A Synthesis of the Many Errors and Learning Processes of Visuomotor Adaptation

Abstract

Visuomotor adaptation has one of the oldest experimental histories in psychology and neuroscience, yet its precise nature has always been a topic of debate. Here we offer a survey and synthesis of recent work on visuomotor adaptation that we hope will prove illuminating for this ongoing dialogue. We discuss three types of error signals that drive learning in adaptation tasks: task performance error, sensory prediction-error, and a binary target hitting error. Each of these errors has been shown to drive distinct learning processes. Namely, both target hitting errors and putative sensory prediction-errors drive an implicit change in visuomotor maps, while task performance error drives learning of explicit strategy use and non-motor decision-making. Each of these learning processes contributes to the overall learning that takes place in visuomotor adaptation tasks, and although the learning processes and error signals are independent, they interact in a complex manner. We outline many task contexts where the operation of these processes is counter-intuitive and offer general guidelines for their control, measurement and interpretation. We believe this new framework unifies several disparate threads of research in sensorimotor adaptation that often seem in conflict. We conclude by explaining how this more nuanced understanding of errors and learning processes could lend itself to the analysis of other types of sensorimotor adaptation, of motor skill learning, of the neural processing underlying sensorimotor adaptation in humans, of animal models and of brain computer interfaces.

The acute effects of aerobic exercise on sensorimotor adaptation in chronic stroke

“…In contrast, behaviour in the type of reach adaptation paradigm used here has a large contribution from explicit learning ( Taylor et al., 2014 ). Here, exercise had a selective influence on improved re-adaptation to a previously encountered perturbation (i.e., savings), which often has a contribution from explicit learning ( Morehead et al., 2015 ). Exercise might have modulated adaptation by modulating explicit learning processes, which might be more prominent in reach adaptation than in gait adaptation.…”

“…The review also suggests that exercise might particularly benefit a form of motor learning termed sensorimotor adaptation: although there were fewer published studies on exercise in sensorimotor adaptation, effect sizes were overall larger with sensorimotor adaptation than with other forms of motor learning ( Wanner et al., 2020 ). An additional benefit of the sensorimotor adaptation paradigm is its long history of study: there are clear theories on its mechanisms and a rich body of knowledge on its characteristics in healthy and clinical populations ( Morehead & de Xivry, 2021 ), including in stroke (e.g., et al., 2011 ). In sensorimotor adaptation, the sensory feedback of a movement is experimentally perturbed, leading to a discrepancy between the predicted sensory outcome and the actual sensory outcome (sensory prediction error) ( Tseng et al., 2007 ).…”

Interactions between sensory prediction error and task error during implicit motor learning

“…Sensorimotor adaptation is an essential feature of human competence, allowing us to flexibly move in novel and changing environments (Kim, Avraham, & Ivry, 2020; John W. Krakauer, Hadjiosif, Xu, Wong, & Haith, 2019; Ryan Morehead & de Xivry, 2021; Shadmehr, Smith, & Krakauer, 2010). Multiple learning processes have been shown to contribute to the performance changes observed in adaptation tasks, including an aiming process which is explicit, volitional, and learns rapidly and a recalibration process which is implicit, automatic, and learns slowly (Haith, Huberdeau, & Krakauer, 2015; Hegele & Heuer, 2010; McDougle, Ivry, & Taylor, 2016; Taylor & Ivry, 2011; Taylor, Krakauer, & Ivry, 2014; Werner et al, 2015).…”

Mechanisms of Human Motor Learning Do Not Function Independently

“…These three mechanisms are SPE-driven learning, explicit learning, and reinforcement learning ( Figure 1 ). Each of these mechanisms is thought to respond to a different kind of feedback signal, and consequently, drive changes in behavior in different (and occasionally opposing) ways and at different rates (Mazzoni and Krakauer, 2006 ; van der Kooij et al, 2018 ; Albert et al, 2020 ; Morehead and Orban de Xivry, 2021 ). In general, the study of these mechanisms has treated them as modular, typically assuming that observed behavior can be described as the summation of the outputs of the individual mechanisms.…”