Variability in error-based and reward-based human motor learning is associated with entorhinal volume
Bibliographic record
Abstract
Abstract Error-based and reward-based processes are critical for motor learning, and are thought to be mediated via distinct neural pathways. However, recent behavioral work in humans suggests that both learning processes are supported by cognitive strategies and that these contribute to individual differences in motor learning ability. While it has been speculated that medial temporal lobe regions may support this strategic component to learning, direct evidence is lacking. Here we first show that faster and more complete learning during error-based visuomotor adaptation is associated with better learning during reward-based shaping of reaching movements. This result suggests that strategic processes, linked to faster and better learning, drive individual differences in both error-based and reward-based motor learning. We then show that right entorhinal cortex volume was larger in good learning individuals—classified across both motor learning tasks—compared to their poorer learning counterparts. This suggests that strategic processes underlying both error- and reward-based learning are linked to neuroanatomical differences in entorhinal cortex. Significance Statement While it is widely appreciated that humans vary greatly in their motor learning abilities, little is known about the processes and neuroanatomical bases that underlie these differences. Here, using a data-driven approach, we show that individual variability in error-based and reward-based motor learning is tightly linked, and related to the use of cognitive strategies. We further show that structural differences in entorhinal cortex predict this intersubject variability in motor learning, with larger entorhinal volumes being associated with better overall error-based and reward-based learning. Together, these findings provide support for the notion that the ability to recruit strategic processes underlies intersubject variability in both error-based and reward-based learning, which itself may be linked to structural differences in medial temporal regions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".