Impact of task demands on the neural processing of facial emotions
Bibliographic record
Abstract
The Early Posterior Negativity (EPN) (~250-350ms post-stimulus) ERP component is a known marker of facial emotion processing however whether the face-sensitive N170 (~100-200ms post-stimulus) component is also sensitive to emotional faces remains debated. Possible causes for the previous inconsistent results are the use of different tasks involving varying degrees of attention to the emotional faces and the lack of control of point-of-gaze on the faces. We investigated whether emotion sensitivity of the N170 and EPN varied as a function of task in a sample of 33 participants. ERPs were recorded in response to the same fearful, joyful, or neutral faces during an explicit emotion discrimination task, a gender discrimination task, and an oddball detection (flower detection) task. Task order was counterbalanced across participants. Using an eye-tracker, fixation was restricted to the nose (i.e., centre of mass) where holistic processing is maximal. Results revealed N170 modulation by emotion with larger responses for fearful than happy or neutral faces in all tasks. As predicted, the EPN was modulated by emotion with larger responses for fearful compared to neutral faces. The EPN was also modulated by task with largest responses during the gender discrimination task, followed by the emotion discrimination task and smallest responses for the oddball detection task. Results suggest that when conditions for holistic processing are maximized with fixation to the centre of mass, the N170 is sensitive to fear irrespective of the degree of attention to the face placed by task demands. Meeting abstract presented at VSS 2015
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".