Perceived gaze direction affects basic cognitive and affective theory of mind processes – an ERP study
Bibliographic record
Abstract
We look at someone's eyes for insight into their mental state. However, little is known about how seeing someone look at or away from us impacts our reasoning about their thoughts (cognitive theory of mind; cTOM) and emotions (affective theory of mind; aTOM). We examined how gaze affects the ability to make cTOM and aTOM judgements and the time course of these cognitive processes. As we usually infer what people are thinking based on where they are looking in their environment, we hypothesized that averted gaze may facilitate cTOM more than direct gaze. In contrast, direct gaze is implicated in emotional responding, suggesting a facilitatory role in aTOM. Thirty participants viewed the same direct and averted gaze faces expressing joy or anger (half female) and completed: 1) an aTOM task (emotion discrimination), 2) a cTOM task (direction of attention discrimination), and 3) a control task (gender discrimination). ERPs were recorded to face onset, and mean amplitude was analysed across 200ms time-windows from 200-800ms over occipito-temporal and parietal sites. Accuracy and reaction times were best/shortest for the control task, intermediate for the aTOM task, and worst/longest for the cTOM task. At occipito-temporal sites, task affected amplitudes around 400-800ms, with the most negative amplitude seen for the cTOM task, followed by the aTOM task, and then the control task, likely reflecting cognitive load. As predicted, gaze direction modulated behaviour in the two TOM tasks, but not the control task. Participants responded faster and more accurately when faces had direct gaze in the aTOM task, and when faces had averted gaze in the cTOM task. An increased positivity was elicited by direct compared to averted gaze in the cTOM task from 600-800ms over parietal sites. Results support a facilitatory role of direct gaze in aTOM and for averted gaze in cTOM tasks. Meeting abstract presented at VSS 2018
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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 | 0.000 | 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".