Maximizing exposure therapy: An inhibitory learning approach

Abstract

Despite the effectiveness of exposure therapy for treating anxiety disorders, a number of patients fail to benefit or experience a return of fear after treatment. Research suggests that anxious individuals show deficits in the mechanisms believed to underlie exposure therapy, such as inhibitory learning. Targeting these processes may help improve the efficacy of exposure-based procedures. The primary aim of this paper is to provide examples to clinicians of how to apply the inhibitory learning model in order to optimize exposure therapy.  Exposure optimization strategies include 1) expectancy violation, 2) deepened extinction, 3) occasional reinforced extinction, 4) removal of safety signals, 5) variability, 6) retrieval cues, 7) multiple contexts, and 8) affect labeling. Case studies illustrate methods of applying these techniques in a variety of anxiety disorders.