Interacting adaptive processes with different timescales underlie short-term motor learning

Abstract

Multiple processes may contribute to motor skill acquisition, but it is thought that many of these processes require sleep or the passage of long periods of time ranging from several hours to many days or weeks. Here we demonstrate that within a timescale of minutes, two distinct fast-acting processes drive motor adaptation. One process responds weakly to error but retains information well, whereas the other responds strongly but has poor retention. This two-state learning system makes the surprising prediction of spontaneous recovery (or adaptation rebound) if error feedback is clamped at zero following an adaptation-extinction training episode. We used a novel paradigm to experimentally confirm this prediction in human motor learning of reaching, and we show that the interaction between the learning processes in this simple two-state system provides a unifying explanation for several different, apparently unrelated, phenomena in motor adaptation including savings, anterograde interference, spontaneous recovery, and rapid unlearning. Our results suggest that motor adaptation depends on at least two distinct neural systems that have different sensitivity to error and retain information at different rates.

Figure 1. Simulations of Motor Adaptation Experiments That Show Savings

(A) Paradigm for basic savings experiment. This paradigm consists of four blocks: (1) a baseline period, (2) initial learning, (3) unlearning, and (4) relearning. Note that adaptation stimulus for the unlearning block is opposite that used in learning blocks, and the number of trials in the unlearning block is adjusted so that on this block's last trial performance is at the baseline level. (B and C) Model simulations of the experiment paradigm shown in (A). The first row (B) shows the raw results of these simulations, while the second row (C) shows a direct comparison of simulated performance in the initial learning and relearning blocks. The different columns display simulation results from the single-state, gain-specific, and multi-rate models, respectively. The single-state model fails to show savings (faster relearning), but the gain-specific and multi-rate models show savings. (D) Paradigm for savings experiment with washout. Note that this paradigm is similar to the paradigm shown in (A), except that a washout block of variable length is inserted prior to the relearning block. (E) The amount of savings found in simulation, as a function of the number of washout trials. The amount of savings is measured as the percent improvement in performance on the 30th trial in the relearning block compared to the 30th trial in the initial learning block. The columns are the same as in (B). The gain-specific and multi-rate models show similar washout of savings; however, in the single-state model there is no savings to wash out.

Figure 2. Simulations of Motor Adaptation in the Error-Clamp Paradigm

(A) Paradigm for simulated error-clamp experiment. This paradigm is similar to the savings paradigm shown in Figure 1A except that the relearning block is replaced by an error-clamp block during which the error that drives adaptation is held at zero. (B) Model simulations of the experiment paradigm shown in (A). The different columns display simulation results form the single-state, gain-specific, and multi-rate models, respectively. The single-state and gain-specific models do not predict changes in motor output from baseline in the error-clamp block, whereas the multi-rate model predicts a transient rebound of motor output in the error-clamp block. This rebound is in the direction of the motor output displayed in the initial learning block, resulting in spontaneous recovery. (C) Paradigm for the error-clamp/relearning experiment. Here a relearning block follows a shortened error-clamp block. This paradigm reproduces the effect of jump-up facilitation seen by Kojima et al. following dark exposure. During dark exposure, monkeys made saccades but received no visual feedback of saccade error. The absence of error feedback may be similar to the zero-error condition produced by the error-clamp. (D) Model simulations of the experiment paradigm shown in (C). The columns are the same as in (B). The multi-rate model predicts that performance at the start of the relearning block is already better than baseline. This jump-up in performance is caused by adaptation rebound in the error-clamp phase. Kojima et al. showed that following a period of dark exposure (during which saccade gain was not measured) monkeys displayed an immediate jump-up in performance at the start of the subsequent relearning block. This finding is predicted by the multi-rate model, but is not predicted by the single-state or gain-specific models.

