Visual Feedback Modulates Aftereffects and Electrophysiological Markers of Prism Adaptation
Bibliographic record
Abstract
Prism adaptation (PA) is both a model for visuomotor learning and a promising treatment for visuospatial neglect after stroke. The task involves reaching for targets while prism glasses horizontally displace the visual field. Adaptation is hypothesized to occur through two processes: strategic recalibration, a rapid self-correction of pointing errors; and spatial realignment, a more gradual adjustment of visuomotor reference frames that produce prism aftereffects (i.e., reaching errors upon glasses removal in the direction opposite to the visual shift). While aftereffects can ameliorate neglect, not all patients respond to PA, and the neural mechanisms underlying successful adaptation are unclear. We investigated the feedback-related negativity (FRN) and the P300 event-related potential (ERP) components as candidate markers of strategic recalibration and spatial realignment, respectively. Healthy young adults wore prism glasses and performed memory-guided reaching toward vertical-line targets. ERPs were recorded in response to three different between-subject error feedback conditions at screen-touch: view of hand and target (Experiment 1), view of hand only (Experiment 2), or view of lines to mark target and hand position (view of hand occluded; Experiment 3). Conditions involving a direct view of the hand-produced stronger aftereffects than indirect hand feedback, and also evoked a P300 that decreased in amplitude as adaptation proceeded. Conversely, the FRN was only seen in conditions involving target feedback, even when aftereffects were smaller. Since conditions producing stronger aftereffects were associated with a phase-sensitive P300, this component may index a "context-updating" realignment process critical for strong aftereffects, whereas the FRN may reflect an error monitoring process related to strategic recalibration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".