Modeling subjective belief states in computational psychiatry: interoceptive inference as a candidate framework

Xiaosi Gu, Thomas H. B. FitzGerald, Karl J. Friston

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


The nascent field computational psychiatry has undergone exponential growth since its inception. To date, much of the published work has focused on choice behaviors, which are primarily modeled within a reinforcement learning framework. While this initial normative effort represents a milestone in psychiatry research, the reality is that many psychiatric disorders are defined by disturbances in subjective states (e.g., depression, anxiety) and associated beliefs (e.g., dysmorphophobia, paranoid ideation), which are not considered in normative models. In this paper, we present interoceptive inference as a candidate framework for modeling subjective-and associated belief-states in computational psychiatry. We first introduce the notion and significance of modeling subjective states in computational psychiatry. Next, we present the interoceptive inference framework, and in particular focus on the relationship between interoceptive inference (i.e., belief updating) and emotions. Lastly, we will use drug craving as an example of subjective states to demonstrate the feasibility of using interoceptive inference to model the psychopathology of subjective states.

Original languageEnglish
Pages (from-to)2405–2412
Issue number8
Early online date22 Jun 2019
Publication statusPublished - Aug 2019

Cite this