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Structure learning in a sensorimotor association task.

Braun DA, Waldert S, Aertsen A, Wolpert DM, Mehring C - PLoS ONE (2010)

Bottom Line: Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects.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.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center for Computational Neuroscience, Freiburg, Germany. dab54@cam.ac.uk

ABSTRACT
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.

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Task description.(A) Subjects had to learn a mapping from a 3×3 stimulus board to a 3×3 action board. The stimulus was presented by lighting up one of the nine squares. The subject then had to press one of the nine response buttons associated to that stimulus. (B) There were six possible mappings with four different structures (S1 to S4). The identity and the random structure comprised only one mapping each. The shift structure consisted of a right-shift and a left-shift mapping. The mirror structure consisted of a horizontal and vertical mirror mapping.
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pone-0008973-g001: Task description.(A) Subjects had to learn a mapping from a 3×3 stimulus board to a 3×3 action board. The stimulus was presented by lighting up one of the nine squares. The subject then had to press one of the nine response buttons associated to that stimulus. (B) There were six possible mappings with four different structures (S1 to S4). The identity and the random structure comprised only one mapping each. The shift structure consisted of a right-shift and a left-shift mapping. The mirror structure consisted of a horizontal and vertical mirror mapping.

Mentions: To investigate features of structure learning, we exposed subjects to a stimulus-response learning task, where the stimulus-response patterns were characterised by different structural constraints. Subjects were presented with nine possible stimuli and could respond with one of nine possible actions (see Figure 1A and Methods for details). This defines a set of nine pairs of stimuli and their associated correct responses, resulting in 362,880 (9!) possible one-to-one sensorimotor mappings. Subjects had to learn six different mappings that were characterised by four different structural features: (1) an identity mapping that constitutes the baseline mapping, as it is most readily learned, (2) two shift mappings, where the correct response was shifted either to the right or to the left compared to the identity mapping, (3) two mirror mappings, where the correct response was mirrored around the vertical or horizontal axis, again compared to the identity mapping, and (4) a random mapping where stimuli and responses were not associated by any apparent rule (see Figure 1B). We counted the number of trials it took subjects to learn any of the mappings to assess their performance.


Structure learning in a sensorimotor association task.

Braun DA, Waldert S, Aertsen A, Wolpert DM, Mehring C - PLoS ONE (2010)

Task description.(A) Subjects had to learn a mapping from a 3×3 stimulus board to a 3×3 action board. The stimulus was presented by lighting up one of the nine squares. The subject then had to press one of the nine response buttons associated to that stimulus. (B) There were six possible mappings with four different structures (S1 to S4). The identity and the random structure comprised only one mapping each. The shift structure consisted of a right-shift and a left-shift mapping. The mirror structure consisted of a horizontal and vertical mirror mapping.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2813299&req=5

pone-0008973-g001: Task description.(A) Subjects had to learn a mapping from a 3×3 stimulus board to a 3×3 action board. The stimulus was presented by lighting up one of the nine squares. The subject then had to press one of the nine response buttons associated to that stimulus. (B) There were six possible mappings with four different structures (S1 to S4). The identity and the random structure comprised only one mapping each. The shift structure consisted of a right-shift and a left-shift mapping. The mirror structure consisted of a horizontal and vertical mirror mapping.
Mentions: To investigate features of structure learning, we exposed subjects to a stimulus-response learning task, where the stimulus-response patterns were characterised by different structural constraints. Subjects were presented with nine possible stimuli and could respond with one of nine possible actions (see Figure 1A and Methods for details). This defines a set of nine pairs of stimuli and their associated correct responses, resulting in 362,880 (9!) possible one-to-one sensorimotor mappings. Subjects had to learn six different mappings that were characterised by four different structural features: (1) an identity mapping that constitutes the baseline mapping, as it is most readily learned, (2) two shift mappings, where the correct response was shifted either to the right or to the left compared to the identity mapping, (3) two mirror mappings, where the correct response was mirrored around the vertical or horizontal axis, again compared to the identity mapping, and (4) a random mapping where stimuli and responses were not associated by any apparent rule (see Figure 1B). We counted the number of trials it took subjects to learn any of the mappings to assess their performance.

Bottom Line: Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects.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.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center for Computational Neuroscience, Freiburg, Germany. dab54@cam.ac.uk

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus