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Fitting the child's mind to the world: adaptive norm‐based coding of facial identity in 8‐year‐olds

2008· article· en· W2133562298 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

VenueDevelopmental Science · 2008
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychologyFace perceptionPerceptionIdentity (music)Norm (philosophy)Coding (social sciences)Developmental psychologyCognitive psychologyFace (sociological concept)Social psychologyLinguisticsNeuroscienceMathematicsStatistics

Abstract

fetched live from OpenAlex

In adults, facial identity is coded by opponent processes relative to an average face or norm, as evidenced by the face identity aftereffect: adapting to a face biases perception towards the opposite identity, so that a previously neutral face (e.g. the average) resembles the identity of the computationally opposite face. We investigated whether children as young as 8 use adaptive norm-based coding to represent faces, a question of interest because 8-year-olds are less accurate than adults at recognizing faces and do not show the adult neural markers of face expertise. We found comparable face identity aftereffects in 8-year-olds and adults: perception of identity in both groups shifted in the direction predicted by norm-based coding. This finding suggests that, by 8 years of age, the adaptive computational mechanisms used to code facial identity are like those of adults and hence that children's immaturities in face processing arise from another source.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.072
GPT teacher head0.312
Teacher spread0.240 · 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