Motor Task Variation Induces Structural Learning

By Daniel A. Braun, Ad Aertsen, D. M. Wolpert, Carsten Mehring. 2009

259 citations recorded by the source corpus.

Summary When we have learned a motor skill, such as cycling or ice-skating, we can rapidly generalize to novel tasks, such as motorcycling or rollerblading [1–8]. Such facilitation of learning could arise through two distinct mechanisms by which the motor system might adjust its control parameters. First, fast learning could simply be a consequence of the proximity of the original and final settings of the control parameters. Second, by structural learning [9–14], the motor system could constrain the parameter adjustments to conform to the control parameters' covariance structure. Thus, facilitation of learning would rely on the novel task parameters' lying on the structure of a lower-dimensional subspace that can be explored more efficiently. To test between these two hypotheses, we exposed subjects to randomly varying visuomotor tasks of fixed structure. Although such randomly varying tasks are thought to prevent learning, we show that when subsequently presented with novel tasks, subjects exhibit three key features of structural learning: facilitated learning of tasks with the same structure, strong reduction in interference normally observed when switching between tasks that require opposite control strategies, and preferential exploration along the learned structure. These results suggest that skill generalization relies on task variation and structural learning.

Subfields: Cognitive Neuroscience, Social Psychology

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Citations in the corpus

One-shot Learning with Memory-Augmented Neural Networks (2016, background): “This kind of ﬂexible adaptation is a celebrated aspect of human learning (Jankowski et al., 2011), manifesting in settings ranging from motor control (Braun et al., 2009) to the acquisition of abstract concepts (Lake et al., 2015).”
Intentions and Motor Representations: the Interface Challenge (2017, background): “In particular, there is evidence that parameter learning and structural learning inmotor tasks can be best described as forms of Bayesian inference and modelling (Braun et al. 2009; Braun et al. 2010).”
Learning latent structure: Carving nature at its joints (2010, background): “The curse of dimensionality can be alleviated if one is willing to make certain assumptions about the structure of the environment or the structure of correct motor strategies [48 ,49 ].”
Error correction, sensory prediction, and adaptation in motor control. (2010, background): “…with a large number of inertial objects would benefit from discovering this structure, as it would allow it to learn control by simply adjusting a small number of parameters, rather than searching in the high-dimensional space of all possible motor commands (Hwang et al., 2006;Braun et al., 2009).”
Meta-Learning with Memory-Augmented Neural Networks (2016, background): “This kind of flexible adaptation is a celebrated aspect of human learning (Jankowski et al., 2011), manifesting in settings ranging from motor control (Braun et al., 2009) to the acquisition of abstract concepts (Lake et al., 2015).”
Structure Learning in a Sensorimotor Association Task (2010, background): “Such structure learning has been recently reported both in cognitive [19–25] and motor neuroscience [26,27,18].”
Principles of sensorimotor learning (2011, background): “After experience, adaptation to the matching perturbation type was accelerated, suggesting that the primitives become aligned with the axis 107 .”
Motor learning. (2010, background): “Roving is predicted to destabilize cooperative behaviour [5] because it would reduce the frequency of repeated interactions between cleaners and clients.”