 The computational relationship between reinforcement learning, social inference, and paranoiaJoseph M Barnby, Mitul A Mehta and Michael Moutoussis PLOS Computational Biology, 2022, vol. 18, issue 7, 1-26 Abstract: Theoretical accounts suggest heightened uncertainty about the state of the world underpin aberrant belief updates, which in turn increase the risk of developing a persecutory delusion. However, this raises the question as to how an agent’s uncertainty may relate to the precise phenomenology of paranoia, as opposed to other qualitatively different forms of belief. We tested whether the same population (n = 693) responded similarly to non-social and social contingency changes in a probabilistic reversal learning task and a modified repeated reversal Dictator game, and the impact of paranoia on both. We fitted computational models that included closely related parameters that quantified the rigidity across contingency reversals and the uncertainty about the environment/partner. Consistent with prior work we show that paranoia was associated with uncertainty around a partner’s behavioural policy and rigidity in harmful intent attributions in the social task. In the non-social task we found that pre-existing paranoia was associated with larger decision temperatures and commitment to suboptimal cards. We show relationships between decision temperature in the non-social task and priors over harmful intent attributions and uncertainty over beliefs about partners in the social task. Our results converge across both classes of model, suggesting paranoia is associated with a general uncertainty over the state of the world (and agents within it) that takes longer to resolve, although we demonstrate that this uncertainty is expressed asymmetrically in social contexts. Our model and data allow the representation of sociocognitive mechanisms that explain persecutory delusions and provide testable, phenomenologically relevant predictions for causal experiments.Author summary: Responding to shifts in inanimate and social environments is important for adaptation and appropriate communication. Studies have demonstrated generic cognitive distortions to the processing of information in shifting contexts to underpin or accompany the development of symptoms of severe mental disorders, such as persecutory delusions. However, given the clear social phenomenology and clinical needs regarding social function which accompany persecutory delusions, explanations that detail how changes in generic cognition dovetail with social cognition are urgently needed. We addressed this gap by measuring the relationship between computational mechanisms governing non-social decision making and social inferences upon reversal of task contingencies, and the impact of pre-existing paranoia. We found that paranoia was related to uncertainty in both non-social and social contexts, and crucially, increased non-social uncertainty was related to changes in sociocognitive parameters. Paranoia was related to context-dependent, asymmetric biases in prior beliefs and belief-updating in social contexts. Importantly, paranoia increased the propensity to explain behaviour shifting away from beliefs about harm intent through alternative attributions. Our model and data bridges non-social and social theory explaining persecutory delusions and provides a mechanistic, phenomenologically relevant framework for causal experiments. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1010326 DOI: 10.1371/journal.pcbi.1010326 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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