Consolidation patterns of human motor memory

Abstract

Can memories be unlearned, or is unlearning a form of acquiring a new memory that competes with the old, effectively masking it? We considered motor memories that were acquired when people learned to use a novel tool. We trained people to reach with tool A and quantified recall in error-clamp trials, i.e., trials in which the memory was reactivated but error-dependent learning was minimized. We measured both the magnitude of the memory and its resistance to change. With passage of time between acquisition and reactivation (up to 24 h), memory of A slowly declined, but with reactivation remained resistant to change. After learning of tool A, brief exposure to tool B brought performance back to baseline, i.e., apparent extinction. Yet, for up to a few minutes after A+B training, output in error-clamp trials increased from baseline to match those who had trained only in A. This spontaneous recovery and convergence demonstrated that B did not produce any unlearning of A. Rather, it masked A with a new memory that was very fragile. We tracked the memory of B as a function of time and found that within minutes it was transformed from a fragile to a more stable state. Therefore, a sudden performance error in a well-learned motor task does not produce unlearning, but rather installs a competing but fragile memory that with passage of time acquires stability. Learning not only engages processes that adapt at multiple timescales, but once practice ends, the fast states are partially transformed into slower states.

Figure 1.

Study protocol and performances during learning of the task. A, Volunteers were divided into two groups (group A and group A+B). They held the handle of a light weight robotic arm and reached to a target. In the first 192 trials, the robot produced a null field (no forces). In the subsequent 384 trials, a curl force field was introduced (field A), perturbing the hand perpendicular to its direction of motion. In group A+B, the force field was reversed in sign for 20 additional trials. The gray bars schematically represent “error-clamp” trials, trials in which the robot produced a stiff channel that allowed us to measure the subject's motor output perpendicular to the direction of motion. After a break of variable length, subjects returned and were asked to hold the robotic tool and perform the task. We quantified the strength of the reactivated memory through 30 error-clamp trials. B, Force output (mean for each group for each trial) during the error-clamp trials in the baseline and learning periods (field A only). Learning of A was similar among the groups, exhibiting the classic double exponential pattern: rapid initial learning followed by slow, gradual learning. Bin size is one trial.

Figure 2.

Performance during test of recall in group A. A, Force output (mean ± SEM) for each subgroup after completion of training in A. The dashed line represents the force output at end of training (average of last 5 error-clamp trials across all subgroups). The output started higher for subjects that acquired the task recently, and decayed slowly during test of recall in all subgroups. Bin size is one trial. B, Magnitude of the memory as a function of time since acquisition. Each bar plot represents the initial force output (bin size is two trials) averaged across subjects in each subgroup (error bars are SEM). C, Fragility of the memory. Force output as a function of trial for each subject was fitted to a single exponential. The decay rates, shown here as mean ± SEM, did not change with passage of time.

Figure 3.

The patterns of spontaneous recovery. A, In the A+B group, the 20 B trials were sufficient to bring the motor output to baseline, as assayed at 0 or 2 min. Yet, in the subsequent error-clamp trials, output spontaneously rose and precisely converged to the output of group A. B, At 10 min after A+B acquisition and for all subsequent time intervals, motor output no longer started at zero and no longer rose during the error-clamp trials. All data points are mean ± SEM. Bin size is one trial.

Figure 4.

The effect of reactivation of memory as a function of time since acquisition. A, Force output as a function of trial after acquisition of A versus after acquisition of B. To estimate the memory of B, we used a boot-strapping procedure to subtract performance of the A group from the A+B group. The memory of B is a result of only 20 training trials. This memory at 0 and 2 min after acquisition is fragile in the sense that when activated in error-clamp trials it decays rapidly to baseline. However, at 10 min and beyond it acquires an increased resistance to trial. By 24 h, the memory of B is no longer measurable. B, Magnitude of memories of A and B as a function of time since acquisition. Each bar plot represents the initial force output (bin size is two trials) averaged across subjects in each subgroup. Error bars are SEM. With passage of time, the memory of A declines gradually, whereas memory of B declines rapidly. C, Fragility of the memory. Force output as a function of trial for each subject was fitted to a single exponential (shown as mean ± SEM). Within minutes after acquisition, the memory of B became more resistant to trial.

Figure 5.

Active/inactive multistate model of motor learning. A, A generative model, describing the learner's hypothesis about the task. As in previous generative models (Kording et al., 2007), the learner assumes that the environment is composed of states with multiple timescales (two timescales are considered here, labeled fast and slow), and the problem of learning is state estimation. In the active condition (left), the learner can sample the environment (i.e., subject is using the tool) and learns from observation. In the inactive condition (right), the learner is not in the context of the task and therefore cannot sample the environment. Passage of time affects the two conditions differently, as specified by the state transition matrices A_a and A_i, referring to the active and inactive conditions, respectively. We assumed that A_a is diagonal but A_i allows for transformation of a portion of the fast states into the slow states. In total, there were five parameters in this model (components of the two matrices). B, Output of the model compared with the measured data. A_a = [0.984, 0; 0, 0.855] and A_I = [0.9999, 0.0043; 0, 0.983]. Error bars indicate SEM.