Individual differences in the emotional modulation of gaze-cuing
Bibliographic record
Abstract
Gaze-cuing refers to the spontaneous orienting of attention towards a gazed-at location, characterised by shorter response times to gazed-at than non-gazed at targets. Previous research suggests that processing of these gaze cues interacts with the processing of facial expression cues to enhance gaze-cuing. However, whether only negative emotions (which signal potential threat or uncertainty) can enhance gaze-cuing is still debated, and whether this emotional modulation varies as a function of individual differences still remains largely unclear. Combining data from seven experiments, we investigated the emotional modulation of gaze-cuing in the general population as a function of participant sex, and self-reported subclinical trait anxiety, depression, and autistic traits. We found that (i) emotional enhancement of gaze-cuing can occur for both positive and negative expressions, (ii) the higher the score on the Attention to Detail subscale of the Autism Spectrum Quotient, the smaller the emotional enhancement of gaze-cuing, especially for happy expressions, and (iii) emotional modulation of gaze-cuing does not vary as a function of participant anxiety, depression or sex, although women display an overall larger gaze-cuing effect than men.
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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 | 0.003 |
| 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".