Human sensorimotor learning: adaptation, skill, and beyond
Highlights
► Human sensorimotor learning is comprised of several component processes. ► Adaptation is updating of internal model based on sensory prediction errors. ► Skill learning is improvement in a controller through trial-and-error reinforcement. ► Use-dependent plasticity is induced by movement repetition and induces biases. ► Explicit and implicit processes influence skill learning.
Introduction
Sensorimotor learning refers to improvement, through practice, in the performance of sensory-guided motor behavior. Here we will focus primarily on learning studies of the hand and arm in humans. Based on our own scientific leanings and limited space for this review, we chose to neglect learning with eyes and legs. It is worthwhile to admit to this effector chauvinism as it raises a question that almost never gets explicitly mentioned in the field of motor learning: how to choose which animal, body part, or task to study? Reductionism applies in motor control as much as in as the rest of science; we need reduced systems in order to build up from the simple to the complex. Sherrington's studies of reflexes across single joints in cats and dogs were predicated on just this kind of reasoning [1]. Thus the sheer richness and variety of learned real-world motor behaviors has been reduced to a small repertoire of laboratory-based learning tasks using different body parts. The crucial question is how interchangeable are these tasks with respect to general insights? It is fairly typical to read a paper where the methods section is task-specific but the discussion assumes the results are about motor learning in general. Thus we would argue that there is a tendency in the field to prematurely lump, and that we should be splitting instead. We focus on goal-directed arm movements because, in our view, they represent an intermediate level of behavior that embodies both low-level motor execution and higher-level cognition.
Psychophysical studies, in which learning is recorded through quantitative movement analysis, reveal regularities and performance patterns at the behavioral level, which suggest organizational principles for learning. Computational modeling offers normative principles, such as optimal Bayesian estimation and minimization of costs, to explain and predict behavioral data. Lesions in patients and stimulation techniques, such as transcranial magnetic (TMS) and direct current stimulation (tDCS), can be used to test the causal role of anatomical structures.
This review, necessarily selective, will describe recent noteworthy studies of goal-directed arm movements, and is organized around the principles of modularity and hierarchy. The text is structured on the premise that motor learning (as a blanket term) consists of multiple component processes, each of which has been studied with particular experimental paradigms. We have divided the sections into what we view as roughly separable components of learning. The order of the sections proceeds from adaptation, to skills, and then to the role of explicit cognitive processes.
Section snippets
Learning rates
Error-based paradigms (prisms, rotations, force fields) have been used extensively to investigate motor learning [2•]. In these paradigms, subjects experience a perturbation of their hand during reaching or pointing movements: lateral displacement by prisms, rotation of movement direction, or lateral forces applied by a robot arm. Specifically, these paradigms have focused on adaptation, a form of learning characterized by gradual improvement in performance in response to altered conditions.
Beyond adaptation in error-based paradigms
Recent studies suggest that other learning processes are active, in addition to adaptation itself, in error-based paradigms. This is important to appreciate – the whole brain is taking part in the experiment, not just the cerebellum updating a forward model.
Optimization and skill
We recently defined skill change operationally as a shift the speed-accuracy trade-off function (SAF) for a task when no systematic perturbation is present [29•]. Adaptation to a perturbation, by contrast, is not a skill because subjects are knocked off their baseline SAF but at best only return to it – their performance is not better than baseline performance. The question is how is skill, that is, improved performance captured as a shift in the SAF, accomplished when there is no systematic
Interaction between implicit and explicit processes during motor learning
Sensorimotor learning, in the form of mirror writing (a form of adaptation), served as the prototypical instantiation of procedural or implicit learning when it was shown to be intact in the amnesic patient HM [38, 39]. Having lost explicit memory, HM did not recall having practiced the motor task before but nevertheless showed motor improvement over days. This very famous result has led, however, to oversimplifications and misunderstandings. That HM could not explicitly recall having done the
Conclusions
Motor learning is a general term that covers multiple model-free and model-based learning processes that are likely to be differentially weighted across tasks and implemented by multiple functional and anatomical brain modules (Figure 1). Specifically, motor learning, at the very least, is made up of adaptation, use-dependent plasticity, operant reinforcement, and explicit cognitive processes. In this framework, it can be conjectured that adaptation and skill learning tasks lie along a spectrum
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
Support was provided by NIH grant R01NS052804 to JWK, and grants from the Gatsby Initiative in Brain Circuitry and the Parkinson's Disease Foundation to PM. We thank Adrian Haith, Vincent Huang, Britne Shabbott, Reza Shadmehr, and Lior Shmuelof for helpful comments on this manuscript.
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