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Record W4387743175 · doi:10.1111/spc3.12910

The effects of visual attention on social behavior

2023· article· en· W4387743175 on OpenAlexafffund
Francesca Capozzi, Alan Kingstone

Bibliographic record

VenueSocial and Personality Psychology Compass · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of British ColumbiaUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Montréal
KeywordsPsychologyInterpersonal communicationPerceptionValence (chemistry)Cognitive psychologyImpression formationSocial cognitionCognitionSocial psychologySocial perceptionAffect (linguistics)Communication

Abstract

fetched live from OpenAlex

Abstract Psychology has made tremendous strides in understanding the effects that social stimuli have on attention. However, one aspect that has received relatively less consideration is the role that attention plays in social interactions. The present review examines how attentional orienting, engagement, and communication affect and shape a diverse array of social processes, including person perception, discrimination, and group structures. Specifically, the empirical evidence reviewed here points to the notions that (1) attentional orienting mediates learning and acquisition of others' attitudes and reflects or reinforces the use of stereotypical social information; (2) attentional engagement increases the accuracy of impression formation and modulates impression valence (positive vs. negative) depending on contextual and cultural factors; (3) attentional communication conveys socially appropriate behavior depending on interpersonal factors such as familiarity, intimacy, and social status. Overall, this review reveals that the links between cognitive and social psychology are strong and bidirectional, binds that hold the potential for many new and exciting discoveries in the near future.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.999

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 studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.080
GPT teacher head0.452
Teacher spread0.373 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

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