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.

Figure 1. Savings and Interference Occur for Rotation Learning at the Wrist

(A) R_wrist on day 1 (group 1, black circles and black curve) and on day 2 (group 2, white squares and dashed curve). Learning is shown by progressive reduction across cycles in the directional error at peak velocity. Points, representing the group average with standard error for each cycle, are fitted by a double-exponential function. There were substantial savings from day 1 to day 2. (B) R_wrist on day 1 (group 1, black circles and black curve) and after interference on day 2 (group 3, white squares and dashed curve). There were no savings from day 1 to day 2 after interference with CR_wrist. (C) Bar graph showing a statistically significant difference in the reduction in mean directional error in the first six cycles for day 1 versus day 2 (groups 1 and 2, mean difference = 9.86°, p < 0.0001). This difference is absent with interference, with no statistically significant difference in the reduction in mean directional error in the first six cycles for day 1 versus day 2 (groups 1 and 3, mean difference = −2.64°, p = 0.22).

Figure 2. Savings Transfers from Arm to Wrist but Not from Wrist to Arm

(A) R_wrist on day 1 (group 1, black circles and black curve) and R_wrist on day 2 after R_arm on day 1 (group 5a, white squares and dashed curve). There were substantial savings from R_arm on day 1 to R_wrist on day 2. Inset: R_wrist on day 1 (group 1, black circles and black curve) and R_wrist on day 2 after R_shoulder on day 1 (group 5b, white squares and dashed curve). There were substantial savings from R_shoulder on day 1 to R_wrist on day 2 (mean difference = 7.75° , p = 0.0157). Inset axes scaled as in main figure. (B) R_arm on day 1 (group 5, black circles and black curve) and R_arm on day 2 after R_wrist on day 1 (group 4, white squares and dashed curve). There were no significant savings from R_wrist on day 1 to R_arm on day 2. Inset: R_shoulder on day 1 (group 1, black circles and black curve) and R_wrist on day 2 after R_shoulder on day 1 (group 5b, white squares and dashed curve). There were no significant savings from R_wrist on day 1 to R_shoulder on day 2 (mean difference = 4.5° , p = 0.1). Inset axes scaled as in main figure. (C) First pair of bars showing a statistically significant difference in the reduction in mean directional error in the first six cycles for R_wrist on day 2 , after R_arm on day 1, compared to R_wrist on day 1 (groups 1 vs. 5a, mean difference = 5.52°, p = 0.01). Second pair of bars showing no statistically significant difference in the reduction in mean directional error in the first six cycles for R_arm on day 2, after R_wrist on day 1, compared to R_arm on day 1 (groups 5a versus group 4, mean difference = 3.52°, p = 0.12).

Figure 3. Rotation Learning at the Wrist Is Not Interfered With by Counter-Rotation Learning at the Arm

(A) R_wrist on day 2, after R_wrist followed by CR_arm 5 min later on day 1 (group 7, white squares, dashed curve). There was savings from R_wrist on day 1 to R_wrist on day 2 despite CR_arm. The thick black curve represents R_wrist on day 1 (group 1). (B) Bar graph showing a statistically significant difference in the reduction in mean directional error in the first six cycles for R_wrist on day 1 versus day 2 (groups 1 and 7, mean difference = 6.49°, p =0.0036). This difference was absent when only CR_arm was learned on day 1, with no statistically significant difference in the reduction in mean directional error in the first six cycles for day 1 versus day 2 (groups 1 and 8, mean difference = 0.328°, p = 0.88).

Figure 4. Antecedent Learning of the Rotation with the Wrist Blocks Subsequent Transfer of the Counter-Rotation from Arm to the Wrist

(A) CR_wrist on day 2, after R_wrist followed by CR_arm 5 min later on day 1 (group 9, white squares, dashed curve). Also shown is CR_wrist on day 1 (group 10, black circles, black curve). There were no savings for CR_wrist on day 2 compared to CR_wrist on day 1. (B) Bar graph showing no statistically significant difference in the reduction in mean directional error in the first six cycles for CR_wrist on day 1 versus day 2 (groups 9 and 10, mean difference = −0.14°, p =0.947).

Figure 5. Simulation Results

Each column displays the movement errors y ⁽ⁿ⁾ − y_t, the two components of the parameter vector (and their linear combination), the two components of the Kalman gain vector k ⁽ⁿ⁾, and the components of parameter uncertainty matrix . For , the plot includes the upper arm estimate , the wrist estimate , and the arm estimate . For P, the plot includes the upper arm variance P _1,1, the wrist variance P _2,2, the covariance P _1,2 (which is equal to P _2,1), and the variance for the arm which is P_a = P _1,1 + P _2,2 + P _1,2 + P _2,1. The context for each training situation is specified by the vector c. All simulations begin at the same initial conditions. (A) Simulation of R_wrist. With each trial, the estimate for the wrist increases toward 30°. Despite the fact that only the wrist context is present, the estimate for the upper arm becomes negative. This is because the uncertainty matrix has negative off-diagonal elements P _1,2, which arise from the prior assumption that motion of the upper arm usually results in motion of the wrist (in extrinsic space). (B) Simulation of R_arm. Errors produce changes in the estimates of both the upper arm and the wrist, resulting in transfer to the wrist. Despite identical initial conditions, learning with the arm is slower than learning with the wrist. (In the subplots, the red line associated with the upper arm is hidden behind the green line associated with the wrist). (C) Simulation of R_wrist followed by CR_arm. Despite the fact that in the naive condition, arm training transferred to the wrist (part B), prior wrist training blocked this transfer. By the end of training, the model acquired R at the wrist and CR at the arm. To see the reason for this, compare the Kalman gain at the start of arm training in this subplot with the same arm training in subplot B. In part C, gain for the upper arm is nearly twice as high as in part B. In contrast, in part C, the gain for the wrist is about half as high as in part B. The prior training with the wrist changed the pattern of generalization.

Figure 6. Simulation Results (Lines) along with the Measured Data (Dots) from All Experiments

In the legend for each subplot, the vertical line refers to a 24-h break. (A) Savings from R_wrist day 1 to R_wrist day 2 (experiment 1). (B) Catastrophic interference when R_wrist on day 1 is followed by CR_wrist on day 1 and R_wrist is relearned on day 2 (experiment 1). (C) R_arm transfers to R_wrist (experiment 2). (D) Learning R_arm is slower than R_wrist, and R_wrist shows little transfer to R_arm (experiment 2). (E) Prior learning of R_wrist blocks interference by CR_arm (experiment 3). (F) Learning of R_wrist interferes anterograde with CR_wrist learned 5 min later (experiment 3). (G) Prior learing of R_wrist blocks transfer of CR_arm to CR_wrist (experiment 4).