Where you aim - not how you aim - affects implicit recalibration in visuomotor adaptation

The influence of explicit strategies on implicit recalibration during visuomotor adaptation has become a central question in motor learning. Because the two systems operate in tandem, explicit strategies could indirectly influence implicit recalibration. However, explicit strategies are not unitary: they may rely on algorithmic-based computations or memory-based retrieval of cached solutions. This raises the possibility that different strategy implementations interact with the cerebellar-based implicit recalibration system in qualitatively distinct ways, especially given that these strategies likely rely on different frontoparietal networks. Here, we tested whether the type of explicit strategy modulates implicit recalibration. Across a set of experiments, we observed subtle differences in the spatial profile of implicit generalization: the algorithmic strategy produced a broader generalization pattern than the retrieval strategy, even after controlling for intertrial decay, generalization structure, and between-target interactions. While this pattern is suggestive of greater flexibility afforded by algorithmic strategy use compared to memory-based retrieval, it could instead arise from increased variability in explicit aiming, which constitutes the input data driving implicit recalibration. Indeed, when we isolated the direct contribution of each strategy to implicit recalibration by rigorously controlling for reach variability and using error-clamp feedback to ensure uniform implicit learning conditions, we found no difference in implicit recalibration across strategies. Together, these findings suggest that while algorithmic and retrieval strategies differ in their behavioral signatures and influence the movement plan, the implicit recalibration process itself remains rigid with respect to the strategy employed. Significance StatementWhether explicit strategies influence implicit recalibration remains debated - a question complicated by the fact that different training conditions give rise to distinct forms of strategy. Because algorithmic and retrieval-based strategies engage partially dissociable frontoparietal networks, they may differentially interface with cerebellar subregions, yielding distinct effects on implicit recalibration, potentially reconciling prior mixed findings. Our results indicate that, once the first order spatial and temporal statistics that typically covary with these different strategies are matched, the strategies themselves do not directly influence implicit recalibration. Instead, the cerebellar-based implicit recalibration system faithfully updates the sensorimotor mapping simply based on the training data it is provided. These findings support prior work suggesting strong independence and stereotyped behavior of implicit recalibration.