Increased attention orienting by fearful faces varies with Stimulus-Onset Asynchrony
Bibliographic record
Abstract
Spontaneous orienting of attention towards an observer’s gaze direction is typically measured using a gaze-cuing paradigm in which a centrally-fixated face shifts its gaze towards (congruent) or away (incongruent) from a peripheral target. The reaction time (RT) difference between incongruent and congruent targets (gaze-orienting effect; GOE) indicates attention orienting based on gaze-cues. Studies have reported an increased GOE when the face expresses fear compared to happy or neutral expressions that might be due to the signaling of threat in the environment. However, the time course of this effect remains unclear. We used a dynamic gaze-cuing paradigm in which a neutral face with direct gaze looked to the side (averted gaze shift) and then either expressed fear or performed a neutral movement (tongue protrusion). The target was then presented after one of five Stimulus-Onset Asynchronies (SOAs; 300, 400, 500, 600, or 700 ms), the time between the gaze shift and the target onset. Overall, RTs decreased with increased SOAs, were faster for fearful than for neutral gaze-cues and faster for congruent than incongruent trials (classic GOE). The GOE was significantly larger for fearful than neutral faces, due to faster RTs for fearful than neutral faces in congruent trials, and this effect of emotion was largest at 400 and 500ms SOA. Thus, fearful expressions and gaze-cues interact to enhance orienting to congruent targets and this interaction is maximal at 400-500ms SOA. These results will be compared to gaze-cue orienting to happy and neutral faces in a second participant group. Meeting abstract presented at VSS 2015
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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) | 0.002 | 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".