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Mikus N, Lamm C, Mathys C. Computational phenotyping of aberrant belief updating in individuals with schizotypal traits and schizophrenia. Biol Psychiatry 2024:S0006-3223(24)01554-3. [PMID: 39218138 DOI: 10.1016/j.biopsych.2024.08.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 08/10/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Psychotic experiences are thought to emerge from various interrelated patterns of disrupted belief updating, such as overestimating the reliability of sensory information and misjudging task volatility. Yet, these substrates have never been jointly addressed under one computational framework and it is not clear to what degree they reflect trait-like computational patterns. METHODS We introduced a novel hierarchical Bayesian model that describes how individuals simultaneously update their beliefs about the task volatility and noise in observation. We applied this model to data from a modified Predictive inference task in a test-retest study with healthy volunteers (N=45, 4 sessions) and examined the relationship between model parameters and schizotypal traits in a larger online sample (N = 437) and in a cohort of patients with schizophrenia (N = 100). RESULTS The interclass correlations were moderate to high for model parameters and excellent for averaged belief trajectories and precision-weighted learning rates estimated through hierarchical Bayesian inference. We found that uncertainty about the task volatility was related to schizotypal traits and to positive symptoms in patients, when learning to gain rewards. In contrast, negative symptoms in patients were associated with more rigid beliefs about observational noise, when learning to avoid losses. CONCLUSION These findings suggest that individuals with schizotypal traits across the psychosis continuum are less likely to learn or utilize higher-order statistical regularities of the environment and showcase the potential of clinically relevant computational phenotypes for differentiating symptom groups in a transdiagnostic manner.
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Affiliation(s)
- Nace Mikus
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Austria; Interacting Minds Centre, Aarhus University, Denmark.
| | - Claus Lamm
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Austria
| | - Christoph Mathys
- Interacting Minds Centre, Aarhus University, Denmark; Translational Neuromodeling Unit, University of Zurich and ETH Zurich, Zurich, Switzerland;; Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy
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2
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Rossi-Goldthorpe R, Silverstein SM, Gold JM, Schiffman J, Waltz JA, Williams TF, Powers AR, Woods SW, Zinbarg RE, Mittal VA, Ellman LM, Strauss GP, Walker EF, Levin JA, Castiello S, Kenney J, Corlett PR. Different learning aberrations relate to delusion-like beliefs with different contents. Brain 2024; 147:2854-2866. [PMID: 38637303 PMCID: PMC11292907 DOI: 10.1093/brain/awae122] [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: 11/28/2023] [Revised: 03/21/2024] [Accepted: 03/24/2024] [Indexed: 04/20/2024] Open
Abstract
The prediction error account of delusions has had success. However, its explanation of delusions with different contents has been lacking. Persecutory delusions and paranoia are the common unfounded beliefs that others have harmful intentions towards us. Other delusions include believing that one's thoughts or actions are under external control or that events in the world have specific personal meaning. We compare learning in two different cognitive tasks, probabilistic reversal learning and Kamin blocking, that have relationships to paranoid and non-paranoid delusion-like beliefs, respectively. We find that clinical high-risk status alone does not result in different behavioural results in the probabilistic reversal learning task but that an individual's level of paranoia is associated with excessive switching behaviour. During the Kamin blocking task, paranoid individuals learned inappropriately about the blocked cue. However, they also had decreased learning about the control cue, suggesting more general learning impairments. Non-paranoid delusion-like belief conviction (but not paranoia) was associated with aberrant learning about the blocked cue but intact learning about the control cue, suggesting specific impairments in learning related to cue combination. We fit task-specific computational models separately to behavioural data to explore how latent parameters vary within individuals between tasks and how they can explain symptom-specific effects. We find that paranoia is associated with low learning rates in the probabilistic reversal learning task and the blocking task. Non-paranoid delusion-like belief conviction is instead related to parameters controlling the degree and direction of similarity between cue updating during simultaneous cue presentation. These results suggest that paranoia and other delusion-like beliefs involve dissociable deficits in learning and belief updating, which, given the transdiagnostic status of paranoia, might have differential utility in predicting psychosis.
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Affiliation(s)
- Rosa Rossi-Goldthorpe
- Interdepartmental Neuroscience Program, Wu Tsai Institute, Yale University, New Haven, CT 06511, USA
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
| | - Steven M Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY 14623, USA
- Department of Neuroscience, University of Rochester Medical Center, Rochester, NY 14623, USA
- Department of Opthalmology, University of Rochester Medical Center, Rochester, NY 14623, USA
| | - James M Gold
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, USA
| | - Jason Schiffman
- Department of Psychological Sciences, University of California Irvine, Irvine, CA 92617, USA
| | - James A Waltz
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, USA
| | - Trevor F Williams
- Department of Psychology, Northwestern University, Evanston, IL 60208-2710, USA
| | - Albert R Powers
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
| | - Richard E Zinbarg
- Department of Psychology, Northwestern University, Evanston, IL 60208-2710, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL 60208-2710, USA
| | - Lauren M Ellman
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA 19122, USA
| | - Gregory P Strauss
- Department of Psychology, University of Georgia, Athens, GA 30602, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA 30322, USA
| | - Jason A Levin
- Department of Psychology, University of Georgia, Athens, GA 30602, USA
| | - Santiago Castiello
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06511, USA
| | - Joshua Kenney
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06511, USA
| | - Philip R Corlett
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06511, USA
- Department of Psychology, Yale University, New Haven, CT 06511, USA
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3
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Liang X, Avram MM, Gibbs-Dean T, Chesney E, Oliver D, Wang S, Obreshkova S, Spencer T, Englund A, Diederen K. Exploring the relationship between frequent cannabis use, belief updating under uncertainty and psychotic-like symptoms. Front Psychiatry 2024; 15:1309868. [PMID: 39114739 PMCID: PMC11304345 DOI: 10.3389/fpsyt.2024.1309868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 06/17/2024] [Indexed: 08/10/2024] Open
Abstract
Background Cannabis users present an important group for investigating putative mechanisms underlying psychosis, as cannabis-use is associated with an increased risk of psychosis. Recent work suggests that alterations in belief-updating under uncertainty underlie psychosis. We therefore compared belief updating under uncertainty between cannabis and non-cannabis users. Methods 49 regular cannabis users and 52 controls completed the Space Game, via an online platform used for behavioral testing. In the task, participants were asked to predict the location of the stimulus based on previous information, under different uncertainty conditions. Mixed effects models were used to identify significant predictors of mean score, confidence, performance error and learning rate. Results Both groups showed decreased confidence in high noise conditions, and increased belief updating in more volatile conditions, suggesting that they could infer the degree and sources of uncertainty. There were no significant effects of group on any of the performance indices. However, within the cannabis group, frequent users showed worse performance than less frequent users. Conclusion Belief updating under uncertainty is not affected by cannabis use status but could be impaired in those who use cannabis more frequently. This finding could show a similarity between frequent cannabis use and psychosis risk, as predictors for abnormal belief-updating.
