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PLoS One. 2010; 5(1): e8973.
Published online 2010 January 29. doi: 10.1371/journal.pone.0008973.
PMCID: PMC2813299
Structure Learning in a Sensorimotor Association Task
Daniel A. Braun,1,2,3* Stephan Waldert,1,2 Ad Aertsen,1,2 Daniel M. Wolpert,3 and Carsten Mehring1,2
1Bernstein Center for Computational Neuroscience, Freiburg, Germany
2Faculty of Biology, Albert-Ludwigs-Universität, Freiburg, Germany
3Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Paul L. Gribble, Editor
The University of Western Ontario, Canada
* E-mail:
Conceived and designed the experiments: DAB AA DMW CM. Performed the experiments: DAB SW. Analyzed the data: DAB. Contributed reagents/materials/analysis tools: SW. Wrote the paper: DAB AA DMW CM.
Received November 17, 2009; Accepted January 13, 2010.
Learning is often understood as an organism's gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments.