MétaCan
Menu
Back to cohort
Record W3175181988 · doi:10.1038/s41598-021-92652-8

Cultural values shape the expression of self-evaluative social emotions

2021· article· en· W3175181988 on OpenAlexfundno aff
Antje von Suchodoletz, Robert Hepach

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsnot available
FundersYork UniversityNew York University Abu Dhabi
KeywordsExpression (computer science)CollectivismPsychologyEmotional expressionFacial expressionSocial psychologyModalitiesCultural valuesIndividualismHofstede's cultural dimensions theoryCognitive psychologyCommunication

Abstract

fetched live from OpenAlex

Social emotions are key to everyday social life and therefore shaped by cultural values in their expression. Prior research has focused on facial expressions of emotions. What is less clear, however, is the extent to which cultural values shape other modalities of emotional expression. In the present study, we applied a novel paradigm using depth sensor imaging technology to capture changes in participants' body posture in real time. We aimed to (1) identify the nuances in the postural expression that are thought to characterize social emotions and (2) assess how individual differences in cultural values impact the postural expression of emotions. Participants in two separate studies were 132 undergraduate college students whose upper-body postural expansion was recorded after they recalled emotion episodes. Positive emotions elevated participants' upper-body posture whereas negative emotions resulted in lowered upper-body posture. The effects on changes in upper-body posture were moderated by participants' self-ratings of the vertical and horizontal dimensions of individualism and collectivism. The findings provide initial evidence of the nuances in the way cultural values influence the postural expression of 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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.993

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.066
GPT teacher head0.390
Teacher spread0.325 · 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

Citations21
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicEvolutionary Psychology and Human BehaviorFrench-language works237,207