Gaze behavior during 3-D face identification is depth cue invariant
Bibliographic record
Abstract
Gaze behavior during scene and object recognition can highlight the relevant information for a task. For example, salience maps-highlighting regions that have heightened luminance, contrast, color, etc. in a scene-can be used to predict gaze targets. Certain tasks, such as face recognition, result in a typical pattern of fixations on high salience features. While local salience of a 2-D feature may contribute to gaze behavior and object recognition, we are perfectly capable of recognizing objects from 3-D depth cues devoid of meaningful 2-D features. Faces can be recognized from pure texture, binocular disparity, or structure-from-motion displays (Dehmoobadsharifabadi & Farivar, 2016; Farivar, Blanke, & Chaudhuri, 2009; Liu, Collin, Farivar, & Chaudhuri, 2005), and yet these displays are devoid of local salient 2-D features. We therefore sought to determine whether gaze behavior is driven by an underlying 3-D representation that is depth-cue invariant or depth-cue specific. By using a face identification task comprising morphs of 3-D facial surfaces, we were able to measure identification thresholds and thereby equate for task difficulty across different depth cues. We found that gaze behavior for faces defined by shading and texture cues was highly comparable, but we observed some deviations for faces defined by binocular disparity. Interestingly, we found no effect of task difficulty on gaze behavior. The results are discussed in the context of depth-cue invariant representations for facial surfaces, with gaze behavior being constrained by low-level limits of depth extraction from specific cues such as binocular disparity.
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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 | 0.001 |
| 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".