Figure 3. Experimental Results from the Force-Field Learning Error-Clamp Paradigm

(A and B) Paradigms for simulated error-clamp experiment. These paradigms are the same as the paradigm shown in Figure 2A, except that here one group (the NP group) of participants is exposed to an initial adaptation to a clockwise viscous-curl force field while the other group (the PN group) is exposed to an initial adaptation in the opposite direction (counterclockwise). (C) Example force trajectories during the course of this learning paradigm. Force trajectories from selected error-clamp trials for one participant in each group are shown as red arrows with tips connected by dashed black lines. The blue line represents the force trajectory required to fully cancel the force field applied during the initial learning block for each participant. The same trials are shown for each participant, and each trial is labeled by a block identifier and the trial number within that block. For example, N97 is the 97th trial in the null-field practice block, A17 is the 17th trial in the initial adaptation block, and F1 is the first trial in the force-channel (error-clamp) block. Since the adaptation requires the production of lateral forces, only lateral forces are shown. Lateral forces (red arrows) in the baseline period are small and inconsistent in direction. However, during the initial adaptation block these lateral forces grow with training so that they nearly cancel the applied force field. After the extinction block, the first trials in the error-clamp block show a near-zero or negative pattern of lateral forces with respect to the forces displayed late in the initial adaptation block. However, by trials 12–15 in the error-clamp block, a small but consistent rebound of the pattern of lateral forces seen during initial adaptation emerges. This rebound substantially fades away by trial 90 in the error-clamp block. (D) The average time course of adaptive changes in the pattern of lateral forces. Data from both the PN and NP groups are averaged together. The adaptation score corresponding to the force pattern displayed on a particular trial was assessed by computing a force-field compensation factor (see Materials and Methods). In short, this force-field compensation factor measures the fraction of (initial adaptation) force field that would be compensated by the pattern of lateral forces displayed on a particular trial by regressing the measured lateral force pattern onto the ideal pattern of lateral forces required to fully compensate the force field. The transient rebound of motor output in the error-clamp block matches the rebound predicted by the multi-rate model. The blue error bars represent experimental data (mean +/− standard error of the mean.). The green line is the best-fit multi-rate model, and the red and purple lines are the best-fit gain-specific and single-state models. The best-fit model parameters (with 95% confidence intervals) for the multi-rate model were A ₁ = 0.992 (0.990–0.994), B ₁ = 0.02 (0.013–0.025), A ₂ = 0.59 (0.43–0.76), and B ₂ = 0.21 (0.10–0.35). (E) Summary of results from NP and PN groups. The asterisks indicate significant difference in lateral forces from baseline. Both groups display significant adaptation rebound by trials 10–20 of the error-clamp block compared to the initial error-clamp trials ( p < 0.01 for both the NP and PN groups taken separately, and p < 0.0001 for all participants taken together) and compared to baseline lateral force levels before learning ( p < 0.01 for the NP group, p < 0.001 for the PN group, and p < 0.0001 for all participants taken together). NP, negative/positive group; PN, positive/negative group.

Figure 4. Simulations of Motor Adaptation with the Multi-Rate Model Explain a Variety of Previously Reported Results, Including Rapid Unlearning and Rapid Downscaling

(A–C) Anterograde interference. (D–F) Rapid unlearning. (G–I) Rapid downscaling. First column (A, D, and G): experiment paradigms. Second column (B, E, and H): Raw simulation results. Blue: initial adaptation. Red, green, and cyan: secondary adaptation after 30, 60, or 120 trials of the initial adaptation, respectively. Third column (C, F, and I): Comparison of adaptation rates for initial and secondary adaptations. Here the learning curves have been shifted so that they all begin at zero and scaled so that the desired performance level is one. In the anterograde interference paradigm (A–C), the multi-rate model predicts that learning the opposite force field proceeds with a slower time constant than initial learning; furthermore, this time constant gets even slower when number of trials in the initial learning block is increased. The multi-rate model predicts that unlearning proceeds with a faster time constant than initial learning (E–F) and the time constant for downscaling is faster still (H–I); however, the time constant for unlearning or downscaling returns to baseline when the number of trials in the initial learning block is increased. In summary, the multi-rate model simultaneously explains the effects of anterograde interference, rapid unlearning, and rapid downscaling. Furthermore this model predicts that anterograde interference will get stronger as the length of the initial adaptation period increases, but that rapid unlearning and rapid downscaling will get weaker as the length of the initial adaptation period increases.

Figure 5. Two Different Internal Realizations of a Linear, Two-State, Multi-Rate System

Any input-output behavior achieved by one realization can be duplicated by the other.