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Affiliation(s)
- Xinyi Liang
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Maria-Mihaela Avram
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Toni Gibbs-Dean
- School of Medicine, Yale University, New Haven, CT, United States
| | - Edward Chesney
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Dominic Oliver
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, National Institute for Health and Care Research (NIHR) Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Simiao Wang
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Stiliyana Obreshkova
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Tom Spencer
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Amir Englund
- Department of Psychiatry, National Institute for Health and Care Research (NIHR) Oxford Health Biomedical Research Centre, Oxford, United Kingdom
- Addictions Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Kelly Diederen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
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4
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Sheffield JM, Brinen AP, Feola B, Heckers S, Corlett PR. Understanding Cognitive Behavioral Therapy for Psychosis Through the Predictive Coding Framework. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100333. [PMID: 38952435 PMCID: PMC11215207 DOI: 10.1016/j.bpsgos.2024.100333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 05/02/2024] [Accepted: 05/04/2024] [Indexed: 07/03/2024] Open
Abstract
Psychological treatments for persecutory delusions, particularly cognitive behavioral therapy for psychosis, are efficacious; however, mechanistic theories explaining why they work rarely bridge to the level of cognitive neuroscience. Predictive coding, a general brain processing theory rooted in cognitive and computational neuroscience, has increasing experimental support for explaining symptoms of psychosis, including the formation and maintenance of delusions. Here, we describe recent advances in cognitive behavioral therapy for psychosis-based psychotherapy for persecutory delusions, which targets specific psychological processes at the computational level of information processing. We outline how Bayesian learning models employed in predictive coding are superior to simple associative learning models for understanding the impact of cognitive behavioral interventions at the algorithmic level. We review hierarchical predictive coding as an account of belief updating rooted in prediction error signaling. We examine how this process is abnormal in psychotic disorders, garnering noisy sensory data that is made sense of through the development of overly strong delusional priors. We argue that effective cognitive behavioral therapy for psychosis systematically targets the way sensory data are selected, experienced, and interpreted, thus allowing for the strengthening of alternative beliefs. Finally, future directions based on these arguments are discussed.
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Affiliation(s)
- Julia M. Sheffield
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Aaron P. Brinen
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Brandee Feola
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Philip R. Corlett
- Department of Psychiatry, Clinical Neuroscience Research Unit, Yale School of Medicine, New Haven, Connecticut
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5
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Aschieri F, Pascarella G, Milesi A, Giromini L. Effects of Covid-19 Emergency and Associated Lockdown Policies on the Normative Expectations for the Personality Assessment Inventory (PAI) and the Rorschach Performance Assessment System (R-PAS). J Pers Assess 2024; 106:448-458. [PMID: 38084875 DOI: 10.1080/00223891.2023.2289461] [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: 10/23/2022] [Accepted: 11/19/2023] [Indexed: 06/13/2024]
Abstract
Standardized personality tests compare the test taker's scores to those of a large sample of individuals representing normative expectations. However, what is psychologically normal in one historical context may not be similarly normal in another, so the recent spread of a new coronavirus, SARS-Cov-2 (COVID-19), may have implications for what should normally be expected of a nonclinical person taking a personality test shortly after this dramatic event. To address this research question, we administered the Personality Assessment Inventory (PAI) and the Rorschach Performance Assessment System (R-PAS) to 60 nonclinical volunteers from Italy and compared their scores with the official normative reference values of the two tests, which had been established before COVID-19. The results of a series of two-sample t-tests indicated that our newly collected sample appeared somewhat less psychologically healthy compared with normative expectations, and these discrepancies were more pronounced on the PAI than on the R-PAS. Implications and future perspectives are discussed.
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Affiliation(s)
- Filippo Aschieri
- European Center for Therapeutic Assessment, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Giulia Pascarella
- Department of Psychology, Università Cattolica del Sacro Cuore, Brescia, Italy
| | - Aurora Milesi
- Department of Psychology, Università Cattolica del Sacro Cuore, Brescia, Italy
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6
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Suthaharan P, Thompson SL, Rossi-Goldthorpe RA, Rudebeck PH, Walton ME, Chakraborty S, Noonan MP, Costa VD, Murray EA, Mathys CD, Groman SM, Mitchell AS, Taylor JR, Corlett PR, Chang SWC. Lesions to the mediodorsal thalamus, but not orbitofrontal cortex, enhance volatility beliefs linked to paranoia. Cell Rep 2024; 43:114355. [PMID: 38870010 PMCID: PMC11231991 DOI: 10.1016/j.celrep.2024.114355] [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: 11/02/2023] [Revised: 04/13/2024] [Accepted: 05/29/2024] [Indexed: 06/15/2024] Open
Abstract
Beliefs-attitudes toward some state of the environment-guide action selection and should be robust to variability but sensitive to meaningful change. Beliefs about volatility (expectation of change) are associated with paranoia in humans, but the brain regions responsible for volatility beliefs remain unknown. The orbitofrontal cortex (OFC) is central to adaptive behavior, whereas the magnocellular mediodorsal thalamus (MDmc) is essential for arbitrating between perceptions and action policies. We assessed belief updating in a three-choice probabilistic reversal learning task following excitotoxic lesions of the MDmc (n = 3) or OFC (n = 3) and compared performance with that of unoperated monkeys (n = 14). Computational analyses indicated a double dissociation: MDmc, but not OFC, lesions were associated with erratic switching behavior and heightened volatility belief (as in paranoia in humans), whereas OFC, but not MDmc, lesions were associated with increased lose-stay behavior and reward learning rates. Given the consilience across species and models, these results have implications for understanding paranoia.
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Affiliation(s)
- Praveen Suthaharan
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA
| | | | - Rosa A Rossi-Goldthorpe
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA
| | | | - Mark E Walton
- Department of Experimental Psychology, Oxford University, Oxford, UK
| | - Subhojit Chakraborty
- Department of Experimental Psychology, Oxford University, Oxford, UK; NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London EC1V 9EL, UK
| | - Maryann P Noonan
- Department of Experimental Psychology, Oxford University, Oxford, UK; Department of Psychology, University of York, York, UK
| | - Vincent D Costa
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR, USA
| | | | - Christoph D Mathys
- Interacting Minds Centre, Aarhus University, Aarhus, Denmark; Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Stephanie M Groman
- Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Neuroscience, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Anesthesia and Critical Care, University of Chicago, Chicago, IL, USA
| | - Anna S Mitchell
- Department of Experimental Psychology, Oxford University, Oxford, UK; School of Psychology, Speech, and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Jane R Taylor
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Philip R Corlett
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA.
| | - Steve W C Chang
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale University, New Haven, CT, USA.
