A computational model for learning from repeated trauma

Abstract

AbstracTraumatic events can lead to lifelong inflexible adaptations in threat perception and behavior which characterize posttraumatic stress disorder (PTSD). This process involves associations between sensory cues and internal states of threat and then generalization of the threat responses to previously neutral cues. However, most formulations neglect adaptations to threat that are not specific to those associations. In order to incorporate non-associative responses to threat, we propose a computational theory of PTSD based on adaptation to the frequency of traumatic events using a reinforcement learning momentum model. Recent threat prediction errors generate momentum that influences subsequent threat perception in novel contexts. This model fits data acquired from a mouse model of PTSD, in which unpredictable footshocks in one context accelerate threat learning in a novel context. The theory is also consistent with epidemiological data showing that PTSD incidence increases with the number of traumatic events, as well as the disproportionate impact of early life trauma. Since the theory proposes that PTSD relates to the average of recent threat prediction errors rather than the strength of a specific association, it makes novel predictions for the treatment of PTSD.