Neuro-computational mechanisms underlying the effects of anxiety and motivation on biased attentional and learning processes

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Computational models in combination with neuroimaging techniques are increasingly being applied to the study of psychiatric disorders. However, their application to healthy individuals that may represent a population at risk to develop these disorders remains unexplored. Here we propose to reveal the neuro-computational mechanisms subserving cognitive biases in healthy individuals that can generate serious and debilitating symptoms through anxiety and depressive disorders. Anxiety and depression are amongst the most common mental health problems worldwide, costing the UK an estimated £77 billion a year. By combining neuroscientific and computational modelling techniques, I aim to further our understanding of the mechanisms which construct cognitive biases within healthy individuals that lead to these problems in well-being.An increasingly influential view proposes that the brain learns about its environment by comparing prior beliefs with sensory evidence, with the aim to generate increasingly accurate predictions about future states. This process can be best understood using computational models of Bayesian inference, which account for how the brain makes inferences about the state of its environment. Specifically, these models propose that by weighting both sensory data and prior belief according to their reliability (or precision), the brain can parsimoniously explain away incoming sensory information. This proposal intends to show that the predictive nature of perception runs in tandem with our long-term memories, motivations and emotions, creating cognitive biases that can transform computational parsimony, into computational pathology. Accordingly, I will investigate how everyday factors such as intrinsic motivation and anxiety can alter estimates of precision or reliability ascribed to prior belief or sensory data, thereby biasing perception and learning. Analysing neurophysiological and behavioural data in combination with Bayesian modelling will provide a mechanistic understanding of this previously unexplored non-optimal inference process. Moreover, by using a class of Bayesian models specifically introduced to assess individual differences, I ultimately aim to determine how inter-individual variation in estimates of precision can account for variability in individual learning under different degrees of anxiety and intrinsic motivation.