Eliminating Direction Specificity in Visuomotor Learning

Abstract

The generalization of learning offers a unique window for investigating the nature of motor learning. Error-based motor learning reportedly cannot generalize to distant directions because the aftereffects are direction specific. This direction specificity is often regarded as evidence that motor adaptation is model-based learning, and is constrained by neuronal tuning characteristics in the primary motor cortices and the cerebellum. However, recent evidence indicates that motor adaptation also involves model-free learning and explicit strategy learning. Using rotation paradigms, here we demonstrate that savings (faster relearning), which is closely related to model-free learning and explicit strategy learning, is also direction specific. However, this new direction specificity can be abolished when the participants receive exposure to the generalization directions via an irrelevant visuomotor gain-learning task. Control evidence indicates that this exposure effect is weakened when direction error signals are absent during gain learning. Therefore, the direction specificity in visuomotor learning is not solely related to model-based learning; it may also result from the impeded expression of model-free learning and explicit strategy learning with untrained directions. Our findings provide new insights into the mechanisms underlying motor learning, and may have important implications for practical applications such as motor rehabilitation.

Significance statement: Motor learning is more useful if it generalizes to untrained scenarios when needed, especially for sports training and motor rehabilitation. However, as a form of motor learning, motor adaptation is typically direction specific. Here we first show that savings with motor adaptation, an index for model-free learning and explicit strategy learning in motor learning, is also direction specific. However, the participants' additional exposure to untrained directions via an irrelevant gain-learning task can enable the complete generalization of learning. Our findings challenge existing models of motor generalization and may have important implications for practical applications.

Keywords: learning specificity; motor adaptation; motor generalization; motor learning.

Figure 1.

Experimental setup and designs. A, An illustration of the experimental setup with reaching targets shown on the screen. B, Experimental designs in various experiments. The arrows indicate the learning and generalization directions. The dashed lines indicate that no trial was performed in those specific experimental phases. The exposure tasks were different among groups. Exp, Experiment.

Figure 2.

Learning and generalization in Experiment 1. A, Learning over trials during training and generalization, shown separately for the 0°, 45°, 90°, and 135° No-Exposure groups. The error bars denote SEMs. Solid lines are fitted exponential learning curves. The first trial (open circle) in the generalization phase is an indicator of the aftereffect. B, The aftereffect as a function of the angular separation. C, The initial learning errors in the training (dotted lines) and generalization (solid lines) phases. D, The directional generalization quantified by savings. *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 3.

Learning and generalization in Experiment 2 with the exposure of the generation direction via a visuomotor gain-learning task. A, Learning during training and generalization at the 0°, 45°, 90°, and 135° directions. The error bars denote SEMs. Solid lines are fitted exponential learning curves. The first open circle during the generalization phase indicates the aftereffect. Note the first panel is the 0° group from Experiment 1 and from the control group who only learned once. B, Learning of the visuomotor gain task for the Gain-Exposure groups. Solid lines are fitted exponential learning curves. C, Initial learning errors before and after the exposure task (red). The shaded gray lines denote the corresponding initial learning errors in Experiment 1. D, The savings is compared between Experiments 1 and 2, which are shown correspondingly by gray and red lines. Expo, Exposure; Pre, before; Post, after.

Figure 4.

Learning and generalization in the washout control conditions with washout after initial training. A, Initial learning errors before and after the exposure task (dash vs solid lines). Red lines denote groups with exposure (Expo+), and gray lines denote groups without exposure (Expo−). B, The savings from the washout conditions (WO+) are compared with those from Experiments 1 and 2 without washout (WO−).

Figure 5.

Results from Experiment 3 where different exposure tasks were used. A, Luminance discrimination as the exposure task in the Attention group. Left, A typical participant's luminance discrimination learning curve. The inset illustrates possible positions of the stimuli, which symmetrically flanked the to-be-generalized target. Right, The training and generalization functions. B, Visuomotor tracking task as the exposure task in the Tracking group. Left, A typical participant's tracking learning curve. The inset illustrates the path of the moving target (red, not shown to the participant), overlaid by exemplary tracking trajectories. Right, The training and generalization functions. C, Rotation learning for other exposure task groups. D, Initial learning errors before and after exposure. Results from Experiments 1 and 2 are shown in shades. E, Savings for different exposure task groups. Error bars denote the SEM. Significant differences from zero are marked. ***p < 0.001, *p < 0.05. Exp, Experiment.