Computational models of motor adaptation under multiple classes of sensorimotor disturbance
The human motor system exhibits remarkable adaptability, enabling us to maintain high levels of performance despite ever-changing requirements. There are many potential sources of error duringmovement to which the motor system may need to adapt: the properties of our bodies or tools may vary over time, either at a dynamic or a kinematic level; our senses may become miscalibrated over time and mislead us as to the state of our bodies or the true location of an intended goal; the relationship between sensory stimuli and movement goals may change. Despite these many varied ways in which our movements may be disturbed, existing models of human motor adaptation have tended to assume just a single adaptive component. In this thesis, I argue that the motor system maintains multiple components of adaptation, corresponding to the multiple potential sources of error to which we are exposed. I outline some of the shortcomings of existing adaptation models in scenarious where multiple kinds of disturbances may be present - in particular examining how different distal learning problems associated with different classes of disturbance can affect adaptation within alternative cerebellar-based learning architectures - and outline the computational challenges associated with extending these existing models. Focusing on the specific problem in which the potential disturbances are miscalibrations of vision and proprioception and changes in arm dynamics during reaching, a unified model of sensory and motor adaptation is derived based on the principle of Bayesian estimation of the disturbances given noisy observations. This model is able to account parsimoniously for previously reported patterns of sensory and motor adaptation during exposure to shifted visual feedback. However the model additionally makes the novel and surprising prediction that adaptation to a force field will also result in sensory adaptation. These predictions are confirmed experimentally. The success of the model strongly supports the idea that the motor system maintains multiple components of adaptation, which it updates according to the principles of Bayesian estimation.