |
| evidence_reliability | High |
| group_high | Ignorant_high |
|---|---|
| group_low | Ignorant_low |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | — |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | peanuts | Same pine-nuts/noodles vignette family; coded as peanuts-style scenario. | Provenance
What is the strength of Hannah's evidence that her noodles are not topped with pine nuts? | ||||||
skeptical_pressure | No | Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong. | Provenance
Hannah notes that the menu says her dish does not contain Mongolian pine nuts. | ||||||
awareness | No | Paper explicitly describes the Ignorant manipulation as the protagonist being unaware of the stakes. | Provenance
the protagonist being ignorant of the stakes involved (ignorant low/ignorant high) | ||||||
evidence | External | Coded to match the original Sripada & Stanley extraction; evidence is from external sources (testimony + menu). | Provenance
Sarah says, 'I heard that Mongolian dishes are often served topped with Mongolian pine nuts.' Hannah notes that the menu says her dish does not contain Mongolian pine nuts. | ||||||
attribution_person | — | DV is evidence-strength (not a knowledge attribution), so self/other knowledge-ascription coding is not applicable. | Provenance
What is the strength of Hannah's evidence... | ||||||
evidence_reliability | High | Coded to match the original Sripada & Stanley extraction; menu. | Provenance
Sarah says, 'I heard that Mongolian dishes are often served topped with Mongolian pine nuts.' Hannah notes that the menu says her dish does not contain Mongolian pine nuts. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ignorant_low | Ignorant low stakes | 62 | 4.25806451613 | 1.88118800858 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column: Ignorant Low__Strength_Evidence. | ||||||
Ignorant_high | Ignorant high stakes | 60 | 4.03333333333 | 1.88631707048 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column: Ignorant High__Strength_Evidence. |
| test | t |
|---|---|
| t | 0.84 |
| f | — |
| chi2 | — |
| z | — |
| df1 | 120 |
| df2 | — |
| p | 0.511 |
| reported_d | — |
| reported_r | — |
| notes | Effect not significant per Figure 2 caption. |
| page | 12 |
|---|---|
| table_ref | Figure 2 |
| tei_id | — |
s2_e6 · Ignorant — Knowledge attribution (Q2) · Between-Subjects · d=0.177466028998 · v=0.0329247734798| effect_id | s2_e6 |
|---|---|
| subgroup | Ignorant — Knowledge attribution (Q2) |
| subgroup_desc | Agreement with 'Hannah knows...' reverse-coded to agreement |
| design | Between-Subjects |
| design_other | — |
| quality_flags | raw_data_recovered; q2_reverse_coded_to_agreement |
| notes | Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes. |
| metric | SMD |
|---|---|
| d | 0.177466028998 |
| v | 0.0329247734798 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; Q2 was reverse-coded to agreement before computing low minus high. |
| scenario | peanuts |
|---|---|
| skeptical_pressure | No |
| awareness | No |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | Ignorant_high |
|---|---|
| group_low | Ignorant_low |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | — |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | peanuts | Same pine-nuts/noodles vignette family; coded as peanuts-style scenario. | Provenance
"Hannah knows her noodles are not topped with pine nuts." | ||||||
skeptical_pressure | No | Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong. | Provenance
Hannah notes that the menu says her dish does not contain Mongolian pine nuts. | ||||||
awareness | No | Paper explicitly describes the Ignorant manipulation as the protagonist being unaware of the stakes. | Provenance
the protagonist being ignorant of the stakes involved (ignorant low/ignorant high) | ||||||
evidence | External | Coded to match the original Sripada & Stanley extraction; evidence is from external sources (testimony + menu). | Provenance
Sarah says, 'I heard that Mongolian dishes are often served topped with Mongolian pine nuts.' Hannah notes that the menu says her dish does not contain Mongolian pine nuts. | ||||||
attribution_person | Other | Participants evaluate a third-person knowledge attribution ('Hannah knows...'). | Provenance
"Hannah knows her noodles are not topped with pine nuts." | ||||||
evidence_reliability | High | Coded to match the original Sripada & Stanley extraction; menu. | Provenance
Sarah says, 'I heard that Mongolian dishes are often served topped with Mongolian pine nuts.' Hannah notes that the menu says her dish does not contain Mongolian pine nuts. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ignorant_low | Ignorant low stakes | 62 | 4.08064516129 | 2.01061435107 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column: Ignorant Low__Know_Prompt. | ||||||
Ignorant_high | Ignorant high stakes | 60 | 3.71666666667 | 2.09188639762 | — | Provenance
Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column: Ignorant High__Know_Prompt. |
| test | t |
|---|---|
| t | -0.98 |
| f | — |
| chi2 | — |
| z | — |
| df1 | 120 |
| df2 | — |
| p | 0.329 |
| reported_d | — |
| reported_r | — |
| notes | Effect not significant per Figure 2 caption. |
| page | 12 |
|---|---|
| table_ref | Figure 2 |
| tei_id | — |
| study_id | 3 |
|---|---|
| label | Experiment 2: Evidence-seeking design (original prompts) |
| objective | Test for stakes effects on how much evidence is needed for knowledge (positive prompt) or can be had while still not knowing (negative prompt), across six scenarios and four stakes levels. |
| study_language | English |
| study_language_other | — |
| design | — |
| design_other | Stakes (4 levels) within-subjects; prompt polarity (positive vs negative) between-subjects; six scenarios presented in a randomized block design. |
| manipulated_factors | Prompt polarity: evidence-seeking positive vs evidence-seeking negative |
| paradigm | Rating how much evidence is needed for knowledge |
| paradigm_other | — |
| notes | Effects are scenario-by-polarity lowest-vs-highest stakes contrasts recovered from raw data; evidence-seeking cleaning is documented in analysis/effect_sizes.qmd. Evidence-seeking d values in this YAML use the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| n_final | 109 |
|---|---|
| recruitment | mTurk |
| recruitment_other | — |
| compensation | money |
| compensation_other | $1.75 |
| characteristics | MTurk sample: 54 females, 55 males; ages 21–74; positive polarity N=58, negative polarity N=51. |
| mean_age | 38.98 |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
| label | other |
|---|---|
| points | — |
| anchors | Numeric free response (whole number of checks); positive prompt allows '0' (knows without checking) and both prompts allow 'never'. |
| direction | Higher numbers indicate more evidence required for knowledge (positive) / more checking still insufficient for knowledge (negative). |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
| knowledge_question_text | How many times does S need to check F before she knows that P? |
|---|---|
| knowledge_question_first | — |
| additional_question_text | How many times can S check F and still not know that P? |
s3_e1 · Experiment 2: Evidence-seeking design -- Paramedic -- positive polarity · Within-Subjects · d=-0.661808915227 · v=0.0276260903612| effect_id | s3_e1 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Paramedic -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Paramedic; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.661808915227 |
| v | 0.0276260903612 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Paramedic Low vs Paramedic 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos. | Provenance
Paramedic: lives... Megan, a paramedic... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (she is told the consequences). | Provenance
Over the radio, Megan is told that there is one person at the scene of the accident... | ||||||
evidence | First Person | Evidence is the agent’s own checking/navigation (first-person). | Provenance
Megan... has GPS on her phone that she can check if necessary... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | Medium | Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium. | Provenance
Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 39 | 1.94871794872 | 1.12270158074 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low column: Paramedic Low. | ||||||
stakes4 | Highest stakes | 39 | 2.76923076923 | 1.34676099668 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high column: Paramedic 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e2 · Experiment 2: Evidence-seeking design -- Vaccine -- positive polarity · Within-Subjects · d=-0.839514293934 · v=0.0227377109024| effect_id | s3_e2 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Vaccine -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Vaccine; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.839514293934 |
| v | 0.0227377109024 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Vaccine Low vs Vaccine 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos. | Provenance
Vaccine: lives... medical researcher... vaccine... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (assistant informs her). | Provenance
Elaine's assistant has informed her that there is one human research participant... | ||||||
evidence | First Person | Evidence is the agent’s own checking/procedure-following (first-person). | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | High | The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability is coded High. | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of the steps correctly. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 48 | 2.47916666667 | 1.58435950323 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low column: Vaccine Low. | ||||||
stakes4 | Highest stakes | 48 | 4.22916666667 | 2.48604259847 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high column: Vaccine 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e3 · Experiment 2: Evidence-seeking design -- Mountaineering -- positive polarity · Within-Subjects · d=-0.683958446104 · v=0.0211432766559| effect_id | s3_e3 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Mountaineering -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Mountaineering; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.683958446104 |
| v | 0.0211432766559 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Mountaineering Low vs Mountaineering 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos. | Provenance
Mountaineering: personal injury... inspect the rope... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the situation (dangerous climb; consequences). | Provenance
Visibility is reducing, making the climb increasingly dangerous... | ||||||
evidence | First Person | Evidence is from the agent’s own inspection/checking (first-person). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
evidence_reliability | High | The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is coded High. | Provenance
How many times does S need to inspect the rope before she knows that it is tied securely? |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 44 | 2.0 | 1.20077494357 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low column: Mountaineering Low. | ||||||
stakes4 | Highest stakes | 44 | 2.88636363636 | 1.38456456241 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high column: Mountaineering 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e4 · Experiment 2: Evidence-seeking design -- Game show -- positive polarity · Within-Subjects · d=-0.871275071557 · v=0.0732423045923| effect_id | s3_e4 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Game show -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Game show; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.871275071557 |
| v | 0.0732423045923 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: GameShow low vs GameShow 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos. | Provenance
Game show: finance... "What is the capital of Tanzania?"... "Dodoma" | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (money at stake in the game show). | Provenance
As this is the final round of the game show, $1,000,000 is at stake... | ||||||
evidence | First Person | Evidence is from the agent’s own memory/consideration (first-person). | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania is Dodoma? | ||||||
evidence_reliability | Medium | The evidence is memory of a recently read list without an additional check, so reliability is coded Medium. | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 27 | 2.66666666667 | 2.44948974278 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low column: GameShow low. | ||||||
stakes4 | Highest stakes | 27 | 5.48148148148 | 3.8567659865 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high column: GameShow 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e5 · Experiment 2: Evidence-seeking design -- Introduction -- positive polarity · Within-Subjects · d=-0.668163278631 · v=0.0224825444801| effect_id | s3_e5 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Introduction -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Introduction; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.668163278631 |
| v | 0.0224825444801 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Intro Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos. | Provenance
Introduction: reputation... wrote down the speaker's name-"Dr. Woodbridge"-in her notebook... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (embarrassment/reputation consequences). | Provenance
If Nicole introduces the guest speaker by the wrong name... reflect very badly... | ||||||
evidence | External | Evidence is from an external written source (notebook). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
evidence_reliability | Medium | The evidence is a notebook record made earlier in the day, an external written record but not an official or independently verified source, so reliability is coded Medium. | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 50 | 1.74 | 0.828325086533 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low column: Intro Low. | ||||||
stakes4 | Highest stakes | 50 | 2.4 | 1.12485826772 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high column: Intro 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e6 · Experiment 2: Evidence-seeking design -- Possessions/Arson -- positive polarity · Within-Subjects · d=-0.813209200727 · v=0.0306234050604| effect_id | s3_e6 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Possessions/Arson -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Possessions/Arson; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.813209200727 |
| v | 0.0306234050604 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Personal Val Low vs Personal Val 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos. | Provenance
Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist. | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The risk/stakes are described within the scenario context. | Provenance
Natalie is living in an area where there have been a series of fires set by arsonists recently. | ||||||
evidence | First Person | Evidence is from the agent’s own checking/inspection (first-person). | Provenance
A week ago, Natalie checked that the sprinklers were working... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to check the sprinklers before she knows that they are working in the X room? | ||||||
evidence_reliability | Medium | A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is coded Medium for current harmonization. | Provenance
A week ago, Natalie checked that the sprinklers were working... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 47 | 1.53191489362 | 0.905323959067 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low column: Personal Val Low. | ||||||
stakes4 | Highest stakes | 47 | 2.82978723404 | 2.06754579294 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high column: Personal Val 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e7 · Experiment 2: Evidence-seeking design -- Paramedic -- negative polarity · Within-Subjects · d=0.0585626171598 · v=0.0377444913971| effect_id | s3_e7 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Paramedic -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Paramedic; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | 0.0585626171598 |
| v | 0.0377444913971 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Paramedic Low vs Paramedic 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos. | Provenance
Paramedic: lives... Megan, a paramedic... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (she is told the consequences). | Provenance
Over the radio, Megan is told that there is one person at the scene of the accident... | ||||||
evidence | First Person | Evidence is the agent’s own checking/navigation (first-person). | Provenance
Megan... has GPS on her phone that she can check if necessary... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | Medium | Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium. | Provenance
Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 35 | 2.25714285714 | 2.06287716185 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low column: Paramedic Low. | ||||||
stakes4 | Highest stakes | 35 | 2.14285714286 | 1.83339699406 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high column: Paramedic 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e8 · Experiment 2: Evidence-seeking design -- Vaccine -- negative polarity · Within-Subjects · d=-0.372724636385 · v=0.034404491619| effect_id | s3_e8 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Vaccine -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Vaccine; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.372724636385 |
| v | 0.034404491619 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Vaccine Low vs Vaccine 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos. | Provenance
Vaccine: lives... medical researcher... vaccine... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (assistant informs her). | Provenance
Elaine's assistant has informed her that there is one human research participant... | ||||||
evidence | First Person | Evidence is the agent’s own checking/procedure-following (first-person). | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | High | The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability is coded High. | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of the steps correctly. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 34 | 2.70588235294 | 1.9467052471 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low column: Vaccine Low. | ||||||
stakes4 | Highest stakes | 34 | 3.79411764706 | 3.64134017757 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high column: Vaccine 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e9 · Experiment 2: Evidence-seeking design -- Mountaineering -- negative polarity · Within-Subjects · d=-0.379749242052 · v=0.0431311478436| effect_id | s3_e9 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Mountaineering -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Mountaineering; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.379749242052 |
| v | 0.0431311478436 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Mountaineering Low vs Mountaineering 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos. | Provenance
Mountaineering: personal injury... inspect the rope... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the situation (dangerous climb; consequences). | Provenance
Visibility is reducing, making the climb increasingly dangerous... | ||||||
evidence | First Person | Evidence is from the agent’s own inspection/checking (first-person). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
evidence_reliability | High | The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is coded High. | Provenance
How many times does S need to inspect the rope before she knows that it is tied securely? |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 36 | 1.86111111111 | 1.26835726778 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low column: Mountaineering Low. | ||||||
stakes4 | Highest stakes | 36 | 2.47222222222 | 1.88961237312 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high column: Mountaineering 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e10 · Experiment 2: Evidence-seeking design -- Game show -- negative polarity · Within-Subjects · d=0.0160686971955 · v=0.0283223978536| effect_id | s3_e10 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Game show -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Game show; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | 0.0160686971955 |
| v | 0.0283223978536 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Game Show Low vs Game Show 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos. | Provenance
Game show: finance... "What is the capital of Tanzania?"... "Dodoma" | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (money at stake in the game show). | Provenance
As this is the final round of the game show, $1,000,000 is at stake... | ||||||
evidence | First Person | Evidence is from the agent’s own memory/consideration (first-person). | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania is Dodoma? | ||||||
evidence_reliability | Medium | The evidence is memory of a recently read list without an additional check, so reliability is coded Medium. | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 33 | 8.15151515152 | 10.5478706741 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low column: Game Show Low. | ||||||
stakes4 | Highest stakes | 33 | 8.0 | 8.15858443604 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high column: Game Show 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e11 · Experiment 2: Evidence-seeking design -- Introduction -- negative polarity · Within-Subjects · d=-0.355430263187 · v=0.0448463453658| effect_id | s3_e11 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Introduction -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Introduction; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.355430263187 |
