A new model for recovery-from-extinction effects in Pavlovian conditioning and exposure therapy

Abstract

Exposure therapy is an effective intervention for anxiety-related problems. A mechanism of this intervention has been the extinction procedure in Pavlovian conditioning, and their findings have provided many effective intervention strategies that can promote the effect of and prevent relapse following exposure sessions. However, traditional associative theories that have explained Pavlovian conditioning cannot comprehensively explain their findings. In particular, it was difficult to explain the recovery-from-extinction effects, which is the reappearance of conditioned response following extinction. In this study, we propose a new associative model that can deal with procedures that promote an effect of extinction and many recovery-from-extinction effects. The cores of this model are that the asymptotic strength of the inhibitory association depends on the degree of excitatory association retrieved in a context in which CS is presented and that the retrieval is determined by the similarity between contexts during reinforcement and non-reinforcement and the present context. Moreover, this model assumes that these similarities change under specific conditions. By adding these assumptions to the traditional framework, many difficulties in explaining these phenomena can be resolved. Our model can provide not only a new perspective in associative learning, but also many implications for exposure therapy.

ABA, ABC, and AAB renewal effects and individual differences of learning on fear conditioning using social stimuli

“…We used Bayesian modeling to examine whether the real data support the numerical simulation of an associative model. Recently, we developed a new mathematical model to explain the recovery-from-extinction effects, including all renewal effects (Nihei et al, 2020).…”

“…data can be described by the model ofNihei et al (2020) and whether individual differences in the estimated parameters are related to other parameters and social anxiety. For the first aim, the discrepancy between the observed data and the model was assessed by qualitative posterior predictive checking (PPC), which evaluates whether posterior predictive values from estimated parameter values resemble the actual data by visual inspection(Kruschke, 2013).…”