Interaction between Model-based and Model-free Mechanisms in Motor Learning

Abstract Motor learning can be driven by distinct mechanisms—habitual, model-free processes, and strategic, model-based processes—depending on the magnitude and context of movement errors. Although small and large errors are known to engage distinct motor learning mechanisms—model-free (implicit) or model-based (explicit), respectively—it remains unclear whether successfully deploying one mechanism might hamper the engagement of the other in subsequent learning tasks. Here, we investigated how prior engagement of a particular mechanism biases future adaptations, even when the new task context typically favours the alternative strategy. Across three experiments (N=82), participants performed reaching movements to targets that either remained fixed or “jumped” mid-movement by small (15°) or large (30°, 45°, or 60°) angles. When first exposed to small errors (15°), participants exhibited persistent aftereffects in subsequent catch trials and stable reaction times (RTs), hallmarks of a model-free, habitual process. Surprisingly, even when switching to larger error magnitudes later, these participants continued to show robust aftereffects and did not elevate RTs— indicating a carryover of model-free learning. Conversely, participants who initially experienced large errors showed minimal aftereffects and flexible RT modulation consistent with model-based strategies; this bias persisted in later sessions with smaller errors, leading to reduced habitual aftereffects. Notably, inserting a washout phase to reset baseline performance did not abolish these mechanistic biases, highlighting that the initial engagement of either model-free or model-based processes leaves a durable imprint on subsequent adaptations. Taken together, these findings demonstrate that motor learning is shaped not only by ongoing task demands (e.g., error magnitude) but also by an individual’s prior learning history. Understanding how initial learning experiences constrain future adaptations has broad implications for designing interventions and training protocols in motor rehabilitation and skill acquisition. Statement of Significance Motor learning involves distinct mechanisms: habitual, model-free processes (driven by gradual stimulus-response associations) and strategic, model-based processes (guided by explicit adjustments). This study demonstrates that initial engagement of one mechanism biases subsequent adaptations, even when task demands shift to favor the alternative. The findings suggest that motor learning is a hierarchical process shaped by cumulative contextual experiences. Our results have highlighted how early learning establishes neural or cognitive frameworks that constrain future adaptations, prioritizing efficiency over flexibility. This has implications for designing motor training or rehabilitation protocols: initiating learning with model-based strategies (via large errors) may preserve adaptability, while model-free training (via small errors) risks anchoring rigid habits. By elucidating how prior mechanisms bias ongoing learning, this work advances our understanding of motor memory interactions and their real-world applications in skill acquisition and recovery.

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