Abstract
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, prism adaptation behaviour is typically explained by reference to a traditional cognitive psychology framework that distinguishes putative functions, such as 'strategic control' versus 'spatial realignment'. This theoretical framework lacks conceptual clarity, quantitative precision and explanatory power. Here, we advocate for an alternative 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 towards mechanistic neurocognitive 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 can advance understanding of the functional and neural mechanisms that implement prism adaptation behaviour, by enabling quantitative tests of hypotheses that go beyond merely descriptive mapping claims that ‘brain area X is (somehow) involved in psychological process Y’.
Highlights
Adaptation is a fundamental property of the nervous system that enables organisms to flexibly reconfigure sensorimotor processing to counteract perturbations that cause performance errors (Shadmehr et al, 2010; Franklin and Wolpert, 2011)
We will argue for the advantages of a computational framework in place of this descriptive account. Key benefits of this formal model framework are that it offers: 1) principled mechanistic explanations of behaviour, 2) which specify the computations that give rise to behaviour, 3) in precise mathematical terms, 4) that enable quantitative tests of behavioural predictions, 5) and characterise information processing in terms that could plausibly be implemented by neural circuits. We argue that this explanatory framework offers a significant conceptual advance, which promises to accelerate progress in understanding the causal bases of prism adaptation behaviour, in terms of the algorithms that drive it, the neural circuits that implement it, and how these interact
What is the relationship between these functional components and the learning dynamics extracted by state-space models? The following sections outline a computational framework (Bayesian decision theory) that clarifies the content of information processing in relation to the visual, proprioceptive and motor systems
Summary
Adaptation is a fundamental property of the nervous system that enables organisms to flexibly reconfigure sensorimotor processing to counteract perturbations that cause performance errors (Shadmehr et al, 2010; Franklin and Wolpert, 2011). Prism after-effects tend to generalise at least partially across space (Bedford, 1989, 1993; Redding and Wallace, 2006b) This contrasts with visuomotor rotation, for instance, where effects drop off sharply with distance from the trained target location (Krakauer et al, 2000). Improved symptomatology after prism adaptation has been reported in patients with complex regional pain syndrome (Sumitani et al, 2007) and Parkinson's disease (Bultitude et al, 2012) This distinctive generalisation/transfer profile of prism adaptation, by contrast with other adaptation paradigms, suggests that this experimental model of sensorimotor integration warrants special attention. We make the case that a computational characterisation of prism adaptation behaviour offers advantages over this traditional functional descriptive approach, and argue the need for an integrated neuro-computational account to further advance understanding within this field
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