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Children recruit distinct neural systems for implicit emotional face processing

2006· article· en· W1975535618 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeuroreport · 2006
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsWomen's College HospitalUniversity of TorontoHealth Sciences CentreSickKids FoundationSunnybrook Health Science Centre
Fundersnot available
KeywordsDisgustPsychologyFusiform gyrusFusiform face areaAmygdalaInsulaParahippocampal gyrusFacial expressionSadnessCognitive psychologyNeural correlates of consciousnessFace perceptionNeuroscienceCognitionPerceptionDevelopmental psychologyCommunicationTemporal lobeAngerClinical psychology

Abstract

fetched live from OpenAlex

The ability to properly distinguish facial emotions has a protracted development, not maturing until well into adolescence. Emotional faces activate emotion-specific neural networks in adults; whether these networks are operational in children is not known. Using an implicit face-processing task in 10-year-old children, we determined that the emotions of fear, disgust and sadness recruited distinct neural systems. These systems included a number of regions typically associated with processing emotions in adults, namely the amygdala and parahippocampal gyrus, insula and cingulate gyrus, as well as the fusiform and superior temporal gyri. Thus, in spite of immature behavioral responses to emotional faces in explicit tasks, neural networks for emotion-specific processing are present in young children.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.634

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)0.0000.000

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 head0.062
GPT teacher head0.308
Teacher spread0.245 · 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