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Barnby JM, Haslbeck JMB, Rosen C, Sharma R, Harrow M. Modelling the longitudinal dynamics of paranoia in psychosis: A temporal network analysis over 20 years. Schizophr Res 2024; 270:465-475. [PMID: 38996524 DOI: 10.1016/j.schres.2024.06.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 06/28/2024] [Accepted: 06/28/2024] [Indexed: 07/14/2024]
Abstract
BACKGROUND Paranoia is a key feature of psychosis that can be highly debilitating. Theories of paranoia mostly interface with short-scale or cross-sectional data models, leaving the longitudinal course of paranoia underspecified. METHODS We develop an empirical characterisation of two aspects of paranoia - persecutory and referential delusions - in individuals with psychosis over 20 years. We examine delusional dynamics by applying a Graphical Vector Autoregression Model to data collected from the Chicago Follow-up Study (n = 135 with a range of psychosis-spectrum diagnoses). We adjusted for age, sex, IQ, and antipsychotic use. RESULTS We found that referential and persecutory delusions are central themes, supported by other primary delusions, and are strongly autoregressive - the presence of referential and persecutory delusions is predictive of their future occurrence. In a second analysis we demonstrate that social factors influence the severity of referential, but not persecutory, delusions. IMPLICATIONS We suggest that persecutory delusions represent central, resistant states in the cognitive landscape, whereas referential beliefs are more flexible, offering an important window of opportunity for intervention. Our data models can be collated with prior biological, computational, and social work to contribute toward a more complete theory of paranoia and provide more time-dependent evidence for optimal treatment targets.
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Affiliation(s)
- J M Barnby
- Social Computation and Representation Lab, Department of Psychology, Royal Holloway, University of London, London, UK; Cultural and Social Neuroscience Group, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, University of London, London, UK.
| | - J M B Haslbeck
- Department of Clinical Psychological Science, Maastricht University, the Netherlands; Department of Psychological Methods, University of Amsterdam, the Netherlands
| | - C Rosen
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - R Sharma
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - M Harrow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
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Diaconescu AO, Karvelis P, Hauke DJ. Rethinking interpersonal judgments: dopamine antagonists impact attributional dynamics. Trends Cogn Sci 2024; 28:693-694. [PMID: 38797602 DOI: 10.1016/j.tics.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024]
Abstract
Barnby et al. investigated the effects of haloperidol, a D2/D3 dopamine antagonist, on social attributions. Using computational modeling, they demonstrate that haloperidol increases belief flexibility, reducing paranoia-like interpretations by enhancing sensitivity to social context and reducing self-relevant perspective taking, offering a mechanistic explanation for its therapeutic potential in schizophrenia.
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Affiliation(s)
- Andreea O Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada.
| | - Povilas Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
| | - Daniel J Hauke
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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Lau IHW, Norman J, Stothard M, Carlisi CO, Moutoussis M. Jumping to attributions during social evaluation. Sci Rep 2024; 14:15447. [PMID: 38965391 PMCID: PMC11224235 DOI: 10.1038/s41598-024-65704-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 06/24/2024] [Indexed: 07/06/2024] Open
Abstract
Social learning is crucial for human relationships and well-being. Self- and other- evaluations are universal experiences, playing key roles in many psychiatric disorders, particularly anxiety and depression. We aimed to deepen our understanding of the computational mechanisms behind social learning, which have been implicated in internalizing conditions like anxiety and depression. We built on prior work based on the Social Evaluation Learning Task (SELT) and introduced a new computational model to better explain rapid initial inferences and progressive refinement during serial social evaluations. The Social Evaluation Learning Task-Revised (SELT-R) was improved by stakeholder input, making it more engaging and suitable for adolescents. A sample of 130 adults from the UK completed the SELT-R and questionnaires assessing symptoms of depression and anxiety. 'Classify-refine' computational models were compared with previously successful Bayesian models. The 'classify-refine' models performed better, providing insight into how people infer the attributes and motives of others. Parameters of the best fitting model from the SELT-R were correlated with Anxiety factor scores, with higher symptoms associated with greater decision noise and higher (less flexible) policy certainty. Our results replicate findings regarding the classify-refine process and set the stage for future investigations into the cognitive mechanisms of self and other evaluations in internalizing disorders.
