Asymmetry in Gaze Direction Discrimination Between the Upper and Lower Visual Fields
Bibliographic record
Abstract
Previous research has shown that gaze direction can only be accurately discriminated within parafoveal limits (∼5° eccentricity) along the horizontal visual field. Beyond this eccentricity, head orientation seems to influence gaze discrimination more than iris cues. The present study examined gaze discrimination performance in the upper visual field (UVF) and lower visual field (LVF), and whether head orientation affects gaze judgments beyond parafoveal vision. Direct and averted gaze faces, in frontal and deviated head orientations, were presented for 150 ms along the vertical meridian while participants maintained central fixation during gaze discrimination judgments. Gaze discrimination was above chance level at all but one eccentricity for the two gaze-head congruent conditions. In contrast, for the incongruent conditions, gaze was discriminated above chance only from -1.5° to +3°, with an asymmetry between the UVF and LVF. Beyond foveal vision, response rates were biased toward head orientation rather than iris eccentricity, occurring in the LVF for both head orientations, and in the UVF for frontal head views. These findings suggest that covert processing of gaze direction involves the integration of eyes and head cues, with congruency of these two social cues driving response differences between the LVF and the UVF.
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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) | 0.003 | 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".