Computational models can be used to enhance the description of behavioral responses that arise from the interaction between the brain and its immediate environment, and mathematical approaches are increasingly being utilized in the attempt to understand how the brain processes sensory data to elicit various behavioral responses. Computational psychiatry is the application of computational models to understanding psychopathology, with the modelling of mood and anxiety disorders becoming an area of particular interest. Various approaches to constructing these models have been suggested. For example, some investigators have proposed that distinguishing between “mood” and “emotions” is a key to computational modeling in studying depression, while others have pointed out that clinical phenomena related to the experience of depression and anxiety may be best accounted for by computational approaches to narratives patients construct to attempt to “make sense” of their biological condition. The growth and dynamic interaction of these various approaches has become an area with increasing potential for clinical application.
This paper presents a broad overview of the existing literature on the use of computational modeling in mood and anxiety disorders, as well as recent publications outlining the application of other related translational methods in psychiatry. We also present some original ideas on how Bayesian computational approaches can help explain the effects of attentional focus on brain function, and the how this relates to the utility of computational modeling for enhancing treatment interventions.
Iliyan Ivanov and Jeffrey Schwartz
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