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Record W2074273764 · doi:10.1177/01461672022811007

Vigilance for Differences: Heightened Impact of Differences on Surprise

2002· article· en· W2074273764 on OpenAlexaff
James M. Olson, Leslie M. Janes

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2002
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsWestern University
Fundersnot available
KeywordsSurprisePsychologyVigilance (psychology)Social psychologyCognitive psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Two experiments tested the hypothesis that humans are vigilant for differences between stimuli in the environment by comparing the amount of surprise elicited by unexpected differences versus unexpected similarities. In Experiment 1, participants performed a weight judgment task where their implicit expectancies about the relative sizes of two wooden blocks were violated. For some participants, the unexpected event was that the two blocks differed in size when participants expected them to be similar in size; for other participants, the unexpected event was that the two blocks were the same size when participants expected them to differ in size. Unexpected differences in size produced greater spontaneous expressions of surprise and mirth than did unexpected similarities in size. Experiment 2 replicated the surprise finding when participants’ expectancies about weight were violated, rather than size. These findings are consistent with the idea that people are more attuned to differences between stimuli in the environment than to similarities.

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)0.001
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.224
GPT teacher head0.351
Teacher spread0.127 · 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

Citations8
Published2002
Admission routes1
Has abstractyes

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