Humans have been shown to adapt their movements when a sudden change to the dynamics of the environment is introduced, a phenomenon called motor adaptation. If the change is reverted, the adaptation is also quickly reverted. Human are also able to adapt to multiple changes in dynamics presented separately, and to be able to switch between adapted movements on the fly. Such switching relies on contextual information which is often noisy or misleading, which affects the switch between adaptations. In this work, we introduce a computational model to explain the behavioral phenomena effected by uncertain contextual information. Specifically, we present a hierarchical model for motor adaptation based on exact Bayesian inference. This model explicitly takes into account contextual information and how the dynamics of context inference affect adaptation and action selection. We show how the proposed model provides a unifying explanation for four different experimentally-established phenomena: (i) effects of sensory cues and proprioceptive information on switching between tasks, (ii) the effects of previously-learned adaptations on switching between tasks, (iii) the effects of training history on behavior in new contexts, in addition to (iv) the well-studied savings, de-adaptation and spontaneous recovery.
Exact Bayesian inference by symbolic disintegration
