Seeing the Errors You Feel Enhances Locomotor Performance but Not Learning

Abstract

SUMMARY In human motor learning, it is thought that the more information we have about our errors, the faster we learn. Here we show that additional error information can lead to improved motor performance without any concomitant improvement in learning. We studied split-belt treadmill walking that drives people to learn a new gait pattern using sensory prediction errors detected by proprioceptive feedback. When we also provided visual error feedback, participants acquired the new walking pattern far more rapidly and showed accelerated restoration of the normal walking pattern during washout. However, when the visual error feedback was removed during either learning or washout, errors reappeared with performance immediately returning to the level expected based on proprioceptive learning alone. These findings support a model with two mechanisms: a dual-rate adaptation process that learns invariantly from sensory prediction error detected by proprioception and a visual feedback dependent process that monitors learning and corrects residual errors, but shows no learning itself. We show that our voluntary correction model accurately predicted behavior in multiple situations where visual feedback was used to change acquisition of new walking patterns while the underlying learning was unaffected. The computational and behavioral framework proposed here suggests that parallel learning and error correction systems allow us to rapidly satisfy task demands without necessarily committing to learning, as the relative permanence of learning may be inappropriate or inefficient when facing environments that are liable to change.

A single exercise bout and locomotor learning after stroke: physiological, behavioural, and computational outcomes

“…; Roemmich et al . ). Interestingly, in contrast to a previous study of locomotor adaptation in healthy subjects (Mawase et al .…”

“…; Roemmich et al . ). Here, n refers to stride number and ‘perturbation’ refers to the maximum step length asymmetry recorded within the first 10 strides of perturbation onset in day 1.…”

“…We tested the feasibility of applying a dual-rate computational model to behavioural data collected from chronic stroke survivors. This model of adaptation proposed by Smith and colleagues (2006) has been shown to successfully capture key features of locomotor adaptation in healthy subjects (Mawase et al 2014;Roemmich et al 2016). In this study, we were particularly interested in modelling the component processes of locomotor adaptation following stroke and better understanding the underlying mechanisms for long-term retention.…”

“…; Roemmich et al . ). Therefore, the computational modelling was used to investigate the component processes of the locomotor learning after stroke and to further understand the underlying mechanisms for long‐term retention.…”

“…Additionally, we tested the feasibility of applying computational modelling to behavioural data collected from chronic stroke survivors. Though the majority of the computational models have been developed to explain error-based learning during reaching (Smith et al 2006;Joiner & Smith, 2008;Lee & Schweighofer, 2009;Huang et al 2011), only a few studies of locomotor adaptation have used model-based approaches, and both included only subjects that were neurologically intact adults (Mawase et al 2014;Roemmich et al 2016). Therefore, the computational modelling was used to investigate the component processes of the locomotor learning after stroke and to further understand the underlying mechanisms for long-term retention.…”