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7
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McNally GP, Jean-Richard-Dit-Bressel P. A Cognitive Pathway to Persistent, Maladaptive Choice. Eur Addict Res 2024; 30:233-242. [PMID: 38865985 DOI: 10.1159/000538103] [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: 12/18/2023] [Accepted: 02/27/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND Correctly recognising that alcohol or other substances are causing problems is a necessary condition for those problems to spur beneficial behaviour change. Yet such recognition is neither immediate nor straightforward. Recognition that one's alcohol or drug use is causing negative consequences often occurs gradually. Contemporary addiction neuroscience has yet to make progress in understanding and addressing these recognition barriers, despite evidence that a lack of problem recognition is a primary impediment to seeking treatment. SUMMARY Based on our recent empirical work, this article shows how recognition barriers can emerge from dual constraints on how we learn about the negative consequences of our actions. One constraint is imposed by the characteristics of negative consequences themselves. A second constraint is imposed by the characteristics of human cognition and information processing. In some people, the joint action of these constraints causes a lack of correct awareness of the consequences of their behaviour and reduced willingness to update that knowledge and behaviour when confronted with counterevidence. KEY MESSAGES This "cognitive pathway" can drive persistent, maladaptive choice.
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Affiliation(s)
- Gavan P McNally
- School of Psychology, UNSW, Sydney, New South Wales, Australia
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8
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Sloan AF, Kittleson AR, Torregrossa LJ, Feola B, Rossi-Goldthorpe R, Corlett PR, Sheffield JM. Belief Updating, Childhood Maltreatment, and Paranoia in Schizophrenia-Spectrum Disorders. Schizophr Bull 2024:sbae057. [PMID: 38701234 DOI: 10.1093/schbul/sbae057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
BACKGROUND AND HYPOTHESIS Exposure to childhood maltreatment-a risk factor for psychosis is associated with paranoia-may impact one's beliefs about the world and how beliefs are updated. We hypothesized that increased exposure to childhood maltreatment is related to volatility-related belief updating, specifically higher expectations of volatility, and that these relationships are strongest for threat-related maltreatment. Additionally, we tested whether belief updating mediates the relationship between maltreatment and paranoia. STUDY DESIGN Belief updating was measured in 75 patients with schizophrenia-spectrum disorders and 76 nonpsychiatric controls using a 3-option probabilistic reversal learning (3PRL) task. A Hierarchical Gaussian Filter (HGF) was used to estimate computational parameters of belief updating, including prior expectations of volatility (μ03). The Childhood Trauma Questionnaire (CTQ) was used to assess cumulative maltreatment, threat, and deprivation exposure. Paranoia was measured using the Positive and Negative Syndrome Scale (PANSS) and the revised Green et al. Paranoid Thoughts Scale (R-GPTS). RESULTS Greater exposure to childhood maltreatment is associated with higher prior expectations of volatility in the whole sample and in individuals with schizophrenia-spectrum disorders. This was specific to threat-related maltreatment, rather than deprivation, in schizophrenia-spectrum disorders. Paranoia was associated with both exposure to childhood maltreatment and volatility priors, but we did not observe a significant indirect effect of volatility priors on the relationship between maltreatment and paranoia. CONCLUSIONS Our study suggests that individuals with schizophrenia-spectrum disorders who were exposed to threatening experiences during childhood expect their environment to be more volatile, potentially facilitating aberrant belief updating and conferring risk for paranoia.
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Affiliation(s)
- Ali F Sloan
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Andrew R Kittleson
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lénie J Torregrossa
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Brandee Feola
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Philip R Corlett
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Julia M Sheffield
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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9
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Powers A, Angelos P, Bond A, Farina E, Fredericks C, Gandhi J, Greenwald M, Hernandez-Busot G, Hosein G, Kelley M, Mourgues C, Palmer W, Rodriguez-Sanchez J, Seabury R, Toribio S, Vin R, Weleff J, Benrimoh D. A computational account of the development and evolution of psychotic symptoms. ARXIV 2024:arXiv:2404.10954v1. [PMID: 38699166 PMCID: PMC11065053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.
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Affiliation(s)
- Albert Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Philip Angelos
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Alexandria Bond
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Emily Farina
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Carolyn Fredericks
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jay Gandhi
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Maximillian Greenwald
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | | | - Gabriel Hosein
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Megan Kelley
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - William Palmer
- Yale University Department of Psychology, New Haven, CT USA
| | | | - Rashina Seabury
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Silmilly Toribio
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Raina Vin
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jeremy Weleff
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, Canada
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10
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Zhao W, Cannon TD. Moral learning and positive schizotypy: Social cognitive mechanisms in psychosis- proneness. Schizophr Res 2024; 266:156-164. [PMID: 38402656 DOI: 10.1016/j.schres.2024.02.023] [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: 04/06/2023] [Revised: 11/06/2023] [Accepted: 02/17/2024] [Indexed: 02/27/2024]
Abstract
BACKGROUND Delusional thoughts such as paranoia and conspiratorial thinking reflect beliefs in others' intentions to do harm. Given the integral role of harm evaluation in moral cognition, a better understanding of how psychosis-prone individuals process others' moral characters may provide insights into social cognitive mechanisms of these types of delusions. METHODS An online sample of 293 participants was drawn from the general population, using Amazon Mechanical Turk. Participants performed a moral inference task, where they predicted and judged the binary choices of two fictitious agents ("good" or "bad") to impose harm under different levels of financial incentives. An investment game involving the same agents then examined participants' trust behavior. Psychosis-proneness was measured with the Multidimensional Schizotypy Scale Brief Edition. RESULTS A set of multiple regressions showed that positive schizotypy was associated with a lower yet more confident pre-experimental expectation of the agent's moral character, lower prediction accuracy of the agent's harm preferences, less belief revision, and undifferentiated perception of the good and bad agents' characters. Positive schizotypy was also related to higher expectations for reciprocity in the investment game, regardless of agent characters. CONCLUSION Our findings suggest that inflexible beliefs associated with psychosis-proneness extend beyond negative prior expectations, also reflecting difficulties in moral learning. The resulting undifferentiated moral impressions might contribute to undue suspicion of benevolent individuals and increased gullibility to malicious ones, potentially further strengthening conspiratorial beliefs.
