The role of observers' gaze behaviour when watching object manipulation tasks: predicting and evaluating the consequences of action
Bibliographic record
Abstract
When watching an actor manipulate objects, observers, like the actor, naturally direct their gaze to each object as the hand approaches and typically maintain gaze on the object until the hand departs. Here, we probed the function of observers' eye movements, focusing on two possibilities: (i) that observers' gaze behaviour arises from processes involved in the prediction of the target object of the actor's reaching movement and (ii) that this gaze behaviour supports the evaluation of mechanical events that arise from interactions between the actor's hand and objects. Observers watched an actor reach for and lift one of two presented objects. The observers' task was either to predict the target object or judge its weight. Proactive gaze behaviour, similar to that seen in self-guided action-observation, was seen in the weight judgement task, which requires evaluating mechanical events associated with lifting, but not in the target prediction task. We submit that an important function of gaze behaviour in self-guided action observation is the evaluation of mechanical events associated with interactions between the hand and object. By comparing predicted and actual mechanical events, observers, like actors, can gain knowledge about the world, including information about objects they may subsequently act upon.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".