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Affiliation(s)
- Isabel H W Lau
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Institute of Cognitive Neuroscience, University College London, London, UK
- Division of Psychology and Language Science, University College London, London, UK
| | - Jessica Norman
- Division of Psychology and Language Science, University College London, London, UK
| | - Melanie Stothard
- Department of Imaging Neuroscience, University College London, London, UK
| | - Christina O Carlisi
- Division of Psychology and Language Science, University College London, London, UK.
| | - Michael Moutoussis
- Department of Imaging Neuroscience, University College London, London, UK
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Quillien T, Tooby J, Cosmides L. Rational inferences about social valuation. Cognition 2023; 239:105566. [PMID: 37499313 DOI: 10.1016/j.cognition.2023.105566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/20/2023] [Accepted: 07/15/2023] [Indexed: 07/29/2023]
Abstract
The decisions made by other people can contain information about the value they assign to our welfare-for example how much they are willing to sacrifice to make us better off. An emerging body of research suggests that we extract and use this information, responding more favorably to those who sacrifice more even if they provide us with less. The magnitude of their trade-offs governs our social responses to them-including partner choice, giving, and anger. This implies that people have well-designed cognitive mechanisms for estimating the weight someone else assigns to their welfare, even when the amounts at stake vary and the information is noisy or sparse. We tested this hypothesis in two studies (N=200; US samples) by asking participants to observe a partner make two trade-offs, and then predict the partner's decisions in other trials. Their predictions were compared to those of a model that uses statistically optimal procedures, operationalized as a Bayesian ideal observer. As predicted, (i) the estimates people made from sparse evidence matched those of the ideal observer, and (ii) lower welfare trade-offs elicited more anger from participants, even when their total payoffs were held constant. These results support the view that people efficiently update their representations of how much others value them. They also provide the most direct test to date of a key assumption of the recalibrational theory of anger: that anger is triggered by cues of low valuation, not by the infliction of costs.
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Affiliation(s)
- Tadeg Quillien
- Center for Evolutionary Psychology, University of California, Santa Barbara, United States of America; Department of Psychological & Brain Sciences, University of California, Santa Barbara, United States of America.
| | - John Tooby
- Center for Evolutionary Psychology, University of California, Santa Barbara, United States of America; Department of Anthropology, University of California, Santa Barbara, United States of America
| | - Leda Cosmides
- Center for Evolutionary Psychology, University of California, Santa Barbara, United States of America; Department of Psychological & Brain Sciences, University of California, Santa Barbara, United States of America
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Barnby JM, Dayan P, Bell V. Formalising social representation to explain psychiatric symptoms. Trends Cogn Sci 2023; 27:317-332. [PMID: 36609016 DOI: 10.1016/j.tics.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 01/06/2023]
Abstract
Recent work in social cognition has moved beyond a focus on how people process social rewards to examine how healthy people represent other agents and how this is altered in psychiatric disorders. However, formal modelling of social representation has not kept pace with these changes, impeding our understanding of how core aspects of social cognition function, and fail, in psychopathology. Here, we suggest that belief-based computational models provide a basis for an integrated sociocognitive approach to psychiatry, with the potential to address important but unexamined pathologies of social representation, such as maladaptive schemas and illusory social agents.
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Affiliation(s)
- Joseph M Barnby
- Social Computation and Cognitive Representation Lab, Department of Psychology, Royal Holloway, University of London, Egham TW20 0EX, UK.
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany; University of Tübingen, Tübingen, 72074, Germany
| | - Vaughan Bell
- Clinical, Educational, and Health Psychology, University College London, London WC1E 7HB, UK; South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
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Barnby J, Raihani N, Dayan P. Knowing me, knowing you: Interpersonal similarity improves predictive accuracy and reduces attributions of harmful intent. Cognition 2022; 225:105098. [DOI: 10.1016/j.cognition.2022.105098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 02/23/2022] [Accepted: 03/15/2022] [Indexed: 11/03/2022]
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Adams RA, Vincent P, Benrimoh D, Friston KJ, Parr T. Everything is connected: Inference and attractors in delusions. Schizophr Res 2022; 245:5-22. [PMID: 34384664 PMCID: PMC9241990 DOI: 10.1016/j.schres.2021.07.032] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 07/21/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023]
Abstract
Delusions are, by popular definition, false beliefs that are held with certainty and resistant to contradictory evidence. They seem at odds with the notion that the brain at least approximates Bayesian inference. This is especially the case in schizophrenia, a disorder thought to relate to decreased - rather than increased - certainty in the brain's model of the world. We use an active inference Markov decision process model (a Bayes-optimal decision-making agent) to perform a simple task involving social and non-social inferences. We show that even moderate changes in some model parameters - decreasing confidence in sensory input and increasing confidence in states implied by its own (especially habitual) actions - can lead to delusions as defined above. Incorporating affect in the model increases delusions, specifically in the social domain. The model also reproduces some classic psychological effects, including choice-induced preference change, and an optimism bias in inferences about oneself. A key observation is that no change in a single parameter is both necessary and sufficient for delusions; rather, delusions arise due to conditional dependencies that create 'basins of attraction' which trap Bayesian beliefs. Simulating the effects of antidopaminergic antipsychotics - by reducing the model's confidence in its actions - demonstrates that the model can escape from these attractors, through this synthetic pharmacotherapy.
