Detecting Gaze Direction in the Horizontal and Vertical Periphery
Bibliographic record
Abstract
Visual search paradigms have previously shown direct gaze (DG) to be detected better than averted gaze (AG). However, our previous eye-tracking research showed that this detection asymmetry is strongly influenced by the eccentricity at which the face is presented (Palanica & Itier, 2011). In four studies, we examined to what extent DG was better detected than AG in the periphery, using various horizontal and vertical eccentricities along the visual field. Stimuli consisted of frontal or deviated head views with direct- or averted gaze and were individually flashed across the screen as participants fixated the centre of the screen and discriminated gaze direction using a two-button press. Experiments 1 (frontal view) and 2 (deviated view) presented faces along the horizontal periphery; Experiments 3 (frontal view) and 4 (deviated view) presented faces along the vertical periphery. When the face was in frontal view, DG was detected faster and more accurately than AG across the entire horizontal visual field, as well as across the vertical visual field tested. When the face was in deviated view and presented along the horizontal periphery, AG was detected faster and more accurately than DG in the periphery, while DG was detected faster in the central visual field. When the face was in deviated view and presented along the vertical periphery, DG tended to be detected faster than AG at some eccentricities, but no RT difference was found. Overall, these findings suggest that gaze direction can be discriminated in both the horizontal and vertical periphery. Importantly, the congruency between gaze direction and head orientation seems to be play an important role. These findings demonstrate that the speed and accuracy of gaze detection is highly dependent on target position and head orientation. Meeting abstract presented at VSS 2013
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".