Dual Adaptation Supports a Parallel Architecture of Motor Memory

Although our understanding of the mechanisms underlying motor adaptation has greatly benefited from previous computational models, the architecture of motor memory is still uncertain. On one hand, two-state models that contain both a fast-learning-fast-forgetting process and a slow-learning-slow-forgetting process explain a wide range of data on motor adaptation, but cannot differentiate whether the fast and slow processes are arranged serially or in parallel and cannot account for learning multiple tasks simultaneously. On the other hand, multiple parallel-state models learn multiple tasks simultaneously but cannot account for a number of motor adaptation data. Here, we investigated the architecture of human motor memory by systematically testing possible architectures via a combination of simulations and a dual visuomotor adaptation experimental paradigm. We found that only one parsimonious model can account for both previous motor adaptation data and our dual-task adaptation data: a fast process that contains a single state is arranged in parallel with a slow process that contains multiple states switched via contextual cues. Our result suggests that during motor adaptation, fast and slow processes are updated simultaneously from the same motor learning errors.