Effects of peripheral eccentricity and head orientation on gaze discrimination
Bibliographic record
Abstract
Visual search tasks support a special role for direct gaze in human cognition, while classic gaze judgment tasks suggest the congruency between head orientation and gaze direction plays a central role in gaze perception. Moreover, whether gaze direction can be accurately discriminated in the periphery using covert attention is unknown. In the present study, individual faces in frontal and in deviated head orientations with a direct or an averted gaze were flashed for 150 ms across the visual field; participants focused on a centred fixation while judging the gaze direction. Gaze discrimination speed and accuracy varied with head orientation and eccentricity. The limit of accurate gaze discrimination was less than ±6° eccentricity. Response times suggested a processing facilitation for direct gaze in fovea, irrespective of head orientation, however, by ±3° eccentricity, head orientation started biasing gaze judgments, and this bias increased with eccentricity. Results also suggested a special processing of frontal heads with direct gaze in central vision, rather than a general congruency effect between eye and head cues. Thus, while both head and eye cues contribute to gaze discrimination, their role differs with eccentricity.
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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 | 0.003 |
| 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".