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The Social Effects of Emotions

2021· review· en· W3186731727 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

VenueAnnual Review of Psychology · 2021
Typereview
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyEmotional expressionCognitive psychologySocial information processingFacial expressionEmotional contagionCognitionSocial cognitionSocial psychologyEmotional intelligenceAffect (linguistics)NegotiationModalitiesCommunication

Abstract

fetched live from OpenAlex

We review the burgeoning literature on the social effects of emotions, documenting the impact of emotional expressions on observers' affect, cognition, and behavior. We find convergent evidence that emotional expressions influence observers' affective reactions, inferential processes, and behaviors across various domains, including close relationships, group decision making, customer service, negotiation, and leadership. Affective reactions and inferential processes mediate the effects of emotional expressions on observers' behaviors, and the relative potency of these mediators depends on the observers' information processing and the perceived appropriateness of the emotional expressions. The social effects of emotions are similar across expressive modalities (face, voice, body, text, symbols). We discuss the findings in relation to emotional contagion, emotional intelligence, emotion regulation, emotions as social information (EASI) theory, and the functionality of emotions in engendering social influence. Finally, we identify gaps in our current understanding of the topic and call for interdisciplinary collaboration and methodological diversification.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.822
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.509
Teacher spread0.442 · 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