Abstract

Research on human motor adaptation has often focused on how people adapt to self-generated or externally-influenced errors. Trial-by-trial adaptation is a person’s response to self-generated errors. Externally-influenced errors applied as catch-trial perturbations are used to calculate a person’s perturbation adaptation rate. Although these adaptation rates are sometimes compared to one another, we show through simulation and empirical data that the two metrics are distinct. We demonstrate that the trial-by-trial adaptation rate, often calculated as a coefficient in a linear regression, is biased under typical conditions. We tested 12 able-bodied subjects moving a cursor on a screen using a computer mouse. Statistically different adaptation rates arise when sub-sets of trials from different phases of learning are analyzed from within a sequence of movement results. We propose a new approach to identify when a person’s learning has stabilized in order to identify steady-state movement trials from which to calculate a more reliable trial-by-trial adaptation rate. Using a Bayesian model of human movement, we show that this analysis approach is more consistent and provides a more confident estimate than alternative approaches. Constraining analyses to steady-state conditions will allow researchers to better decouple the multiple concurrent learning processes that occur while a person makes goal-directed movements. Streamlining this analysis may help broaden the impact of motor adaptation studies, perhaps even enhancing their clinical usefulness.

Highlights

  • The way that people adapt their movements provides important insight into the motor learning processes of the brain [1,2,3,4]

  • Patients suffering from motor deficits or using prostheses will often display different motor abilities that can be observed as changes in error correction rates

  • We present a new approach to limit some of the biases with current motor analysis techniques

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Summary

Introduction

The way that people adapt their movements provides important insight into the motor learning processes of the brain [1,2,3,4]. Motor adaptation, is a key aspect in the study of human movement, in understanding its deficit and developing corresponding rehabilitative strategies [5,6]. Adaptation rate is often discussed in the literature, but that metric’s definition varies based on the context. Adaptation rates are often cited [2, 7,8,9,10,11] but are difficult to compare across conditions and studies due to differences in quantities measured, movement amplitude, system noise, and calculation methods

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