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.

人类运动适应（human motor adaptation）领域的研究通常聚焦于个体如何适应自身产生的或外部诱发的误差。逐试次适应（trial-by-trial adaptation）指个体针对自身产生的误差所做出的响应。以探测试次（catch-trial）扰动形式施加的外部诱发误差，被用于计算个体的扰动适应率。尽管此类适应率有时会被相互比较，但我们通过模拟与实证数据证明，这两类指标存在本质差异。我们证实，通常以线性回归（linear regression）系数计算得到的逐试次适应率，在典型实验条件下存在偏倚。我们招募12名健康受试者，通过电脑鼠标在屏幕上操控光标完成运动任务。当对运动序列结果中不同学习阶段的试次子集进行分析时，会得到具有统计学差异的适应率。为此我们提出一种新方法，用于识别个体学习趋于稳定的节点，从而筛选出稳态运动试次，以此计算可靠性更高的逐试次适应率。借助人类运动贝叶斯模型（Bayesian model），我们证明该分析方法相较于其他替代方案，一致性更强，估计结果的置信度也更高。将分析限定于稳态条件下，有助于研究者更好地分离个体在完成目标导向运动时同时发生的多种学习过程。优化该分析流程，或可扩大运动适应研究的影响力，甚至提升其临床应用价值。