Instrumental-Motor Transfer: The Relative Value of Competing Movement Goals Modulates Implicit Motor Learning
Year: 2026
Type: preprint
Abstract: Motor learning depends on the integration of several, often competing signals, including reward and punishment (which guide learning the value of actions via reinforcement learning) and sensorimotor errors (which refine the execution of actions via motor adaptation). If and how these signals interact remains controversial. Here we show that the learned value of actions can fundamentally alter how the motor adaptation system learns from errors. Human participants learned the reward values of different movement targets and subsequently underwent implicit motor learning at those targets. Across several experiments, motor learning was robustly attenuated when it brought the limb toward previously punished movement directions. Moreover, learning was globally suppressed when punished targets were themselves the movement goal. Learning was not reciprocally enhanced when it pushed movements toward previously rewarded directions, revealing a valence asymmetry. Using computational modeling, we present a novel mechanism to explain our effects: the motor adaptation system tunes its error sensitivity according to a movement-specific value gradient. Our findings point to a close interaction between reinforcement learning and motor memory — a kind of “instrumental-motor transfer” – where learned action values modulate error-based motor adaptation. Significance In order to ensure our motor behavior yields our desired outcomes, we must not only learn which actions lead to which outcomes (via reinforcement learning) but also how to precisely execute those actions to achieve the desired outcome (by calibrating our actions via implicit motor adaptation). While it is clear that reinforcement learning and motor adaptation operate at different levels of an action selection hierarchy, it is unclear if they interact. Knowing if and how these processes interact is critical for understanding the fundamental algorithms and various neural circuits underlying real-world motor behavior. Here, we use behavioral data and computational modeling to reveal a novel interaction between the two processes, where motor adaptation is suppressed when it would bring the hand toward a previously punished movement goal.
Language: English
Topic: Motor Control and Adaptation
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7166474044