Motor Task Variation Induces Structural Learning

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 [1Bernstein N.A. The Co-ordination and Regulation of Movements. Pergamon Press, Oxford, NY1967Google Scholar, 2Poggio T. Bizzi E. Generalization in vision and motor control.Nature. 2004; 431: 768-774Crossref PubMed Scopus (234) Google Scholar, 3Abeele S. Bock O. Mechanisms for sensorimotor adaptation to rotated visual input.Exp. Brain Res. 2001; 139: 248-253Crossref PubMed Scopus (64) Google Scholar, 4Bock O. Schneider S. Bloomberg J. Conditions for interference versus facilitation during sequential sensorimotor adaptation.Exp. Brain Res. 2001; 138: 359-365Crossref PubMed Scopus (109) Google Scholar, 5Roller C.A. Cohen H.S. Kimball K.T. Bloomberg J.J. Variable practice with lenses improves visuo-motor plasticity.Brain Res. Cogn. Brain Res. 2001; 12: 341-352Crossref PubMed Scopus (48) Google Scholar, 6Thoroughman K.A. Shadmehr R. Learning of action through adaptive combination of motor primitives.Nature. 2000; 407: 742-747Crossref PubMed Scopus (668) Google Scholar, 7Welch R.B. Bridgeman B. Anand S. Browman K.E. Alternating prism exposure causes dual adaptation and generalization to a novel displacement.Percept. Psychophys. 1993; 54: 195-204Crossref PubMed Scopus (152) Google Scholar, 8McCracken H.D. Stelmach G.E. A test of the schema theory of discrete motor learning.J. Mot. Behav. 1977; 9: 193-201Crossref PubMed Scopus (68) Google Scholar]. 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 [9Pearl J. Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann Publishers, San Mateo, CA.1988Google Scholar, 10Boyen, X., Friedman, N., and Koller, D. (1999). Discovering the hidden structure of complex dynamic systems. Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence (UAI-99), Morgan Kaufmann, 91–100.Google Scholar, 11Friedman, N. (1998). The Bayesian structural EM algorithm. Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence (UAI-98), Morgan Kaufmann, 129–139.Google Scholar, 12Jordan M.I. Learning in Graphical Models.1st MIT Press Edition. MIT Press, Cambridge, MA1999Google Scholar, 13Buntine W.L. A guide to the literature on learning probabilistic networks from data.IEEE Transactions on Knowledge and Data Engineering. 1996; 8: 195-210Crossref Scopus (315) Google Scholar, 14Åström K.J. Wittenmark B. Adaptive Control.Second Edition. Addison-Wesley, Reading, MA1995Google Scholar], 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.