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Record W1986221171 · doi:10.1371/journal.pone.0060128

Optimal Eye-Gaze Fixation Position for Face-Related Neural Responses

2013· article· en· W1986221171 on OpenAlexafffund
Younes Zerouali, Jean‐Marc Lina, Boutheina Jemel

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversité de MontréalÉcole de Technologie SupérieureHôpital Rivière-des-Prairies
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGazeFixation (population genetics)NasionEye movementVisual fieldFace perceptionStimulus (psychology)PsychologyPerceptionEye trackingScalpVisual perceptionSalientArtificial intelligenceCommunicationComputer visionCognitive psychologyComputer scienceNeuroscienceBiologyAnatomy

Abstract

fetched live from OpenAlex

It is generally agreed that some features of a face, namely the eyes, are more salient than others as indexed by behavioral diagnosticity, gaze-fixation patterns and evoked-neural responses. However, because previous studies used unnatural stimuli, there is no evidence so far that the early encoding of a whole face in the human brain is based on the eyes or other facial features. To address this issue, scalp electroencephalogram (EEG) and eye gaze-fixations were recorded simultaneously in a gaze-contingent paradigm while observers viewed faces. We found that the N170 indexing the earliest face-sensitive response in the human brain was the largest when the fixation position is located around the nasion. Interestingly, for inverted faces, this optimal fixation position was more variable, but mainly clustered in the upper part of the visual field (around the mouth). These observations extend the findings of recent behavioral studies, suggesting that the early encoding of a face, as indexed by the N170, is not driven by the eyes per se, but rather arises from a general perceptual setting (upper-visual field advantage) coupled with the alignment of a face stimulus to a stored face template.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.149
Threshold uncertainty score1.000

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)0.0010.001

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 head0.100
GPT teacher head0.289
Teacher spread0.189 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2013
Admission routes2
Has abstractyes

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