On the contributions of vision and proprioception to the representation of hand-near targets
Bibliographic record
Abstract
Performance is often improved when targets are presented in space near the hands rather than far from the hands. Performance in hand-near space may be improved because participants can use proprioception from the nearby limb and hand to provide a narrower and more resolute frame of reference. An equally compelling alternative is that targets appearing near the hand fall within the receptive fields of visual-tactile bimodal cells, recruiting them to assist in the visual representation of targets that appear near but not far from the hand. We distinguished between these two alternatives by capitalizing on research showing that vision and proprioception have differential effects on the precision of target representation (van Beers RJ, Sittig AC, Denier van der Gon JJ. Exp Brain Res 122: 367-377, 1998). Participants performed an in-to-center reaching task to an array of central target locations with their right hand, while their left hand rested near (beneath) or far from the target array. Reaching end-point accuracy, variability, time, and speed were assessed. We predicted that if proprioception contributes to the representation of hand-near targets, then error variability in depth will be smaller in the hand-near condition than in the hand-far condition. By contrast, if vision contributes to the representation of hand-near targets, then error variability along the lateral dimension will be smaller in the hand-near than in the hand-far condition. Our results showed that the placement of the hand near the targets reduced end-point error variability along the lateral dimension only. The results suggest that hand-near targets are represented with greater visual resolution than far targets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".