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Abstract Previous research has demonstrated significant inter-individual variability in the recruitment of the fast-explicit and slow-implicit processes during motor adaptation. In addition, we previously identified qualitative individual differences in adaptation linked to the formation and updating of new memory processes. Here, we investigated quantitative and qualitative differences in visuomotor adaptation with a design incorporating repeated learning and forgetting blocks, allowing for precise estimation of individual learning and forgetting rates in fast-slow adaptation models. Participants engaged in a two-day online visuomotor adaptation task. They first adapted to a 30-degree perturbation to eight targets in three blocks separated by short blocks of no feedback trials. Approximately 24 hours later, they performed a no-feedback retention block and a relearning block. We clustered the participants into strong and weak learners based on adaptation levels at the end of day one and fitted a fast-slow system to the adaptation data. Strong learners exhibited a strong negative correlation between the estimated slow and fast processes, which predicted 24-hour retention and savings, respectively, supporting the engagement of a fast-slow system. The pronounced individual differences in the recruitment of the two processes were attributed to wide ranges of estimated learning rates. Conversely, weak learners exhibited a positive correlation between the two estimated processes, as well as retention but no savings, supporting the engagement of a single slow system. Finally, both during baseline and adaptation, reaction times were shorter for weak learners. Our findings thus revealed two distinct ways to learn in visuomotor adaptation and highlight the necessity of considering both quantitative and qualitative individual differences in studies of motor learning. 

Abstract Humans exhibit large interindividual differences in motor learning ability. However, most previous studies have examined properties common across populations, with less emphasis on interindividual differences. We hypothesized here, based on our previous experimental and computational motor adaptation studies, that individual differences in effective learning rates between a generalist memory module that assumes environmental continuity and specialist modules that are responsive to trial-by-trial environmental changes could explain both large population-wise and individual-wise differences in dual tasks adaptation under block and random schedules. Participants adapted to two opposing force fields, either sequentially with alternating training blocks or simultaneously with random sequences. As previously reported, in the block training schedule, all participants adapted to the force field presented in a block but showed large interference in the subsequent opposing force field blocks, such that adapting to the two force fields was impossible. In contrast, in the random training schedule, participants could adapt to the two conflicting tasks simultaneously as a group; however, large interindividual variability was observed. A modified MOSAIC computational model of motor learning equipped with one generalist module and two specialist modules explained the observed behavior and variability for wide parameter ranges: when the predictions errors were large and consistent as in block schedules, the generalist module was selected to adapt quickly. In contrast, the specialist modules were selected when they more accurately predicted the changing environment than the generalist, as during random schedules; this resulted in consolidated memory specialized to each environment, but only when the ratio of learning rates of the generalist to specialists was relatively small. This dynamic selection process plays a crucial role in explaining the individual differences observed in motor learning abilities. 

Abstract Riding a bicycle is considered a durable skill that cannot be forgotten. Here, novice participants practiced riding a reversed bicycle, in which a reversing gear inverted the handlebar’s rotation. Although learning to ride the reversed bicycle was possible, it was slow, highly variable, implicit, and followed an S-shape pattern. In the initial learning phase, failed attempts to ride the normal bicycle indicated strong interference between the two bicycle skills. While additional practice decreased this interference effect, a subset of learners could not ride either bicycle after eight sessions of practice. Experienced riders who performed extensive practice could switch bicycles without failed attempts and exhibited similar performance (i.e., similar handlebar oscillations) on both bicycles. However, their performance on the normal bicycle was worse than that of the novice bicycle riders at baseline. In conclusion, “unlearning” of the normal bicycle skill precedes the initial learning of the reversed bicycle skill, and a signature of such unlearning is still present following extensive practice.