| v | 0.0448463453658 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Introductions Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos. | Provenance
Introduction: reputation... wrote down the speaker's name-"Dr. Woodbridge"-in her notebook... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (embarrassment/reputation consequences). | Provenance
If Nicole introduces the guest speaker by the wrong name... reflect very badly... | ||||||
evidence | External | Evidence is from an external written source (notebook). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
evidence_reliability | Medium | The evidence is a notebook record made earlier in the day, an external written record but not an official or independently verified source, so reliability is coded Medium. | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 32 | 1.65625 | 0.970845158911 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low column: Introductions Low. | ||||||
stakes4 | Highest stakes | 32 | 2.09375 | 1.44488809702 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high column: Intro 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s3_e12 · Experiment 2: Evidence-seeking design -- Possessions/Arson -- negative polarity · Within-Subjects · d=-0.435233708389 · v=0.0578572704137| effect_id | s3_e12 |
|---|---|
| subgroup | Experiment 2: Evidence-seeking design -- Possessions/Arson -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Possessions/Arson; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.435233708389 |
| v | 0.0578572704137 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Pvalue Low vs Pvalue 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos. | Provenance
Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist. | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The risk/stakes are described within the scenario context. | Provenance
Natalie is living in an area where there have been a series of fires set by arsonists recently. | ||||||
evidence | First Person | Evidence is from the agent’s own checking/inspection (first-person). | Provenance
A week ago, Natalie checked that the sprinklers were working... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to check the sprinklers before she knows that they are working in the X room? | ||||||
evidence_reliability | Medium | A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is coded Medium for current harmonization. | Provenance
A week ago, Natalie checked that the sprinklers were working... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 26 | 2.15384615385 | 1.61721508013 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low column: Pvalue Low. | ||||||
stakes4 | Highest stakes | 26 | 3.11538461538 | 2.67322910469 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high column: Pvalue 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
| study_id | 4 |
|---|---|
| label | Appendix IV: Symmetrical Experiment (follow-up evidence-seeking prompts) |
| objective | Follow-up evidence-seeking experiment using symmetrical prompts ('minimum' vs 'maximum') to reduce asymmetry between positive and negative polarities. |
| study_language | English |
| study_language_other | — |
| design | — |
| design_other | Stakes (4 levels) within-subjects; prompt polarity (positive vs negative) between-subjects; prompts modified to use minimum/maximum wording. |
| manipulated_factors | Prompt polarity: evidence-seeking positive vs evidence-seeking negative; Prompt wording: minimum vs maximum (symmetrical prompts) |
| paradigm | Rating how much evidence is needed for knowledge |
| paradigm_other | — |
| notes | Effects are scenario-by-polarity lowest-vs-highest stakes contrasts recovered from raw data; evidence-seeking cleaning is documented in analysis/effect_sizes.qmd. Evidence-seeking d values in this YAML use the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| n_final | 105 |
|---|---|
| recruitment | mTurk |
| recruitment_other | — |
| compensation | money |
| compensation_other | $1.75 |
| characteristics | MTurk sample: 45 females, 59 males; ages 21–65; additional exclusions include prior completion of the first evidence-seeking experiment. |
| mean_age | 37.09 |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
| label | other |
|---|---|
| points | — |
| anchors | Numeric free response (whole number of checks); positive prompt allows '0' and both prompts allow 'never'. |
| direction | Higher numbers indicate more evidence required for knowledge / more checking still insufficient for knowledge. |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
| knowledge_question_text | What is the minimum number of times S needs to check F before she knows that P? |
|---|---|
| knowledge_question_first | — |
| additional_question_text | What is the maximum number of times S can check F and not know that P? |
s4_e1 · Appendix IV: Symmetrical evidence-seeking experiment -- Paramedic -- positive polarity · Within-Subjects · d=-0.431103113867 · v=0.0179003744693| effect_id | s4_e1 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Paramedic -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Paramedic; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.431103113867 |
| v | 0.0179003744693 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Para Low vs Para 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos. | Provenance
Paramedic: lives... Megan, a paramedic... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (she is told the consequences). | Provenance
Over the radio, Megan is told that there is one person at the scene of the accident... | ||||||
evidence | First Person | Evidence is the agent’s own checking/navigation (first-person). | Provenance
Megan... has GPS on her phone that she can check if necessary... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | Medium | Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium. | Provenance
Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 47 | 2.23404255319 | 1.59090849021 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; low column: Para Low. | ||||||
stakes4 | Highest stakes | 47 | 3.0 | 1.94489297794 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; high column: Para 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e2 · Appendix IV: Symmetrical evidence-seeking experiment -- Vaccine -- positive polarity · Within-Subjects · d=-0.615892037585 · v=0.0207344359567| effect_id | s4_e2 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Vaccine -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Vaccine; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.615892037585 |
| v | 0.0207344359567 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Vacc Low vs Vacc 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos. | Provenance
Vaccine: lives... medical researcher... vaccine... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (assistant informs her). | Provenance
Elaine's assistant has informed her that there is one human research participant... | ||||||
evidence | First Person | Evidence is the agent’s own checking/procedure-following (first-person). | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | High | The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability is coded High. | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of the steps correctly. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 44 | 2.86363636364 | 2.25770936695 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; low column: Vacc Low. | ||||||
stakes4 | Highest stakes | 44 | 4.70454545455 | 3.57367341108 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; high column: Vacc 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e3 · Appendix IV: Symmetrical evidence-seeking experiment -- Mountaineering -- positive polarity · Within-Subjects · d=-0.510345860764 · v=0.0244069491794| effect_id | s4_e3 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Mountaineering -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Mountaineering; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.510345860764 |
| v | 0.0244069491794 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Mount Low vs Mount 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos. | Provenance
Mountaineering: personal injury... inspect the rope... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the situation (dangerous climb; consequences). | Provenance
Visibility is reducing, making the climb increasingly dangerous... | ||||||
evidence | First Person | Evidence is from the agent’s own inspection/checking (first-person). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
evidence_reliability | High | The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is coded High. | Provenance
How many times does S need to inspect the rope before she knows that it is tied securely? |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 35 | 2.82857142857 | 1.20014004785 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; low column: Mount Low. | ||||||
stakes4 | Highest stakes | 35 | 3.6 | 1.76901434836 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; high column: Mount 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e4 · Appendix IV: Symmetrical evidence-seeking experiment -- Game show -- positive polarity · Within-Subjects · d=-0.505090368385 · v=0.0184715642471| effect_id | s4_e4 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Game show -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Game show; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.505090368385 |
| v | 0.0184715642471 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Game Low vs Game 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos. | Provenance
Game show: finance... "What is the capital of Tanzania?"... "Dodoma" | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (money at stake in the game show). | Provenance
As this is the final round of the game show, $1,000,000 is at stake... | ||||||
evidence | First Person | Evidence is from the agent’s own memory/consideration (first-person). | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania is Dodoma? | ||||||
evidence_reliability | Medium | The evidence is memory of a recently read list without an additional check, so reliability is coded Medium. | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 41 | 4.0 | 4.28368999812 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; low column: Game Low. | ||||||
stakes4 | Highest stakes | 41 | 6.60975609756 | 5.91978905359 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; high column: Game 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e5 · Appendix IV: Symmetrical evidence-seeking experiment -- Introduction -- positive polarity · Within-Subjects · d=-0.206779836189 · v=0.0220955487438| effect_id | s4_e5 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Introduction -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Introduction; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.206779836189 |
| v | 0.0220955487438 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Intro Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos. | Provenance
Introduction: reputation... wrote down the speaker's name-"Dr. Woodbridge"-in her notebook... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (embarrassment/reputation consequences). | Provenance
If Nicole introduces the guest speaker by the wrong name... reflect very badly... | ||||||
evidence | External | Evidence is from an external written source (notebook). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
evidence_reliability | Medium | The evidence is a notebook record made earlier in the day, an external written record but not an official or independently verified source, so reliability is coded Medium. | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 45 | 2.22222222222 | 1.18492210885 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; low column: Intro Low. | ||||||
stakes4 | Highest stakes | 45 | 2.46666666667 | 1.17936808966 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; high column: Intro 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e6 · Appendix IV: Symmetrical evidence-seeking experiment -- Possessions/Arson -- positive polarity · Within-Subjects · d=-0.751472126025 · v=0.0302478033315| effect_id | s4_e6 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Possessions/Arson -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Possessions/Arson; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.751472126025 |
| v | 0.0302478033315 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Pval Low vs Pval 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos. | Provenance
Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist. | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The risk/stakes are described within the scenario context. | Provenance
Natalie is living in an area where there have been a series of fires set by arsonists recently. | ||||||
evidence | First Person | Evidence is from the agent’s own checking/inspection (first-person). | Provenance
A week ago, Natalie checked that the sprinklers were working... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to check the sprinklers before she knows that they are working in the X room? | ||||||
evidence_reliability | Medium | A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is coded Medium for current harmonization. | Provenance
A week ago, Natalie checked that the sprinklers were working... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 46 | 1.91304347826 | 1.17049062755 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; low column: Pval Low. | ||||||
stakes4 | Highest stakes | 46 | 3.34782608696 | 2.43326384654 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv; high column: Pval 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e7 · Appendix IV: Symmetrical evidence-seeking experiment -- Paramedic -- negative polarity · Within-Subjects · d=-0.198074155086 · v=0.0460036063285| effect_id | s4_e7 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Paramedic -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Paramedic; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.198074155086 |
| v | 0.0460036063285 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Para Low vs Para 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos. | Provenance
Paramedic: lives... Megan, a paramedic... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (she is told the consequences). | Provenance
Over the radio, Megan is told that there is one person at the scene of the accident... | ||||||
evidence | First Person | Evidence is the agent’s own checking/navigation (first-person). | Provenance
Megan... has GPS on her phone that she can check if necessary... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | Medium | Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium. | Provenance
Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 30 | 1.83333333333 | 0.912870929175 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; low column: Para Low. | ||||||
stakes4 | Highest stakes | 30 | 2.03333333333 | 1.09806517404 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; high column: Para 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e8 · Appendix IV: Symmetrical evidence-seeking experiment -- Vaccine -- negative polarity · Within-Subjects · d=-0.0235111844073 · v=0.033935712758| effect_id | s4_e8 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Vaccine -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Vaccine; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.0235111844073 |
| v | 0.033935712758 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Vacc Low vs Vacc 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos. | Provenance
Vaccine: lives... medical researcher... vaccine... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (assistant informs her). | Provenance
Elaine's assistant has informed her that there is one human research participant... | ||||||
evidence | First Person | Evidence is the agent’s own checking/procedure-following (first-person). | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | High | The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability is coded High. | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of the steps correctly. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 30 | 3.36666666667 | 2.93002687211 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; low column: Vacc Low. | ||||||
stakes4 | Highest stakes | 30 | 3.43333333333 | 2.7377732373 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; high column: Vacc 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e9 · Appendix IV: Symmetrical evidence-seeking experiment -- Mountaineering -- negative polarity · Within-Subjects · d=-0.319806387682 · v=0.0156962540102| effect_id | s4_e9 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Mountaineering -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Mountaineering; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.319806387682 |
| v | 0.0156962540102 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Mount Low vs Mount 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos. | Provenance
Mountaineering: personal injury... inspect the rope... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the situation (dangerous climb; consequences). | Provenance
Visibility is reducing, making the climb increasingly dangerous... | ||||||
evidence | First Person | Evidence is from the agent’s own inspection/checking (first-person). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
evidence_reliability | High | The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is coded High. | Provenance
How many times does S need to inspect the rope before she knows that it is tied securely? |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 39 | 2.15384615385 | 1.91309148457 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; low column: Mount Low. | ||||||
stakes4 | Highest stakes | 39 | 2.84615384615 | 2.39009426745 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; high column: Mount 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e10 · Appendix IV: Symmetrical evidence-seeking experiment -- Game show -- negative polarity · Within-Subjects · d=-0.480340771592 · v=0.058763399638| effect_id | s4_e10 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Game show -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Game show; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.480340771592 |
| v | 0.058763399638 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Game Low vs Game 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos. | Provenance
Game show: finance... "What is the capital of Tanzania?"... "Dodoma" | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (money at stake in the game show). | Provenance
As this is the final round of the game show, $1,000,000 is at stake... | ||||||
evidence | First Person | Evidence is from the agent’s own memory/consideration (first-person). | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania is Dodoma? | ||||||
evidence_reliability | Medium | The evidence is memory of a recently read list without an additional check, so reliability is coded Medium. | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 23 | 3.60869565217 | 2.5179199647 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; low column: Game Low. | ||||||
stakes4 | Highest stakes | 23 | 4.95652173913 | 3.06710199121 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; high column: Game 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e11 · Appendix IV: Symmetrical evidence-seeking experiment -- Introduction -- negative polarity · Within-Subjects · d=-0.217503158496 · v=0.0100083057383| effect_id | s4_e11 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Introduction -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Introduction; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.217503158496 |
| v | 0.0100083057383 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Intro Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos. | Provenance
Introduction: reputation... wrote down the speaker's name-"Dr. Woodbridge"-in her notebook... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (embarrassment/reputation consequences). | Provenance
If Nicole introduces the guest speaker by the wrong name... reflect very badly... | ||||||
evidence | External | Evidence is from an external written source (notebook). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
evidence_reliability | Medium | The evidence is a notebook record made earlier in the day, an external written record but not an official or independently verified source, so reliability is coded Medium. | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 34 | 1.97058823529 | 1.19304281509 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; low column: Intro Low. | ||||||
stakes4 | Highest stakes | 34 | 2.29411764706 | 1.73256530359 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; high column: Intro 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s4_e12 · Appendix IV: Symmetrical evidence-seeking experiment -- Possessions/Arson -- negative polarity · Within-Subjects · d=-0.443709626053 · v=0.0539589673019| effect_id | s4_e12 |
|---|---|
| subgroup | Appendix IV: Symmetrical evidence-seeking experiment -- Possessions/Arson -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Possessions/Arson; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.443709626053 |
| v | 0.0539589673019 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Pval Low vs Pval 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos. | Provenance
Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist. | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The risk/stakes are described within the scenario context. | Provenance
Natalie is living in an area where there have been a series of fires set by arsonists recently. | ||||||
evidence | First Person | Evidence is from the agent’s own checking/inspection (first-person). | Provenance
A week ago, Natalie checked that the sprinklers were working... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to check the sprinklers before she knows that they are working in the X room? | ||||||
evidence_reliability | Medium | A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is coded Medium for current harmonization. | Provenance
