Emotional gaze increases target temporal processing
Bibliographic record
Abstract
Human attention is spontaneously oriented in the direction of eye gaze, with such gaze following behavior enhanced when a face shows an emotional expression. Here we investigated how emotional eye gaze affects the temporal precision of target perception. Participants viewed a face that either maintained a neutral expression or displayed an emotional reaction (fearful or happy) upon averting its gaze to the left or right. Two peripheral targets were presented, one to the left and another one to the right of the face. These targets were temporally offset by 0ms, 50ms, 100ms, or 150ms. Participants performed a temporal order judgement, reporting on which target appeared first (left or right). Data showed greater accuracy when the first target appeared at the gazed-at location, demonstrating that eye gaze facilitates temporal perception. Greater sensitivity for target timing at gazed-at locations was also supported by steeper response curves (reflecting proportion of right vs. left responses) and was further modulated by the emotional expression of the face, such that increased temporal target perception was present when faces displayed emotional expressions (fearful and happy) but not when they remained neutral. Together, these findings show that eye-gaze perception enhances the temporal processing of targets at gazed-at locations, and particularly so when the face display an emotional expression. This potentially reflects an adaptive mechanism that facilitates rapid responses to biologically relevant emotional face stimuli.
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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 | 0.001 |
| 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) | 0.003 | 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".