Common Mechanisms of Learning in Motor and Cognitive Systems

Abstract

Rapid progress in our understanding of the brain's learning mechanisms has been accomplished over the past decade, particularly with conceptual advances, including representing behavior as a dynamical system, large-scale neural population recordings, and new methods of analysis of neuronal populations. However, motor and cognitive systems have been traditionally studied with different methods and paradigms. Recently, some common principles, evident in both behavior and neural activity, that underlie these different types of learning have become to emerge. Here we review results from motor and cognitive learning, relying on different techniques and studying different systems to understand the mechanisms of learning. Movement is intertwined with cognitive operations, and its dynamics reflect cognitive variables. Training, in either motor or cognitive tasks, involves recruitment of previously unresponsive neurons and reorganization of neural activity in a low dimensional manifold. Mapping of new variables in neural activity can be very rapid, instantiating flexible learning of new tasks. Communication between areas is just as critical a part of learning as are patterns of activity within an area emerging with learning. Common principles across systems provide a map for future research.

Figure 1.

A, Saccade vigor (peak velocity normalized by amplitude) across subjects as they deliberated between two on-screen options. Vigor increased over the course of deliberation and was significantly greater toward the preferred option at the end of deliberation. Data from Korbisch et al. (2022). B, Neural population activity in M1 cortex corresponding to motor planning for different reach directions is pushed apart with increasing cued reward from small through large. For Jackpot rewards, the activity for different reach directions collapses back toward each other, diminishing their discriminability. We projected neural activity grouped by trial conditions defined by reward and direction and then averaged into a 3D space reflecting reward information (Reward Axis) and target information (Target Axis 1 and 2). The units (population neural activity, spikes/s) on the three axes are the same. Adjacent reach directions (dot color) are connected by a ring for each reward (line color). Data from Smoulder et al. (2023). C, Area under the receiver operating characteristic (ROC) curve for prefrontal neurons recorded during training, while the subject achieved low (left) or high-performance sessions (right). Dark red colors represent ability of neurons to discriminate between different displays that needed to be maintained in memory. Data from Tang et al. (2019).