A week ago, Natalie checked that the sprinklers were working... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 30 | 1.56666666667 | 0.727932041795 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; low column: Pval Low. | ||||||
stakes4 | Highest stakes | 30 | 2.2 | 1.88277125169 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv; high column: Pval 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
| study_id | 5 |
|---|---|
| label | Appendix IV: Matched Experiment (follow-up; '0' option removed) |
| objective | Follow-up evidence-seeking experiment with matched prompts by removing the '0' response option from the positive polarity prompts. |
| study_language | English |
| study_language_other | — |
| design | — |
| design_other | Stakes (4 levels) within-subjects; prompt polarity (positive vs negative) between-subjects; '0' response option removed to create matched design. |
| manipulated_factors | Prompt polarity: evidence-seeking positive vs evidence-seeking negative; Response options: '0' removed in positive prompts |
| paradigm | Rating how much evidence is needed for knowledge |
| paradigm_other | — |
| notes | Effects are scenario-by-polarity lowest-vs-highest stakes contrasts recovered from raw data; evidence-seeking cleaning is documented in analysis/effect_sizes.qmd. Evidence-seeking d values in this YAML use the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| n_final | 89 |
|---|---|
| recruitment | mTurk |
| recruitment_other | — |
| compensation | money |
| compensation_other | $1.75 |
| characteristics | MTurk sample: 33 females, 56 males; ages 20–70; positive polarity N=45, negative polarity N=44. |
| mean_age | 34.71 |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
| label | other |
|---|---|
| points | — |
| anchors | Numeric free response (whole number of checks); '0' option removed; both prompts allow 'never'. |
| direction | Higher numbers indicate more evidence required for knowledge / more checking still insufficient for knowledge. |
| page | — |
|---|---|
| table_ref | — |
| tei_id | — |
| knowledge_question_text | Modified: enter a whole number... (no '0' option). |
|---|---|
| knowledge_question_first | — |
| additional_question_text | — |
s5_e1 · Appendix IV: Matched evidence-seeking experiment -- Paramedic -- positive polarity · Within-Subjects · d=-0.864738958369 · v=0.029921005076| effect_id | s5_e1 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Paramedic -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Paramedic; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.864738958369 |
| v | 0.029921005076 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Para Low vs Para 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos. | Provenance
Paramedic: lives... Megan, a paramedic... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (she is told the consequences). | Provenance
Over the radio, Megan is told that there is one person at the scene of the accident... | ||||||
evidence | First Person | Evidence is the agent’s own checking/navigation (first-person). | Provenance
Megan... has GPS on her phone that she can check if necessary... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | Medium | Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium. | Provenance
Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 43 | 2.02325581395 | 1.53511861017 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; low column: Para Low. | ||||||
stakes4 | Highest stakes | 43 | 3.90697674419 | 2.67095447081 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; high column: Para 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e2 · Appendix IV: Matched evidence-seeking experiment -- Vaccine -- positive polarity · Within-Subjects · d=-0.831369146354 · v=0.0373725222341| effect_id | s5_e2 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Vaccine -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Vaccine; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.831369146354 |
| v | 0.0373725222341 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Vacc Low vs Vacc 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos. | Provenance
Vaccine: lives... medical researcher... vaccine... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (assistant informs her). | Provenance
Elaine's assistant has informed her that there is one human research participant... | ||||||
evidence | First Person | Evidence is the agent’s own checking/procedure-following (first-person). | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | High | The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability is coded High. | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of the steps correctly. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 34 | 3.5 | 2.27303028283 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; low column: Vacc Low. | ||||||
stakes4 | Highest stakes | 34 | 5.73529411765 | 3.04818697758 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; high column: Vacc 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e3 · Appendix IV: Matched evidence-seeking experiment -- Mountaineering -- positive polarity · Within-Subjects · d=-0.947446942608 · v=0.049281922459| effect_id | s5_e3 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Mountaineering -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Mountaineering; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.947446942608 |
| v | 0.049281922459 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Mount Low vs Mount 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos. | Provenance
Mountaineering: personal injury... inspect the rope... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the situation (dangerous climb; consequences). | Provenance
Visibility is reducing, making the climb increasingly dangerous... | ||||||
evidence | First Person | Evidence is from the agent’s own inspection/checking (first-person). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
evidence_reliability | High | The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is coded High. | Provenance
How many times does S need to inspect the rope before she knows that it is tied securely? |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 26 | 2.65384615385 | 1.01753850806 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; low column: Mount Low. | ||||||
stakes4 | Highest stakes | 26 | 3.92307692308 | 1.59807576599 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; high column: Mount 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e4 · Appendix IV: Matched evidence-seeking experiment -- Game show -- positive polarity · Within-Subjects · d=-1.08022663846 · v=0.0370733112632| effect_id | s5_e4 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Game show -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Game show; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -1.08022663846 |
| v | 0.0370733112632 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Game Low vs Game 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos. | Provenance
Game show: finance... "What is the capital of Tanzania?"... "Dodoma" | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (money at stake in the game show). | Provenance
As this is the final round of the game show, $1,000,000 is at stake... | ||||||
evidence | First Person | Evidence is from the agent’s own memory/consideration (first-person). | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania is Dodoma? | ||||||
evidence_reliability | Medium | The evidence is memory of a recently read list without an additional check, so reliability is coded Medium. | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 40 | 2.9 | 3.00256300773 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; low column: Game Low. | ||||||
stakes4 | Highest stakes | 40 | 10.25 | 9.14204151582 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; high column: Game 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e5 · Appendix IV: Matched evidence-seeking experiment -- Introduction -- positive polarity · Within-Subjects · d=-0.394938706522 · v=0.0250901114773| effect_id | s5_e5 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Introduction -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Introduction; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.394938706522 |
| v | 0.0250901114773 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Intro Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos. | Provenance
Introduction: reputation... wrote down the speaker's name-"Dr. Woodbridge"-in her notebook... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (embarrassment/reputation consequences). | Provenance
If Nicole introduces the guest speaker by the wrong name... reflect very badly... | ||||||
evidence | External | Evidence is from an external written source (notebook). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
evidence_reliability | Medium | The evidence is a notebook record made earlier in the day, an external written record but not an official or independently verified source, so reliability is coded Medium. | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 41 | 2.0487804878 | 1.11694269128 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; low column: Intro Low. | ||||||
stakes4 | Highest stakes | 41 | 2.51219512195 | 1.22723166557 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; high column: Intro 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e6 · Appendix IV: Matched evidence-seeking experiment -- Possessions/Arson -- positive polarity · Within-Subjects · d=-0.491607487397 · v=0.0426130224156| effect_id | s5_e6 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Possessions/Arson -- positive polarity |
| subgroup_desc | Lowest vs highest stakes; Possessions/Arson; positive polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.491607487397 |
| v | 0.0426130224156 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Pval Low vs Pval 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos. | Provenance
Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist. | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The risk/stakes are described within the scenario context. | Provenance
Natalie is living in an area where there have been a series of fires set by arsonists recently. | ||||||
evidence | First Person | Evidence is from the agent’s own checking/inspection (first-person). | Provenance
A week ago, Natalie checked that the sprinklers were working... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to check the sprinklers before she knows that they are working in the X room? | ||||||
evidence_reliability | Medium | A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is coded Medium for current harmonization. | Provenance
A week ago, Natalie checked that the sprinklers were working... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 41 | 1.9512195122 | 1.67259109636 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; low column: Pval Low. | ||||||
stakes4 | Highest stakes | 41 | 2.90243902439 | 2.16569709388 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv; high column: Pval 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e7 · Appendix IV: Matched evidence-seeking experiment -- Paramedic -- negative polarity · Within-Subjects · d=-0.146300512989 · v=0.0952413634855| effect_id | s5_e7 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Paramedic -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Paramedic; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.146300512989 |
| v | 0.0952413634855 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Para Low vs Para 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos. | Provenance
Paramedic: lives... Megan, a paramedic... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (she is told the consequences). | Provenance
Over the radio, Megan is told that there is one person at the scene of the accident... | ||||||
evidence | First Person | Evidence is the agent’s own checking/navigation (first-person). | Provenance
Megan... has GPS on her phone that she can check if necessary... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | Medium | Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium. | Provenance
Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 18 | 3.33333333333 | 1.60879933308 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; low column: Para Low. | ||||||
stakes4 | Highest stakes | 18 | 3.55555555556 | 1.42342677748 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; high column: Para 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e8 · Appendix IV: Matched evidence-seeking experiment -- Vaccine -- negative polarity · Within-Subjects · d=-0.468635005912 · v=0.0370716846331| effect_id | s5_e8 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Vaccine -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Vaccine; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.468635005912 |
| v | 0.0370716846331 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Vacc Low vs Vacc 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos. | Provenance
Vaccine: lives... medical researcher... vaccine... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The vignette describes the stakes to the protagonist (assistant informs her). | Provenance
Elaine's assistant has informed her that there is one human research participant... | ||||||
evidence | First Person | Evidence is the agent’s own checking/procedure-following (first-person). | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
How many times does Elaine need to consult her check list before she knows that she is making the vaccine correctly? | ||||||
evidence_reliability | High | The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability is coded High. | Provenance
Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of the steps correctly. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 28 | 2.67857142857 | 2.03767426301 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; low column: Vacc Low. | ||||||
stakes4 | Highest stakes | 28 | 3.85714285714 | 2.91502221215 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; high column: Vacc 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e9 · Appendix IV: Matched evidence-seeking experiment -- Mountaineering -- negative polarity · Within-Subjects · d=-0.329402378246 · v=0.0855303160901| effect_id | s5_e9 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Mountaineering -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Mountaineering; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.329402378246 |
| v | 0.0855303160901 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | High |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Mount Low vs Mount 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos. | Provenance
Mountaineering: personal injury... inspect the rope... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the situation (dangerous climb; consequences). | Provenance
Visibility is reducing, making the climb increasingly dangerous... | ||||||
evidence | First Person | Evidence is from the agent’s own inspection/checking (first-person). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to inspect the rope before she knows that it is tied securely? | ||||||
evidence_reliability | High | The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is coded High. | Provenance
How many times does S need to inspect the rope before she knows that it is tied securely? |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 18 | 3.05555555556 | 0.998364675929 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; low column: Mount Low. | ||||||
stakes4 | Highest stakes | 18 | 3.44444444444 | 1.33822631614 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; high column: Mount 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e10 · Appendix IV: Matched evidence-seeking experiment -- Game show -- negative polarity · Within-Subjects · d=-0.635638288618 · v=0.0608271017613| effect_id | s5_e10 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Game show -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Game show; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | -0.635638288618 |
| v | 0.0608271017613 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Game Low vs Game 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos. | Provenance
Game show: finance... "What is the capital of Tanzania?"... "Dodoma" | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (money at stake in the game show). | Provenance
As this is the final round of the game show, $1,000,000 is at stake... | ||||||
evidence | First Person | Evidence is from the agent’s own memory/consideration (first-person). | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania is Dodoma? | ||||||
evidence_reliability | Medium | The evidence is memory of a recently read list without an additional check, so reliability is coded Medium. | Provenance
Debra has recently read a list... and the city "Dodoma" pops into her head. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 26 | 2.57692307692 | 2.08178917133 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; low column: Game Low. | ||||||
stakes4 | Highest stakes | 26 | 4.15384615385 | 2.82407234599 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; high column: Game 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e11 · Appendix IV: Matched evidence-seeking experiment -- Introduction -- negative polarity · Within-Subjects · d=0.0 · v=0.039714932657| effect_id | s5_e11 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Introduction -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Introduction; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | 0.0 |
| v | 0.039714932657 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | External |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Intro Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos. | Provenance
Introduction: reputation... wrote down the speaker's name-"Dr. Woodbridge"-in her notebook... | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | Stakes are described within the scenario (embarrassment/reputation consequences). | Provenance
If Nicole introduces the guest speaker by the wrong name... reflect very badly... | ||||||
evidence | External | Evidence is from an external written source (notebook). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr. Woodbridge"? | ||||||
evidence_reliability | Medium | The evidence is a notebook record made earlier in the day, an external written record but not an official or independently verified source, so reliability is coded Medium. | Provenance
Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day. |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 36 | 2.58333333333 | 1.87273672927 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; low column: Intro Low. | ||||||
stakes4 | Highest stakes | 36 | 2.58333333333 | 1.64533973912 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; high column: Intro 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
s5_e12 · Appendix IV: Matched evidence-seeking experiment -- Possessions/Arson -- negative polarity · Within-Subjects · d=0.255925346834 · v=0.0544689106788| effect_id | s5_e12 |
|---|---|
| subgroup | Appendix IV: Matched evidence-seeking experiment -- Possessions/Arson -- negative polarity |
| subgroup_desc | Lowest vs highest stakes; Possessions/Arson; negative polarity; raw-data recovered. |
| design | Within-Subjects |
| design_other | Lowest and highest stakes are repeated measures within the polarity-specific sample. |
| quality_flags | raw_data_recovered; evidence_seeking_cleaning_rule |
| notes | Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically. |
| metric | SMD |
|---|---|
| d | 0.255925346834 |
| v | 0.0544689106788 |
| computed_from | groups |
| needs_review | false |
| notes | Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| scenario | other |
|---|---|
| skeptical_pressure | No |
| awareness | Yes |
| evidence | First Person |
| attribution_person | Other |
| evidence_reliability | Medium |
| group_high | stakes4 |
|---|---|
| group_low | stakes1 |
| sign_convention | d = mean(low) - mean(high) |
| other_notes | Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source columns: Pval Low vs Pval 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically. |
| moderator | value | reason | evidence | ||||||
|---|---|---|---|---|---|---|---|---|---|
scenario | other | Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos. | Provenance
Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist. | ||||||
skeptical_pressure | No | No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as a separate effect. | Provenance
How many times can Elaine consult her check list and still not know that she is making the vaccine correctly? | ||||||
awareness | Yes | The risk/stakes are described within the scenario context. | Provenance
Natalie is living in an area where there have been a series of fires set by arsonists recently. | ||||||
evidence | First Person | Evidence is from the agent’s own checking/inspection (first-person). | Provenance
A week ago, Natalie checked that the sprinklers were working... | ||||||
attribution_person | Other | Prompt concerns whether the protagonist knows (third-person attribution). | Provenance
+ How many times does S need to check the sprinklers before she knows that they are working in the X room? | ||||||
evidence_reliability | Medium | A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is coded Medium for current harmonization. | Provenance
A week ago, Natalie checked that the sprinklers were working... |
| group_id | label | n | mean | sd | se | provenance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
stakes1 | Lowest stakes | 27 | 2.85185185185 | 2.31556113325 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; low column: Pval Low. | ||||||
stakes4 | Highest stakes | 27 | 2.2962962963 | 2.0156086085 | — | Provenance
Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv; high column: Pval 3. |
| test | — |
|---|---|
| t | — |
| f | — |
| chi2 | — |
| z | — |
| df1 | — |
| df2 | — |
| p | — |
| reported_d | — |
| reported_r | — |
| notes | — |
schema_version: '1.2'
paper:
paper_id: francis2019stakesscalesskepticism
citation: Francis, K. B., Beaman, C. P., & Hansen, N. (2019). Stakes, Scales, and Skepticism. Ergo, 6(16).
