Humans have been shown to adapt their movements when a sudden or gradual change to the dynamics of the environment are introduced, a phenomenon called motor adaptation. If the change is reverted, the adaptation is also quickly reverted. Humans 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, affecting the switch between known adaptations. Recently, computational models for motor adaptation and context inference have been introduced, which contain components for context inference and Bayesian motor adaptation. These models were used to show the effects of context inference on learning rates across different experiments. We expanded on these works by using a simplified version of the recently-introduced COIN model to show that the effects of context inference on motor adaptation and control go even further than previously shown. Here, we used this model to simulate classical motor adaptation experiments from previous works and showed that context inference, and how it is affected by the presence and reliability of feedback, effect a host of behavioral phenomena that had so far required multiple hypothesized mechanisms, lacking a unified explanation. Concretely, we show that the reliability of direct contextual information, as well as noisy sensory feedback, typical of many experiments, effect measurable changes in switching-task behavior, as well as in action selection, that stem directly from probabilistic context inference.
|Publication status||Published - 3 Jul 2023|