Prism adaptation in virtual and natural contexts: Evidence for a flexible adaptive process
Bibliographic record
Abstract
Prism exposure when aiming at a visual target in a virtual condition (e.g., when the hand is represented by a video representation) produces no or only small adaptations (after-effects), whereas prism exposure in a natural condition produces large after-effects. Some researchers suggested that this difference may arise from distinct adaptive processes, but other studies suggested a unique process. The present study reconciled these conflicting interpretations. Forty participants were divided into two groups: One group used visual feedback of their hand (natural context), and the other group used computer-generated representational feedback (virtual context). Visual feedback during adaptation was concurrent or terminal. All participants underwent laterally displacing prism perturbation. The results showed that the after-effects were twice as large in the "natural context" than in the "virtual context". No significant differences were observed between the concurrent and terminal feedback conditions. The after-effects generalized to untested targets and workspace. These results suggest that prism adaptation in virtual and natural contexts involves the same process. The smaller after-effects in the virtual context suggest that the depth of adaptation is a function of the degree of convergence between the proprioceptive and visual information that arises from the hand.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".