Computation in Psychotherapy, or How Computational Psychiatry Can Aid Learning-Based Psychological Therapies

Abstract

Learning-based therapies, such as cognitive-behavioral therapy, are used worldwide, and their efficacy is endorsed by health and research funding agencies. However, the mechanisms behind both their strengths and their weaknesses are inadequately understood. Here we describe how advances in computational modeling may help formalize and test hypotheses regarding how patients make inferences, which are core postulates of these therapies. Specifically, we highlight the relevance of computations with regard to the development, maintenance, and therapeutic change in psychiatric disorders. A Bayesian approach helps delineate which apparent inferential biases and aberrant beliefs are in fact near-normative, given patients’ current concerns, and which are not. As examples, we formalize three hypotheses. First, high-level dysfunctional beliefs should be treated as beliefs over models of the world. There is a need to test how, and whether, people apply these high-level beliefs to guide the formation of lower level beliefs important for real-life decision making, conditional on their experiences. Second, during the genesis of a disorder, maladaptive beliefs grow because more benign alternative schemas are discounted during belief updating. Third, we propose that when patients learn within therapy but fail to benefit in real life, this can be accounted for by a mechanism that we term overaccommodation, similar to that used to explain fear reinstatement. Beyond these specifics, an ambitious collaborative research program between computational psychiatry researchers, therapists, and experts-by-experience needs to form testable predictions out of factors claimed to be important for therapy.

Motivation and cognitive control in depression

“…Cognitive control deficits can strengthen those beliefs, but are also strengthened by them. Interestingly, there is already some progress on studying the maladaptive beliefs within the framework of computational psychiatry (Moutoussis et al, 2017). Finally, lowered levels of cognitive control are related to maladaptive use of emotion regulation strategies such as rumination which contribute to the onset and maintenance of depressive symptoms .…”

Motivation and Cognitive Control in Depression

“…Cognitive control deficits can strengthen those beliefs, but are also strengthened by them. Interestingly, there is already some progress on studying the maladaptive beliefs within the framework of computational psychiatry (Moutoussis et al, 2017). Finally, lowered levels of cognitive control are related to maladaptive use of emotion regulation strategies such as rumination (Nolen-Hoeksema et al, 2008) which contribute to the onset and maintenance of depressive symptoms (Joormann & Vanderlind, 2014).…”

Anxiety, avoidance, and sequential evaluation

“…This phenomenon (similar to failures of model-free RL algorithms to adjust to reward revaluation without extensive relearning; 39 ) may offer at least a partial answer to a classic puzzle in pathological avoidance, i.e. why it is so resistant to extinction 40 , and to the unfortunately high rates of anxiety recurrence following treatment 41 .…”