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Abstract:
The nascent field of computational psychiatry includes the application of computational and statistical methods to understand dysfunction in psychiatric disease through the medium of normal information processing and cognition. I will discuss our attempts to use Bayesian decision theory to provide a framework for understanding disorders – coarsely, solving the wrong problem, solving the right problem incorrectly, or solving the right problem correctly, but in the wrong environment. I will illustrate aspects of these flaws using examples drawn from anhedonia, rumination and learned helplessness.