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Affiliation(s)
- Wanchen Zhao
- Department of Psychology, Yale University, United States of America.
| | - Tyrone D Cannon
- Department of Psychology, Yale University, United States of America
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11
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Kazinka R, Kwashie AND, Pratt DN, Vilares I, MacDonald AW. Value Representations of Spite Sensitivity in Psychosis on the Minnesota Trust Game. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:429-436. [PMID: 38096987 PMCID: PMC10999326 DOI: 10.1016/j.bpsc.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/20/2023] [Accepted: 11/05/2023] [Indexed: 02/17/2024]
Abstract
BACKGROUND Spite sensitivity provides a valuable construct to understand persecutory ideation and its underlying neural mechanisms. We examined the relationship between persecution and spite sensitivity in psychosis to identify their neural substrates. METHODS In a 3T magnetic resonance imaging scanner, 49 participants with psychosis played the Minnesota Trust Game, in which they decided whether to take a small amount of money or trust a partner to choose between fair and unfair distributions of money. In some conditions, the partner benefited from the unfair option, while in others, the partner lost money. Participants who were untrusting in the second condition (suspiciousness) showed heightened sensitivity to spite. Behavioral measures included mistrust during the 2 conditions of the game, which were compared with Brief Psychiatric Rating Scale persecution and computational modeling. Functional connectivity and blood oxygen level-dependent analyses were also conducted on a priori regions during spite-sensitive decisions. RESULTS Behavioral results replicated previous findings; participants who experienced more persecutory ideation trusted less, specifically in the suspiciousness condition. Functional connectivity findings showed that decreased connectivity between the orbitofrontal cortex-insula and the left frontoparietal network was associated with increased persecutory ideation and estimated spite-guilt (a marker of spite sensitivity). Additionally, we found differences between conditions in caudate nucleus, medial prefrontal cortex, and lateral orbitofrontal cortex activation. CONCLUSIONS These findings provide a new perspective on the origin of positive symptoms by identifying primary brain circuits that are related to both spite sensitivity and persecutory ideation.
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Affiliation(s)
- Rebecca Kazinka
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota; Department of Psychiatry and Behavioral Medicine, University of Minnesota School of Medicine, Minneapolis, Minnesota
| | | | - Danielle N Pratt
- Department of Psychology, Northwestern University, Evanston, Illinois
| | - Iris Vilares
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota
| | - Angus W MacDonald
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota.
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12
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Zhou T, Ho YY, Lee RX, Fath AB, He K, Scott J, Bajwa N, Hartley ND, Wilde J, Gao X, Li C, Hong E, Nassar MR, Wimmer RD, Singh T, Halassa MM, Feng G. Enhancement of mediodorsal thalamus rescues aberrant belief dynamics in a mouse model with schizophrenia-associated mutation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.08.574745. [PMID: 38260581 PMCID: PMC10802391 DOI: 10.1101/2024.01.08.574745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Optimizing behavioral strategy requires belief updating based on new evidence, a process that engages higher cognition. In schizophrenia, aberrant belief dynamics may lead to psychosis, but the mechanisms underlying this process are unknown, in part, due to lack of appropriate animal models and behavior readouts. Here, we address this challenge by taking two synergistic approaches. First, we generate a mouse model bearing patient-derived point mutation in Grin2a (Grin2aY700X+/-), a gene that confers high-risk for schizophrenia and recently identified by large-scale exome sequencing. Second, we develop a computationally trackable foraging task, in which mice form and update belief-driven strategies in a dynamic environment. We found that Grin2aY700X+/- mice perform less optimally than their wild-type (WT) littermates, showing unstable behavioral states and a slower belief update rate. Using functional ultrasound imaging, we identified the mediodorsal (MD) thalamus as hypofunctional in Grin2aY700X+/- mice, and in vivo task recordings showed that MD neurons encoded dynamic values and behavioral states in WT mice. Optogenetic inhibition of MD neurons in WT mice phenocopied Grin2aY700X+/- mice, and enhancing MD activity rescued task deficits in Grin2aY700X+/- mice. Together, our study identifies the MD thalamus as a key node for schizophrenia-relevant cognitive dysfunction, and a potential target for future therapeutics.
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Affiliation(s)
- Tingting Zhou
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Yi-Yun Ho
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Ray X Lee
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Amanda B Fath
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Kathleen He
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Jonathan Scott
- Department of Neuroscience, Tufts University School of Medicine
| | - Navdeep Bajwa
- Department of Neuroscience, Tufts University School of Medicine
| | - Nolan D Hartley
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard
| | - Jonathan Wilde
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Xian Gao
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Cui Li
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Evan Hong
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | | | - Ralf D Wimmer
- Department of Neuroscience, Tufts University School of Medicine
| | - Tarjinder Singh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard
| | | | - Guoping Feng
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard
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13
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Hauke DJ, Charlton CE, Schmidt A, Griffiths JD, Woods SW, Ford JM, Srihari VH, Roth V, Diaconescu AO, Mathalon DH. Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1176-1185. [PMID: 37536567 DOI: 10.1016/j.bpsc.2023.07.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/18/2023] [Accepted: 07/20/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND Mismatch negativity reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals who are at clinical high risk for psychosis (CHR-P). Here, we adopted a computational approach to develop a mechanistic model of mismatch negativity reductions in CHR-P individuals and patients early in the course of schizophrenia. METHODS Electroencephalography was recorded in 38 CHR-P individuals (15 converters), 19 patients early in the course of schizophrenia (≤5 years), and 44 healthy control participants during three different auditory oddball mismatch negativity paradigms including 10% duration, frequency, or double deviants, respectively. We modeled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated electroencephalography amplitudes over sensor space and time. RESULTS Both low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P individuals and patients early in the course of schizophrenia compared with healthy control participants. Moreover, low-level precision-weighted prediction errors were significantly different in CHR-P individuals who later converted to psychosis compared with nonconverters. CONCLUSIONS Our results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model seems to capture pathophysiological mechanisms that are relevant to early psychosis and the risk for future psychosis in CHR-P individuals and may serve as predictive biomarkers and mechanistic targets for the development of novel treatments.
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Affiliation(s)
- Daniel J Hauke
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
| | - Colleen E Charlton
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - André Schmidt
- Department of Psychiatry, University of Basel, Basel, Switzerland
| | - John D Griffiths
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Scott W Woods
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Judith M Ford
- Mental Health Service, Veterans Affairs San Francisco Health Care System, San Francisco, California; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
| | - Vinod H Srihari
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Volker Roth
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Andreea O Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Daniel H Mathalon
- Mental Health Service, Veterans Affairs San Francisco Health Care System, San Francisco, California; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
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14
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Hird EJ, Diederen K, Leucht S, Jensen KB, McGuire P. The Placebo Effect in Psychosis: Why It Matters and How to Measure It. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:605-613. [PMID: 37881581 PMCID: PMC10593894 DOI: 10.1016/j.bpsgos.2023.02.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/04/2022] [Accepted: 02/20/2023] [Indexed: 03/07/2023] Open
Abstract
Psychosis is characterized by unusual percepts and beliefs in the form of hallucinations and delusions. Antipsychotic medication, the primary treatment for psychosis, is often ineffective and accompanied by severe side effects, but research has not identified an effective alternative in several decades. One reason that clinical trials fail is that patients with psychosis tend to show a significant therapeutic response to inert control treatments, known as the placebo effect, which makes it difficult to distinguish drug effects from placebo effects. Conversely, in clinical practice, a strong placebo effect may be useful because it could enhance the overall treatment response. Identifying factors that predict large placebo effects could improve the future outlook of psychosis treatment. Biomarkers of the placebo effect have already been suggested in pain and depression, but not in psychosis. Quantifying markers of the placebo effect would have the potential to predict placebo effects in psychosis clinical trials. Furthermore, the placebo effect and psychosis may represent a shared neurocognitive mechanism in which prior beliefs are weighted against new sensory information to make inferences about reality. Examining this overlap could reveal new insights into the mechanisms underlying psychosis and indicate novel treatment targets. We provide a narrative review of the importance of the placebo effect in psychosis and propose a novel method to assess it.
