Retinal limb position and the use of visual feedback during manual aiming
Bibliographic record
Abstract
Recently, we have observed that providing vision between 0.8 and 1.4 m/s (i.e., prior to peak velocity) resulted in movement endpoint distributions as precise as with full vision. This limb velocity range coincides with seeing the limb at approximately 18-20° of visual eccentricity (i.e., peak rod density on the retina). As such, it is possible that the common observation that vision can be used rather early in the trajectory is related to the visual eccentricity of the limb when vision is provided. We thus aimed to determine if the use of vision to control a goal-directed movement varies as a function of retinal limb position. Sixteen (16) participants completed a discrete reaching movement following a 2 viewing distances (30, 50 cm) X 4 vision conditions (early-, late-, full- and no-vision) experimental design. The early and late vision conditions yielded eccentricity ranges of 11° to 15°, 17° to 22°, and 25° to 33°, as well as between 34 and 58 ms of vision. Movement endpoint analyses revealed that trials in the early, late and no-vision conditions were not significantly different from each other but were significantly less accurate and precise than full vision trials (Fs > 17; ps < .01). While these results suggest that the use of vision during limb movements is not directly tied to the neuroanatomy of the eye, we were surprised not to reproduce significant effects of brief visual samples on the control of a voluntary movement. This research was supported by NSERC.Acknowledgments: This research was supported by NSERC.
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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 | 0.008 |
| 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) | 0.002 | 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".