Uncovering the Role of the Human Hippocampus in Procedural Motor Learning: Insights from Implicit Sensorimotor Adaptation
Bibliographic record
Abstract
Recent evidence suggests that the human hippocampus, traditionally associated with declarative memory, plays a role in motor sequence learning (MSL). However, the classic MSL paradigm depends initially on declarative learning. Thus, it is critical to discern whether the participation of the hippocampus relates to its canonical role or to processing a general aspect of learning that transcends the declarative/non-declarative distinction. To address this issue, here we turned to visuomotor adaptation (VMA)-a type of motor learning involving skill recalibration-which unlike MSL can be easily manipulated to eliminate the explicit component. We examined the broader involvement of the hippocampus in procedural motor learning by using diffusion MRI in a sample of males and females to assess structural plasticity associated with memory consolidation in VMA and an implicit-only version (IVMA). We found that both VMA and IVMA engaged the left posterior hippocampus in a learning-specific manner. Remarkably, while VMA induced only transient hippocampal alterations, IVMA elicited structural changes that persisted overnight, underscoring the reliance on implicit learning for enduring neuroplasticity. As expected, training on both tasks impacted the microstructure of the cerebellum and the motor and posterior parietal cortex. Notably, the temporal dynamics of changes in these regions paralleled those of the left hippocampus, suggesting that motor and limbic regions operate together as part of the same network. Collectively, our findings support an active role of the hippocampus in implicit motor learning and argue for a unified function in memory encoding regardless of the declarative or non-declarative nature of the task.
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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 | 0.000 |
| 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 | 0.001 |
| 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".