Skip to main content

ORIGINAL RESEARCH article

Front. Behav. Neurosci.
Sec. Learning and Memory
Volume 18 - 2024 | doi: 10.3389/fnbeh.2024.1466970
This article is part of the Research Topic Reinforcement feedback in motor learning: neural underpinnings of skill refinement View all 5 articles

Reinforcement learning in motor skill acquisition: Using the reward positivity to understand the mechanisms underlying short-and long-term behavior adaptation

Provisionally accepted
Mariane F. B. Bacelar Mariane F. B. Bacelar 1*Keith R. Lohse Keith R. Lohse 2Juliana O. Parma Juliana O. Parma 3Matthew W. Miller Matthew W. Miller 4
  • 1 Department of Kinesiology, Boise State University, Boise, United States
  • 2 School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States
  • 3 San Francisco State University, San Francisco, California, United States
  • 4 School of Kinesiology, College of Education, Auburn University, Auburn, Alabama, United States

The final, formatted version of the article will be published soon.

    According to reinforcement learning, humans adjust their behavior based on the difference between actual and anticipated outcomes (i.e., prediction error) with the main goal of maximizing rewards through their actions. Despite offering a strong theoretical framework to understand how we acquire motor skills, very few studies have investigated reinforcement learning predictions and its underlying mechanisms in motor skill acquisition. Thus, in the present study, we explored a 134-person dataset consisting of learners' feedback-evoked brain activity (reward positivity; RewP) and motor accuracy during the practice phase and delayed retention test to investigate whether these variables interacted according to reinforcement learning predictions. Results showed a non-linear relationship between RewP and trial accuracy, which was moderated by the learners' performance level. Specifically, high-performing learners were more sensitive to violations in reward expectations compared to low-performing learners, likely because they developed a stronger representation of the skill and were able to rely on more stable outcome predictions. Furthermore, contrary to our prediction, the average RewP during acquisition did not predict performance on the delayed retention test. Together, these findings support the use of reinforcement learning models to understand short-term behavior adaptation and highlight the complexity of the motor skill consolidation process, which would benefit from a multi-mechanistic approach to further our understanding of this phenomenon.

    Keywords: motor learning, reward-prediction errors, graded feedback, EEG, mixed-effects modeling

    Received: 18 Jul 2024; Accepted: 16 Oct 2024.

    Copyright: © 2024 F. B. Bacelar, Lohse, O. Parma and Miller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mariane F. B. Bacelar, Department of Kinesiology, Boise State University, Boise, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.