Simulating the computational mechanisms of cognitive and behavioral psychotherapeutic interventions: Insights from active inference

Cognitive-behavioral therapy (CBT) leverages interactions between thoughts, feelings, and actions. In this paper, we use neurocomputational modelling to deepen our understanding of the mechanisms underlying these interactions and how therapeutic interventions can produce behavioral change through different mechanisms in different cases. We describe an active inference model that allows formal simulations of interactions between cognitive interventions (i.e., cognitive restructuring) and behavioral interventions (i.e., exposure) in producing adaptive behavior change (i.e., reducing maladaptive avoidance behavior). Using the example of Spider Phobia, we show simulations indicating that when conscious beliefs about safety/danger have strong interactions with affective/behavioral outcomes, behavioral change during exposure therapy is mediated by changes in these beliefs, preventing generalization. In contrast, when these interactions are weakened, behavior change leads to generalized learning (i.e., “over-writing” the implicit beliefs about action-outcome mappings that directly produce avoidance). The individual is therefore equipped to face any new context, safe or dangerous, remaining in a content state without the need for avoidance behavior – increasing resilience from a CBT perspective. However, this beneficial effect only held if cognitive restructuring induced uncertain beliefs about whether the spider is safe or not, but not when it first induced strong beliefs in safety. These results show how the same changes in behavior during CBT can be due to distinct underlying mechanisms, and predict lower rates of relapse when cognitive interventions focus on inducing uncertainty and on reducing the effects of automatic negative thoughts on behavior.