short_label: Francis et al. (2019)
doi: 10.3998/ergo.12405314.0006.016
published: 'Yes'
year: 2019
language: English
language_other: null
research_objective: Investigate the scalarity of stakes effects on knowledge judgments by varying stakes magnitude across
multiple scenarios using evidence-fixed and evidence-seeking paradigms, and replicate Sripada & Stanley (2012).
data_availability:
data_available_online: 'Yes'
url: https://researchdata.reading.ac.uk/205/
notes: 'Open University of Reading dataset (DOI: 10.17864/1947.205); source ZIPs/CSVs and README copied to out/external
for reproducible extraction.'
notes: Effect sizes were recovered from the public raw data; analysis/effect_sizes.qmd is the source of truth for computed
d and v values. For evidence-seeking effects, YAML stores raw low-minus-high d values; downstream meta-analysis reverses evidence-seeking effects programmatically. Study 1 negative-polarity evidence-fixed effects are reverse-coded to align with knowledge-attribution
direction.
studies:
- study_id: 1
label: 'Experiment 1: Evidence-fixed design (multiple scenarios; 4 stakes levels; polarity manipulation)'
language: English
language_other: null
objective: Test whether varying stakes magnitude (4 levels) affects agreement with knowledge attributions/denials across
six scenarios, and whether this interacts with prompt polarity ('knows' vs 'doesn't know').
sample:
n_final: 97
recruitment: mTurk
recruitment_other: null
compensation: money
compensation_other: $1.75
characteristics: 'MTurk sample: 44 females, 52 males, 1 non-binary; ages 20–71; positive polarity N=55, negative polarity
N=42.'
mean_age: 39.64
mean_age_prov:
page: null
quote: leaving a final sample of 97 participants (44 females, 52 males, 1 non-binary gender identity) between 20 and
71 years old (M = 39.64 years, SD = 12.00 years).
tei_id: null
table_ref: null
provenance:
page: null
quote: One hundred and twenty participants were recruited from MTurk and paid $1.75 each... leaving a final sample of
97 participants... Participants were randomly assigned to the positive polarity condition (N = 55) or the negative
polarity condition (N = 42).
tei_id: null
table_ref: null
design: null
design_other: '2 (polarity: know vs doesn''t know; between-subjects) × 4 (stakes scale: 1–4; within-subjects) mixed design;
six scenarios presented in a randomized block design.'
manipulated_factors:
- 'Prompt polarity: ''knows'' vs ''doesn''t know'''
paradigm: Agreement with knowledge claim
paradigm_other: null
scale:
label: Likert 7-point
points: 7
anchors: 1 = strongly disagree; 7 = strongly agree
direction: 'Higher numbers indicate stronger agreement with the prompt sentence (note: control Prompt 2 uses a reversed
Likert scale).'
provenance:
page: null
quote: Prompt 1... "You know the coin landed heads" 1 (strongly disagree) -7 (strongly agree)... Prompt 2... "You don't
know that the coin landed heads" 1 (strongly agree) -7 (strongly disagree).
tei_id: null
table_ref: null
measures:
knowledge_question_text: 'To what extent do you agree or disagree with the following claim: Subject x knows that P / doesn''t
know that P (scenario-specific).'
knowledge_question_first: null
additional_question_text: null
scenarios:
- scenario_code: paramedic
scenario_type: Paramedic GPS/wrong-turn scenario; stakes scaled by severity of consequences (lives).
high_stakes_text: 'High stakes example: school bus carrying 50 children on fire; wrong turn → children die.'
low_stakes_text: 'Low stakes example: one person with a broken arm; wrong turn → inconvenienced.'
provenance:
page: null
quote: 'Paramedic (low): "there is one person... with a broken arm... If Megan makes a wrong turn... will be inconvenienced".
Higher stakes: "a school bus carrying 50 children... on fire... the children will die."'
tei_id: null
table_ref: null
- scenario_code: vaccine
scenario_type: Vaccine checklist scenario; stakes scaled by number/severity of harms (lives).
high_stakes_text: 'High stakes example: 100 participants die after excruciating pain if steps not followed.'
low_stakes_text: 'Low stakes example: 1 participant gets mild cold-like symptoms if steps not followed.'
provenance:
page: null
quote: 'Low: "one human research participant... will give them mild cold-like symptoms." High: "100 human research participants...
will kill them all after several days of excruciating pain."'
tei_id: null
table_ref: null
- scenario_code: mountaineering
scenario_type: Mountaineering rope-inspection scenario; stakes scaled by injury severity (physical injury).
high_stakes_text: 'High stakes example: 1,000-foot drop; fall would be fatal.'
low_stakes_text: 'Low stakes example: 5-foot drop; minor injuries possible.'
provenance:
page: null
quote: 'Low: "drop... around 5 feet... minor injuries". High: "drop... around 1,000 feet... fatal".'
tei_id: null
table_ref: null
- scenario_code: game_show
scenario_type: Game show trivia scenario; stakes scaled by money at stake.
high_stakes_text: 'High stakes example: $1,000,000 at stake.'
low_stakes_text: 'Low stakes example: $1 at stake.'
provenance:
page: null
quote: 'Low: "only $1 is at stake". High: "$1,000,000 is at stake".'
tei_id: null
table_ref: null
- scenario_code: introduction
scenario_type: Guest-speaker name introduction scenario; stakes scaled by embarrassment/reputation.
high_stakes_text: 'High stakes example: national television interview; wrong name → very embarrassed and reflects badly
on university reputation.'
low_stakes_text: 'Low stakes example: lunch with 5 colleagues; wrong name → slightly embarrassed.'
provenance:
page: null
quote: 'Low: "lunch... 5 colleagues... slightly embarrassed". High: "national television... thousands... very embarrassed...
reflect very badly... university''s reputation".'
tei_id: null
table_ref: null
- scenario_code: arson
scenario_type: Arson/sprinkler system scenario; stakes scaled by value of possessions (including baby).
high_stakes_text: 'High stakes example: nursery room where baby sleeps; sprinklers failing puts baby at risk.'
low_stakes_text: 'Low stakes example: storage room with garbage/recycling at risk.'
provenance:
page: null
quote: 'Low: "storage room... garbage and recycling". High: "nursery room, where her baby sleeps... the baby, is at
risk from arson".'
tei_id: null
table_ref: null
effects:
- effect_id: s1_e1
subgroup: 'Experiment 1: Evidence-fixed design -- Paramedic -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Paramedic; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Paramedic: lives... Megan... paramedic... accident...'
tei_id: null
table_ref: null
reason: Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Megan is familiar with the surrounding area... traveling on the right route to get to the accident.
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Over the radio, Megan is told that there is one person at the scene of the accident...
tei_id: null
table_ref: null
reason: The stakes are conveyed to the protagonist within the vignette (she is told the consequences).
evidence:
provenance:
page: null
quote: Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Evidence comes from the agent’s own navigation resources/knowledge (familiarity + checking GPS).
attribution_person:
provenance:
page: null
quote: Paramedic +Subject x... knows that she will make it to the accident without taking a wrong turn.
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Subject x knows...").
evidence_reliability:
provenance:
page: null
quote: Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Paramedic Low vs Paramedic 3.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 55
mean: 5.50909090909
sd: 1.34539994429
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low
column: Paramedic Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 55
mean: 5.67272727273
sd: 1.3479002251
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high
column: Paramedic 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.121513595094
v: 0.011522200282
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
quality_flags:
- raw_data_recovered
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data.
- effect_id: s1_e2
subgroup: 'Experiment 1: Evidence-fixed design -- Vaccine -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Vaccine; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Vaccine: lives... medical researcher... vaccine...'
tei_id: null
table_ref: null
reason: Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Elaine has done this before, and she has a check list that specifies all of the steps...
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Elaine's assistant has informed her that there is one human research participant...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (assistant informs her).
evidence:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of
the steps correctly.
tei_id: null
table_ref: null
reason: Evidence is based on the agent’s own checking/procedure-following (consulting a checklist).
attribution_person:
provenance:
page: null
quote: Elaine knows that she is making the vaccine correctly
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Elaine knows...").
evidence_reliability:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of
the steps correctly.
tei_id: null
table_ref: null
reason: The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability
is coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Vaccine Low vs Vaccine 3.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 55
mean: 5.98181818182
sd: 1.14650691864
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low
column: Vaccine Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 55
mean: 6.07272727273
sd: 1.11976428496
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high
column: Vaccine 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.0802223158773
v: 0.0153833192653
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
quality_flags:
- raw_data_recovered
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data.
- effect_id: s1_e3
subgroup: 'Experiment 1: Evidence-fixed design -- Mountaineering -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Mountaineering; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Mountaineering: personal injury... mountain climbing expedition... inspect the rope...'
tei_id: null
table_ref: null
reason: Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Visibility is reducing, making the climb increasingly dangerous...
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Visibility is reducing... the drop... If not tied together securely... injuries...
tei_id: null
table_ref: null
reason: The stakes are described within the scenario (agent is in the situation and consequences are described).
evidence:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Evidence comes from the agent’s own inspection/checking (first-person).
attribution_person:
provenance:
page: null
quote: Mountaineering +Subject x... knows that the rope is tied securely.
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Subject x knows...").
evidence_reliability:
provenance:
page: null
quote: How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is
coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Mountaineering Low vs Mountaineering 3.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 55
mean: 5.83636363636
sd: 1.21355975243
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low
column: Mountaineering Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 55
mean: 5.96363636364
sd: 1.03572548135
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high
column: Mountaineering 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.112815214964
v: 0.0146515699543
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
quality_flags:
- raw_data_recovered
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data.
- effect_id: s1_e4
subgroup: 'Experiment 1: Evidence-fixed design -- Game show -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Game show; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Game show: finance... "What is the capital of Tanzania?" ... "Dodoma"'
tei_id: null
table_ref: null
reason: Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Emma has recently read a list of the most obscure world capitals and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: As this is the final round of the game show, $1,000,000 is at stake...
tei_id: null
table_ref: null
reason: Stakes are described as part of the protagonist’s situation (game show winnings/losses).
evidence:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own memory (first-person evidence).
attribution_person:
provenance:
page: null
quote: Game show +Subject x... knows that the capital of Tanzania is Dodoma.
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Subject x knows...").
evidence_reliability:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: The evidence is memory of a recently read list without an additional check, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: GameShow low vs GameShow 3.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 55
mean: 5.27272727273
sd: 1.45874813726
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low
column: GameShow low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 55
mean: 5.21818181818
sd: 1.37019745929
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high
column: GameShow 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: 0.0385435142968
v: 0.00648211042106
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
quality_flags:
- raw_data_recovered
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data.
- effect_id: s1_e5
subgroup: 'Experiment 1: Evidence-fixed design -- Introduction -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Introduction; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Introduction: reputation... introduce a guest speaker... "Dr. Woodbridge"'
tei_id: null
table_ref: null
reason: Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: If Nicole introduces the guest speaker by the wrong name... it will reflect very badly...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario as consequences of misnaming the speaker.
evidence:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: Evidence is from an external written source (notebook with the name).
attribution_person:
provenance:
page: null
quote: Introduction +Subject x... knows that the guest speakers name is "Dr. Woodbridge".
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Subject x knows...").
evidence_reliability:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: The evidence is a notebook record made earlier in the day, an external written record but not an official
or independently verified source, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Intro Low vs Intro 3.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 55
mean: 5.98181818182
sd: 1.22460740634
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low
column: Intro Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 55
mean: 6.12727272727
sd: 1.01934157134
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high
column: Intro 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.129102557916
v: 0.0126670930616
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
quality_flags:
- raw_data_recovered
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data.
- effect_id: s1_e6
subgroup: 'Experiment 1: Evidence-fixed design -- Possessions/Arson -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Possessions/Arson; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist.'
tei_id: null
table_ref: null
reason: Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Only a functioning sprinkler system can stop a fire set by an arsonist.
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Natalie is living in an area where there have been a series of fires set by arsonists recently.
tei_id: null
table_ref: null
reason: The risk/stakes are described within the scenario context.
evidence:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: Evidence comes from the agent’s own prior checking/inspection (first-person).
attribution_person:
provenance:
page: null
quote: Arson +Subject x... knows that the sprinklers are working in the x room.
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Subject x knows...").
evidence_reliability:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is
coded Medium for current harmonization.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Personal Val Low vs Personal Val 3.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 55
mean: 5.90909090909
sd: 1.00503781526
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; low
column: Personal Val Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 55
mean: 5.89090909091
sd: 1.19679707232
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv; high
column: Personal Val 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: 0.0164528734539
v: 0.0159562267938
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
quality_flags:
- raw_data_recovered
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data.
- effect_id: s1_e7
subgroup: 'Experiment 1: Evidence-fixed design -- Paramedic -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Paramedic; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Paramedic: lives... Megan... paramedic... accident...'
tei_id: null
table_ref: null
reason: Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Megan is familiar with the surrounding area... traveling on the right route to get to the accident.
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Over the radio, Megan is told that there is one person at the scene of the accident...
tei_id: null
table_ref: null
reason: The stakes are conveyed to the protagonist within the vignette (she is told the consequences).
evidence:
provenance:
page: null
quote: Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Evidence comes from the agent’s own navigation resources/knowledge (familiarity + checking GPS).
attribution_person:
provenance:
page: null
quote: Paramedic +Subject x... knows that she will make it to the accident without taking a wrong turn.
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Subject x knows...").
evidence_reliability:
provenance:
page: null
quote: Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Paramedic Low vs Paramedic 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded
knowledge-attribution scores (8 - raw agreement with denial).'
groups:
- group_id: stakes1
label: Lowest stakes
n: 42
mean: 5.42857142857
sd: 1.54829342941
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low
column: Paramedic Low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 42
mean: 5.64285714286
sd: 1.30330590108
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high
column: Paramedic 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.149740006799
v: 0.0210717079245
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing
d.
quality_flags:
- raw_data_recovered
- negative_polarity_reverse_coded
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded
to knowledge-attribution direction before computing d.
