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Impairment in Holistic Face Processing Following Early Visual Deprivation

2004· article· en· W2155820912 on OpenAlexaff
Richard Le Grand, Catherine J. Mondloch, Daphne Maurer, Henry P. Brent

Bibliographic record

VenuePsychological Science · 2004
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsHospital for Sick ChildrenMcMaster UniversityBrock UniversityUniversity of Victoria
Fundersnot available
KeywordsPsychologyNeural substrateVisual processingFace (sociological concept)Set (abstract data type)Cognitive psychologyTask (project management)Contrast (vision)Control (management)CommunicationDevelopmental psychologyCognitionArtificial intelligencePerceptionComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Unlike most objects, faces are processed holistically: They are processed as a whole rather than as a collection of independent features. We examined the role of early visual experience in the development of this type of processing of faces by using the composite-face task, a measure of holistic processing, to test patients deprived of visual experience during infancy. Visually normal control subjects showed the expected composite-face effect: They had difficulty perceiving that the top halves of two faces were the same when the top halves were aligned with different bottom halves. Performance improved when holistic processing was disrupted by misaligning the top and bottom halves. Deprived patients, in contrast, showed no evidence of holistic processing, and in fact performed significantly better than control subjects when top and bottom halves were aligned. These findings suggest that early visual experience is necessary to set up or maintain the neural substrate that leads to holistic processing of faces.

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)0.000
metaresearch head score (Gemma)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.113
GPT teacher head0.425
Teacher spread0.312 · 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

Citations357
Published2004
Admission routes1
Has abstractyes

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