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Record W1523744566 · doi:10.1002/ejsp.2043

On the interactive influence of facial appearance and explicit knowledge in social categorization

2014· article· en· W1523744566 on OpenAlexafffund
Nicholas O. Rule, Konstantin O. Tskhay, Jonathan B. Freeman, Nalini Ambady

Bibliographic record

VenueEuropean Journal of Social Psychology · 2014
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyCategorizationPerceptionSocial psychologyCognitive psychologySocial perceptionFace perceptionSocial cognitionCognitionArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Although people form impressions of others with ease, sometimes one's initial perceptions of individuals conflict with what one knows about them. Here, we aimed to investigate the process by which explicit knowledge about people interacts with initial perceptions on the basis of cues from facial appearance. Participants memorized the sexual orientations of men's faces wherein half of the targets were encoded with a sexual orientation opposite to their actual orientation. Subsequent categorization showed that perceivers favored appearance‐based information when temporally constrained but favored explicit knowledge about group membership with increased viewing time. Additionally, real‐time measures of participants' categorizations showed greater vacillation between appearance‐based cues and explicit knowledge as viewing time increased. These findings suggest that explicit knowledge does not simply overrule appearance‐based cues past a particular threshold but that the two may interact recurrently with top‐down knowledge directing attention and perception at later processing. Copyright © 2014 John Wiley & Sons, Ltd.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.961
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

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

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 head0.037
GPT teacher head0.362
Teacher spread0.325 · 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 teacher head, 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

Citations39
Published2014
Admission routes2
Has abstractyes

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