Contextual Cues and Transition Statistics Drive Expression of Competing Motor Memories
Bibliographic record
Abstract
Abstract Learning multiple motor skills without interference and expressing the correct one in a changing environment is a fundamental challenge. Contextual cues are known to help separate these memories, but how they interact during retrieval is not well understood. We investigated how the stability, recency, and transitional statistics of learning environments influence this process. Across six visuomotor adaptation experiments, participants learned opposing rotations (Tasks A and B) tagged with distinct contextual cues under different schedules (blocked or interleaved) and were tested in stable or dynamic environments. We found that while contextual cues can successfully separate memories, expression is systematically biased by learned transition statistics: towards more stable memories after imbalanced training, and towards more recent memories when stabilities are matched. Critically, when the stable statistics of training mismatched the volatile statistics of testing, cue-based retrieval collapsed, and behavior was dominated by these stability or recency biases. Conversely, learning in a high-entropy, interleaved environment enabled precise, cue-appropriate expression regardless of the testing schedule. These results demonstrate that memory retrieval is not cue-driven but arises from an arbitration process between cues and transition priors. Our findings reveal that memory retrieval involves weighting sensory information against latent priors derived from the history of context transitions. This work provides a unifying theoretical framework for understanding adaptive memory expression, positing that the brain leverages the learned statistical structure of the environment to infer which memory to recall, thereby balancing cue-driven selection with the stability and predictability of past experience. This principle offers a unifying explanation for interference, spontaneous recovery, and the benefits of variable practice, providing a more holistic model of adaptive motor behavior. Statement of Significance How does a tennis player instantly switch between a forehand and a backhand? Our work reveals a fundamental principle of how the brain organizes and retrieves memories. We demonstrate that recalling a skill is not just about recognizing a contextual cue, but about an internal process of integrating that cue with the learned statistics of the environment, such as the stability and recency of past experiences. This finding provides a unifying framework for phenomena like interference and spontaneous recovery. It has significant implications for designing more effective training in sports and rehabilitation, where structuring practice around environmental statistics can optimize learning and promote flexible skill application.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".