Direction of visual shift and hand congruency enhance spatial realignment during visuomotor adaptation.
Bibliographic record
Abstract
Although prism adaptation has been studied extensively for over 100 years to better understand how the motor system adapts to sensory perturbations, very few studies have systematically studied how the combination of the hand used to adapt, and the direction of visual shift, might influence adaptation. Given that sensory inputs and motor outputs from the same side are processed (at least initially) in the same hemisphere, we wondered whether there might be differences in how people adapt when the hand used and the direction of visual shift were congruent (e.g., adapting to rightward shifting prisms with the right hand), compared to incongruent (e.g., adapting to rightward shifting prisms with the left hand). In Experiment 1 we re-analyzed a previously published dataset (Striemer, Enns, & Whitwell, 2019) in which healthy adults (n=17) adapted to 17° leftward or rightward optically displacing prisms using their left or right hand (tested in separate sessions, counterbalanced). Our results revealed a “congruency effect” such that adaptation aftereffects were significantly larger for reaches performed without visual feedback (i.e., straight-ahead pointing) when the direction of prism shift and the hand used were congruent, compared to incongruent. We replicated this same congruency effect in Experiment 2 in a new group of participants (n=25). We suggest that a better understanding of the cognitive and neural mechanisms underlying the congruency effect will allow researchers to build more precise models of visuomotor learning, and may lead to the development of more effective applications of prism adaptation for the treatment of attentional disorders following brain damage.
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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 | 0.000 | 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".