Optimal eye-fixation positions for face perception: A combined ERP and eye-tracking study
Bibliographic record
Abstract
Previous research has outlined the existence of a saliency map among facial features, with the eye region being the most salient feature in a face. In addition, event-related potential (ERP) studies showed that the face-sensitive N170 component is larger in response to isolated eyes relative to other face features, and even to a whole face. Although these results suggest that the N170 could be mainly triggered by the eye-region, there is as yet no direct investigation of the N170 response profile when viewers fixate specific facial features within a whole face context. To address this question, EEG and eye-tracking measurements were recorded and monitored simultaneously to allow an accurate sampling of electrical brain signals from fixated face regions, while participants viewed faces in upright or inverted presentations. ERPs were averaged by gaze location (eyes, inion, eyebrows, nose, mouth and jaws). We also introduce a novel analysis procedure, neuroelectrical heat maps, that allowed mapping the amplitude of the N170 responses associated to specific eye-gaze fixations (measured by the eye-tracker) on face displays. Our results revealed that the optimal fixation position on an upright face (i.e., eliciting the largest N170s) is located around the nasion (triangle between to two eyes and the upper ridge of the nose). Interestingly for inverted faces, the optimal positions are mainly clustered in the upper part of the visual field (around the mouth). Our results suggest that the N170 is not driven by the eyes per se, but could rather arise from a general perceptual setting (upper-visual field advantage). It is also possible that the upper part of faces (eyes) serves as an artificial horizon to align a face stimulus on 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 | 0.000 | 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) | 0.001 | 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".