Using brain potentials to understand prism adaptation: the error-related negativity and the P300
Bibliographic record
Abstract
Prism adaptation (PA) is both a perceptual-motor learning task as well as a promising rehabilitation tool for visuo-spatial neglect (VSN)-a spatial attention disorder often experienced after stroke resulting in slowed and/or inaccurate motor responses to contralesional targets. During PA, individuals are exposed to prism-induced shifts of the visual-field while performing a visuo-guided reaching task. After adaptation, with goggles removed, visuomotor responding is shifted to the opposite direction of that initially induced by the prisms. This visuomotor aftereffect has been used to study visuomotor learning and adaptation and has been applied clinically to reduce VSN severity by improving motor responding to stimuli in contralesional (usually left-sided) space. In order to optimize PA's use for VSN patients, it is important to elucidate the neural and cognitive processes that alter visuomotor function during PA. In the present study, healthy young adults underwent PA while event-related potentials (ERPs) were recorded at the termination of each reach (screen-touch), then binned according to accuracy (hit vs. miss) and phase of exposure block (early, middle, late). Results show that two ERP components were evoked by screen-touch: an error-related negativity (ERN), and a P300. The ERN was consistently evoked on miss trials during adaptation, while the P300 amplitude was largest during the early phase of adaptation for both hit and miss trials. This study provides evidence of two neural signals sensitive to visual feedback during PA that may sub-serve changes in visuomotor responding. Prior ERP research suggests that the ERN reflects an error processing system in medial-frontal cortex, while the P300 is suggested to reflect a system for context updating and learning. Future research is needed to elucidate the role of these ERP components in improving visuomotor responses among individuals with VSN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".