Movements are inherently variable: one never throws a dart or a basketball in exactly the same way twice. On the face of it, this variability in behavior is detrimental to performance, preventing one from consistently hitting the bull's-eye or making the basket. However, computational theories posit that motor variability may also serve a functional role, enabling exploration and learning of more efficient movements. This creates an intriguing duality: while variability should be minimized for short-term motor performance (to act reliably), it should be maximized for long-term performance (to promote learning). During practice, variability might be useful for developing motor skill. When it's game time, however, variability should be suppressed to the greatest extent possible. Might the central nervous system set the amount of variability in a context-appropriate fashion? This study will investigate the neural correlates of motor variability and establish the connections between neural variability, behavioral performance, and learning.
Neural variability lies at the heart of several theoretical computational models, from implementations of probabilistic computation to Hebbian learning rules. Although the importance of variability has been well recognized, the structure and regulation of neural variability within the central nervous system is not well understood. This project coordinates a program of experiments and new analytical techniques to examine the structure of neural variability in the motor system. It seeks to establish, first, how variability depends on behavioral demands, and second, how variability impacts learning. To achieve this, many neurons of the motor and premotor cortices will be studied simultaneously during performance of demanding behaviors. By studying two distinct areas in the motor pathway, the impacts of noise on motor planning and execution can be examined separately. Furthermore, population recordings can be leveraged to decompose variability into three conceptually distinct components: (1) variability that is related to the task (signal variability), (2) trial-to-trial variability shared among neurons, and (3) private variability within each neuron. The investigators will explore how variability of each type is modulated by task context and learning. These decompositions will yield insight into the mechanisms of variability generation during performance.
