Computational Psychiatry (Oct 2018)

A Bayesian Account of Psychopathy: A Model of Lacks Remorse and Self-Aggrandizing

  • Aaron Prosser,
  • Karl J. Friston,
  • Nathan Bakker,
  • Thomas Parr

DOI
https://doi.org/10.1162/cpsy_a_00016
Journal volume & issue
Vol. 2
pp. 92 – 140

Abstract

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This article proposes a formal model that integrates cognitive and psychodynamic psychotherapeutic models of psychopathy to show how two major psychopathic traits called lacks remorse and self-aggrandizing can be understood as a form of abnormal Bayesian inference about the self. This model draws on the predictive coding (i.e., active inference) framework, a neurobiologically plausible explanatory framework for message passing in the brain that is formalized in terms of hierarchical Bayesian inference. In summary, this model proposes that these two cardinal psychopathic traits reflect entrenched maladaptive Bayesian inferences about the self, which defend against the experience of deep-seated, self-related negative emotions, specifically shame and worthlessness. Support for the model in extant research on the neurobiology of psychopathy and quantitative simulations are provided. Finally, we offer a preliminary overview of a novel treatment for psychopathy that rests on our Bayesian formulation.

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