Motor Variability Arises from a Slow Random Walk in Neural State

Abstract

Even well practiced movements cannot be repeated without variability. This variability is thought to reflect "noise" in movement preparation or execution. However, we show that, for both professional baseball pitchers and macaque monkeys making reaching movements, motor variability can be decomposed into two statistical components, a slowly drifting mean and fast trial-by-trial fluctuations about the mean. The preparatory activity of dorsal premotor cortex/primary motor cortex neurons in monkey exhibits similar statistics. Although the neural and behavioral drifts appear to be correlated, neural activity does not account for trial-by-trial fluctuations in movement, which must arise elsewhere, likely downstream. The statistics of this drift are well modeled by a double-exponential autocorrelation function, with time constants similar across the neural and behavioral drifts in two monkeys, as well as the drifts observed in baseball pitching. These time constants can be explained by an error-corrective learning processes and agree with learning rates measured directly in previous experiments. Together, these results suggest that the central contributions to movement variability are not simply trial-by-trial fluctuations but are rather the result of longer-timescale processes that may arise from motor learning.

Feedback from Network States Generates Variability in a Probabilistic Olfactory Circuit

“…The behavioral response of individuals to a defined sensory stimulus varies from trial to trial, even when it is predictable on average. Although variability may limit task performance, both behavioral and theoretical analyses suggest that it can also be a creative element of behavioral strategies (Thrun, 1992;Hessler and Doupe, 1999;Ö lveczky et al, 2005;Tumer and Brainard, 2007;Chaisanguanthum et al, 2014). In foraging animals, behavioral variation over short and long timescales allows efficient exploration of environments with unevenly distributed resources (Charnov, 1976;Humphries et al, 2010).…”

“…Game-theoretical approaches suggest that variable strategies are often the best responses to unpredictable conditions, particularly in the presence of competitors or predictors (Harsanyi, 1973). At a neuronal level, intrinsically generated variability provides a substrate for reward learning, and increased variability has been linked to enhanced learning in motor tasks (Ö lveczky et al, 2005;Tumer and Brainard, 2007;Chaisanguanthum et al, 2014).…”

Individual Differences in Motor Noise and Adaptation Rate Are Optimally Related

“…Modeling (Cheng and Sabes, 2006, 2007; van Beers, 2009) and physiologic (Churchland et al, 2006; Chaisanguanthum et al, 2014) studies have divided the multiple sources of motor noise into planning noise and execution noise (see Fig. 1 A ).…”

Methods for Measuring Swallowing Pressure Variability Using High-Resolution Manometry

“…Whenever a motor action is performed repeatedly, there is inherent variability in the kinematics and the outcome of each movement [25,26]. This variability can arise from noise or other subtle differences in neural and muscle activity [27][28][29][30][31][32][33] that can occur both during movement preparation [34][35][36][37] and execution [37][38][39]. Movement variability is sensitive to the sensorimotor and timing demands of a task.…”

“…Movement variability is an integral part of motor learning [29,[42][43][44]. We can modulate the degree of variability used during motor learning, as either part of a reward [45] or error-based learning process [46], and this modulation in variability eventually decreases once we master a new motor skill [47][48][49].…”