Computational Psychiatry & Decision-making

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  • doi pdf Computational Psychiatry
  • Huys QJM
  • Encyclopædia of Computational Neuroscience (2014) 1-10
  • Computational Psychiatry is a heterogeneous field at the intersection of computational neuroscience and psychiatry. Incorporating methods from psychiatry, psychology, neuroscience, behavioural economics and machine learning, computational psychiatry focuses on building mathematical models of neural or cognitive phenomena relevant to psychiatric diseases. The models span a wide range - from biologically detailed models of neurons or networks to abstract models describing high-level cognitive abilities of an organism. Psychiatric diseases are conceptualized as either an extreme of normal function, or as a consequence of alterations in parts of the model.

    As in computational neuroscience more generally, the building of models forces key concepts to be made concrete and hidden assumptions to be made explicit. One critical functions of these models in the setting of psychiatry are their ability to bridge between low-level biological and high-level cognitive features. While many neurobiological alterations are known, the exclusively atheoretical focus of standard psychiatric nosology on high-level symptoms has as yet prevented an integration of these bodies of knowledge. David Marr pointed out that models at different levels may be independent (Marr, 1982). Nevertheless, implementational details may constrain functions at the computational level. The models used in computational psychiatry make these constraints explicit, and thereby aim to provide normative conduits between the different levels at which neural systems are analysed (Stephan et al., 2006; Huys et al., 2011; Hasler, 2012; Montague et al., 2012). This in turn allows for a principled approach to study dysfunctions, and indeed may allow the dysfunctions observed in psychiatry to inform neuroscience in general.

    Practically, it underpins hopes that computational techniques may facilitate the development of a psychiatric nomenclature based on an understanding of the underlying neuroscience. Computational models enhance experimental designs by allowing more intricate neural and/or cognitive processes to be inferred from complex features of the data, often via Bayesian inference. These aspects motivate hopes that it may facilitate the development of clinical treatment decision tools informed by advances in neuroscience.