- effect_id: s1_e8
subgroup: 'Experiment 1: Evidence-fixed design -- Vaccine -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Vaccine; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Vaccine: lives... medical researcher... vaccine...'
tei_id: null
table_ref: null
reason: Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Elaine has done this before, and she has a check list that specifies all of the steps...
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Elaine's assistant has informed her that there is one human research participant...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (assistant informs her).
evidence:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of
the steps correctly.
tei_id: null
table_ref: null
reason: Evidence is based on the agent’s own checking/procedure-following (consulting a checklist).
attribution_person:
provenance:
page: null
quote: Elaine knows that she is making the vaccine correctly
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Elaine knows...").
evidence_reliability:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of
the steps correctly.
tei_id: null
table_ref: null
reason: The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability
is coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Vaccine Low vs Vaccine 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded
knowledge-attribution scores (8 - raw agreement with denial).'
groups:
- group_id: stakes1
label: Lowest stakes
n: 42
mean: 5.61904761905
sd: 1.43054451431
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low
column: Vaccine Low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 42
mean: 5.54761904762
sd: 1.51741726905
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high
column: Vaccine 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: 0.0484386042375
v: 0.0103158241577
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing
d.
quality_flags:
- raw_data_recovered
- negative_polarity_reverse_coded
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded
to knowledge-attribution direction before computing d.
- effect_id: s1_e9
subgroup: 'Experiment 1: Evidence-fixed design -- Mountaineering -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Mountaineering; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Mountaineering: personal injury... mountain climbing expedition... inspect the rope...'
tei_id: null
table_ref: null
reason: Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Visibility is reducing, making the climb increasingly dangerous...
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Visibility is reducing... the drop... If not tied together securely... injuries...
tei_id: null
table_ref: null
reason: The stakes are described within the scenario (agent is in the situation and consequences are described).
evidence:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Evidence comes from the agent’s own inspection/checking (first-person).
attribution_person:
provenance:
page: null
quote: Mountaineering +Subject x... knows that the rope is tied securely.
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Subject x knows...").
evidence_reliability:
provenance:
page: null
quote: How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is
coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Mountaineering Low vs Mountaineering 3. For this negative-polarity evidence-fixed effect, group means are
reverse-coded knowledge-attribution scores (8 - raw agreement with denial).'
groups:
- group_id: stakes1
label: Lowest stakes
n: 42
mean: 5.40476190476
sd: 1.79510885341
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low
column: Mountaineering Low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 42
mean: 5.33333333333
sd: 1.72027602247
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high
column: Mountaineering 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: 0.0406284919837
v: 0.0125311746321
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing
d.
quality_flags:
- raw_data_recovered
- negative_polarity_reverse_coded
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded
to knowledge-attribution direction before computing d.
- effect_id: s1_e10
subgroup: 'Experiment 1: Evidence-fixed design -- Game show -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Game show; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Game show: finance... "What is the capital of Tanzania?" ... "Dodoma"'
tei_id: null
table_ref: null
reason: Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Emma has recently read a list of the most obscure world capitals and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: As this is the final round of the game show, $1,000,000 is at stake...
tei_id: null
table_ref: null
reason: Stakes are described as part of the protagonist’s situation (game show winnings/losses).
evidence:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own memory (first-person evidence).
attribution_person:
provenance:
page: null
quote: Game show +Subject x... knows that the capital of Tanzania is Dodoma.
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Subject x knows...").
evidence_reliability:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: The evidence is memory of a recently read list without an additional check, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: GameShow low vs GameShow 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded
knowledge-attribution scores (8 - raw agreement with denial).'
groups:
- group_id: stakes1
label: Lowest stakes
n: 42
mean: 5.33333333333
sd: 1.60284300262
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low
column: GameShow low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 42
mean: 5.11904761905
sd: 1.65577159012
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high
column: GameShow 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: 0.131502174867
v: 0.0104410020413
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing
d.
quality_flags:
- raw_data_recovered
- negative_polarity_reverse_coded
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded
to knowledge-attribution direction before computing d.
- effect_id: s1_e11
subgroup: 'Experiment 1: Evidence-fixed design -- Introduction -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Introduction; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Introduction: reputation... introduce a guest speaker... "Dr. Woodbridge"'
tei_id: null
table_ref: null
reason: Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: If Nicole introduces the guest speaker by the wrong name... it will reflect very badly...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario as consequences of misnaming the speaker.
evidence:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: Evidence is from an external written source (notebook with the name).
attribution_person:
provenance:
page: null
quote: Introduction +Subject x... knows that the guest speakers name is "Dr. Woodbridge".
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Subject x knows...").
evidence_reliability:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: The evidence is a notebook record made earlier in the day, an external written record but not an official
or independently verified source, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Intro Low vs Intro 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded knowledge-attribution
scores (8 - raw agreement with denial).'
groups:
- group_id: stakes1
label: Lowest stakes
n: 42
mean: 5.97619047619
sd: 1.19935135392
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low
column: Intro Low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 42
mean: 5.7380952381
sd: 1.62389960956
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high
column: Intro 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: 0.166792146986
v: 0.0187820334615
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing
d.
quality_flags:
- raw_data_recovered
- negative_polarity_reverse_coded
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded
to knowledge-attribution direction before computing d.
- effect_id: s1_e12
subgroup: 'Experiment 1: Evidence-fixed design -- Possessions/Arson -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Possessions/Arson; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist.'
tei_id: null
table_ref: null
reason: Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: Only a functioning sprinkler system can stop a fire set by an arsonist.
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Natalie is living in an area where there have been a series of fires set by arsonists recently.
tei_id: null
table_ref: null
reason: The risk/stakes are described within the scenario context.
evidence:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: Evidence comes from the agent’s own prior checking/inspection (first-person).
attribution_person:
provenance:
page: null
quote: Arson +Subject x... knows that the sprinklers are working in the x room.
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ("Subject x knows...").
evidence_reliability:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is
coded Medium for current harmonization.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Personal Val Low vs Personal Val 3. For this negative-polarity evidence-fixed effect, group means are reverse-coded
knowledge-attribution scores (8 - raw agreement with denial).'
groups:
- group_id: stakes1
label: Lowest stakes
n: 42
mean: 5.78571428571
sd: 1.3710546819
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; low
column: Personal Val Low. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 42
mean: 5.80952380952
sd: 1.41831392923
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv; high
column: Personal Val 3. Negative-polarity responses reverse-coded as 8 - raw agreement with denial.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.0170691726829
v: 0.0217680938563
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd using paired lowest/highest stakes responses.
Negative-polarity agreement-with-denial responses were reverse-coded to knowledge-attribution direction before computing
d.
quality_flags:
- raw_data_recovered
- negative_polarity_reverse_coded
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Negative-polarity agreement-with-denial responses were reverse-coded
to knowledge-attribution direction before computing d.
notes: Effects are scenario-by-polarity lowest-vs-highest stakes contrasts recovered from raw data. Study 1 negative-polarity
evidence-fixed effects reverse-code agreement with “doesn’t know” to knowledge-attribution direction before computing
d.
- study_id: 2
label: 'Appendix II: Registered replication of Sripada & Stanley (2012) (pine nuts)'
language: English
language_other: null
objective: Replicate Sripada & Stanley’s evidence-fixed design across three vignette pairs (basic; implicit/explicit; ignorant),
measuring (i) evidence strength and (ii) agreement with a knowledge attribution.
sample:
n_final: 367
recruitment: mTurk
recruitment_other: null
compensation: money
compensation_other: $0.50
characteristics: 'MTurk sample: 183 females, 246 males, 1 non-binary; ages 20–67; 63 suspicious/VPS responses removed;
assigned to six conditions (N=58–68 per condition).'
mean_age: 35.99
mean_age_prov:
page: null
quote: Four hundred and thirty participants... between 20 and 67 years (M = 35.99 years, SD = 10.55 years)... Following
screening procedures, 63 VPS and further suspicious responses were flagged and removed.
tei_id: null
table_ref: null
provenance:
page: null
quote: Four hundred and thirty participants... recruited from MTurk and paid $0.50... Following screening procedures,
63 VPS and further suspicious responses were flagged and removed... randomly assigned to one of six conditions (N
= 58 -68 per condition).
tei_id: null
table_ref: null
design: Between-Subjects
design_other: 3 vignette pairs × 2 stakes (low vs high) between-subjects; each participant reads one vignette and answers
Q1 (evidence strength) then Q2 (knowledge agreement).
manipulated_factors:
- 'Vignette pair type: Basic vs Implicit/Explicit vs Ignorant'
paradigm: Agreement with knowledge claim
paradigm_other: null
scale:
label: Likert 7-point
points: 7
anchors: 'Q1: 1 = Very weak evidence; 7 = Very strong evidence. Q2: 1 = Strongly agree; 7 = Strongly disagree.'
direction: Higher numbers indicate stronger evidence (Q1). For Q2, anchors are Strongly agree (1) to Strongly disagree
(7) (paper reports effects in terms of 'levels of agreement').
provenance:
page: null
quote: Participants responded to the first question (evidence prompt) along a 7-point Likert-type scale (1-Very weak
evidence, 7-Very strong evidence) and to the second prompt (knowledge prompt) along another 7-point Likert-type scale
(1-Strongly agree, 7-Strongly disagree).
tei_id: null
table_ref: null
measures:
knowledge_question_text: '"Hannah knows her noodles are not topped with pine nuts."'
knowledge_question_first: null
additional_question_text: What is the strength of Hannah's evidence that her noodles are not topped with pine nuts?
scenarios:
- scenario_code: peanuts
scenario_type: Pine-nuts/noodles vignette (Sripada & Stanley 2012 replication).
high_stakes_text: null
low_stakes_text: null
provenance:
page: null
quote: What is the strength of Hannah's evidence that her noodles are not topped with pine nuts?
tei_id: null
table_ref: null
effects:
- effect_id: s2_e1
subgroup: Basic — Evidence strength (Q1)
subgroup_desc: Strength of evidence rating (7-point)
design: Between-Subjects
design_other: null
moderators:
scenario: peanuts
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: null
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: What is the strength of Hannah's evidence that her noodles are not topped with pine nuts?
tei_id: null
table_ref: null
reason: This is the Sripada & Stanley-style pine-nuts restaurant vignette (coded as peanuts-style scenario).
skeptical_pressure:
provenance:
page: null
quote: Hannah notes that the menu says her dish does not contain pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong.
awareness:
provenance:
page: null
quote: Hannah is very much aware of this, and has known this for a very long time.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; the Basic vignette protagonist is aware of the allergy
stakes.
evidence:
provenance:
page: null
quote: Hannah notes that the menu says her dish does not contain pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; evidence is from an external written source (the menu).
attribution_person:
provenance:
page: null
quote: What is the strength of Hannah's evidence...
tei_id: null
table_ref: null
reason: DV is evidence-strength (not a knowledge attribution), so self/other knowledge-ascription coding is not applicable.
evidence_reliability:
provenance:
page: null
quote: Sarah says, 'The noodles may be topped with pine nuts.' Hannah notes that the menu says her dish does not contain
pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; menu.
contrast:
group_high: Basic_high
group_low: Basic_low
sign_convention: d = mean(low) - mean(high)
other_notes: null
groups:
- group_id: Basic_low
label: Basic low stakes
n: 58
mean: 3.93103448276
sd: 1.82441597447
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column:
Basic Low__Strength_Evidence.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: Basic_high
label: Basic high stakes
n: 68
mean: 2.89705882353
sd: 1.82960484994
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column:
Basic High__Strength_Evidence.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
reported_test:
test: t
t: 3.17
f: null
chi2: null
z: null
df1: 124
df2: null
p: 0.002
reported_d: 0.57
reported_r: null
notes: 'Direction reported: strength of evidence higher in low-stakes scenario.'
provenance:
page: 12
quote: In the strength of evidence comparison (left panel) there was a medium effect of stakes in the basic vignette
pair, (t(124) = 3.17, p =.002, d = 0.57) with strength of evidence higher in the low stakes scenario.
tei_id: null
table_ref: Figure 2
effect_size:
metric: SMD
d: 0.565873199091
v: 0.0332179460976
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd.
quality_flags:
- raw_data_recovered
notes: Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes.
paradigm: Other
paradigm_other: Strength of evidence rating
- effect_id: s2_e2
subgroup: Basic — Knowledge attribution (Q2)
subgroup_desc: Agreement with 'Hannah knows...' reverse-coded to agreement
design: Between-Subjects
design_other: null
moderators:
scenario: peanuts
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: '"Hannah knows her noodles are not topped with pine nuts."'
tei_id: null
table_ref: null
reason: This is the Sripada & Stanley-style pine-nuts restaurant vignette (coded as peanuts-style scenario).
skeptical_pressure:
provenance:
page: null
quote: Hannah notes that the menu says her dish does not contain pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong.
awareness:
provenance:
page: null
quote: Hannah is very much aware of this, and has known this for a very long time.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; the Basic vignette protagonist is aware of the allergy
stakes.
evidence:
provenance:
page: null
quote: Hannah notes that the menu says her dish does not contain pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; evidence is from an external written source (the menu).
attribution_person:
provenance:
page: null
quote: '"Hannah knows her noodles are not topped with pine nuts."'
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ('Hannah knows...').
evidence_reliability:
provenance:
page: null
quote: Sarah says, 'The noodles may be topped with pine nuts.' Hannah notes that the menu says her dish does not contain
pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; menu.
contrast:
group_high: Basic_high
group_low: Basic_low
sign_convention: d = mean(low) - mean(high)
other_notes: null
groups:
- group_id: Basic_low
label: Basic low stakes
n: 58
mean: 3.72413793103
sd: 1.84289154423
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column:
Basic Low__Know_Prompt.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: Basic_high
label: Basic high stakes
n: 68
mean: 2.91176470588
sd: 1.70806073258
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column:
Basic High__Know_Prompt.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
reported_test:
test: t
t: -2.57
f: null
chi2: null
z: null
df1: 124
df2: null
p: 0.011
reported_d: 0.46
reported_r: null
notes: 'Direction reported: levels of agreement higher in low-stakes scenario.'
provenance:
page: 12
quote: For the levels of agreement comparison (right panel) there was a smaller effect of stakes in the basic vignette
pair, (t(124) = -.2.57, p =.011, d = 0.46) with levels of agreement are higher in the low stakes scenario.
tei_id: null
table_ref: Figure 2
effect_size:
metric: SMD
d: 0.458627340663
v: 0.0327819403839
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; Q2 was reverse-coded to agreement before computing
low minus high.
quality_flags:
- raw_data_recovered
- q2_reverse_coded_to_agreement
notes: Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes.
paradigm: Agreement with knowledge claim
paradigm_other: null
- effect_id: s2_e3
subgroup: Implicit/Explicit — Evidence strength (Q1)
subgroup_desc: Strength of evidence rating (7-point)
design: Between-Subjects
design_other: null
moderators:
scenario: peanuts
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: null
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: What is the strength of Hannah's evidence that her noodles are not topped with pine nuts?
tei_id: null
table_ref: null
reason: Same pine-nuts/noodles vignette family; coded as peanuts-style scenario.
skeptical_pressure:
provenance:
page: null
quote: Hannah notes that the menu says her dish does not contain pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong.
awareness:
provenance:
page: null
quote: 'Implicit Low Stakes: "Hannah likes the taste of most foods and is not a very picky eater."'
