Rethinking post-traumatic stress disorder - A predictive processing perspective

Neurosci Biobehav Rev. 2020 Jun:113:448-460. doi: 10.1016/j.neubiorev.2020.04.014. Epub 2020 Apr 19.

Abstract

Predictive processing has become a popular framework in neuroscience and computational psychiatry, where it has provided a new understanding of various mental disorders. Here, we apply the predictive processing account to post-traumatic stress disorder (PTSD). We argue that the experience of a traumatic event in Bayesian terms can be understood as a perceptual hypothesis that is subsequently given a very high a-priori likelihood due to its (life-) threatening significance; thus, this hypothesis is re-selected although it does not fit the actual sensory input. Based on this account, we re-conceptualise the symptom clusters of PTSD through the lens of a predictive processing model. We particularly focus on re-experiencing symptoms as the hallmark symptoms of PTSD, and discuss the occurrence of flashbacks in terms of perceptual and interoceptive inference. This account provides not only a new understanding of the clinical profile of PTSD, but also a unifying framework for the corresponding pathologies at the neurobiological level. Finally, we derive directions for future research and discuss implications for psychological and pharmacological interventions.

Keywords: Bayesian brain; active inference; belief updating; expectation; post-traumatic stress disorder; predictive processing.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Bayes Theorem
  • Humans
  • Psychiatry*
  • Stress Disorders, Post-Traumatic*