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Record W2110353111 · doi:10.1371/journal.pone.0027132

Infant Pupil Diameter Changes in Response to Others' Positive and Negative Emotions

2011· article· en· W2110353111 on OpenAlexafffund
Elena Geangu, Petra Hauf, Rishi Bhardwaj, Wolfram Bentz

Bibliographic record

VenuePLoS ONE · 2011
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHappinessArousalPsychologyPupilDistressDevelopmental psychologyValence (chemistry)Stimulus (psychology)Pupillary responsePerceptionAudiologyPupil diameterPupillometrySocial psychologyClinical psychologyMedicineCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

It has been suggested that infants resonate emotionally to others' positive and negative affect displays, and that these responses become stronger towards emotions with negative valence around the age of 12-months. In this study we measured 6- and 12-month-old infants' changes in pupil diameter when presented with the image and sound of peers experiencing happiness, distress and an emotionally neutral state. For all participants the perception of another's distress triggered larger pupil diameters. Perceiving other's happiness also induced larger pupil diameters but for shorter time intervals. Importantly, we also found evidence for an asymmetry in autonomous arousal towards positive versus negative emotional displays. Larger pupil sizes for another's distress compared to another's happiness were recorded shortly after stimulus onset for the older infants, and in a later time window for the 6-month-olds. These findings suggest that arousal responses for negative as well as for positive emotions are present in the second half of the first postnatal year. Importantly, an asymmetry with stronger responses for negative emotions seems to be already present at this age.

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)0.000
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 score0.632
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.095
GPT teacher head0.305
Teacher spread0.210 · 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

Citations162
Published2011
Admission routes2
Has abstractyes

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