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

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.