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Record W2971534650 · doi:10.1111/jopy.12509

Attention promotes accurate impression formation

2019· article· en· W2971534650 on OpenAlexafffund
Francesca Capozzi, Lauren J. Human, Jelena Ristic

Bibliographic record

VenueJournal of Personality · 2019
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaFonds de Recherche du Québec-Société et CultureCanada Excellence Research Chairs, Government of Canada
KeywordsReadabilityPsychologyPersonalityImpression formationAssociation (psychology)Social perceptionPerceptionExtraversion and introversionSocial psychologyEye trackingBig Five personality traitsDevelopmental psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: An ability to form accurate impressions of others is vital for adaptive social behavior in humans. Here, we examined if attending to persons more is associated with greater accuracy in personality impressions. METHOD: We asked 42 observers (36 females; mean age = 21 years, age range = 18-28; expected power = 0.96) to form personality impressions of unacquainted individuals (i.e., targets) from video interviews while their attentional behavior was assessed using eye tracking. We examined whether (a) attending more to targets benefited accuracy, (b) attending to specific body parts (e.g., face vs. body) drove this association, and (c) targets' ease of personality readability modulated these effects. RESULTS: Paying more attention to a target was associated with forming more accurate personality impressions. Attention to the whole person contributed to this effect, with this association occurring independently of targets' ease of readability. CONCLUSIONS: These findings show that attending more to a person is associated with increased accuracy and thus suggest that attention promotes social adaption by supporting accurate social perception.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.996

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.046
GPT teacher head0.364
Teacher spread0.318 · 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.

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

Citations16
Published2019
Admission routes2
Has abstractyes

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