Structure Learning in Bayesian Sensorimotor Integration

PLoS Comput Biol. 2015 Aug 25;11(8):e1004369. doi: 10.1371/journal.pcbi.1004369. eCollection 2015 Aug.


Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Computational Biology
  • Feedback, Sensory / physiology*
  • Female
  • Hand / physiology
  • Humans
  • Learning / physiology*
  • Male
  • Psychomotor Performance / physiology*

Grants and funding

This study was supported by the DFG, Emmy Noether grant BR4164/1-1. ( The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.