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Record W2750611741 · doi:10.1098/rspb.2017.1054

Fearful but not happy expressions boost face detection in human infants

2017· article· en· W2750611741 on OpenAlexaff
Laurie Bayet, Paul C. Quinn, Rafael Laboissière, Roberto Caldara, Kang Lee, Olivier Pascalis

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2017
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentMinistère de l'Enseignement Supérieur et de la Recherche
KeywordsPsychologyAttentional biasFacial expressionDevelopmental psychologyInfant developmentCognitive psychologyAudiologyCognitionCommunicationNeuroscience

Abstract

fetched live from OpenAlex

Human adults show an attentional bias towards fearful faces, an adaptive behaviour that relies on amygdala function. This attentional bias emerges in infancy between 5 and 7 months, but the underlying developmental mechanism is unknown. To examine possible precursors, we investigated whether 3.5-, 6- and 12-month-old infants show facilitated detection of fearful faces in noise, compared to happy faces. Happy or fearful faces, mixed with noise, were presented to infants ( N = 192), paired with pure noise. We applied multivariate pattern analyses to several measures of infant looking behaviour to derive a criterion-free, continuous measure of face detection evidence in each trial. Analyses of the resulting psychometric curves supported the hypothesis of a detection advantage for fearful faces compared to happy faces, from 3.5 months of age and across all age groups. Overall, our data show a readiness to detect fearful faces (compared to happy faces) in younger infants that developmentally precedes the previously documented attentional bias to fearful faces in older infants and adults.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.106
GPT teacher head0.330
Teacher spread0.224 · 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 designBench or experimental
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

Citations29
Published2017
Admission routes1
Has abstractyes

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