Emotion specificity of gaze cueing in a danger vigilance context.
Bibliographic record
Abstract
In attentional cueing paradigms in which gaze direction of emotional faces serve as the cue, the magnitude of the cueing effect varies with emotion and context (e.g., threat-related; Dawel et al., 2015; Bayliss et al., 2010). In these studies, however, only two emotions were compared, making it difficult to ascertain whether the influence of emotion is highly specific (e.g., to fearful faces in a threat context) or generalises to other emotions of similar valence or arousal. To examine this question we used a Posner style cueing task in which the gaze direction of a range of emotional faces, namely happy (positive, high arousal), sad (negative, low arousal), angry, fearful, disgust (all negative, high arousal) provided non-predictive cues to the location of a forthcoming threatening or neutral target. We set a danger vigilance context by asking participants (n=64) to indicate whether each target animal was dangerous. Controlling for state anxiety (STAI), gaze cueing was significant for all emotions, ps< .001, but varied across them ps< .01. To examine the influence of valence we used happy (M=38ms) as the reference category; only sad faces differed (M=31ms), eliciting a smaller cueing effect. To examine the influence of arousal we used sad as the reference category; disgusted (M=34ms), fearful (M=47ms), and happy faces elicited larger cueing effects, with no difference for angry faces (M=32ms). These results suggest that in the context of multiple emotions cueing effects are complex. Happy faces and faces indicating indirect proximal threat (disgust, fear) showed comparable gaze cueing compared to merely negative faces (sad), with direct threat (angry) having an intermediate effect. Our findings suggest that in a threat context cueing effects are not limited to threat-related or negative emotions, but generalise across all high-arousal emotions. We are currently investigating whether this pattern holds in other contexts (e.g., pleasant, disgust-inducing). Meeting abstract presented at VSS 2016
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 | 0.002 |
| 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 | 0.001 |
| 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".