Event Related Potentials Associated With The Modulation Of Spatial Attention By Emotional Faces
Bibliographic record
Abstract
Being able to attend to the location others are gazing at is crucial to understand people's intentions. This orientation of attention by gaze has been studied using target detection tasks in which a central face cue gazing to the side precedes the appearance of a lateral target. Participants are faster to respond to the gazed-at location (congruent trials) than to the non-gazed at location (incongruent trials). Whether this gaze orienting effect (GOE) is modulated by facial expressions is still debated. A GOE enhancement has been reported with fearful compared to neutral faces and attributed to increased arousal or emotional valence. In the present experiment, we compared the effects of fearful and surprised expressions, previously unstudied, on the GOE at the behavioral and neural level, using the ERP technique. In a classic target detection task, dynamic faces with averted gaze and displaying fear, surprise or no expression, were presented to 20 participants. The GOE was found for all emotions on reaction times. However compared to neutral faces, the GOE was significantly enhanced when the target was preceded by fearful and surprised faces, for which it did not differ. At the ERP level, an enhanced P1 amplitude in response to the target was found for congruent compared to incongruent trials for all emotions. Thus, greater allocation of attentional resources at the gazed-at location was found for fear and surprise compared with neutral emotions only on reaction times. It remains possible that a differential emotional effect would be seen on ERPs in response to the cue, which we are currently investigating.
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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) | 0.002 | 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".