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Affiliation(s)
- Emily J. Hird
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England
| | - Kelly Diederen
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England
| | - Stefan Leucht
- Department of Psychiatry and Psychotherapy, Technical University of Munich, Munich, Germany
| | - Karin B. Jensen
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Philip McGuire
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England
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15
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Lemonde AC, Iyer SN, Malla A, Rangaswamy T, Padmavati R, Mohan G, Taksal A, Gariepy G, Joober R, Boksa P, Shah JL. Differential Trajectories of Delusional Content and Severity Over 2 Years of Early Intervention for Psychosis: Comparison Between Chennai, India, and Montréal, Canada. Schizophr Bull 2023; 49:1032-1041. [PMID: 36897303 PMCID: PMC10318872 DOI: 10.1093/schbul/sbad007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
BACKGROUND There exist few direct studies of delusional content in psychosis across geo-cultural contexts, especially those in which treatment protocols and measures are comparable. To directly examine an illness outcome that is potentially culturally mediated, this study investigated the baseline presentation and longitudinal trajectory of delusions in first-episode psychosis (FEP) across 2 similar treatment settings in Montréal (Canada) and Chennai (India). STUDY DESIGN Patients entering an early intervention program for FEP in Chennai (N = 168) and Montréal (N = 165) were compared on site-level differences in the presentation of delusions across specific time points over 2 years of treatment. Delusions were measured using the Scale for Assessment of Positive Symptoms. Chi-square and regression analyses were conducted. STUDY RESULTS At baseline, delusions were more frequent in Montréal than in Chennai (93% vs 80%, respectively; X2(1) = 12.36, P < .001). Thematically, delusions of grandiosity, religiosity, and mind reading were more common in Montréal than in Chennai (all P < .001); however, these baseline differences did not persist over time. Regression revealed a significant time-by-site interaction in the longitudinal course of delusions, which differs from the trajectory of other FEP-positive symptom domains. CONCLUSIONS To the best of our knowledge, this is the first direct comparison of delusions in similar programs for FEP across 2 different geo-cultural contexts. Our findings support the notion that delusion themes follow consistent ordinal patterns across continents. Future work is needed to unpack the differences in severity that present at baseline and minor differences in content.
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Affiliation(s)
- Ann-Catherine Lemonde
- Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Montréal, Canada
| | - Srividya N Iyer
- Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Montréal, Canada
| | - Ashok Malla
- Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Montréal, Canada
| | | | | | - Greeshma Mohan
- Schizophrenia Research Foundation (SCARF), Chennai, India
| | - Aarati Taksal
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Montréal, Canada
| | | | - Ridha Joober
- Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Montréal, Canada
| | - Patricia Boksa
- Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Canada
| | - Jai L Shah
- Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Montréal, Canada
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16
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Xia L, Gu R, Lin Y, Qin J, Luo W, Luo YJ. Explaining reversal learning deficits in anxiety with electrophysiological evidence. J Psychiatr Res 2023; 164:270-280. [PMID: 37390622 DOI: 10.1016/j.jpsychires.2023.06.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/21/2023] [Accepted: 06/23/2023] [Indexed: 07/02/2023]
Abstract
Reversal learning is a crucial aspect of behavioral flexibility that plays a significant role in environmental adaptation and development. While previous studies have established a link between anxiety and impaired reversal learning ability, the underlying mechanisms behind this association remain unclear. This study employed a probabilistic reversal learning task with electroencephalographic recording to investigate these mechanisms. Participants were divided into two groups based on their scores on Spielberger's State-Trait Anxiety Inventory: high trait-anxiety (HTA) and low trait-anxiety (LTA), consisting of 50 individuals in each group. The results showed that the HTA group had poorer reversal learning performance than the LTA group, including a lower tendency to shift to the new optimal option after rule reversals (reversal-shift). The study also examined event-related potentials elicited by reversals and found that although the N1 (related to attention allocation), feedback-related negativity (FRN: related to belief updating), and P3 (related to response inhibition) were all sensitive to the grouping factor, only the FRN elicited by reversal-shift mediated the relationship between anxiety and the number/reaction time of reversal-shift. From these findings, we suggest that abnormalities in belief updating may contribute to the impaired reversal learning performance observed in anxious individuals. In our opinion, this study sheds light on potential targets for interventions aimed at improving behavioral flexibility in anxious individuals.
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Affiliation(s)
- Lisheng Xia
- School of Psychology, Guizhou Normal University, Guiyang, 550025, China
| | - Ruolei Gu
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Yongling Lin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Jianqiang Qin
- School of Psychology, Guizhou Normal University, Guiyang, 550025, China
| | - Wenbo Luo
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China; Key Laboratory of Brain and Cognitive Neuroscience, Liaoning Province, Dalian, 116029, China.
| | - Yue-Jia Luo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China; School of Social Development and Management, University of Health and Rehabilitation Sciences, Qingdao, 266113, China
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17
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Sheffield JM, Smith R, Suthaharan P, Leptourgos P, Corlett PR. Relationships between cognitive biases, decision-making, and delusions. Sci Rep 2023; 13:9485. [PMID: 37301915 PMCID: PMC10257713 DOI: 10.1038/s41598-023-36526-1] [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: 09/27/2022] [Accepted: 06/05/2023] [Indexed: 06/12/2023] Open
Abstract
Multiple measures of decision-making under uncertainty (e.g. jumping to conclusions (JTC), bias against disconfirmatory evidence (BADE), win-switch behavior, random exploration) have been associated with delusional thinking in independent studies. Yet, it is unknown whether these variables explain shared or unique variance in delusional thinking, and whether these relationships are specific to paranoia or delusional ideation more broadly. Additionally, the underlying computational mechanisms require further investigation. To investigate these questions, task and self-report data were collected in 88 individuals (46 healthy controls, 42 schizophrenia-spectrum) and included measures of cognitive biases and behavior on probabilistic reversal learning and explore/exploit tasks. Of those, only win-switch rate significantly differed between groups. In regression, reversal learning performance, random exploration, and poor evidence integration during BADE showed significant, independent associations with paranoia. Only self-reported JTC was associated with delusional ideation, controlling for paranoia. Computational parameters increased the proportion of variance explained in paranoia. Overall, decision-making influenced by strong volatility and variability is specifically associated with paranoia, whereas self-reported hasty decision-making is specifically associated with other themes of delusional ideation. These aspects of decision-making under uncertainty may therefore represent distinct cognitive processes that, together, have the potential to worsen delusional thinking across the psychosis spectrum.
