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Record W2318887624 · doi:10.1037/a0015689

Difficulté des jeunes enfants à comprendre la dissimulation des émotions.

2009· article· fr· W2318887624 on OpenAlexaff
Mélanie Perron, Pierre Gosselin

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2009
Typearticle
Languagefr
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPsychologyCharacter (mathematics)Facial expressionDevelopmental psychologySocial psychologyCommunication

Abstract

fetched live from OpenAlex

The authors investigated the understanding of emotion dissimulation in school-age children. Sixty participants were read short stories in which a main character expressed an emotion or hid an emotion from other characters. The participants were asked to identify the emotion felt by the main characters and to indicate the facial expressions they would display. Then they were asked what emotions the main characters felt while they were displaying these expressions, and what the beliefs of the other story characters would be as to the emotion felt by the main characters. The results revealed that children from 5 to 6 years of age have a partial understanding of emotion dissimulation. They were accurate in finding the emotion felt by the main characters when questioned the first time. They were also accurate in choosing the expressions the main characters would display to hide their emotions. However, they were often inaccurate as to the felt emotions of the main characters when questioned the second time. Compared with 9- and 10-year-olds, the younger children had more difficulty understanding the simultaneous character of felt and displayed emotions. Five- and 6-year-olds were also less accurate than the older children when asked to indicate the beliefs of the other characters in stories where felt emotions were hidden.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.345
Teacher spread0.299 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

Explore more

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