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Record W2043929732 · doi:10.1037/0096-1523.33.1.1

Temporal integration in face perception: Evidence of configural processing of temporally separated face parts.

2007· article· en· W2043929732 on OpenAlexaff
David Anaki, Jennifer Boyd, Morris Moscovitch

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2007
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of TorontoBaycrest Hospital
Fundersnot available
KeywordsInversion (geology)PerceptionFace perceptionInterval (graph theory)Computer sciencePattern recognition (psychology)Artificial intelligenceVisual perceptionFace (sociological concept)Computer visionSpeech recognitionPsychologyMathematicsGeologyNeuroscience

Abstract

fetched live from OpenAlex

Temporal integration is the process by which temporally separated visual components are combined into a unified representation. Although this process has been studied in object recognition, little is known about temporal integration in face perception and recognition. In the present study, the authors investigated the characteristics and time boundaries of facial temporal integration. Whole faces of nonfamous and famous people were segmented horizontally into 3 parts and presented in sequence, with varying interval lengths between parts. Inversion and misalignment effects were found at short intervals (0-200 ms). Moreover, their magnitude was comparable to those found with whole-face presentations. These effects were eliminated, or substantially reduced, when the delay interval was 700 ms. Order of parts presentation did not influence the pattern of inversion effects obtained within each temporal delay condition. These results demonstrate that temporal integration of faces occurs in a temporary and limited visual buffer. Moreover, they indicate that only integrated faces can undergo configural processing.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.127
GPT teacher head0.432
Teacher spread0.304 · 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 designBench or experimental
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

Citations36
Published2007
Admission routes1
Has abstractyes

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