Hierarchical bayesian models of social inference for probing persecutory delusional ideation

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 x 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.