Impact of task demands and fixation to features on the time course of facial emotion processing
Bibliographic record
Abstract
The time course of facial emotion processing remains debated. Recent studies have suggested that facial features specific to an emotion are critical for accurate facial emotion discrimination. We investigated whether fixating on these diagnostic facial features impacted the time course of facial emotion processing and whether this varied as a function of task. ERPs were recorded in response to presentations of fearful, joyful or neutral faces while fixation was restricted to the left eye, right eye, nose or mouth using an eye-tracker. In the explicit emotion discrimination task, emotion only impacted the Early Posterior Negativity (EPN) (230- 250ms post-stimulus) that was largest for fearful faces, followed by happy, and smallest for neutral faces. In the oddball detection task where participants responded to flower target images, fearful and neutral faces elicited larger responses than happy faces on the P1 component (80- 130ms post-stimulus); fearful faces also elicited larger responses than neutral faces on the face-sensitive N170 component (120-220ms post-stimulus), but no modulation by emotion was seen on the EPN. In addition, in both tasks, the N170 was larger for fixation to the left and right eye compared to the nose and mouth regardless of facial emotion. Thus, fixation on diagnostic features did not impact the time course of emotion processing but the N170 response to eyes suggests the involvement of an eye-detector in the face structural encoding stages. Results also suggest earlier processing of emotion in expression-irrelevant compared to expression-relevant tasks. Meeting abstract presented at VSS 2014
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".