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; no ignorance manipulation is present.
evidence:
provenance:
page: null
quote: Hannah notes that the menu says her dish does not contain pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; evidence is from an external written source (the menu).
attribution_person:
provenance:
page: null
quote: What is the strength of Hannah's evidence...
tei_id: null
table_ref: null
reason: DV is evidence-strength (not a knowledge attribution), so self/other knowledge-ascription coding is not applicable.
evidence_reliability:
provenance:
page: null
quote: Sarah says, 'The noodles may be topped with pine nuts.' Hannah notes that the menu says her dish does not contain
pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; menu.
contrast:
group_high: ImplicitExplicit_high
group_low: ImplicitExplicit_low
sign_convention: d = mean(low) - mean(high)
other_notes: null
groups:
- group_id: ImplicitExplicit_low
label: Implicit/Explicit low stakes
n: 58
mean: 3.62068965517
sd: 1.94510258789
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column:
Implicit Low__Strength_Evidence.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: ImplicitExplicit_high
label: Implicit/Explicit high stakes
n: 61
mean: 3.4262295082
sd: 1.96179353648
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column:
Explicit High__Strength_Evidence.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
reported_test:
test: t
t: 0.54
f: null
chi2: null
z: null
df1: 117
df2: null
p: 0.588
reported_d: null
reported_r: null
notes: Effect not significant per Figure 2 caption.
provenance:
page: 12
quote: 'The effect of stakes was not significant in the other vignettes [Implicit/Explicit: t(117) = 0.54, p = .588;
...].'
tei_id: null
table_ref: Figure 2
effect_size:
metric: SMD
d: 0.0995353183474
v: 0.0336764491585
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd.
quality_flags:
- raw_data_recovered
notes: Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes.
paradigm: Other
paradigm_other: Strength of evidence rating
- effect_id: s2_e4
subgroup: Implicit/Explicit — Knowledge attribution (Q2)
subgroup_desc: Agreement with 'Hannah knows...' reverse-coded to agreement
design: Between-Subjects
design_other: null
moderators:
scenario: peanuts
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: '"Hannah knows her noodles are not topped with pine nuts."'
tei_id: null
table_ref: null
reason: Same pine-nuts/noodles vignette family; coded as peanuts-style scenario.
skeptical_pressure:
provenance:
page: null
quote: Hannah notes that the menu says her dish does not contain pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong.
awareness:
provenance:
page: null
quote: 'Implicit Low Stakes: "Hannah likes the taste of most foods and is not a very picky eater."'
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; no ignorance manipulation is present.
evidence:
provenance:
page: null
quote: Hannah notes that the menu says her dish does not contain pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; evidence is from an external written source (the menu).
attribution_person:
provenance:
page: null
quote: '"Hannah knows her noodles are not topped with pine nuts."'
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ('Hannah knows...').
evidence_reliability:
provenance:
page: null
quote: Sarah says, 'The noodles may be topped with pine nuts.' Hannah notes that the menu says her dish does not contain
pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; menu.
contrast:
group_high: ImplicitExplicit_high
group_low: ImplicitExplicit_low
sign_convention: d = mean(low) - mean(high)
other_notes: null
groups:
- group_id: ImplicitExplicit_low
label: Implicit/Explicit low stakes
n: 58
mean: 3.79310344828
sd: 1.92635116117
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column:
Implicit Low__Know_Prompt.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: ImplicitExplicit_high
label: Implicit/Explicit high stakes
n: 61
mean: 3.27868852459
sd: 1.87199668394
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column:
Explicit High__Know_Prompt.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
reported_test:
test: t
t: -1.48
f: null
chi2: null
z: null
df1: 117
df2: null
p: 0.142
reported_d: null
reported_r: null
notes: Effect not significant per Figure 2 caption.
provenance:
page: 12
quote: 'Once again there was no significant effect of stakes in the other vignettes [Implicit/Explicit: t(117) = -1.48,
p = .142; ...].'
tei_id: null
table_ref: Figure 2
effect_size:
metric: SMD
d: 0.270934142758
v: 0.0339432476044
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; Q2 was reverse-coded to agreement before computing
low minus high.
quality_flags:
- raw_data_recovered
- q2_reverse_coded_to_agreement
notes: Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes.
paradigm: Agreement with knowledge claim
paradigm_other: null
- effect_id: s2_e5
subgroup: Ignorant — Evidence strength (Q1)
subgroup_desc: Strength of evidence rating (7-point)
design: Between-Subjects
design_other: null
moderators:
scenario: peanuts
skeptical_pressure: 'No'
awareness: 'No'
evidence: External
attribution_person: null
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: What is the strength of Hannah's evidence that her noodles are not topped with pine nuts?
tei_id: null
table_ref: null
reason: Same pine-nuts/noodles vignette family; coded as peanuts-style scenario.
skeptical_pressure:
provenance:
page: null
quote: Hannah notes that the menu says her dish does not contain Mongolian pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong.
awareness:
provenance:
page: null
quote: the protagonist being ignorant of the stakes involved (ignorant low/ignorant high)
tei_id: null
table_ref: null
reason: Paper explicitly describes the Ignorant manipulation as the protagonist being unaware of the stakes.
evidence:
provenance:
page: null
quote: Sarah says, 'I heard that Mongolian dishes are often served topped with Mongolian pine nuts.' Hannah notes that
the menu says her dish does not contain Mongolian pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; evidence is from external sources (testimony + menu).
attribution_person:
provenance:
page: null
quote: What is the strength of Hannah's evidence...
tei_id: null
table_ref: null
reason: DV is evidence-strength (not a knowledge attribution), so self/other knowledge-ascription coding is not applicable.
evidence_reliability:
provenance:
page: null
quote: Sarah says, 'I heard that Mongolian dishes are often served topped with Mongolian pine nuts.' Hannah notes that
the menu says her dish does not contain Mongolian pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; menu.
contrast:
group_high: Ignorant_high
group_low: Ignorant_low
sign_convention: d = mean(low) - mean(high)
other_notes: null
groups:
- group_id: Ignorant_low
label: Ignorant low stakes
n: 62
mean: 4.25806451613
sd: 1.88118800858
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column:
Ignorant Low__Strength_Evidence.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: Ignorant_high
label: Ignorant high stakes
n: 60
mean: 4.03333333333
sd: 1.88631707048
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column:
Ignorant High__Strength_Evidence.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
reported_test:
test: t
t: 0.84
f: null
chi2: null
z: null
df1: 120
df2: null
p: 0.511
reported_d: null
reported_r: null
notes: Effect not significant per Figure 2 caption.
provenance:
page: 12
quote: 'The effect of stakes was not significant... [ ... Ignorant: t(120) = 0.84, p =.511].'
tei_id: null
table_ref: Figure 2
effect_size:
metric: SMD
d: 0.119302333567
v: 0.0328540310837
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd.
quality_flags:
- raw_data_recovered
notes: Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes.
paradigm: Other
paradigm_other: Strength of evidence rating
- effect_id: s2_e6
subgroup: Ignorant — Knowledge attribution (Q2)
subgroup_desc: Agreement with 'Hannah knows...' reverse-coded to agreement
design: Between-Subjects
design_other: null
moderators:
scenario: peanuts
skeptical_pressure: 'No'
awareness: 'No'
evidence: External
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: '"Hannah knows her noodles are not topped with pine nuts."'
tei_id: null
table_ref: null
reason: Same pine-nuts/noodles vignette family; coded as peanuts-style scenario.
skeptical_pressure:
provenance:
page: null
quote: Hannah notes that the menu says her dish does not contain Mongolian pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; no explicit prompt says that the menu might be wrong.
awareness:
provenance:
page: null
quote: the protagonist being ignorant of the stakes involved (ignorant low/ignorant high)
tei_id: null
table_ref: null
reason: Paper explicitly describes the Ignorant manipulation as the protagonist being unaware of the stakes.
evidence:
provenance:
page: null
quote: Sarah says, 'I heard that Mongolian dishes are often served topped with Mongolian pine nuts.' Hannah notes that
the menu says her dish does not contain Mongolian pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; evidence is from external sources (testimony + menu).
attribution_person:
provenance:
page: null
quote: '"Hannah knows her noodles are not topped with pine nuts."'
tei_id: null
table_ref: null
reason: Participants evaluate a third-person knowledge attribution ('Hannah knows...').
evidence_reliability:
provenance:
page: null
quote: Sarah says, 'I heard that Mongolian dishes are often served topped with Mongolian pine nuts.' Hannah notes that
the menu says her dish does not contain Mongolian pine nuts.
tei_id: null
table_ref: sripadastanley2012empiricaltestsinterest.yaml
reason: Coded to match the original Sripada & Stanley extraction; menu.
contrast:
group_high: Ignorant_high
group_low: Ignorant_low
sign_convention: d = mean(low) - mean(high)
other_notes: null
groups:
- group_id: Ignorant_low
label: Ignorant low stakes
n: 62
mean: 4.08064516129
sd: 2.01061435107
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; low column:
Ignorant Low__Know_Prompt.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: Ignorant_high
label: Ignorant high stakes
n: 60
mean: 3.71666666667
sd: 2.09188639762
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Replication_Experiment/xReplic_data_post_removal.csv; high column:
Ignorant High__Know_Prompt.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
reported_test:
test: t
t: -0.98
f: null
chi2: null
z: null
df1: 120
df2: null
p: 0.329
reported_d: null
reported_r: null
notes: Effect not significant per Figure 2 caption.
provenance:
page: 12
quote: 'Once again there was no significant effect of stakes... [ ... Ignorant: t(120) = -0.98, p =.329].'
tei_id: null
table_ref: Figure 2
effect_size:
metric: SMD
d: 0.177466028998
v: 0.0329247734798
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; Q2 was reverse-coded to agreement before computing
low minus high.
quality_flags:
- raw_data_recovered
- q2_reverse_coded_to_agreement
notes: Raw-data recovered values replace previously missing or rounded reported-d-only effect sizes.
paradigm: Agreement with knowledge claim
paradigm_other: null
- study_id: 3
label: 'Experiment 2: Evidence-seeking design (original prompts)'
language: English
language_other: null
objective: Test for stakes effects on how much evidence is needed for knowledge (positive prompt) or can be had while still
not knowing (negative prompt), across six scenarios and four stakes levels.
sample:
n_final: 109
recruitment: mTurk
recruitment_other: null
compensation: money
compensation_other: $1.75
characteristics: 'MTurk sample: 54 females, 55 males; ages 21–74; positive polarity N=58, negative polarity N=51.'
mean_age: 38.98
mean_age_prov:
page: null
quote: leaving a final sample of 109 participants (54 females, 55 males) between 21 and 74 years old (M = 38.98 years,
SD = 11.76 years).
tei_id: null
table_ref: null
provenance:
page: null
quote: One hundred and twenty participants were recruited from MTurk and paid $1.75... leaving a final sample of 109
participants... Participants were randomly assigned to a positive polarity condition (N = 58) or a negative polarity
condition (N = 51).
tei_id: null
table_ref: null
design: null
design_other: Stakes (4 levels) within-subjects; prompt polarity (positive vs negative) between-subjects; six scenarios
presented in a randomized block design.
manipulated_factors:
- 'Prompt polarity: evidence-seeking positive vs evidence-seeking negative'
paradigm: Rating how much evidence is needed for knowledge
paradigm_other: null
scale:
label: other
points: null
anchors: Numeric free response (whole number of checks); positive prompt allows '0' (knows without checking) and both
prompts allow 'never'.
direction: Higher numbers indicate more evidence required for knowledge (positive) / more checking still insufficient
for knowledge (negative).
provenance:
page: null
quote: 'enter a whole number: 1, 2, 3 . . . etc. If you think Elaine knows without having to check, write "0"... If
you think Elaine will never know... write "never".'
tei_id: null
table_ref: null
measures:
knowledge_question_text: How many times does S need to check F before she knows that P?
knowledge_question_first: null
additional_question_text: How many times can S check F and still not know that P?
scenarios: []
effects:
- effect_id: s3_e1
subgroup: 'Experiment 2: Evidence-seeking design -- Paramedic -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Paramedic; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Paramedic: lives... Megan, a paramedic...'
tei_id: null
table_ref: null
reason: Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Over the radio, Megan is told that there is one person at the scene of the accident...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (she is told the consequences).
evidence:
provenance:
page: null
quote: Megan... has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/navigation (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Paramedic Low vs Paramedic 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 39
mean: 1.94871794872
sd: 1.12270158074
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low
column: Paramedic Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 39
mean: 2.76923076923
sd: 1.34676099668
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high
column: Paramedic 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.661808915227
v: 0.0276260903612
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e2
subgroup: 'Experiment 2: Evidence-seeking design -- Vaccine -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Vaccine; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Vaccine: lives... medical researcher... vaccine...'
tei_id: null
table_ref: null
reason: Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Elaine's assistant has informed her that there is one human research participant...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (assistant informs her).
evidence:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/procedure-following (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of
the steps correctly.
tei_id: null
table_ref: null
reason: The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability
is coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Vaccine Low vs Vaccine 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 48
mean: 2.47916666667
sd: 1.58435950323
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low
column: Vaccine Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 48
mean: 4.22916666667
sd: 2.48604259847
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high
column: Vaccine 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.839514293934
v: 0.0227377109024
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e3
subgroup: 'Experiment 2: Evidence-seeking design -- Mountaineering -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Mountaineering; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Mountaineering: personal injury... inspect the rope...'
tei_id: null
table_ref: null
reason: Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Visibility is reducing, making the climb increasingly dangerous...
tei_id: null
table_ref: null
reason: Stakes are described within the situation (dangerous climb; consequences).
evidence:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own inspection/checking (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is
coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Mountaineering Low vs Mountaineering 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 44
mean: 2.0
sd: 1.20077494357
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low
column: Mountaineering Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 44
mean: 2.88636363636
sd: 1.38456456241
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high
column: Mountaineering 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.683958446104
v: 0.0211432766559
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e4
subgroup: 'Experiment 2: Evidence-seeking design -- Game show -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Game show; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Game show: finance... "What is the capital of Tanzania?"... "Dodoma"'
tei_id: null
table_ref: null
reason: Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: As this is the final round of the game show, $1,000,000 is at stake...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (money at stake in the game show).