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Affiliation(s)
- Rick A Adams
- Centre for Medical Image Computing, Dept of Computer Science, University College London, 90 High Holborn, London WC1V 6LJ, UK; Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, Russell Square House, 10-12 Russell Square, London WC1B 5EH, UK.
| | - Peter Vincent
- Sainsbury Wellcome Centre, University College London, 25 Howland St, London W1T 4JG, UK
| | - David Benrimoh
- Department of Psychiatry, McGill University, H3G 1A4 QC, Canada
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London WC1N 3AR, UK
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Barnby JM, Mehta MA, Moutoussis M. The computational relationship between reinforcement learning, social inference, and paranoia. PLoS Comput Biol 2022; 18:e1010326. [PMID: 35877675 PMCID: PMC9352206 DOI: 10.1371/journal.pcbi.1010326] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/04/2022] [Accepted: 06/23/2022] [Indexed: 11/18/2022] Open
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.
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Affiliation(s)
- Joseph M. Barnby
- Department of Psychology, Royal Holloway, University of London, London, United Kingdom
- Cultural and Social Neuroscience Group, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, University of London, London, United Kingdom
- Neuropharmacology Group, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, University of London, London, United Kingdom
| | - Mitul A. Mehta
- Cultural and Social Neuroscience Group, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, University of London, London, United Kingdom
- Neuropharmacology Group, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, University of London, London, United Kingdom
| | - Michael Moutoussis
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- Max-Planck–UCL Centre for Computational Psychiatry and Ageing, University College London, London, United Kingdom
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Vass Á, Polner B. Paranoia and game theory: Altered interpersonal functioning through the lens of interactive games. Schizophr Res 2022; 241:116-118. [PMID: 35121435 DOI: 10.1016/j.schres.2022.01.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 01/13/2022] [Accepted: 01/16/2022] [Indexed: 10/19/2022]
Affiliation(s)
- Ágota Vass
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary; Department of Cognitive Science, Budapest University of Technology and Economics, Budapest, Hungary.
| | - Bertalan Polner
- Department of Cognitive Science, Budapest University of Technology and Economics, Budapest, Hungary
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Greenburgh A, Barnby JM, Delpech R, Kenny A, Bell V, Raihani N. What motivates avoidance in paranoia? Three failures to find a betrayal aversion effect. JOURNAL OF EXPERIMENTAL SOCIAL PSYCHOLOGY 2021. [DOI: 10.1016/j.jesp.2021.104206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Rossi-Goldthorpe RA, Leong YC, Leptourgos P, Corlett PR. Paranoia, self-deception and overconfidence. PLoS Comput Biol 2021; 17:e1009453. [PMID: 34618805 PMCID: PMC8525769 DOI: 10.1371/journal.pcbi.1009453] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 10/19/2021] [Accepted: 09/15/2021] [Indexed: 02/04/2023] Open
Abstract
Self-deception, paranoia, and overconfidence involve misbeliefs about the self, others, and world. They are often considered mistaken. Here we explore whether they might be adaptive, and further, whether they might be explicable in Bayesian terms. We administered a difficult perceptual judgment task with and without social influence (suggestions from a cooperating or competing partner). Crucially, the social influence was uninformative. We found that participants heeded the suggestions most under the most uncertain conditions and that they did so with high confidence, particularly if they were more paranoid. Model fitting to participant behavior revealed that their prior beliefs changed depending on whether the partner was a collaborator or competitor, however, those beliefs did not differ as a function of paranoia. Instead, paranoia, self-deception, and overconfidence were associated with participants' perceived instability of their own performance. These data are consistent with the idea that self-deception, paranoia, and overconfidence flourish under uncertainty, and have their roots in low self-esteem, rather than excessive social concern. The model suggests that spurious beliefs can have value-self-deception is irrational yet can facilitate optimal behavior. This occurs even at the expense of monetary rewards, perhaps explaining why self-deception and paranoia contribute to costly decisions which can spark financial crashes and devastating wars.
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Affiliation(s)
- Rosa A. Rossi-Goldthorpe
- Department of Psychiatry, Yale University, New Haven, Connecticut, United States of America
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, United States of America
| | - Yuan Chang Leong
- Department of Psychology, University of Chicago, Chicago, Illinois, United States of America
| | - Pantelis Leptourgos
- Department of Psychiatry, Yale University, New Haven, Connecticut, United States of America
| | - Philip R. Corlett
- Department of Psychiatry, Yale University, New Haven, Connecticut, United States of America
- Wu Tsai Institute, Yale University, New Haven, Connecticut, United States of America
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