Influence of initial hand and target position on reach errors in optic ataxic and normal subjects
Bibliographic record
Abstract
Recent neurophysiological studies suggest that reach planning areas in the posterior parietal cortex encode both target and initial hand position in gaze-centered coordinates, which could be used to calculate a desired movement vector. We tested how varying gaze, target position, and initial hand position affected reach errors in two left unilateral optic ataxia patients with right PPC damage and seven neurologically intact controls. Both controls' and patients' reaching errors revealed an influence of target position in gaze-centered coordinates; however, both patients' mean errors were offset toward the left, with greater errors when the target was in their left visual field, consistent with the damage to the right PPC. Control subjects also showed a large quasi-independent shoulder-centered influence of target position. This effect was much less present in patient C.F., who had more medial damage to the PPC. In contrast, for patient O.K., who had more lateral PPC damage, the shoulder-centered effect was larger and interacted with the gaze-centered influence of target position. All subjects' errors also revealed a shoulder-centered influence of the initial hand position, with larger influences on the patients' reaching errors. Both patients also showed an interactive influence of the shoulder-centered and gaze-centered initial hand positions. These results suggest that the target and the hand are compared at more than one level in the visuomotor pathway in multiple reference frames, and these comparisons are then integrated. Depending on the location of the damage within the PPC, these comparisons are disrupted, changing the relative influence of hand and target position in different reference frames on the final reaching movement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".