Eye-hand coordination: Differential effects of obstacle position on reach trajectories, grasp and gaze locations.
Bibliographic record
Abstract
When we reach out to pick up an object, we rarely collide with any non-target objects, even if our workspace is cluttered. This seemingly simple task requires the coordination of motor, attentional, and perceptual systems. Even though previous research has investigated the effects of non-target objects on reach trajectories, their effects on eye-hand coordination remains to be determined. The current investigation utilized an eye-hand coordination paradigm, where a reaching and grasping task was performed in the presence of a non-target-object positioned exclusively in the right or left workspace of each right-handed participant. Non-target objects varied in their closeness to the subject and reach-path, between the starting location of the hand and the target-object of the reach. A control condition, where only the target was present, was also included. When non-target objects were presented on the right, greater reach durations and larger deviations in reach trajectories were produced than during the target-only condition. These effects increased further as the "obstacle" was placed closer to the subject or reach-path. Right-sided "obstacles" also pushed the final grasp and gaze locations on the target, shifting them to the left – away from the "obstacles". Unlike reach trajectory, final grasp and gaze locations were not influenced by the nearness of the non-target object to the subject or reach-path. As risk of collision increases for right-sided obstacles, a more trial-by-trial approach may have been taken to trajectory planning, though not in guiding the hand to the target. During trials in which non-target objects appeared in any of the leftward positions, none of these measures were affected. Results from the current studies demonstrate how the arrangement of clutter in an environment can differentially affect eye-hand coordination when reaching for an object. Meeting abstract presented at VSS 2013
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".