The Gaze Cueing Effect and Its Enhancement by Facial Expressions Are Impacted by Task Demands: Direct Comparison of Target Localization and Discrimination Tasks
Bibliographic record
Abstract
The gaze cueing effect is characterized by faster attentional orienting to a gazed-at than a non-gazed-at target. This effect is often enhanced when the gazing face bears an emotional expression, though this finding is modulated by a number of factors. Here, we tested whether the type of task performed might be one such modulating factor. Target localization and target discrimination tasks are the two most commonly used gaze cueing tasks, and they arguably differ in cognitive resources, which could impact how emotional expression and gaze cues are integrated to orient attention. In a within-subjects design, participants performed both target localization and discrimination gaze cueing tasks with neutral, happy, and fearful faces. The gaze cueing effect for neutral faces was greatly reduced in the discrimination task relative to the localization task, and the emotional enhancement of the gaze cueing effect was only present in the localization task and only when this task was performed first. These results suggest that cognitive resources are needed for gaze cueing and for the integration of emotional expressions and gaze cues. We propose that a shift toward local processing may be the mechanism by which the discrimination task interferes with the emotional modulation of gaze cueing. The results support the idea that gaze cueing can be greatly modulated by top-down influences and cognitive resources and thus taps into endogenous attention. Results are discussed within the context of the recently proposed EyeTune model of social attention.
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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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".