Generalization of Motor Learning Depends on the History of Prior Action

Abstract

Generalization of motor learning refers to our ability to apply what has been learned in one context to other contexts. When generalization is beneficial, it is termed transfer, and when it is detrimental, it is termed interference. Insight into the mechanism of generalization may be acquired from understanding why training transfers in some contexts but not others. However, identifying relevant contextual cues has proven surprisingly difficult, perhaps because the search has mainly been for cues that are explicit. We hypothesized instead that a relevant contextual cue is an implicit memory of action with a particular body part. To test this hypothesis we considered a task in which participants learned to control motion of a cursor under visuomotor rotation in two contexts: by moving their hand through motion of their shoulder and elbow, or through motion of their wrist. Use of these contextual cues led to three observations: First, in naive participants, learning in the wrist context was much faster than in the arm context. Second, generalization was asymmetric so that arm training benefited subsequent wrist training, but not vice versa. Third, in people who had prior wrist training, generalization from the arm to the wrist was blocked. That is, prior wrist training appeared to prevent both the interference and transfer that subsequent arm training should have caused. To explain the data, we posited that the learner collected statistics of contextual history: all upper arm movements also move the hand, but occasionally we move our hands without moving the upper arm. In a Bayesian framework, history of limb segment use strongly affects parameter uncertainty, which is a measure of the covariance of the contextual cues. This simple Bayesian prior dictated a generalization pattern that largely reproduced all three findings. For motor learning, generalization depends on context, which is determined by the statistics of how we have previously used the various parts of our limbs.

Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration

“…These parameters reflect the statistics of the usual fluctuations in sensory calibration errors and motor plant dynamics, which the sensorimotor system must adapt to on an ongoing basis. (Similar assumptions have previously been made elsewhere [Körding, Tenenbaum, & Shadmehr, 2007;Krakauer, Mazzoni, Ghazizadeh, Ravindran, & Shadmehr, 2006]). We propose that the patterns of adaptation and the sensory after-effects exhibited by subjects correspond to optimal inference of the disturbances r t within this full generative model, given the observations on each trial.…”

Specificity of Speech Motor Learning

“…Krakauer et al (2006) report another rather specific example of generalization. They show that learning generalizes from the shoulder to wrist but not vice versa.…”

Modifiability of Generalization in Dynamics Learning

“…Generalization of motor learning has been documented in studies involving the alteration of visual feedback during movement (Bedford 1993;Caithness et al 2004;Ghahramani and Wolpert 1997;Ghahramani et al 1996;Ghilardi et al 1995;Krakauer et al 1999Krakauer et al , 2000Krakauer et al , 2006Tong et al 2002;Vetter et al 1999;Wang and Sainburg 2004a). Previous experiments have shown that this so-called visuomotor learning can generalize broadly across the workspace under certain conditions (Bedford 1993;Ghilardi et al 1995;Krakauer et al 2000;Vetter et al 1999).…”

“…Generalization has been described between movements that differ in terms of speed (Goodbody and Wolpert 1998), amplitude (Goodbody and Wolpert 1998;Krakauer et al 2000), direction (Bedford 1993;Donchin et al 2003;Gandolfo et al 1996;Ghilardi et al 1995;Huang and Shadmehr 2007;Krakauer et al 2000;Thoroughman and Shadmehr 2000;Thoroughman and Taylor 2005;Vetter et al 1999), path (Conditt et al 1997), workspace location (Hwang et al 2003;Malfait et al 2002;Shadmehr and Moussavi 2000), or the effector used (Criscimagna-Hemminger et al 2003;Dizio and Lackner 1995;Krakauer et al 2006;Malfait and Ostry 2004;Wang and Sainburg 2004a,b;Witney and Wolpert 2003). In the present paper we have focused on the extent to which the pattern of generalization for dynamics learning is modifiable.…”