Gaze following from another’s perspective
Bibliographic record
Abstract
Recent research shows that gaze following relies on both the computation of a gazer’s line-of-sight (gaze directionality) and an understanding of their mental state (adopting their visual perspective). Here we examined how these two mechanisms operated when participants responded from the gazer’s mental perspective. We devised a task in which a central avatar gazed at a peripheral target or distractor before returning its gaze to the center. Response targets invoked either a combined (i.e., both the observer and the avatar see an 8) or a dissociated mental representation (i.e., observer sees an E while the avatar sees a 3). Participants were asked to localize the target from the avatar’s perspective. Crossing the line-of-sight and mental perspective conditions provided the test cases to examine target performance when the line-of-sight and mental perspective are spatially combined and when they are spatially dissociated. Participants overall performed with high accuracy. They were faster to localize targets in conditions in which both the line-of-sight and avatar’s mental perspective cued the target relative to conditions in which one of the two signals were inconsistent with the target’s location. Inconsistent visual perspective between the observer and the avatar invoked a larger detriment on target performance when the avatar’s line of sight was spatially incongruent with the target relative to when it was spatially congruent with the target. Thus, humans can follow gaze from another gazer’s perspective, with the computations of the gazer’s line-of-sight and their mental state operating similarly as in typical gaze following measured from the observer’s perspective.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".