Behavioral/Systems/Cognitive Dual Adaptation Supports a Parallel Architecture of Motor Memory

Although our understanding of the mechanisms underlyingmotor adaptation has greatly benefited from previous computational mod-els, 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 onmotor adaptation, but cannot differentiate whether the fast and slowprocesses are arranged serially or inparallel andcannot account for learningmultiple tasks simultaneously.On theother 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 andadual visuomotor adaptation experimental paradigm.We found that only oneparsimoniousmodel canaccount for both previousmotor 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 containsmultiple states switched via contextual cues. Our result suggests that duringmotor adaptation, fast and slow processes are updated simultaneously from the samemotor learning errors