The proximity of visual landmarks impacts reaching performance
Bibliographic record
Abstract
The control of goal-directed reaching movements is thought to rely upon egocentric visual information derived from the visuomotor networks of the dorsal visual pathway. However, recent research (Krigolson and Heath, 2004) suggests it is also possible to make allocentric comparisons between a visual background and a target object to facilitate reaching accuracy. Here we sought to determine if the effectiveness of these allocentric comparisons is reduced as distance between a visual background and a target object increases. To accomplish this, participants completed memory-guided reaching movements to targets presented in an otherwise empty visual background or positioned within a proximal, medial, or distal visual background. Our results indicated that the availability of a proximal or medial visual background reduced endpoint variability relative to reaches made without a visual background. Interestingly, we found that endpoint variability was not reduced when participants reached to targets framed within a distal visual background. Such findings suggest that allocentric visual information is used to facilitate reaching performance; however, the fidelity by which such cues are used appears linked to the proximity of veridical target location. Importantly, these data also suggest that information from both the dorsal and ventral visual streams can be integrated to facilitate the online control of reaching movements.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".