Estimating properties of the fast and slow adaptive processes during sensorimotor adaptation

Experience of a prediction error recruits multiple motor learning processes: some that learn strongly from error but have weak retention, some that learn weakly from error but exhibit strong retention. These processes are not generally observable, but are inferred from their collective influence on behavior. Is there a robust way to uncover the hidden processes? A standard approach is to consider a state-space model where the hidden states change following experience of error, and then fit the model to the measured data by minimizing the squared error between measurement and model prediction. We found that this least-squares algorithm (LMSE) often yielded unrealistic predictions about the hidden states, possibly due to its neglect of the stochastic nature of error-based learning. We found that behavioral data during adaptation was better explained by a system in which both error-based learning and movement production were stochastic processes. To uncover the hidden states of learning, we developed a generalized Expectation Maximization (EM) algorithm. In simulation, we found that while LMSE tracked the measured data marginally better than EM, EM was far more accurate in unmasking the timecourses and properties of the hidden states of learning. In a power analysis designed to measure the effect of an intervention on sensorimotor learning, EM significantly reduced the number of subjects that were required for effective hypothesis testing. In summary, we developed a new approach for analysis of data in sensorimotor experiments. The new algorithm improved the ability to uncover the multiple processes that contribute to learning from error.

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