Format

Send to

Choose Destination
PLoS One. 2013;8(2):e55352. doi: 10.1371/journal.pone.0055352. Epub 2013 Feb 8.

Credit assignment during movement reinforcement learning.

Author information

1
Department of Behavioral Sciences, University of Rio Grande, Rio Grande, Ohio, USA.

Abstract

We often need to learn how to move based on a single performance measure that reflects the overall success of our movements. However, movements have many properties, such as their trajectories, speeds and timing of end-points, thus the brain needs to decide which properties of movements should be improved; it needs to solve the credit assignment problem. Currently, little is known about how humans solve credit assignment problems in the context of reinforcement learning. Here we tested how human participants solve such problems during a trajectory-learning task. Without an explicitly-defined target movement, participants made hand reaches and received monetary rewards as feedback on a trial-by-trial basis. The curvature and direction of the attempted reach trajectories determined the monetary rewards received in a manner that can be manipulated experimentally. Based on the history of action-reward pairs, participants quickly solved the credit assignment problem and learned the implicit payoff function. A Bayesian credit-assignment model with built-in forgetting accurately predicts their trial-by-trial learning.

PMID:
23408972
PMCID:
PMC3568147
DOI:
10.1371/journal.pone.0055352
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center