Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator

Daniel Blustein, Ahmed Shehata, Kevin Englehart and Jonathon Sensinger

PLOS Computational Biology, 2018, vol. 14, issue 12, 1-15

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.Author summary: By observing the learning rate of a person making a movement under new conditions, researchers can better understand how the nervous system handles uncertainty. Patients suffering from motor deficits or using prostheses will often display different motor abilities that can be observed as changes in error correction rates. Here we show that previous approaches to determining error correction rates are affected by the overall learning rate within the subset of trials selected for analysis. We use real-world data collected from people controlling a computer cursor and simulations of how a person’s nervous system operates to show the limitations of current approaches. We also present a new approach to limit some of the biases with current motor analysis techniques.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006501

DOI: 10.1371/journal.pcbi.1006501

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