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Record W4385708613 · doi:10.1521/soco.2023.41.4.365

Decoding the Silent Language of the Eyes: Occluding the Eye Region Impacts Understanding and Sharing of Emotions With Others

2023· article· en· W4385708613 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Cognition · 2023
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsMcGill University
Fundersnot available
KeywordsSocioemotional selectivity theoryPsychologyEmpathyEmotional valenceValence (chemistry)Cognitive psychologyRecallTheory of mindSocial psychologyCognitionDevelopmental psychology

Abstract

fetched live from OpenAlex

Emotional expressions can be recognized from the eye region alone. However, it remains unknown how reading emotions from the eyes impacts downstream abilities that build on basic emotion recognition, including understanding (i.e., affective theory of mind) and sharing of emotions (i.e., affective empathy). In three experiments we investigated how occluding the eye region of emotional faces impacted judgments of affective theory of mind and affective empathy. Participants viewed emotional faces with eye regions covered by opaque occluders, transparent occluders, or no occluders. In Experiments 1 and 2, participants reported the protagonists’ emotional valence and intensity. In Experiment 3, participants rated their own empathy and emotional valence for the protagonist. When eyes were occluded, protagonists were judged to feel more neutral and less intense emotion and were empathized with less. This provides one of the first direct links between eye region information and the complex socioemotional processes of inferring and sharing emotions.

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.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.163
GPT teacher head0.341
Teacher spread0.178 · 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