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Hierarchical Bayesian models of social inference for probing persecutory delusional ideation


Diaconescu, Andreea Oliviana; Wellstein, Katharina V; Kasper, Lars; Mathys, Christoph; Stephan, Klaas Enno (2020). Hierarchical Bayesian models of social inference for probing persecutory delusional ideation. Journal of Abnormal Psychology, 129(6):556-569.

Abstract

While persecutory delusions (PDs) have been linked to fallacies of reasoning and social inference, computational characterizations of delusional tendencies are rare. Here, we examined 151 individuals from the general population on opposite ends of the PD spectrum (Paranoia Checklist [PCL]). Participants made trial-wise predictions in a probabilistic lottery, guided by advice from a more informed human and a nonsocial cue. Additionally, 2 frames differentially emphasized causes of invalid advice: (a) the adviser’s possible intentions (dispositional frame) or (b) the rules of the game (situational frame). We applied computational modeling to examine possible reasons for group differences in behavior. Comparing different models, we found that a hierarchical Bayesian model (hierarchical Gaussian filter) explained participants’ responses better than other learning models. Model parameters determining participants’ belief updates about the adviser’s fidelity and the contribution of prior beliefs about fidelity to trial-wise decisions, respectively, showed significant Group × Frame interactions: High PCL scorers held more rigid beliefs about the adviser’s fidelity across both experimental frames and relied less on advice in situational frames than low scorers. These results suggest that PD tendencies are associated with rigid beliefs and prevent adaptive use of social information in “safe” contexts. This supports previous proposals of a link between PD and aberrant social inference. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Abstract

While persecutory delusions (PDs) have been linked to fallacies of reasoning and social inference, computational characterizations of delusional tendencies are rare. Here, we examined 151 individuals from the general population on opposite ends of the PD spectrum (Paranoia Checklist [PCL]). Participants made trial-wise predictions in a probabilistic lottery, guided by advice from a more informed human and a nonsocial cue. Additionally, 2 frames differentially emphasized causes of invalid advice: (a) the adviser’s possible intentions (dispositional frame) or (b) the rules of the game (situational frame). We applied computational modeling to examine possible reasons for group differences in behavior. Comparing different models, we found that a hierarchical Bayesian model (hierarchical Gaussian filter) explained participants’ responses better than other learning models. Model parameters determining participants’ belief updates about the adviser’s fidelity and the contribution of prior beliefs about fidelity to trial-wise decisions, respectively, showed significant Group × Frame interactions: High PCL scorers held more rigid beliefs about the adviser’s fidelity across both experimental frames and relied less on advice in situational frames than low scorers. These results suggest that PD tendencies are associated with rigid beliefs and prevent adaptive use of social information in “safe” contexts. This supports previous proposals of a link between PD and aberrant social inference. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Scopus Subject Areas:Health Sciences > Psychiatry and Mental Health
Life Sciences > Biological Psychiatry
Uncontrolled Keywords:Clinical Psychology
Language:English
Date:1 August 2020
Deposited On:08 Jan 2021 12:06
Last Modified:24 Jun 2024 01:40
Publisher:American Psychological Association
ISSN:0021-843X
OA Status:Closed
Publisher DOI:https://doi.org/10.1037/abn0000500
Project Information:
  • : FunderSNSF
  • : Grant IDPZ00P3_167952
  • : Project TitleNeurocomputational Modelling of Delusions and its Clinical Utility for Psychosis
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