Structure Learning in a Sensorimotor Association Task
By Daniel A. Braun, S. Waldert, Ad Aertsen, D. M. Wolpert, Carsten Mehring. 2010
40 citations recorded by the source corpus.
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.
Subfields: Cognitive Neuroscience, Social Psychology, Reinforcement Learning
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- George Dimitri Konidaris
- Andrew Barto
- Matthew Botvinick
- J. Urgen Schmidhuber
- Peter S. Dayan
- Yael Niv
- Gianluca Baldassarre
- Sergey Levine
In Social Psychology
In Reinforcement Learning
- George Dimitri Konidaris
- Bruno Castro da Silva
- Andrew Barto
- Sergey Levine
- Shie Mannor
- Jan Peters
- Philip S. Thomas
- Peter Stone
Citations in the corpus
- Learning Parameterized Skills (2012, methodology): “…in the skill manifold, which are important, for instance, for classes of movements whose description requires more than one DMP. Finally, Braun et al. (2010) discuss how Bayesian modeling can be used to explain experimental data from cognitive and motor neuroscience that supports the…”