Looking eye to eye: Face context and featural fixation modulate early neural markers of face perception
Bibliographic record
Abstract
The N170 is an early ERP component sensitive to faces, but also to eye fixation within a face context, or to eye regions (i.e., two eyes) presented in isolation. Here we investigated the role of face context by comparing N170 modulations when facial features (left eye, right eye, nasion, nose, and mouth) were fixated within a full face or in isolation. Fixation on the desired feature was continuously enforced using a gaze-contingent eye-tracking procedure. We further assessed the N170 response to a single isolated eye compared to the classically used eye region. The N170 was largest and most delayed when fixated features were presented in isolation compared to in a face context, with the largest difference seen for the mouth. For faces, fixation on the left or right eye elicited the largest N170 response compared to nasion, nose, or mouth fixation, reproducing recent findings. However, for isolated features, the response pattern was more complex and varied with hemisphere. Specifically, the N170 response to an isolated mouth was as large as the response to the left eye in the left hemisphere, but was smaller to the response to the right eye in the right hemisphere. Isolated nose fixation showed the most delayed N170 response in both hemispheres. The isolated eye region yielded a larger and shorter N170 compared to a single isolated eye, irrespective of eye or nasion fixation. These results provide support for different neural mechanisms for facial features in isolation compared to within a full face context, and highlight the importance of featural fixation in modulating early neural responses. These findings also provide novel evidence of 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. Meeting abstract presented at VSS 2016
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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) | 0.002 | 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".