Towards a neuro-computational account of prism adaptation

Prism adaptation has a long history as an experimental paradigm used to investigate the functional and neural processes that underlie sensorimotor control. In the neuropsychology literature, functional explanations of prism adaptation are typically framed within a traditional cognitive psychology 'box-and-arrow' framework that distinguishes putative component functions thought to give rise to behaviour (i.e. 'strategic control' versus 'spatial realignment'). However, this kind of theoretical framework lacks precision and explanatory power. Here, we advocate for a computational framework that offers several advantages: 1) an algorithmic explanatory account of the computations and operations that drive behaviour; 2) expressed in quantitative mathematical terms; 3) embedded within a principled theoretical framework (Bayesian decision theory, state-space modelling); 4) that offers a means to generate and test quantitative behavioural predictions. This computational framework offers a route toward mechanistic explanations of prism adaptation behaviour. Thus it constitutes a conceptual advance compared to the traditional theoretical framework. In this paper, we illustrate how Bayesian decision theory and state-space models offer principled explanations for a range of behavioural phenomena in the field of prism adaptation (e.g. visual capture, magnitude of visual versus proprioceptive realignment, spontaneous recovery and dynamics of adaptation memory). We argue that this explanatory framework offers to advance understanding of the functional and neural mechanisms that implement prism adaptation behaviour, by enabling quantitative tests of hypotheses that go beyond mere descriptive mapping claims that "brain area X is (somehow) involved in psychological process Y".