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Affiliation(s)
- Julia M Sheffield
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1601 23rd Ave S, Nashville, TN, 37209, USA.
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, USA
| | | | - Pantelis Leptourgos
- Department of Psychiatry, Yale University, New Haven, USA
- University of Lille, Lille, France
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18
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Buades-Rotger M, Smeijers D, Gallardo-Pujol D, Krämer UM, Brazil IA. Aggressive and psychopathic traits are linked to the acquisition of stable but imprecise hostile expectations. Transl Psychiatry 2023; 13:197. [PMID: 37296151 DOI: 10.1038/s41398-023-02497-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 05/12/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Individuals with hostile expectations (HEX) anticipate harm from seemingly neutral or ambiguous stimuli. However, it is unclear how HEX are acquired, and whether specific components of HEX learning can predict antisocial thought, conduct, and personality. In an online sample of healthy young individuals (n = 256, 69% women), we administered a virtual shooting task and applied computational modelling of behaviour to investigate HEX learning and its constellation of correlates. HEX acquisition was best explained by a hierarchical reinforcement learning mechanism. Crucially, we found that individuals with relatively higher self-reported aggressiveness and psychopathy developed stronger and less accurate hostile beliefs as well as larger prediction errors. Moreover, aggressive and psychopathic traits were associated with more temporally stable hostility representations. Our study thus shows that aggressiveness and psychopathy are linked with the acquisition of robust yet imprecise hostile beliefs through reinforcement learning.
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Affiliation(s)
- Macià Buades-Rotger
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
- Department of Neurology, University of Lübeck, Lübeck, Germany.
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain.
| | - Danique Smeijers
- Division Diagnostics, Research, and Education, Forensic Psychiatric Center Pompestichting, Nijmegen, The Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - David Gallardo-Pujol
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institute of Neurosciences, Barcelona, Spain
| | - Ulrike M Krämer
- Department of Neurology, University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany
| | - Inti A Brazil
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
- Division Diagnostics, Research, and Education, Forensic Psychiatric Center Pompestichting, Nijmegen, The Netherlands.
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19
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Benrimoh D, Fisher V, Mourgues C, Sheldon AD, Smith R, Powers AR. Barriers and solutions to the adoption of translational tools for computational psychiatry. Mol Psychiatry 2023; 28:2189-2196. [PMID: 37280282 PMCID: PMC10611570 DOI: 10.1038/s41380-023-02114-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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/25/2023] [Accepted: 05/05/2023] [Indexed: 06/08/2023]
Abstract
Computational psychiatry is a field aimed at developing formal models of information processing in the human brain, and how alterations in this processing can lead to clinical phenomena. There has been significant progress in the development of tasks and how to model them, presenting an opportunity to incorporate computational psychiatry methodologies into large- scale research projects or into clinical practice. In this viewpoint, we explore some of the barriers to incorporation of computational psychiatry tasks and models into wider mainstream research directions. These barriers include the time required for participants to complete tasks, test-retest reliability, limited ecological validity, as well as practical concerns, such as lack of computational expertise and the expense and large sample sizes traditionally required to validate tasks and models. We then discuss solutions, such as the redesigning of tasks with a view toward feasibility, and the integration of tasks into more ecologically valid and standardized game platforms that can be more easily disseminated. Finally, we provide an example of how one task, the conditioned hallucinations task, might be translated into such a game. It is our hope that interest in the creation of more accessible and feasible computational tasks will help computational methods make more positive impacts on research as well as, eventually, clinical practice.
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Affiliation(s)
- David Benrimoh
- McGill University School of Medicine, Montreal, QC, Canada
| | - Victoria Fisher
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Andrew D Sheldon
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Albert R Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA.
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20
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McNally GP, Jean-Richard-Dit-Bressel P, Millan EZ, Lawrence AJ. Pathways to the persistence of drug use despite its adverse consequences. Mol Psychiatry 2023; 28:2228-2237. [PMID: 36997610 PMCID: PMC10611585 DOI: 10.1038/s41380-023-02040-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/10/2023] [Accepted: 03/15/2023] [Indexed: 04/01/2023]
Abstract
The persistence of drug taking despite its adverse consequences plays a central role in the presentation, diagnosis, and impacts of addiction. Eventual recognition and appraisal of these adverse consequences is central to decisions to reduce or cease use. However, the most appropriate ways of conceptualizing persistence in the face of adverse consequences remain unclear. Here we review evidence that there are at least three pathways to persistent use despite the negative consequences of that use. A cognitive pathway for recognition of adverse consequences, a motivational pathway for valuation of these consequences, and a behavioral pathway for responding to these adverse consequences. These pathways are dynamic, not linear, with multiple possible trajectories between them, and each is sufficient to produce persistence. We describe these pathways, their characteristics, brain cellular and circuit substrates, and we highlight their relevance to different pathways to self- and treatment-guided behavior change.
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Affiliation(s)
- Gavan P McNally
- School of Psychology, UNSW Sydney, Sydney, NSW, 2052, Australia.
| | | | - E Zayra Millan
- School of Psychology, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Andrew J Lawrence
- Florey Institute of Neuroscience and Mental Health, Parkville, VIC, 3010, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, 3010, Australia
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21
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Sharp PB, Fradkin I, Eldar E. Hierarchical inference as a source of human biases. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023; 23:476-490. [PMID: 35725986 DOI: 10.3758/s13415-022-01020-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
The finding that human decision-making is systematically biased continues to have an immense impact on both research and policymaking. Prevailing views ascribe biases to limited computational resources, which require humans to resort to less costly resource-rational heuristics. Here, we propose that many biases in fact arise due to a computationally costly way of coping with uncertainty-namely, hierarchical inference-which by nature incorporates information that can seem irrelevant. We show how, in uncertain situations, Bayesian inference may avail of the environment's hierarchical structure to reduce uncertainty at the cost of introducing bias. We illustrate how this account can explain a range of familiar biases, focusing in detail on the halo effect and on the neglect of base rates. In each case, we show how a hierarchical-inference account takes the characterization of a bias beyond phenomenological description by revealing the computations and assumptions it might reflect. Furthermore, we highlight new predictions entailed by our account concerning factors that could mitigate or exacerbate bias, some of which have already garnered empirical support. We conclude that a hierarchical inference account may inform scientists and policy makers with a richer understanding of the adaptive and maladaptive aspects of human decision-making.
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Affiliation(s)
- Paul B Sharp
- Department of Psychology, Hebrew University of Jerusalem, 9190501, Jerusalem, Israel
- Department of Cognitive and Brain Sciences, Hebrew University of Jerusalem, 9190501, Jerusalem, Israel
| | - Isaac Fradkin
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, WC1B 5EH, UK
- Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK
| | - Eran Eldar
- Department of Psychology, Hebrew University of Jerusalem, 9190501, Jerusalem, Israel.
