Computational models of motor adaptation under multiple classes of sensorimotor disturbance
Abstract
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.