Eye Movements When Observing Predictable and Unpredictable Actions
Bibliographic record
Abstract
We previously showed that, when observers watch an actor performing a predictable block-stacking task, the coordination between the observer's gaze and the actor's hand is similar to the coordination between the actor's gaze and hand. Both the observer and the actor direct gaze to forthcoming grasp and block landing sites and shift their gaze to the next grasp or landing site at around the time the hand contacts the block or the block contacts the landing site. Here we compare observers' gaze behavior in a block manipulation task when the observers did and when they did not know, in advance, which of two blocks the actor would pick up first. In both cases, observers managed to fixate the target ahead of the actor's hand and showed proactive gaze behavior. However, these target fixations occurred later, relative to the actor's movement, when observers did not know the target block in advance. In perceptual tests, in which observers watched animations of the actor reaching partway to the target and had to guess which block was the target, we found that the time at which observers were able to correctly do so was very similar to the time at which they would make saccades to the target block. Overall, our results indicate that observers use gaze in a fashion that is appropriate for hand movement planning and control. This in turn suggests that they implement representations of the manual actions required in the task and representations that direct task-specific eye movements.
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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 | 0.007 |
| 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 | 0.001 |
| 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".