- Department of Cognitive and Brain Sciences, Hebrew University of Jerusalem, 9190501, Jerusalem, Israel.
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Fromm SP, Wieland L, Klettke A, Nassar MR, Katthagen T, Markett S, Heinz A, Schlagenhauf F. Computational mechanisms of belief updating in relation to psychotic-like experiences. Front Psychiatry 2023; 14:1170168. [PMID: 37215663 PMCID: PMC10196365 DOI: 10.3389/fpsyt.2023.1170168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 04/07/2023] [Indexed: 05/24/2023] Open
Abstract
Introduction Psychotic-like experiences (PLEs) may occur due to changes in weighting prior beliefs and new evidence in the belief updating process. It is still unclear whether the acquisition or integration of stable beliefs is altered, and whether such alteration depends on the level of environmental and belief precision, which reflects the associated uncertainty. This motivated us to investigate uncertainty-related dynamics of belief updating in relation to PLEs using an online study design. Methods We selected a sample (n = 300) of participants who performed a belief updating task with sudden change points and provided self-report questionnaires for PLEs. The task required participants to observe bags dropping from a hidden helicopter, infer its position, and dynamically update their belief about the helicopter's position. Participants could optimize performance by adjusting learning rates according to inferred belief uncertainty (inverse prior precision) and the probability of environmental change points. We used a normative learning model to examine the relationship between adherence to specific model parameters and PLEs. Results PLEs were linked to lower accuracy in tracking the outcome (helicopter location) (β = 0.26 ± 0.11, p = 0.018) and to a smaller increase of belief precision across observations after a change point (β = -0.003 ± 0.0007, p < 0.001). PLEs were related to slower belief updating when participants encountered large prediction errors (β = -0.03 ± 0.009, p = 0.001). Computational modeling suggested that PLEs were associated with reduced overall belief updating in response to prediction errors (βPE = -1.00 ± 0.45, p = 0.028) and reduced modulation of updating at inferred environmental change points (βCPP = -0.84 ± 0.38, p = 0.023). Discussion We conclude that PLEs are associated with altered dynamics of belief updating. These findings support the idea that the process of balancing prior belief and new evidence, as a function of environmental uncertainty, is altered in PLEs, which may contribute to the development of delusions. Specifically, slower learning after large prediction errors in people with high PLEs may result in rigid beliefs. Disregarding environmental change points may limit the flexibility to establish new beliefs in the face of contradictory evidence. The present study fosters a deeper understanding of inferential belief updating mechanisms underlying PLEs.
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Affiliation(s)
- Sophie Pauline Fromm
- Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin Institute of Health CCM, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Lara Wieland
- Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin Institute of Health CCM, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Arne Klettke
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Matthew R. Nassar
- Carney Institute for Brain Science, Brown University, Providence, RI, United States
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Teresa Katthagen
- Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin Institute of Health CCM, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andreas Heinz
- Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin Institute of Health CCM, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Florian Schlagenhauf
- Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin Institute of Health CCM, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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23
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Sandhu TR, Xiao B, Lawson RP. Transdiagnostic computations of uncertainty: towards a new lens on intolerance of uncertainty. Neurosci Biobehav Rev 2023; 148:105123. [PMID: 36914079 DOI: 10.1016/j.neubiorev.2023.105123] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/21/2023] [Accepted: 03/08/2023] [Indexed: 03/13/2023]
Abstract
People radically differ in how they cope with uncertainty. Clinical researchers describe a dispositional characteristic known as "intolerance of uncertainty", a tendency to find uncertainty aversive, reported to be elevated across psychiatric and neurodevelopmental conditions. Concurrently, recent research in computational psychiatry has leveraged theoretical work to characterise individual differences in uncertainty processing. Under this framework, differences in how people estimate different forms of uncertainty can contribute to mental health difficulties. In this review, we briefly outline the concept of intolerance of uncertainty within its clinical context, and we argue that the mechanisms underlying this construct may be further elucidated through modelling how individuals make inferences about uncertainty. We will review the evidence linking psychopathology to different computationally specified forms of uncertainty and consider how these findings might suggest distinct mechanistic routes towards intolerance of uncertainty. We also discuss the implications of this computational approach for behavioural and pharmacological interventions, as well as the importance of different cognitive domains and subjective experiences in studying uncertainty processing.
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Affiliation(s)
- Timothy R Sandhu
- Department of Psychology, Downing Place, University of Cambridge, CB2 3EB, UK; MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 7EF, UK.
| | - Bowen Xiao
- Department of Psychology, Downing Place, University of Cambridge, CB2 3EB, UK
| | - Rebecca P Lawson
- Department of Psychology, Downing Place, University of Cambridge, CB2 3EB, UK; MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 7EF, UK
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24
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McFadyen J, Dolan RJ. Spatiotemporal Precision of Neuroimaging in Psychiatry. Biol Psychiatry 2023; 93:671-680. [PMID: 36376110 DOI: 10.1016/j.biopsych.2022.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/20/2022] [Accepted: 08/12/2022] [Indexed: 12/23/2022]
Abstract
Aberrant patterns of cognition, perception, and behavior seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between spatial and temporal resolutions inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography, often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illnesses such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications that seek to drive a mechanistic understanding of psychopathology and the realization of preclinical translation.
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Affiliation(s)
- Jessica McFadyen
- UCL Max Planck Centre for Computational Psychiatry and Ageing Research and Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Raymond J Dolan
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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25
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Suthaharan P, Corlett PR. Assumed shared belief about conspiracy theories in social networks protects paranoid individuals against distress. Sci Rep 2023; 13:6084. [PMID: 37055504 PMCID: PMC10100615 DOI: 10.1038/s41598-023-33305-w] [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: 10/07/2022] [Accepted: 04/10/2023] [Indexed: 04/15/2023] Open
Abstract
Paranoia is the belief that others intend you harm. It is related to conspiracy theories, wherein those others represent an organized faction, coordinating the harm against self and others, and violating societal norms. Current psychological studies of paranoid conspiracy theorizing focus either on the individual or their broader social network. Likewise, theories of belief formation and updating often contain individual level processes as well as broader interpersonal and organizational factors. Here we examine paranoia and conspiracy theorizing in terms of individual behavioral predictors (performance on a probabilistic reversal learning task which assays belief updating) as well as social sensing: we ask participants to report the features of their social network, including whether their friends and acquaintances share their paranoid conspiratorial beliefs. We find that people who believe paranoid conspiracy theories expect more volatility during the task. They also assume that members of their social network share their paranoid beliefs. Critically, those participants with larger social networks and greater assumed shared belief tend to harbor their conspiratorial beliefs with less emotional distress and expect less volatility in the task. This is evidence that, like political and religious beliefs, conspiracy theories may flourish under a sacred canopy of belief consensus. These data suggest that friends and acquaintances may serve as sources of credulity and moving between them may sustain conspiracy beliefs when there is detraction. This hybrid individual/social account may shed light on clinical paranoia and persecutory delusion, wherein disability is defined normatively, and social supports are fewer.
