Somatosensory working memory in human reinforcement-based motor learning
Bibliographic record
Abstract
Recent studies using visuomotor adaptation and sequence learning tasks have assessed the involvement of working memory in the visuospatial domain. The capacity to maintain previously performed movements in working memory is perhaps even more important in reinforcement-based learning to repeat accurate movements and avoid mistakes. Using this kind of task in the present work, we tested the relationship between somatosensory working memory and motor learning. The first experiment involved separate memory and motor learning tasks. In the memory task, the participant's arm was displaced in different directions by a robotic arm, and the participant was asked to judge whether a subsequent test direction was one of the previously presented directions. In the motor learning task, participants made reaching movements to a hidden visual target and were provided with positive feedback as reinforcement when the movement ended in the target zone. It was found that participants that had better somatosensory working memory showed greater motor learning. In a second experiment, we designed a new task in which learning and working memory trials were interleaved, allowing us to study participants' memory for movements they performed as part of learning. As in the first experiment, we found that participants with better somatosensory working memory also learned more. Moreover, memory performance for successful movements was better than for movements that failed to reach the target. These results suggest that somatosensory working memory is involved in reinforcement motor learning and that this memory preferentially keeps track of reinforced movements. NEW & NOTEWORTHY The present work examined somatosensory working memory in reinforcement-based motor learning. Working memory performance was reliably correlated with the extent of learning. With the use of a paradigm in which learning and memory trials were interleaved, memory was assessed for movements performed during learning. Movements that received positive feedback were better remembered than movements that did not. Thus working memory does not track all movements equally but is biased to retain movements that were rewarded.
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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".