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Record W4377011036 · doi:10.1037/xge0001422

Perceived similarity explains beliefs about possibility.

2023· article· en· W4377011036 on OpenAlexafffund
Brandon W. Goulding, Ori Friedman

Bibliographic record

VenueJournal of Experimental Psychology General · 2023
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of WaterlooUniversity of Winnipeg
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSimilarity (geometry)PsychologyCounterfactual thinkingPsycINFOSocial psychologyRealmPerceptionCognitive psychologyMEDLINEComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

= 1,472) we explore whether American adults' beliefs about possibility are driven by perceptions of similarity to known events. We find that people's confidence in the possibility of hypothetical future events is strongly predicted by how similar they think the events are to events that have already happened. We find that perceived similarity explains possibility ratings better than how desirable people think the events are, or how morally good or bad they think it would be to accomplish them. We also show that similarity to past events is a better predictor of people's beliefs about future possibilities than counterfactual similarity or similarity to events in fiction. We find mixed evidence regarding whether prompting participants to consider similarity shifts their beliefs about possibility. Our findings suggest that people may reflexively use memories of known events to guide their inferences about what is possible. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.158
GPT teacher head0.392
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes2
Has abstractyes

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