Fearful, surprised, happy, and angry facial expressions modulate gaze-oriented attention: Behavioral and ERP evidence
Bibliographic record
Abstract
The impact of emotions on gaze-oriented attention was investigated in non-anxious participants. A neutral face cue with straight gaze was presented, which then averted its gaze to the side while remaining neutral or expressing an emotion (fear/surprise in Exp.1 and anger/happiness in Exp.2). Localization of a subsequent target was faster at the gazed-at location (congruent condition) than at the non-gazed-at location (incongruent condition). This Gaze-Orienting Effect (GOE) was enhanced for fear, surprise, and anger, compared to neutral expressions which did not differ from happy expressions. In addition, Event Related Potentials (ERPs) to the target showed a congruency effect on P1 for fear and surprise and a left lateralized congruency effect on P1 for happy faces, suggesting that target visual processing was also influenced by attention to gaze and emotions. Finally, at cue presentation, early postero-lateral (Early Directing Attention Negativity (EDAN)) and later antero-lateral (Anterior Directing Attention Negativity (ADAN)) attention-related ERP components were observed, reflecting, respectively, the shift of attention and its holding at gazed-at locations. These two components were not modulated by emotions. Together, these findings show that the processing of social signals such as gaze and facial expression interact rather late and in a complex manner to modulate spatial attention.
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 | 0.000 | 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".