Enhanced Visual Processing When Reaching for Targets Presented Near the Hands
Bibliographic record
Abstract
Placing a hand near a target seems to influence how it is processed. One possible explanation for near-hand effects is that bimodal neuron recruitment contributes to a more robust representation of targets appearing near the hands in comparison to targets far from the hands. Neurophysiological studies have shown that near-hand targets recruit visual-tactile bimodal cells, and that the response of these cells varies with the distance between the target and nearby hand. The purpose of the current study is to determine if the representation of target location for reaching is influenced by the presence of the hand near the target. Participants reached for targets that appeared either near or far from (1) the participant's invisible resting left hand or (2) a visual cue (absence of left-hand). We predicted that if hand-proximity effects arise from the recruitment of visual-tactile bimodal cells then participants should reach for targets more quickly and with greater accuracy and precision when the hand is in the workspace, and that these measures should vary with the distance between the hand and target. Right-hand reaching movements were tracked using a motion tracker to measure movement timing, end-point accuracy and precision. Our results showed that when the resting hand was present there was a reduction in spatial error, error variability, and movement time when compared to the no-hand condition. Likewise, these measures varied significantly with the distance between the target and hand. Overall, these results suggest that the visual representation of the target is enhanced through the recruitment of multisensory resources when the target appears near but not far from the hand. Meeting abstract presented at VSS 2014
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".