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

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