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Paranoia and Belief Updating During the COVID-19 Crisis

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Paranoia and Belief Updating During the COVID-19 Crisis ARTICLES https://doi.org/10.1038/s41562-021-01176-8 Paranoia and belief updating during the COVID-19 crisis Praveen Suthaharan 1,12, Erin J. Reed 2,3,12, Pantelis Leptourgos1, Joshua G. Kenney1, Stefan Uddenberg 4, Christoph D. Mathys 5,6,7,8, Leib Litman9, Jonathan Robinson9, Aaron J. Moss 9, Jane R. Taylor1,10, Stephanie M. Groman1 and Philip R. Corlett 1,10,11 ✉ The COVID-19 pandemic has made the world seem less predictable. Such crises can lead people to feel that others are a threat. Here, we show that the initial phase of the pandemic in 2020 increased individuals’ paranoia and made their belief updating more erratic. A proactive lockdown made people’s belief updating less capricious. However, state-mandated mask-wearing increased paranoia and induced more erratic behaviour. This was most evident in states where adherence to mask-wearing rules was poor but where rule following is typically more common. Computational analyses of participant behaviour suggested that people with higher paranoia expected the task to be more unstable. People who were more paranoid endorsed conspiracies about mask-wearing and potential vaccines and the QAnon conspiracy theories. These beliefs were associated with erratic task behaviour and changed priors. Taken together, we found that real-world uncertainty increases paranoia and influences labora- tory task behaviour. rises, from terrorist attacks1 to viral pandemics, are fer- between options with different reward probabilities to learn the best tile grounds for paranoia2, the belief that others bear mali- option (Fig. 1b)7. The best option changed and, part way through cious intent towards us. Paranoia may be driven by altered the task, the underlying probabilities became more difficult to dis- C 3 social inferences or by domain-general mechanisms for process- tinguish, increasing unexpected uncertainty and blurring the dis- ing uncertainty4,5. The COVID-19 pandemic increased real-world tinction between probabilistic errors and errors that signified a shift uncertainty and provided an unprecedented opportunity to track in the underlying contingencies. Participants were forewarned that the impact of an unfolding crisis on human beliefs. the best option may change but not when or how often7. Hence, the We examined self-rated paranoia6 alongside social and task assayed belief formation and updating under uncertainty7. The non-social belief updating in computer-based tasks (Fig. 1a) span- challenge was to harbour beliefs that are robust to noise but sensi- ning three time periods: before the pandemic lockdown; during tive to real contingency changes7. lockdown; and into reopening. We further explored the impact Before the pandemic, people who were more paranoid (scoring of state-level pandemic responses on beliefs and behaviour. We in the clinical range on standard scales6,8) were more likely to switch hypothesized that paranoia would increase during the pandemic, their choices between options, even after positive feedback5. We perhaps driven by the need to explain and understand real-world compared those data (gathered via the Amazon Mechanical Turk volatility1. Furthermore, we expected that real-world volatility Marketplace in the USA between December 2017 and August 2018; would change individuals’ sensitivity to task-based volatility, caus- Supplementary Table 1) to a new task version with identical contin- ing them to update their beliefs in a computerized task accordingly5. gencies but framed socially (Fig. 1a). Instead of selecting between Finally, since different states responded more or less vigorously to decks of cards (‘non-social task’), participants (Supplementary Table the pandemic and the residents of those states complied with those 1) chose between three potential collaborators who might increase policies differently, we expected that efforts to quell the pandemic or decrease their score. These data were gathered during January would change perceived real-world volatility and thus paranoid 2020, before the World Health Organization declared a global pan- ideation and task-based belief updating. We did not preregister our demic. Participants with higher paranoia switched more frequently experiments. Our interests evolved as the pandemic did. We chose than participants with low paranoia after receiving positive feed- to continue gathering data on participants’ belief updating and back in both; however, there were no substantial behavioural dif- leverage publicly available data in an effort to explore and explain ferences between tasks (Supplementary Fig. 2a; win-switch rate: 2 the differences we observed. F(1, 198) = 0.918, P = 