Abstract
Psychological therapies, such as CBT, are an important part of the treatment of a range of psychiatric disorders such as depression and anxiety. There is a growing desire to understand the mechanisms by which such therapies effect change so as to improve treatment outcomes. Here we argue that adopting a computational framework may be one such approach. Computational psychiatry aims to provide a theoretical framework for moving between higher-level psychological states (like emotions, decisions and beliefs) to neural circuits, by modeling these constructs mathematically. These models are explicit hypotheses that contain quantifiable variables and parameters derived from each individual's behavior. This approach has two advantages. Firstly, some of the variables described by these models appears to reflect the neural activity of specific brain regions. Secondly, the parameters estimated by these models may offer a unique description of a patient's symptoms which can be used to both tailor therapy and track its effect. In doing so this approach may offer some additional granularity in understanding how psychological therapies, such as CBT, are working. Although this field shows significant promise, we also highlight several of the key hurdles that must first be overcome before clinical translation of computational insights can be realized.
Highlights
There is growing recognition that, to move forward, the field of psychological therapy needs to return to its scientific roots and become more mechanism focused
Computational Psychological Therapy reviews [3, 4]. The focus of this piece is how a computational framework may translate to psychological therapies by allowing us to get closer to the generating mechanisms of symptoms and distress [5, 6]
We primarily focus on the application of “reinforcement learning” models [7] to understanding and evaluating cognitive behavior therapy (CBT)
Summary
There is growing recognition that, to move forward, the field of psychological therapy needs to return to its scientific roots and become more mechanism focused. There are many approaches derived from or compatible with computational modeling, including active inference [10] and perceptual control theory (PCT) [11, 12] which make different predictions about the relationship between internal states and behavior [for example in PCT, the control of sensory input through behavior, see [11]]. It is beyond the scope of this article to cover them all, and we focus on reinforcement learning and CBT as examples, as two of the most widespread frameworks. We discuss some of the challenges that currently limit the translation of computational psychiatry into clinical practice
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