An eye (region) sensitivity during early face perception: The N170 is modulated by facial context and featural fixation
Bibliographic record
Abstract
The N170 is a face-sensitive ERP component that also demonstrates a particular sensitivity to the eyes. Specifically, the N170 responds maximally to isolated eye regions (i.e., two eyes), as well as to eye fixations within a face. Here I compared N170 modulations when facial features (left eye, right eye, nasion, nose, and mouth) were fixated in isolation or within a full face. Fixation on the desired feature was continuously enforced using a gaze-contingent eye-tracking procedure. In order to further assess this eye sensitivity, I also compared the N170 response to single isolated eyes and the classically-used eye region. The N170 was largest and most delayed when features were fixated in isolation, compared to equivalent fixations in a full face. An eye sensitivity within a face context was observed, with larger N170 amplitudes elicited when the left or right eye was fixated. Mouth fixation yielded the smallest and most delayed N170 within a face, and showed the largest amplitude difference between fixation in isolation and fixation within a face. For isolated features, single eyes did not differ from mouths, yielding significantly larger and faster N170 responses compared to isolated noses. Alternatively, isolated eye regions elicited consistently larger and shorter N170 responses compared to single isolated eyes, irrespective of eye or nasion fixation. These results highlight the importance of the eyes in early face perception, and provide compelling support for an interplay between featural and holistic neural mechanisms. These findings also provide novel evidence of an increased sensitivity to the presence of two symmetric eyes within the eye region compared to only one eye, consistent with an eye region detector rather than an eye detector per se.
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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 | 0.001 |
| 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".