Perception of gaze direction using 3D virtual reality displays. Effect of Sclera and Head Orientation.
Bibliographic record
Abstract
Perceived gaze direction results from a mechanism that takes into account the orientation of the eyes and the head relative to the observer (Todorovic 2006). Studies using two-dimensional images have shown that head orientation influence perceived gaze direction (Wollaston illusion; see Langton 2000). However, this effect has not been studied using 3D images; it is possible that 3D cues modulate interactions between head and gaze direction. We clarify this issue by using 3D virtual reality displays of digitized faces while manipulating eye and head orientation. We also studied the effect of the white sclera on perceived gaze direction by replacing human eyes by macaque monkey eyes. We used an Oculus rift for stimulus presentation. The stimulus presentation and data collection were conducted using Unreal Engine 4. Within a virtual world, emotionally neutral human heads with human or monkey eyes with 3 head and 7 eye orientations were presented. Subjects (n=9) judged whether gaze pointed right or left relative to them. A Weibull function was fit to the data; α (point of equality) and β (slope) were determined for each subject. We found a head orientation bias corresponding to a repulsive effect only when using human-eyes (rightward head rotation; αMedian 2.89° vs 0.05°; signed-rank test; p = 0.0039; leftward head rotation; αMedian -2.74° vs 0.05°; signed-rank test; p = 0.0195). We also found that the slope of the psychometric curves was steeper using human-eyes (human-eyes vs monkey-eyes, βmedian 12.14 vs 3.87; signed-rank test; p = 0.0053). Subjects showed longer reaction times with monkey eyes (median difference= 61ms; Kruskal-Wallis p< 0.001). Our results showed that the Wollaston illusion is present in 3D displays, however, it vanishes when the white sclera is not present. This indicates that the white sclera in human eyes substantially contribute to improve the accuracy of gaze direction discrimination. Meeting abstract presented at VSS 2018
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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 | 0.001 | 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".