Abstract
The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.
Highlights
This article is about set switching and action selection during the execution of cued responses
Summary In summary, we have created a generative model that illustrates the itinerant and dynamic sensorimotor constructs that might be used by the brain to predict cued sequential behaviours and set switching in response to changing contingencies
That this relatively simple model has implicitly modelled a number of apparently disparate processes in cognitive neuroscience: for example, Bayes-optimal sensorimotor integration, evidence accumulation, anticipation, short term memory, action selection, set switching and a simple form of reversal learning. We mean this in the straightforward sense that to perform accurately, the simulated agent has to remember the sequence of cues in terms of delay period activity in the premotor and prefrontal cortex [101], encoded here in terms of conditional beliefs about the dynamics of hidden states
Summary
This article is about set switching and action selection during the execution of cued responses. It offers a straightforward account of dopamine in optimising behaviour in the context of (Bayesoptimal) predictive coding. The emergent role of dopamine is to report the precision or salience of perceptual cues that portend a predictable sequence of sensorimotor events. In this sense, it mediates the affordance of cues that elicit motor behaviour [2]; in much the same way that attention mediates the salience of cues in the perceptual domain. We hope to establish a central role for dopamine in this inference and implicit action selection
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