Gaze strategies while grasping: What are you looking at?!
Bibliographic record
Abstract
Eye movements and visuomotor behaviour operate in sequence - we look at the objects with which we are going to interact. Although eye movements have been well documented during a variety of activities such as walking, sports, typing, reading, and driving, relatively few studies have investigated gaze strategies during object grasping. One that has suggests that gaze supports the planning and control of actions by marking key positions to which the fingertips are directed (Johansson et al., 2001). To date, however, the precise location of eye fixations while grasping objects of varying size and shape have not been well characterized. The purpose of this study was to investigate where people look, relative to where they grasp, when reaching out to pick up centrally placed symmetrical blocks. Eye movement and grasping kinematic recordings were integrated into the same frame of reference via MotionMonitor software. Gaze position was reported at grasp-related kinematically defined time points: first fixation, maximum grip aperture (MGA), and object contact. Overall, fixations were found to be concentrated on the top half of the block, with the majority of fixations clustered along the object's midline. During first fixation, gaze points were clustered on the top central edge of the object, corresponding with the eventual index finger grasp point. At MGA, a significant shift in gaze position frequency was observed, with a greater concentration of gaze fixations around the object's center of mass. This monitoring of the object's center was also observed during object contact. These results suggest that during the planning of the grasp, prior to movement onset, eye gaze targets the grasp point for the index finger on the object. However, during the reach itself, the center of mass becomes more of a concentrated focus as it is during perceptual tasks (Kowler et al., 1995).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".