Task-Specific Sensorimotor Adaptation to Reversing Prisms
Bibliographic record
Abstract
We tested between three levels of visuospatial adaptation (global map, parallel feature modules, and parallel sensorimotor transformations) by training subjects to reach and grasp virtual objects viewed through a left-right reversing prism, with either visual location or orientation feedback. Even though spatial information about the global left-right reversal was present in every training session, subjects trained with location feedback reached to the correct location but with the wrong (reversed) grasp orientation. Subjects trained with orientation feedback showed the opposite pattern. These errors were task-specific and not feature-specific; subjects trained to correctly grasp visually reversed-oriented bars failed to show knowledge of the reversal when asked to point to the end locations of these bars. These results show that adaptation to visuospatial distortion--even global reversals--is implemented through learning rules that operate on parallel sensorimotor transformations (e.g., reach vs. grasp).
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".