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Record W2566570021 · doi:10.1093/scan/nsw166

The special status of sad infant faces: age and valence differences in adults’ cortical face processing

2016· article· en· W2566570021 on OpenAlexaff
Tyler Colasante, Sarah I. Mossad, Joanna Dudek, David Haley

Bibliographic record

VenueSocial Cognitive and Affective Neuroscience · 2016
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsThe Scarborough HospitalCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsPsychologyValence (chemistry)Facial expressionDevelopmental psychologyElectroencephalographyFace perceptionAffect (linguistics)Young adultAudiologyCognitive psychologyPerceptionNeuroscienceCommunicationMedicine

Abstract

fetched live from OpenAlex

Understanding the relative and joint prioritization of age- and valence-related face characteristics in adults' cortical face processing remains elusive because these two characteristics have not been manipulated in a single study of neural face processing. We used electroencephalography to investigate adults' P1, N170, P2 and LPP responses to infant and adult faces with happy and sad facial expressions. Viewing infant vs adult faces was associated with significantly larger P1, N170, P2 and LPP responses, with hemisphere and/or participant gender moderating this effect in select cases. Sad faces were associated with significantly larger N170 responses than happy faces. Sad infant faces were associated with significantly larger N170 responses in the right hemisphere than all other combinations of face age and face valence characteristics. We discuss the relative and joint neural prioritization of infant face characteristics and negative facial affect, and their biological value as distinct caregiving and social cues.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.048
GPT teacher head0.309
Teacher spread0.261 · 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

Citations26
Published2016
Admission routes1
Has abstractyes

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