CUED Publications database

Structure learning in a sensorimotor association task.

Braun, DA and Waldert, S and Aertsen, A and Wolpert, DM and Mehring, C (2010) Structure learning in a sensorimotor association task. PLoS One, 5. e8973-.

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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.

Item Type: Article
Uncontrolled Keywords: Bayes Theorem Humans Learning Motor Cortex
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:31
Last Modified: 22 Apr 2021 06:28
DOI: 10.1371/journal.pone.0008973