Movement drift in optic ataxia reveals deficits in hand state estimation in oculocentric coordinates.
Bibliographic record
Abstract
When vision is removed, limb position has been shown to progressively drift during repetitive arm movements. The posterior parietal cortex (PPC) is known to be involved in the processing of multisensory information, the formation of internal hand estimate, and online motor control. Here, we compared hand position drift between healthy controls and 2 patients with PPC damage to gain insight into the mechanisms underlying movement drift and investigate the possible role of the PPC in this process. To do so, we asked participants to perform back-and-forth movements between 2 targets, in the dark and under different gaze fixation conditions. Each individual participant consistently drifted to the same end position for a given hand and gaze condition. We found that the final drift distance was related to small systematic errors made on the very first trial in the dark, with an approximate 3.5 fold increase in magnitude. Furthermore, PPC damage resulted in greater movement drift in patients when the unseen hand was in the contralesional oculocentric space and also when the target was located in the lower visual field. We conclude that the PPC is involved in the proprioceptive representation of hand position in oculocentric coordinates used for reach planning and motor control. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.002 |
| 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) | 0.002 | 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".