A popular model of human sensorimotor learning suggests that a fast process and a slow process work in parallel to produce the canonical learning curve (Smith et al., 2006). Recent evidence supports the subdivision of sensorimotor learning into explicit and implicit processes that simultaneously subserve task performance (Taylor et al., 2014). We set out to test whether these two accounts of learning processes are homologous. Using a recently developed method to assay explicit and implicit learning directly in a sensorimotor task, along with a computational modeling analysis, we show that the fast process closely resembles explicit learning and the slow process approximates implicit learning. In addition, we provide evidence for a subdivision of the slow/implicit process into distinct manifestations of motor memory. We conclude that the two-state model of motor learning is a close approximation of sensorimotor learning, but it is unable to describe adequately the various implicit learning operations that forge the learning curve. Our results suggest that a wider net be cast in the search for the putative psychological mechanisms and neural substrates underlying the multiplicity of processes involved in motor learning.

Computations underlying cognitive strategies in human motor learning are poorly understood. Here we investigate such strategies in a common sensorimotor transformation task. We show that strategies assume two forms, likely reflecting distinct working memory representations: discrete caching of stimulus-response contingencies, and time-consuming parametric computations. Reaction times and errors suggest that both strategies are employed during learning, and trade off based on task complexity. Experiments using pressured preparation time further support dissociable strategies: In response caching, time pressure elicits multi-modal distributions of movements; during parametric computations, time pressure elicits a shifting distribution of movements between visual targets and distal goals, consistent with analog re-computing of a movement plan. A generalization experiment reveals that discrete and parametric strategies produce, respectively, more localized or more global transfer effects. These results describe how qualitatively distinct cognitive representations are leveraged for motor learning and produce downstream consequences for behavioral flexibility.

Sensorimotor adaptation tasks have been used to characterize processes responsible for calibrating the mapping between desired outcomes and motor commands. Research has focused on how this form of error-based learning occurs in an implicit and automatic manner. However, recent work has revealed the operation of multiple learning processes, even in this simple form of learning. This review focuses on the contribution of cognitive strategies and heuristics to sensorimotor learning, and how these processes enable humans to rapidly explore and evaluate novel solutions to enable flexible, goal-oriented behavior. This new work points to limitations in current computational models, and how these must be updated to describe the conjoint impact of multiple processes in sensorimotor learning.

Generalization is a fundamental aspect of behavior, allowing for the transfer of knowledge from one context to another. The details of this transfer are thought to reveal how the brain represents what it learns. Generalization has been a central focus in studies of sensorimotor adaptation, and its pattern has been well characterized: Learning of new dynamic and kinematic transformations in one region of space tapers off in a Gaussian-like fashion to neighboring untrained regions, echoing tuned population codes in the brain. In contrast to common allusions to generalization in cognitive science, generalization in visually guided reaching is usually framed as a passive consequence of neural tuning functions rather than a cognitive feature of learning. While previous research has presumed that maximum generalization occurs at the instructed task goal or the actual movement direction, recent work suggests that maximum generalization may occur at the location of an explicitly accessible movement plan. Here we provide further support for plan-based generalization, formalize this theory in an updated model of adaptation, and test several unexpected implications of the model. First, we employ a generalization paradigm to parameterize the generalization function and ascertain its maximum point. We then apply the derived generalization function to our model and successfully simulate and fit the time course of implicit adaptation across three behavioral experiments. We find that dynamics predicted by plan-based generalization are borne out in the data, are contrary to what traditional models predict, and lead to surprising implications for the behavioral, computational, and neural characteristics of sensorimotor adaptation. The pattern of generalization is thought to reveal how the motor system represents learned actions. Recent work has made the intriguing suggestion that maximum generalization in sensorimotor adaptation tasks occurs at the location of the learned movement plan. Here we support this interpretation, develop a novel model of motor adaptation that incorporates plan-based generalization, and use the model to successfully predict surprising dynamics in the time course of adaptation across several conditions.

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