Gaze-oriented Attention in fearful and happy facial expressions varies with autistic traits
Bibliographic record
Abstract
Gaze is an important social signal used to determine others’ intentions. Previous gaze-cuing studies have shown faster responses to gazed-at targets than to non-gazed-at targets. This gaze orienting effect (GOE) reflects the spontaneous orientation of attention toward gaze direction, and is negatively correlated with the autism quotient (AQ) score indexing autistic traits in the general population. Thus, in accordance with clinical observations of autistic individuals, participants with many autistic traits seem to show a weaker gaze following than those with little autistic traits. Recent studies have also shown that attention orienting by gaze is modulated by the expression of the face cue. It remains unknown, however, whether this emotional effect varies as a function of autistic traits. The present ERP study investigated whether autistic traits impact the modulation of the GOE by fearful and happy emotions. Two groups were tested, one group with low AQ scores (<18, less autistic traits) and the other with high AQ scores (>26, more autistic traits). Stimuli consisted of centrally presented neutral faces dynamically averting their gaze to the side before expressing fear or happiness. Participants localized the subsequently presented target as fast as possible while maintaining their gaze centered. The GOE was modulated by emotions only in the high AQ group due to a decreased GOE for happy compared to fearful faces. While responses to fearful faces were similar in both groups, the GOE for happy faces was smaller in individuals with high compared to low AQ. In addition, ERP amplitudes to the target (P1 component) showed a gaze congruency effect in the low but not in the high AQ group indicating group differences in the neural processes involved in gaze-oriented attention. The results suggest a diminished sensitivity to social signals such as happy faces and gaze in individuals with many autistic traits. Meeting abstract presented at VSS 2013
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".