Optimal Eye-Gaze Fixation Position for Face-Related Neural Responses
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".