Investigating premotor reaching biases after prism adaptation
Bibliographic record
Abstract
Prism adaptation (PA) is both a visuomotor learning task and potential treatment for spatial neglect after stroke. While PA’s aftereffects can improve neglect symptoms, therapeutic benefits vary across individuals, possibly due to differences in neglect subtypes. Neglect symptoms can be described along an information processing pathway, yielding perceptual (input) and premotor (output) neglect subtypes. There is some evidence that PA mainly benefits persons with premotor neglect. We investigated whether PA modulates the premotor stage of information processing by examining whether PA could induce a premotor bias in healthy adults. We measured perceptual and premotor biases using a speeded reach task that compares the initiation time of leftward and rightward reaches to lateralized targets from different hand start positions. Using a randomized mixed experimental design, 30 right-handed healthy adults completed this speeded reach task before and after either left-shifting (n = 15) or right-shifting (n = 15) PA. As hypothesized, left-shifting PA speeded initiation time specifically for reaches in the rightward direction, regardless of target location (p = .02, ηp2 = .18), suggesting that PA induced a premotor bias in the direction of the prism aftereffect. These findings have implications for PA’s underlying mechanisms, which can inform visuomotor learning theories and PA’s use as a treatment for spatial neglect.
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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) | 0.000 | 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) | 0.001 | 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".