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Record W2169011357 · doi:10.1177/0956797612464500

Social Transmission of the Sensory Benefits of Eye Widening in Fear Expressions

2013· article· en· W2169011357 on OpenAlexaff
Daniel H. Lee, Joshua M. Susskind, Adam K. Anderson

Bibliographic record

VenuePsychological Science · 2013
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologySensory systemCognitive psychologyTransmission (telecommunications)Social psychologyTelecommunications

Abstract

fetched live from OpenAlex

Facial expressions may have originated from a primitive sensory regulatory function that was then co-opted and further shaped for the purposes of social utility. In the research reported here, we tested such a hypothesis by investigating the functional origins of fear expressions for both the expresser and the observer. We first found that fear-based eye widening enhanced target discrimination in the available visual periphery of the expresser by 9.4%. We then found that fear-based eye widening enhanced observers' discrimination of expressers' gaze direction and facilitated observers' responses when locating eccentric targets. We present evidence that this benefit was driven by neither the perceived emotion nor attention but, rather, by an enhanced physical signal originating from greater exposure of the iris and sclera. These results highlight the coevolution of sensory and social regulatory functions of emotional expressions by showing that eye widening serves to enhance processing of important environmental events in the visual fields of both expresser and observer.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.121
GPT teacher head0.385
Teacher spread0.264 · 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

Citations106
Published2013
Admission routes1
Has abstractyes

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