evidence:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own memory/consideration (first-person).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania
is Dodoma?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: The evidence is memory of a recently read list without an additional check, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: GameShow low vs GameShow 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 27
mean: 2.66666666667
sd: 2.44948974278
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low
column: GameShow low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 27
mean: 5.48148148148
sd: 3.8567659865
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high
column: GameShow 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.871275071557
v: 0.0732423045923
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e5
subgroup: 'Experiment 2: Evidence-seeking design -- Introduction -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Introduction; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Introduction: reputation... wrote down the speaker''s name-"Dr. Woodbridge"-in her notebook...'
tei_id: null
table_ref: null
reason: Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: If Nicole introduces the guest speaker by the wrong name... reflect very badly...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (embarrassment/reputation consequences).
evidence:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Evidence is from an external written source (notebook).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: The evidence is a notebook record made earlier in the day, an external written record but not an official
or independently verified source, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Intro Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 50
mean: 1.74
sd: 0.828325086533
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low
column: Intro Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 50
mean: 2.4
sd: 1.12485826772
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high
column: Intro 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.668163278631
v: 0.0224825444801
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e6
subgroup: 'Experiment 2: Evidence-seeking design -- Possessions/Arson -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Possessions/Arson; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist.'
tei_id: null
table_ref: null
reason: Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Natalie is living in an area where there have been a series of fires set by arsonists recently.
tei_id: null
table_ref: null
reason: The risk/stakes are described within the scenario context.
evidence:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own checking/inspection (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to check the sprinklers before she knows that they are working in the X room?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is
coded Medium for current harmonization.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Personal Val Low vs Personal Val 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 47
mean: 1.53191489362
sd: 0.905323959067
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; low
column: Personal Val Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 47
mean: 2.82978723404
sd: 2.06754579294
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv; high
column: Personal Val 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.813209200727
v: 0.0306234050604
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e7
subgroup: 'Experiment 2: Evidence-seeking design -- Paramedic -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Paramedic; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Paramedic: lives... Megan, a paramedic...'
tei_id: null
table_ref: null
reason: Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Over the radio, Megan is told that there is one person at the scene of the accident...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (she is told the consequences).
evidence:
provenance:
page: null
quote: Megan... has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/navigation (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Paramedic Low vs Paramedic 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 35
mean: 2.25714285714
sd: 2.06287716185
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low
column: Paramedic Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 35
mean: 2.14285714286
sd: 1.83339699406
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high
column: Paramedic 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: 0.0585626171598
v: 0.0377444913971
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e8
subgroup: 'Experiment 2: Evidence-seeking design -- Vaccine -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Vaccine; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Vaccine: lives... medical researcher... vaccine...'
tei_id: null
table_ref: null
reason: Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Elaine's assistant has informed her that there is one human research participant...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (assistant informs her).
evidence:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/procedure-following (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of
the steps correctly.
tei_id: null
table_ref: null
reason: The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability
is coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Vaccine Low vs Vaccine 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 34
mean: 2.70588235294
sd: 1.9467052471
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low
column: Vaccine Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 34
mean: 3.79411764706
sd: 3.64134017757
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high
column: Vaccine 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.372724636385
v: 0.034404491619
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e9
subgroup: 'Experiment 2: Evidence-seeking design -- Mountaineering -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Mountaineering; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Mountaineering: personal injury... inspect the rope...'
tei_id: null
table_ref: null
reason: Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Visibility is reducing, making the climb increasingly dangerous...
tei_id: null
table_ref: null
reason: Stakes are described within the situation (dangerous climb; consequences).
evidence:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own inspection/checking (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is
coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Mountaineering Low vs Mountaineering 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 36
mean: 1.86111111111
sd: 1.26835726778
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low
column: Mountaineering Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 36
mean: 2.47222222222
sd: 1.88961237312
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high
column: Mountaineering 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.379749242052
v: 0.0431311478436
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e10
subgroup: 'Experiment 2: Evidence-seeking design -- Game show -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Game show; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Game show: finance... "What is the capital of Tanzania?"... "Dodoma"'
tei_id: null
table_ref: null
reason: Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: As this is the final round of the game show, $1,000,000 is at stake...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (money at stake in the game show).
evidence:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own memory/consideration (first-person).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania
is Dodoma?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: The evidence is memory of a recently read list without an additional check, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Game Show Low vs Game Show 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 33
mean: 8.15151515152
sd: 10.5478706741
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low
column: Game Show Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 33
mean: 8.0
sd: 8.15858443604
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high
column: Game Show 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: 0.0160686971955
v: 0.0283223978536
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e11
subgroup: 'Experiment 2: Evidence-seeking design -- Introduction -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Introduction; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Introduction: reputation... wrote down the speaker''s name-"Dr. Woodbridge"-in her notebook...'
tei_id: null
table_ref: null
reason: Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: If Nicole introduces the guest speaker by the wrong name... reflect very badly...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (embarrassment/reputation consequences).
evidence:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Evidence is from an external written source (notebook).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: The evidence is a notebook record made earlier in the day, an external written record but not an official
or independently verified source, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Introductions Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 32
mean: 1.65625
sd: 0.970845158911
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low
column: Introductions Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 32
mean: 2.09375
sd: 1.44488809702
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high
column: Intro 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.355430263187
v: 0.0448463453658
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s3_e12
subgroup: 'Experiment 2: Evidence-seeking design -- Possessions/Arson -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Possessions/Arson; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist.'
tei_id: null
table_ref: null
reason: Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Natalie is living in an area where there have been a series of fires set by arsonists recently.
tei_id: null
table_ref: null
reason: The risk/stakes are described within the scenario context.
evidence:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own checking/inspection (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to check the sprinklers before she knows that they are working in the X room?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is
coded Medium for current harmonization.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Pvalue Low vs Pvalue 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 26
mean: 2.15384615385
sd: 1.61721508013
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; low
column: Pvalue Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 26
mean: 3.11538461538
sd: 2.67322910469
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv; high
column: Pvalue 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.435233708389
v: 0.0578572704137
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
notes: Effects are scenario-by-polarity lowest-vs-highest stakes contrasts recovered from raw data; evidence-seeking cleaning
is documented in analysis/effect_sizes.qmd. Evidence-seeking d values in this YAML use the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
- study_id: 4
label: 'Appendix IV: Symmetrical Experiment (follow-up evidence-seeking prompts)'
language: English
language_other: null
objective: Follow-up evidence-seeking experiment using symmetrical prompts ('minimum' vs 'maximum') to reduce asymmetry
between positive and negative polarities.
sample:
n_final: 105
recruitment: mTurk
recruitment_other: null
compensation: money
compensation_other: $1.75
characteristics: 'MTurk sample: 45 females, 59 males; ages 21–65; additional exclusions include prior completion of the
first evidence-seeking experiment.'
mean_age: 37.09
mean_age_prov:
page: null
quote: leaving a final sample of 105 participants (45 females, 59 males) between 21 and 65 years old (M = 37.09 years,
SD = 10.67 years).
tei_id: null
table_ref: null
provenance:
page: null
quote: Following screening procedures... leaving a final sample of 105 participants (45 females, 59 males) between 21
and 65 years old (M = 37.09 years, SD = 10.67 years).
tei_id: null
table_ref: null
design: null
design_other: Stakes (4 levels) within-subjects; prompt polarity (positive vs negative) between-subjects; prompts modified
to use minimum/maximum wording.
manipulated_factors:
- 'Prompt polarity: evidence-seeking positive vs evidence-seeking negative'
- 'Prompt wording: minimum vs maximum (symmetrical prompts)'
paradigm: Rating how much evidence is needed for knowledge
paradigm_other: null
scale:
label: other
points: null
anchors: Numeric free response (whole number of checks); positive prompt allows '0' and both prompts allow 'never'.
direction: Higher numbers indicate more evidence required for knowledge / more checking still insufficient for knowledge.
provenance:
page: null
quote: positive... "minimum numbers of times" ... negative... "maximum number of times"... enter a whole number... If
you think Elaine knows without having to check, write "0"... If you think Elaine will never know... write "never".
tei_id: null
table_ref: null
measures:
knowledge_question_text: What is the minimum number of times S needs to check F before she knows that P?
knowledge_question_first: null
additional_question_text: What is the maximum number of times S can check F and not know that P?
scenarios: []
effects:
- effect_id: s4_e1
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Paramedic -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Paramedic; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Paramedic: lives... Megan, a paramedic...'
tei_id: null
table_ref: null
reason: Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Over the radio, Megan is told that there is one person at the scene of the accident...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (she is told the consequences).
evidence:
provenance:
page: null
quote: Megan... has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/navigation (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Para Low vs Para 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 47
mean: 2.23404255319
sd: 1.59090849021
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
low column: Para Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 47
mean: 3.0
sd: 1.94489297794
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
high column: Para 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.431103113867
v: 0.0179003744693
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e2
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Vaccine -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Vaccine; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Vaccine: lives... medical researcher... vaccine...'
tei_id: null
table_ref: null
reason: Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Elaine's assistant has informed her that there is one human research participant...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (assistant informs her).
evidence:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/procedure-following (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of
the steps correctly.
tei_id: null
table_ref: null
reason: The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability
is coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Vacc Low vs Vacc 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 44
mean: 2.86363636364
sd: 2.25770936695
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
low column: Vacc Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 44
mean: 4.70454545455
sd: 3.57367341108
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
high column: Vacc 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.615892037585
v: 0.0207344359567
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e3
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Mountaineering -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Mountaineering; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Mountaineering: personal injury... inspect the rope...'
tei_id: null
table_ref: null
reason: Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Visibility is reducing, making the climb increasingly dangerous...
tei_id: null
table_ref: null
reason: Stakes are described within the situation (dangerous climb; consequences).
evidence:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own inspection/checking (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is
coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Mount Low vs Mount 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 35
mean: 2.82857142857
sd: 1.20014004785
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
low column: Mount Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 35
mean: 3.6
sd: 1.76901434836
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
high column: Mount 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.510345860764
v: 0.0244069491794
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e4
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Game show -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Game show; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Game show: finance... "What is the capital of Tanzania?"... "Dodoma"'
tei_id: null
table_ref: null
reason: Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: As this is the final round of the game show, $1,000,000 is at stake...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (money at stake in the game show).
evidence:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own memory/consideration (first-person).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania
is Dodoma?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: The evidence is memory of a recently read list without an additional check, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Game Low vs Game 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 41
mean: 4.0
sd: 4.28368999812
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
low column: Game Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 41
mean: 6.60975609756
sd: 5.91978905359
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
high column: Game 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.505090368385
v: 0.0184715642471
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e5
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Introduction -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Introduction; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Introduction: reputation... wrote down the speaker''s name-"Dr. Woodbridge"-in her notebook...'
tei_id: null
table_ref: null
reason: Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: If Nicole introduces the guest speaker by the wrong name... reflect very badly...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (embarrassment/reputation consequences).
evidence:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Evidence is from an external written source (notebook).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: The evidence is a notebook record made earlier in the day, an external written record but not an official
or independently verified source, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Intro Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 45
mean: 2.22222222222
sd: 1.18492210885
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
low column: Intro Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 45
mean: 2.46666666667
sd: 1.17936808966
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
high column: Intro 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.206779836189
v: 0.0220955487438
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e6
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Possessions/Arson -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Possessions/Arson; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist.'
tei_id: null
table_ref: null
reason: Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Natalie is living in an area where there have been a series of fires set by arsonists recently.
tei_id: null
table_ref: null
reason: The risk/stakes are described within the scenario context.
evidence:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own checking/inspection (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to check the sprinklers before she knows that they are working in the X room?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is
coded Medium for current harmonization.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Pval Low vs Pval 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 46
mean: 1.91304347826
sd: 1.17049062755
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
low column: Pval Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 46
mean: 3.34782608696
sd: 2.43326384654
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv;
high column: Pval 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.751472126025
v: 0.0302478033315
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e7
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Paramedic -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Paramedic; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Paramedic: lives... Megan, a paramedic...'
tei_id: null
table_ref: null
reason: Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Over the radio, Megan is told that there is one person at the scene of the accident...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (she is told the consequences).
evidence:
provenance:
page: null
quote: Megan... has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/navigation (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Para Low vs Para 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 30
mean: 1.83333333333
sd: 0.912870929175
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
low column: Para Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 30
mean: 2.03333333333
sd: 1.09806517404
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
high column: Para 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.198074155086
v: 0.0460036063285
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e8
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Vaccine -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Vaccine; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Vaccine: lives... medical researcher... vaccine...'
tei_id: null
table_ref: null
reason: Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Elaine's assistant has informed her that there is one human research participant...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (assistant informs her).
evidence:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/procedure-following (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of
the steps correctly.
tei_id: null
table_ref: null
reason: The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability
is coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Vacc Low vs Vacc 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 30
mean: 3.36666666667
sd: 2.93002687211
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
low column: Vacc Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 30
mean: 3.43333333333
sd: 2.7377732373
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
high column: Vacc 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.0235111844073
v: 0.033935712758
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e9
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Mountaineering -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Mountaineering; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Mountaineering: personal injury... inspect the rope...'
tei_id: null
table_ref: null
reason: Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Visibility is reducing, making the climb increasingly dangerous...
tei_id: null
table_ref: null
reason: Stakes are described within the situation (dangerous climb; consequences).
evidence:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own inspection/checking (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is
coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Mount Low vs Mount 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 39
mean: 2.15384615385
sd: 1.91309148457
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
low column: Mount Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 39
mean: 2.84615384615
sd: 2.39009426745
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
high column: Mount 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.319806387682
v: 0.0156962540102
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e10
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Game show -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Game show; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Game show: finance... "What is the capital of Tanzania?"... "Dodoma"'
tei_id: null
table_ref: null
reason: Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: As this is the final round of the game show, $1,000,000 is at stake...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (money at stake in the game show).
evidence:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own memory/consideration (first-person).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania
is Dodoma?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: The evidence is memory of a recently read list without an additional check, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Game Low vs Game 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 23
mean: 3.60869565217
sd: 2.5179199647
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
low column: Game Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 23
mean: 4.95652173913
sd: 3.06710199121
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
high column: Game 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.480340771592
v: 0.058763399638
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e11
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Introduction -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Introduction; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Introduction: reputation... wrote down the speaker''s name-"Dr. Woodbridge"-in her notebook...'
tei_id: null
table_ref: null
reason: Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: If Nicole introduces the guest speaker by the wrong name... reflect very badly...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (embarrassment/reputation consequences).
evidence:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Evidence is from an external written source (notebook).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: The evidence is a notebook record made earlier in the day, an external written record but not an official
or independently verified source, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Intro Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 34
mean: 1.97058823529
sd: 1.19304281509
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
low column: Intro Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 34
mean: 2.29411764706
sd: 1.73256530359
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
high column: Intro 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.217503158496
v: 0.0100083057383
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s4_e12
subgroup: 'Appendix IV: Symmetrical evidence-seeking experiment -- Possessions/Arson -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Possessions/Arson; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist.'
