Which feature is fixated modulates the N170 regardless of facial expression
Bibliographic record
Abstract
Facial expressions represent an important part of non-verbal communication used in everyday life. The N170 is widely regarded as a face-sensitive potential and has been linked to facial structural encoding, however it remains debated whether the N170 is modulated by facial expressions of emotion. We investigated how attention to facial features affects the early stages of emotion perception during an implicit emotion processing task. ERPs were recorded in response to presentations of fearful, joyful, or neutral facial expressions while fixation was restricted to the left eye, right eye, nose, or mouth using an eye tracker. Participants’ task was to discriminate the face gender. Enhanced N170 amplitudes and longer latencies were found when participants were fixated on the left and right eyes compared to the mouth and nose irrespective of emotion. Importantly, the N170 was not modulated by emotion. The results support the view that the N170 component is not sensitive to the facial expression in an implicit emotional task. In contrast, which feature is fixated modulates this component. As the eyes have been shown to be the diagnostic feature used to correctly categorize face gender, it could be that attention to the diagnostic feature is what drives N170 modulation with emotion in previous studies not controlling for fixation. This idea is currently being tested using an explicit emotional task with the same stimuli. Meeting abstract presented at VSS 2013
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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 | 0.002 |
| 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) | 0.003 | 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".