Cluttered environments: Differential effects of obstacle position on grasp and gaze locations.
Bibliographic record
Abstract
Previous research has investigated the effects of nontarget objects (NTOs) on reach trajectories, but their effects on eye-hand coordination remain to be determined. The current investigation utilized an eye-hand coordination paradigm, where a reaching and grasping task was performed in the presence of an NTO positioned exclusively in the right or left workspace of each right-handed participant. NTOs 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 an NTO was presented on the right (ipsilateral to the reaching hand), it pushed the final grasp and gaze locations on the target, shifting them to the left-away from the "obstacle." The impact of the ipsilateral NTO was increased as it was moved into positions closer to the participant that were of greater obstruction to the hand and arm. In contrast, when the NTO was contralateral, the risk of collision was low and participants developed a set reach plan that was repeated nearly identically for each contralateral NTO position. Our findings also indicate that the "invasiveness" of the NTO positions had a greater effect on grasp than it did on gaze position-demonstrating how the arrangement of clutter in an environment can differentially affect gaze and grasp when reaching for an object. (PsycINFO Database Record
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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 | 0.003 |
| 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 | 0.001 |
| 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".