Gaze audits food items for bite points during human withdraw-to-eat movements
Bibliographic record
Abstract
Abstract Food handling and eating are central to the skill of primate hand movements, and their analysis can provide insights into the evolutionary origins of hand use and its generalization to other behaviors, such as tool use. Vision contributes differently to the reach, grasp, and withdraw-to-eat components of hand use when eating, suggesting that these component movements are controlled by different visuomotor networks with distinct evolutionary histories. This study examines the role of gaze in mediating the withdraw-to-eat movement in human participants eating various food items, including candy, donuts, carrots, bananas, and apples, or pantomiming the eating movements for some of these items. Eye-tracking and frame-by-frame video analyses are used to describe gaze, gaze duration, gaze disengagement, eye blinking, and hand preference in eating each food item. The results show that gaze first identifies points on a food item that the dominant hand can grasp and then identifies points on the food item that the mouth can bite. The hand and finger shaping movements of both the initial grasp and subsequent food handling aid in exposing targets on the food for grasping and biting. The comparison of real and pantomime eating suggests that only real food items possess the affordances that elicit gaze patterns associated with identifying online targets for grasps and bites. The findings are discussed in relation to idea that gaze has a feature-detector-like role linking food cues to the skilled movements of hand shaping to grasp a food item and then to orient a food item to the mouth for biting.
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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".