tei_id: null
table_ref: null
reason: Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Natalie is living in an area where there have been a series of fires set by arsonists recently.
tei_id: null
table_ref: null
reason: The risk/stakes are described within the scenario context.
evidence:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own checking/inspection (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to check the sprinklers before she knows that they are working in the X room?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is
coded Medium for current harmonization.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Pval Low vs Pval 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 30
mean: 1.56666666667
sd: 0.727932041795
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
low column: Pval Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 30
mean: 2.2
sd: 1.88277125169
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv;
high column: Pval 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.443709626053
v: 0.0539589673019
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
notes: Effects are scenario-by-polarity lowest-vs-highest stakes contrasts recovered from raw data; evidence-seeking cleaning
is documented in analysis/effect_sizes.qmd. Evidence-seeking d values in this YAML use the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
- study_id: 5
label: 'Appendix IV: Matched Experiment (follow-up; ''0'' option removed)'
language: English
language_other: null
objective: Follow-up evidence-seeking experiment with matched prompts by removing the '0' response option from the positive
polarity prompts.
sample:
n_final: 89
recruitment: mTurk
recruitment_other: null
compensation: money
compensation_other: $1.75
characteristics: 'MTurk sample: 33 females, 56 males; ages 20–70; positive polarity N=45, negative polarity N=44.'
mean_age: 34.71
mean_age_prov:
page: null
quote: leaving a final sample of 89 participants (33 females, 56 males) between 20 and 70 years old (M = 34.71 years,
SD = 10.84 years).
tei_id: null
table_ref: null
provenance:
page: null
quote: leaving a final sample of 89 participants... Participants were randomly assigned to a positive polarity condition
(N = 45) or a negative polarity condition (N = 44).
tei_id: null
table_ref: null
design: null
design_other: Stakes (4 levels) within-subjects; prompt polarity (positive vs negative) between-subjects; '0' response option
removed to create matched design.
manipulated_factors:
- 'Prompt polarity: evidence-seeking positive vs evidence-seeking negative'
- 'Response options: ''0'' removed in positive prompts'
paradigm: Rating how much evidence is needed for knowledge
paradigm_other: null
scale:
label: other
points: null
anchors: Numeric free response (whole number of checks); '0' option removed; both prompts allow 'never'.
direction: Higher numbers indicate more evidence required for knowledge / more checking still insufficient for knowledge.
provenance:
page: null
quote: the additional option to write "0"... was removed... Modified enter a whole number... If you think Elaine will
never know... write "never"
tei_id: null
table_ref: null
measures:
knowledge_question_text: 'Modified: enter a whole number... (no ''0'' option).'
knowledge_question_first: null
additional_question_text: null
scenarios: []
effects:
- effect_id: s5_e1
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Paramedic -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Paramedic; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Paramedic: lives... Megan, a paramedic...'
tei_id: null
table_ref: null
reason: Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Over the radio, Megan is told that there is one person at the scene of the accident...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (she is told the consequences).
evidence:
provenance:
page: null
quote: Megan... has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/navigation (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Para Low vs Para 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 43
mean: 2.02325581395
sd: 1.53511861017
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
low column: Para Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 43
mean: 3.90697674419
sd: 2.67095447081
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
high column: Para 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.864738958369
v: 0.029921005076
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e2
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Vaccine -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Vaccine; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Vaccine: lives... medical researcher... vaccine...'
tei_id: null
table_ref: null
reason: Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Elaine's assistant has informed her that there is one human research participant...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (assistant informs her).
evidence:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/procedure-following (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of
the steps correctly.
tei_id: null
table_ref: null
reason: The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability
is coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Vacc Low vs Vacc 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 34
mean: 3.5
sd: 2.27303028283
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
low column: Vacc Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 34
mean: 5.73529411765
sd: 3.04818697758
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
high column: Vacc 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.831369146354
v: 0.0373725222341
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e3
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Mountaineering -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Mountaineering; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Mountaineering: personal injury... inspect the rope...'
tei_id: null
table_ref: null
reason: Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Visibility is reducing, making the climb increasingly dangerous...
tei_id: null
table_ref: null
reason: Stakes are described within the situation (dangerous climb; consequences).
evidence:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own inspection/checking (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is
coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Mount Low vs Mount 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 26
mean: 2.65384615385
sd: 1.01753850806
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
low column: Mount Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 26
mean: 3.92307692308
sd: 1.59807576599
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
high column: Mount 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.947446942608
v: 0.049281922459
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e4
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Game show -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Game show; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Game show: finance... "What is the capital of Tanzania?"... "Dodoma"'
tei_id: null
table_ref: null
reason: Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: As this is the final round of the game show, $1,000,000 is at stake...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (money at stake in the game show).
evidence:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own memory/consideration (first-person).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania
is Dodoma?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: The evidence is memory of a recently read list without an additional check, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Game Low vs Game 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 40
mean: 2.9
sd: 3.00256300773
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
low column: Game Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 40
mean: 10.25
sd: 9.14204151582
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
high column: Game 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -1.08022663846
v: 0.0370733112632
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e5
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Introduction -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Introduction; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Introduction: reputation... wrote down the speaker''s name-"Dr. Woodbridge"-in her notebook...'
tei_id: null
table_ref: null
reason: Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: If Nicole introduces the guest speaker by the wrong name... reflect very badly...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (embarrassment/reputation consequences).
evidence:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Evidence is from an external written source (notebook).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: The evidence is a notebook record made earlier in the day, an external written record but not an official
or independently verified source, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Intro Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 41
mean: 2.0487804878
sd: 1.11694269128
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
low column: Intro Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 41
mean: 2.51219512195
sd: 1.22723166557
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
high column: Intro 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.394938706522
v: 0.0250901114773
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e6
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Possessions/Arson -- positive polarity'
subgroup_desc: Lowest vs highest stakes; Possessions/Arson; positive polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist.'
tei_id: null
table_ref: null
reason: Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Natalie is living in an area where there have been a series of fires set by arsonists recently.
tei_id: null
table_ref: null
reason: The risk/stakes are described within the scenario context.
evidence:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own checking/inspection (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to check the sprinklers before she knows that they are working in the X room?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is
coded Medium for current harmonization.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Pval Low vs Pval 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 41
mean: 1.9512195122
sd: 1.67259109636
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
low column: Pval Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 41
mean: 2.90243902439
sd: 2.16569709388
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv;
high column: Pval 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.491607487397
v: 0.0426130224156
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e7
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Paramedic -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Paramedic; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Paramedic: lives... Megan, a paramedic...'
tei_id: null
table_ref: null
reason: Scenario is a paramedic accident/GPS vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Over the radio, Megan is told that there is one person at the scene of the accident...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (she is told the consequences).
evidence:
provenance:
page: null
quote: Megan... has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/navigation (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Megan is familiar with the surrounding area, she has GPS on her phone that she can check if necessary...
tei_id: null
table_ref: null
reason: Familiarity plus GPS are ordinary fallible navigation resources, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Para Low vs Para 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 18
mean: 3.33333333333
sd: 1.60879933308
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
low column: Para Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 18
mean: 3.55555555556
sd: 1.42342677748
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
high column: Para 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.146300512989
v: 0.0952413634855
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e8
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Vaccine -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Vaccine; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Vaccine: lives... medical researcher... vaccine...'
tei_id: null
table_ref: null
reason: Scenario is a vaccine-checklist vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Elaine's assistant has informed her that there is one human research participant...
tei_id: null
table_ref: null
reason: The vignette describes the stakes to the protagonist (assistant informs her).
evidence:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take...
tei_id: null
table_ref: null
reason: Evidence is the agent’s own checking/procedure-following (first-person).
attribution_person:
provenance:
page: null
quote: How many times does Elaine need to consult her check list before she knows that she is making the vaccine
correctly?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Elaine... has a check list that specifies all of the steps she needs to take... Elaine is following all of
the steps correctly.
tei_id: null
table_ref: null
reason: The vignette states that the checklist specifies the steps and the agent is following them correctly, so reliability
is coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Vacc Low vs Vacc 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 28
mean: 2.67857142857
sd: 2.03767426301
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
low column: Vacc Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 28
mean: 3.85714285714
sd: 2.91502221215
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
high column: Vacc 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.468635005912
v: 0.0370716846331
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e9
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Mountaineering -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Mountaineering; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: High
moderators_coding:
scenario:
provenance:
page: null
quote: 'Mountaineering: personal injury... inspect the rope...'
tei_id: null
table_ref: null
reason: Scenario is a mountaineering rope-inspection vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Visibility is reducing, making the climb increasingly dangerous...
tei_id: null
table_ref: null
reason: Stakes are described within the situation (dangerous climb; consequences).
evidence:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own inspection/checking (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: How many times does S need to inspect the rope before she knows that it is tied securely?
tei_id: null
table_ref: null
reason: The relevant evidence is repeated rope inspection for a directly inspectable condition, so reliability is
coded High.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Mount Low vs Mount 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 18
mean: 3.05555555556
sd: 0.998364675929
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
low column: Mount Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 18
mean: 3.44444444444
sd: 1.33822631614
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
high column: Mount 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.329402378246
v: 0.0855303160901
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e10
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Game show -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Game show; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Game show: finance... "What is the capital of Tanzania?"... "Dodoma"'
tei_id: null
table_ref: null
reason: Scenario is a game-show trivia vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: As this is the final round of the game show, $1,000,000 is at stake...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (money at stake in the game show).
evidence:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own memory/consideration (first-person).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to spend considering her answer before she knows that the capital of Tanzania
is Dodoma?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Debra has recently read a list... and the city "Dodoma" pops into her head.
tei_id: null
table_ref: null
reason: The evidence is memory of a recently read list without an additional check, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Game Low vs Game 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 26
mean: 2.57692307692
sd: 2.08178917133
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
low column: Game Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 26
mean: 4.15384615385
sd: 2.82407234599
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
high column: Game 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: -0.635638288618
v: 0.0608271017613
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e11
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Introduction -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Introduction; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: External
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Introduction: reputation... wrote down the speaker''s name-"Dr. Woodbridge"-in her notebook...'
tei_id: null
table_ref: null
reason: Scenario is a guest-speaker introduction/name-check vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: If Nicole introduces the guest speaker by the wrong name... reflect very badly...
tei_id: null
table_ref: null
reason: Stakes are described within the scenario (embarrassment/reputation consequences).
evidence:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Evidence is from an external written source (notebook).
attribution_person:
provenance:
page: null
quote: + How many minutes does S need to check her notebook before she knows that the guest speakers name is "Dr.
Woodbridge"?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: Siena wrote down the speaker's name-"Dr. Woodbridge"-in her notebook earlier in the day.
tei_id: null
table_ref: null
reason: The evidence is a notebook record made earlier in the day, an external written record but not an official
or independently verified source, so reliability is coded Medium.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Intro Low vs Intro 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 36
mean: 2.58333333333
sd: 1.87273672927
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
low column: Intro Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 36
mean: 2.58333333333
sd: 1.64533973912
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
high column: Intro 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: 0.0
v: 0.039714932657
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
- effect_id: s5_e12
subgroup: 'Appendix IV: Matched evidence-seeking experiment -- Possessions/Arson -- negative polarity'
subgroup_desc: Lowest vs highest stakes; Possessions/Arson; negative polarity; raw-data recovered.
design: Within-Subjects
design_other: Lowest and highest stakes are repeated measures within the polarity-specific sample.
moderators:
scenario: other
skeptical_pressure: 'No'
awareness: 'Yes'
evidence: First Person
attribution_person: Other
evidence_reliability: Medium
moderators_coding:
scenario:
provenance:
page: null
quote: 'Arson: personal value... Only a functioning sprinkler system can stop a fire set by an arsonist.'
tei_id: null
table_ref: null
reason: Scenario is an arson/sprinkler-system vignette; not bank/peanuts/bridge/typos.
skeptical_pressure:
provenance:
page: null
quote: How many times can Elaine consult her check list and still not know that she is making the vaccine correctly?
tei_id: null
table_ref: null
reason: No explicit doubt or counterconsideration is introduced in the vignette; prompt polarity is represented as
a separate effect.
awareness:
provenance:
page: null
quote: Natalie is living in an area where there have been a series of fires set by arsonists recently.
tei_id: null
table_ref: null
reason: The risk/stakes are described within the scenario context.
evidence:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: Evidence is from the agent’s own checking/inspection (first-person).
attribution_person:
provenance:
page: null
quote: + How many times does S need to check the sprinklers before she knows that they are working in the X room?
tei_id: null
table_ref: null
reason: Prompt concerns whether the protagonist knows (third-person attribution).
evidence_reliability:
provenance:
page: null
quote: A week ago, Natalie checked that the sprinklers were working...
tei_id: null
table_ref: null
reason: A week-old sprinkler check is treated as ordinary past verification projected forward, so reliability is
coded Medium for current harmonization.
contrast:
group_high: stakes4
group_low: stakes1
sign_convention: d = mean(low) - mean(high)
other_notes: 'Lowest vs highest stakes only; intermediate stakes levels are not included in this extracted effect. Source
columns: Pval Low vs Pval 3. Evidence-seeking d in YAML uses the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.'
groups:
- group_id: stakes1
label: Lowest stakes
n: 27
mean: 2.85185185185
sd: 2.31556113325
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
low column: Pval Low.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
- group_id: stakes4
label: Highest stakes
n: 27
mean: 2.2962962963
sd: 2.0156086085
se: null
provenance:
page: null
quote: 'Computed in analysis/effect_sizes.qmd from Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv;
high column: Pval 3.'
tei_id: null
table_ref: analysis/effect_sizes_raw_data.csv
effect_size:
metric: SMD
d: 0.255925346834
v: 0.0544689106788
computed_from: groups
needs_review: false
notes: Computed from post-removal raw CSV in analysis/effect_sizes.qmd; blank/'never'/non-positive responses were excluded
and log-MAD outlier removal was applied over the scenario's four stakes cells. YAML d uses the raw low-minus-high evidence-seeking convention; downstream meta-analysis reverses evidence-seeking effects programmatically.
quality_flags:
- raw_data_recovered
- evidence_seeking_cleaning_rule
notes: Published article reports omnibus four-level stakes analyses rather than this exact low-high, polarity-specific
contrast; contrast recovered from open raw data. Evidence-seeking d values use the raw low-minus-high scale; downstream harmonization reverses evidence-seeking effects programmatically.
notes: Effects are scenario-by-polarity lowest-vs-highest stakes contrasts recovered from raw data; evidence-seeking cleaning
is documented in analysis/effect_sizes.qmd. Evidence-seeking d values in this YAML use the raw low-minus-high convention; downstream meta-analysis reverses evidence-seeking effects programmatically.