A study from UC San Diego uncovers how learning new movements physically reshapes the brain's wiring, highlighting the thalamocortical pathway's role and introducing a novel analytical method to track these changes.

How do we come to feel that our movements are truly our own? A new study reveals that agency arises not from imitation, but from the messy, vital process of trial and error — a finding with big implications for rehabilitation, virtual reality, and more.

Speech auditory-motor adaptation to a formant-shift perturbation and de-adaptation after the perturbation is removed both depend more on total amount of time spent in the corresponding environment than on the number of practice trials.

Background: Despite their potential, the use of serious games within immersive virtual reality (iVR) for enhancing motor skills in older adults remains relatively unexplored. In this study, we developed a self-adaptive serious game in iVR called…

Background The primary motor cortex (M1) is central to motor learning processes, and an increasing number of studies have suggested its role in balance control. However, the specific role of M1 in bal...

Skilled performers need skillful and adaptive movement strategies to solve tasks effectively. Typically, performers learn these strategies with instruction-based teaching methods where coaches offer performers a correct solution. Inspired by recent evidence from decision neuroscience, we asked whether skilled performers learn strategy choices better with an evaluation-based training strategy (reinforcement learning). To address this question, we conducted a three-day learning experiment with skilled alpine ski racers (n=98) designed to improve their performance on flat slopes on slaloms with four strategies at their disposal to achieve this goal. We compared performance and strategy choices of three groups: a reinforcement learning group, that only received feedback about their race times after every run, a supervised (free choice) learning group, that received strategy instructions from their coach, and a supervised (target skill) learning group, being coached to use the theoretically optimal strategy for skiing well on flats. We found that despite making similar strategy choices, the skiers in the reinforcement learning group, showed greater improvements in their race times during the training sessions than their counterparts in the supervised (free choice) learning group and outperformed them during a subsequent retention test. Surprisingly, the skiers in the reinforcement learning group even showed descriptively (but not significantly) better performance than those in the supervised (target skill) learning group. Our findings show that reinforcement learning can be an effective training strategy for improving strategy choices and performance among skilled performers, even among the best ones. ### Competing Interest Statement The authors have declared no competing interest.