Reduction in social learning and increased policy uncertainty about harmful intent is associated with pre-existing paranoid beliefs: evidence from modelling a modified serial dictator game.

Abstract

<p>Current computational models suggest that paranoia may be explained by stronger higher-order beliefs about others and increased sensitivity to environments. However, it is unclear whether this applies to social contexts, and whether it is specific to harmful intent attributions, the live expression of paranoia. We sought to fill this gap this by fitting a computational model to data (n = 1754) from a modified serial dictator game, to explore whether pre-existing paranoia could be accounted by specific alterations to cognitive parameters characterising harmful intent attributions. We constructed a ‘Bayesian brain’ model of others’ intent, which we fitted to harmful intent and self-interest attributions made over 18 trials, across three different partners. We found that pre-existing paranoia was associated with greater uncertainty about other’s actions. It moderated the relationship between learning rates and harmful intent attributions, making harmful intent attributions less reliant on prior interactions. Overall, the magnitude of harmful intent attributions was directly related to their uncertainty, and importantly, the opposite was true for self-interest attributions. Our results explain how pre-existing paranoia may be the result of an increased need to attend to immediate experiences in determining intentional threat, at the expense of what is already known, and more broadly, they suggest that environments that induce greater probabilities of harmful intent attributions may also induce states of uncertainty, potentially as an adaptive mechanism to better detect threatening others. Importantly, we suggest that if paranoia were able to be explained exclusively by core domain-general alterations we wouldn’t observe differential parameter estimates underlying harmful-intent and self-interest attributions.</p>

Knowing me, knowing you: Interpersonal similarity improves predictive accuracy and reduces attributions of harmful intent

“…Specifically, more paranoid people were more likely to make competitive decisions in Phase 1 of our experiment and attributed more harmful intent to their partners in Phase 2 (replicating Barnby et al 2020a;Greenburgh et al 2019;Raihani & Bell 2017, Saalfeld et al 2018). Due to this increased baseline similarity and perception of the harmful intentions of others, more paranoid participants should have been more accurate in predicting the decisions of competitive partners in Phase 2.…”

“…This is particularly apparent in psychiatric disorders, many of which involve distressing changes in inferences about the social orientations of others. For example, persecutory beliefs are associated with biases in social impression formation (Barnby et al, 2020a;Diaconescu et al, 2020;Lincoln et al, 2010;Raihani & Bell 2017;Saalfeld et al 2018;Wellstein et al, 2020), and a defining feature of paranoia is an exaggerated belief that harm will occur, and that other people intend for it to happen (Freeman & Garety, 2000). In experimental settings, more paranoid people attribute more harmful intentions to others, including in scenarios where the partner's true intentions are ambiguous (Barnby et al 2020b;Greenburgh et al 2019;Raihani & Bell 2017;Saalfeld et al 2018).…”

The computational relationship between reinforcement learning, social inference, and paranoia

“…After comparing original belief-based (Barnby et al, 2020a), extended belief-based (Figure 2), and associative (Text S1) social attribution models (see methods), we found the extended belief-based social attribution model fitted the data best. We were able to recapitulate observed data with our winning model (see Figure S7) and recovered our parameters very well (Figure S11).…”

“…We used conceptually similar probabilistic social and non-social tasks in the same large population to detect such key cognitive differences. Building on previous work (Barnby et al, 2020a), we built separate computational models to capture behavioural (choice) and inferential differences within each task. Each model quantified decision/inferential uncertainty as precision in the agent's decision making, or precision of an agent's beliefs about how closely their partner's decisions reflected their true intent, respectively.…”

What motivates avoidance in paranoia? Three failures to find a betrayal aversion effect

“…Martin and Penn, 2001) as well as recent computational results suggesting a hypersensitivity to social information in psychiatric disorders where paranoia is a common feature (Henco et al, 2020). We note this computational study tested probabilistic reward learning and therefore employed a vastly different task design to the present study, however another computational study employing a game theoretical paradigm has similarly suggested that paranoia in the general population involves a greater sensitivity to current social context (Barnby et al, 2020).…”