Report Motor Task Variation Induces Structural Learning

Institute of Biology IIIAlbert Ludwigs UniversityD-79104 FreiburgGermanySummaryWhen we have learned a motor skill, such as cycling or ice-skating, we can rapidly generalize to novel tasks, such asmotorcycling or rollerblading [1–8]. Such facilitation oflearning could arise through two distinct mechanisms bywhich the motor system might adjust its control parameters.First, fast learning could simply be a consequence of theproximity of the original and ﬁnal settings of the controlparameters. Second, by structural learning [9–14], the motorsystem could constrain the parameter adjustments toconform to the control parameters’ covariance structure.Thus, facilitation of learning would rely on the novel taskparameters’ lying on the structure of a lower-dimensionalsubspace that can be explored more efﬁciently. To testbetween these two hypotheses, we exposed subjects torandomly varying visuomotor tasks of ﬁxed structure.Although such randomly varying tasks are thought toprevent learning, we show that when subsequently pre-sented with novel tasks, subjects exhibit three key featuresof structural learning: facilitated learning of tasks with thesame structure, strong reduction in interference normallyobserved when switching between tasks that require oppo-site control strategies, and preferential exploration alongthe learned structure. These results suggest that skill gener-alization relies on task variation and structural learning.Results and DiscussionMotorlearningisoftenregardedasaprocessoflearninganewmapping from sensory inputs to motor outputs [1–8]. Suchmappings can be represented, for example, by simple feed-forward neural networks, and learning can be achieved byadjusting synaptic weight parameters in these networks(e.g., radial basis function networks) [2, 6, 15, 16]. The solutionof a control problem can then be represented as a setting ofthese parameters—Figure 1A shows a schematic of a simpletwo-parameter system. For example, the red setting couldbe the solution for riding a racing bike, and the blue settingcould be the solution for riding a mountain bike. Learning thecontrol process of a mountain bike when having alreadylearned that of a racing bike corresponds then to changingthe parameter setting from red to blue. Thus, learningbecomes a search through parameter space. How could wespeed up this learning process? Clearly, if the old and newparametersettingsareclosetoeachotherinparameterspace,then learning can be fast. However, there is another possibleway to speed up learning. If we have ridden many differenttypes of bicycles, we might have extracted general rules forhow the control parameters covary for different bicycles.That is, the set of bicycles may not span the entire parameterspace, but lie on a low-dimensional subspace (e.g., the thickblack line in Figure 1A), termed a structure. Learning sucha structure would be beneﬁcial in guiding exploration of theparameter space for a new bicycle. It would allow us to intro-duce a new metaparameter (with setting m) that adjusts thecontrol parameters to move along the lower-dimensionalstructure in parameter space (Figure 1B). Therefore, whenwe are presented with a new task on the same structure, thesearch is restricted to a subspace of the full parameter space(e.g., the control subspace for the class of all bikes), therebyspeeding up learning.Such structural learning [9–14] would have three clear bene-ﬁts. First, by reducing the search space from a high-dimen-sional space to a low-dimensional space, the efﬁciency ofany learning algorithm will be dramatically improved [17]. Therefore, we expect structure-speciﬁc facilitation for tasksthat conform to a learned structure; that is, learning shouldbe faster compared to that for tasks that lie off the structure(Figure1A,greensetting). Second,whentwotasksthatrequireopposite control strategies, such as opposing visuomotorrotations, are learned consecutively, the ﬁrst task makes itmore difﬁcult to learn the second task (anterograde interfer-ence), and the second task wipes out memory of the ﬁrst(retrograde interference) [18, 19]. However, if two opposingperturbations (e.g., 660 visuomotor rotations) could belearned as part of the same structure (e.g., rotation structure),thenwewouldexpectalow-dimensional,high-speedpathwaybetween the parameter settings for the two opposing pertur-bations. This should be reﬂected in reduced anterograde andretrograde interference between opposing tasks (structure-speciﬁc interference reduction). Third, when moving betweentasks belonging to the same structure, the controller shouldpreferentially explore along the structure (the thick black linein Figure 1A) and reduce deviations from the structure. More-over,forataskthatliesoffthecurrentstructure(greensetting),theinitialexploration(greenarrowwhenstartingfromreddisk)should still lie preferentially along the structure (structure-speciﬁc exploration). To investigate structural learning, we devised a series ofexperiments in which subjects were exposed to visuomotortransformations in different virtual reality environments.Numerous studies have shown that subjects can rapidly adaptto a ﬁxed visuomotor transformation, such as a rotationinduced by prism glasses [7]. Here, however, we varied theparameters of such visuomotor transformations randomlyover trials, while leaving the structure of the transformation