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The Shaping of the Face Space in Early Infancy: Becoming a Native Face Processor

2010· article· en· W2108583157 on OpenAlexaff
Alan Slater, Paul C. Quinn, David J. Kelly, Kang Lee, Christopher A. Longmore, Paula R. McDonald, Olivier Pascalis

Bibliographic record

VenueChild Development Perspectives · 2010
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsPsychologyCategorizationFace perceptionPerceptionFace (sociological concept)Cognitive psychologySpace (punctuation)Developmental psychologySociologyNeuroscienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Face perception remains one of the most intensively researched areas in psychology and allied disciplines, and there has been much debate regarding the early origins and experiential determinants of face processing. This article reviews studies, the majority of which have appeared in the past decade, that discuss possible mechanisms underlying face perception at birth and document the prominent role of experience in shaping infants' face-processing abilities. In the first months of life, infants develop a preference for female and own-race faces and become better able to recognize and categorize own-race and own-species faces. This perceptual narrowing and shaping of the "face space" forms a foundation for later face expertise in childhood and adulthood and testifies to the remarkable plasticity of the developing visual system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.035
GPT teacher head0.289
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations65
Published2010
Admission routes1
Has abstractyes

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