Scan patterns during the processing of facial expression versus identity: An exploration of task-driven and stimulus-driven effects
Bibliographic record
Abstract
Perceptual studies suggest that processing facial identity emphasizes upper-face information, whereas processing expressions of anger or happiness emphasizes the lower-face. The two goals of the present study were to determine (a) if the distributions of eye fixations reflect these upper/lower-face biases, and (b) whether this bias is task- or stimulus-driven. We presented a target face followed by a probe pair of morphed faces, neither of which was identical to the target. Subjects judged which of the pair was more similar to the target face while eye movements were recorded. In Experiment 1 the probe pair always differed from each other in both identity and expression on each trial. In one block subjects judged which probe face was more similar to the target face in identity, and in a second block subjects judged which probe face was more similar to the target face in expression. In Experiment 2 the two probe faces differed in either expression or identity, but not both. Subjects were not informed which dimension differed, but simply asked to judge which probe face was more similar to the target face. We found that subjects scanned the upper-face more than the lower-face during the identity task but the lower-face more than the upper-face during the expression task in Experiment 1 (task-driven effects), with significantly less variation in bias in Experiment 2 (stimulus-driven effects). We conclude that fixations correlate with regional variations of diagnostic information in different processing tasks, but that these reflect top-down task-driven guidance of information acquisition more than stimulus-driven effects.
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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 | 0.004 |
| 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) | 0.001 | 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".