0.339, ηP = 0.0009, BF10 = 1.07; anecdotal evi- dence for null hypothesis of no difference between tasks lose-stay 2 Results rate: F(1, 198) = 3.121, P = 0.08, ηp = 0.002, BF10 = 3.24; moderate evi- Relating paranoia to task-derived belief updating. We admin- dence for the alternative hypothesis, a difference between tasks; istered a probabilistic reversal learning task. Participants chose Supplementary Fig. 2b). There were also no differences in points 1Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, CT, USA. 2Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA. 3Yale MD-PhD Program, Yale School of Medicine, New Haven, CT, USA. 4Booth School of Business, University of Chicago, Chicago, IL, USA. 5Interacting Minds Center, Aarhus University, Aarhus, Denmark. 6Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich, Zurich, Switzerland. 7Swiss Federal Institute of Technology Zurich, Zurich, Switzerland. 8Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy. 9CloudResearch, New York, NY, USA. 10Department of Psychology, Yale University, New Haven, CT, USA. 11Wu Tsai Institute, Yale University, New Haven, CT, USA. 12These authors contributed equally: Praveen Suthaharan, Erin J. Reed. ✉e-mail: [email protected] Nature HUMAN Behaviour | www.nature.com/nathumbehav ARTICLES NATURE HUMAN BEHAVIOUR a 100 90 80 70 60 50 40 30 20 Reward probability (%) 10 1 80 160 b Initial beliefs Meta-volatility ω3 0 µ3 X3,t X3,t + 1 Volatility X2-X3 coupling K Tonic volatility 0 µ2 ω2 X2,t X2,t + 1 Reward probabilities X1,t X1,t + 1 Win or lose Decision (t) model –µ β = e 3 Stay or switch Fig. 1 | Probabilistic reversal learning and hierarchical Gaussian filter. Depictions of our behavioural tasks and computational model used to ascertain belief-updating behaviour. a, Non-social and social task stimuli and reward contingency schedule. b, Hierarchical model for capturing changes in beliefs under task environment volatility. (BF10 = 0.163, strong evidence for the null hypothesis) or reversals Computational modelling. Probabilistic reversal learning involves achieved (BF10 = 0.210, strong evidence for the null hypothesis) decision-making under uncertainty. The reasons for decisions may between social and non-social tasks. not be manifest in simple counts of choices or errors. By modelling Nature HUMAN Behaviour | www.nature.com/nathumbehav NATURE HUMAN BEHAVIOUR ARTICLES participants’ choices, we could estimate latent processes9. We sup- parameters that did not differ by the social or non-social framing of posed that they continually updated a probabilistic representation the task. People with higher paranoia expected more volatility and of the task (a generative model), which guided their behaviour10,11. reward initially, had a higher learning rate for unexpected events To estimate their generative models, we identified: (1) a set of prior assumptions about how events are caused by the environment a b (the perceptual model); and (2) the behavioural consequences of their posterior beliefs about options and outcomes (the response B model10,11). Inverting the response model also entailed inverting the perceptual model and yielded a mapping from task cues to the 10,11 beliefs that caused participants’ responses (Fig. 1b). 2 The perceptual model (Fig. 1b) consists of three hierarchical lay- 1 ers of belief about the task, represented as probability distributions 1 that encode belief content and uncertainty: (1) reward belief (what 3 0 was the outcome?); (2) contingency beliefs (what are the current val- ω 0 –1 ues of the options (decks/collaborators)?); and (3) volatility beliefs –1 (how do option values change over time?). Each layer updates the –2 layer above it in light of evolving experiences, which engender pre- –2 diction errors and drive learning proportionally to current variance. Each belief layer has an initial mean �0, which for simplicity we refer Low High Low High to as the prior belief, although strictly speaking the prior belief is 0 0 the Gaussian distribution with mean � and variance σ . �2 and �3 encode the evolution rate of the environment at the correspond- Volatility belief 1 ing level (contingencies and volatility). Higher values imply a more rapid tonic level of change. The higher the expected uncertainty 0 0 (that is, ‘I expect variable outcomes’), the less surprising an atypical –1 0 outcome may be and the less it drives belief updates (‘this variation 3 µ –2 –2 is normal’). � captures sensitivity to perceived phasic or unexpected changes in the task and underwrites perceived change in the under- –3 lying statistics of the environment (that is, ‘the world is changing’), –4 –4 which may call for more wholesale belief revision. The layers of beliefs are fed through a sigmoid response function (Fig. 1b). We Low High Low High made the
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