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Affiliation(s)
- Praveen Suthaharan
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, CT, USA
| | - Philip R Corlett
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA.
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, CT, USA.
- Department of Psychology, Yale University, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
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26
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Gibbs-Dean T, Katthagen T, Tsenkova I, Ali R, Liang X, Spencer T, Diederen K. Belief updating in psychosis, depression and anxiety disorders: A systematic review across computational modelling approaches. Neurosci Biobehav Rev 2023; 147:105087. [PMID: 36791933 DOI: 10.1016/j.neubiorev.2023.105087] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/31/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023]
Abstract
Alterations in belief updating are proposed to underpin symptoms of psychiatric illness, including psychosis, depression, and anxiety. Key parameters underlying belief updating can be captured using computational modelling techniques, aiding the identification of unique and shared deficits, and improving diagnosis and treatment. We systematically reviewed research that applied computational modelling to probabilistic tasks measuring belief updating in stable and volatile (changing) environments, across clinical and subclinical psychosis (n = 17), anxiety (n = 9), depression (n = 9) and transdiagnostic samples (n = 9). Depression disorders related to abnormal belief updating in response to the valence of rewards, evidenced in both stable and volatile environments. Whereas psychosis and anxiety disorders were associated with difficulties adapting to changing contingencies specifically, indicating an inflexibility and/or insensitivity to environmental volatility. Higher-order learning models revealed additional difficulties in the estimation of overall environmental volatility across psychosis disorders, showing increased updating to irrelevant information. These findings stress the importance of investigating belief updating in transdiagnostic samples, using homogeneous experimental and computational modelling approaches.
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Affiliation(s)
- Toni Gibbs-Dean
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Teresa Katthagen
- Department of Psychiatry and Neuroscience CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Iveta Tsenkova
- Psychological Medicine, Institute of Psychiatry, Psychology and neuroscience, King's College London, UK
| | - Rubbia Ali
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Xinyi Liang
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Thomas Spencer
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Kelly Diederen
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
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27
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Kreis I, Zhang L, Mittner M, Syla L, Lamm C, Pfuhl G. Aberrant uncertainty processing is linked to psychotic-like experiences, autistic traits, and is reflected in pupil dilation during probabilistic learning. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023:10.3758/s13415-023-01088-2. [PMID: 36977966 PMCID: PMC10390366 DOI: 10.3758/s13415-023-01088-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/07/2023] [Indexed: 03/30/2023]
Abstract
Aberrant belief updating due to misestimation of uncertainty and an increased perception of the world as volatile (i.e., unstable) has been found in autism and psychotic disorders. Pupil dilation tracks events that warrant belief updating, likely reflecting the adjustment of neural gain. However, whether subclinical autistic or psychotic symptoms affect this adjustment and how they relate to learning in volatile environments remains to be unraveled. We investigated the relationship between behavioral and pupillometric markers of subjective volatility (i.e., experience of the world as unstable), autistic traits, and psychotic-like experiences in 52 neurotypical adults with a probabilistic reversal learning task. Computational modeling revealed that participants with higher psychotic-like experience scores overestimated volatility in low-volatile task periods. This was not the case for participants scoring high on autistic-like traits, who instead showed a diminished adaptation of choice-switching behavior in response to risk. Pupillometric data indicated that individuals with higher autistic- or psychotic-like trait and experience scores differentiated less between events that warrant belief updating and those that do not when volatility was high. These findings are in line with misestimation of uncertainty accounts of psychosis and autism spectrum disorders and indicate that aberrancies are already present at the subclinical level.
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Affiliation(s)
- Isabel Kreis
- Department of Psychology, UiT - The Arctic University of Norway, Tromsø, Norway.
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Lei Zhang
- Department of Psychology, UiT - The Arctic University of Norway, Tromsø, Norway
- Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
- Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, UK
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Matthias Mittner
- Department of Psychology, UiT - The Arctic University of Norway, Tromsø, Norway
| | - Leonard Syla
- Department of Psychology, UiT - The Arctic University of Norway, Tromsø, Norway
| | - Claus Lamm
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
- Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria
| | - Gerit Pfuhl
- Department of Psychology, UiT - The Arctic University of Norway, Tromsø, Norway
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
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28
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Fleming LM, Lemonde AC, Benrimoh D, Gold JM, Taylor JR, Malla A, Joober R, Iyer SN, Lepage M, Shah J, Corlett PR. Using dimensionality-reduction techniques to understand the organization of psychotic symptoms in persistent psychotic illness and first episode psychosis. Sci Rep 2023; 13:4841. [PMID: 36964175 PMCID: PMC10039017 DOI: 10.1038/s41598-023-31909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 03/17/2023] [Indexed: 03/26/2023] Open
Abstract
Psychotic disorders are highly heterogeneous. Understanding relationships between symptoms will be relevant to their underlying pathophysiology. We apply dimensionality-reduction methods across two unique samples to characterize the patterns of symptom organization. We analyzed publicly-available data from 153 participants diagnosed with schizophrenia or schizoaffective disorder (fBIRN Data Repository and the Consortium for Neuropsychiatric Phenomics), as well as 636 first-episode psychosis (FEP) participants from the Prevention and Early Intervention Program for Psychosis (PEPP-Montreal). In all participants, the Scale for the Assessment of Positive Symptoms (SAPS) and Scale for the Assessment of Negative Symptoms (SANS) were collected. Multidimensional scaling (MDS) combined with cluster analysis was applied to SAPS and SANS scores across these two groups of participants. MDS revealed relationships between items of SAPS and SANS. Our application of cluster analysis to these results identified: 1 cluster of disorganization symptoms, 2 clusters of hallucinations/delusions, and 2 SANS clusters (asocial and apathy, speech and affect). Those reality distortion items which were furthest from auditory hallucinations had very weak to no relationship with hallucination severity. Despite being at an earlier stage of illness, symptoms in FEP presentations were similarly organized. While hallucinations and delusions commonly co-occur, we found that their specific themes and content sometimes travel together and sometimes do not. This has important implications, not only for treatment, but also for research-particularly efforts to understand the neurocomputational and pathophysiological mechanism underlying delusions and hallucinations.
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Affiliation(s)
- Leah M Fleming
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Department, Yale University, New Haven, CT, USA
| | | | - David Benrimoh
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
| | - James M Gold
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jane R Taylor
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Ashok Malla
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
- The Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Qubec, Canada
| | - Ridha Joober
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
- The Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Qubec, Canada
| | - Srividya N Iyer
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
- The Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Qubec, Canada
| | - Martin Lepage
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
- The Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Qubec, Canada
| | - Jai Shah
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
- The Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Qubec, Canada
| | - Philip R Corlett
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
- Department of Psychology, Yale University, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
- Connecticut Mental Health Center, 34 Park St, New Haven, CT, 06519, USA.
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29
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Hird EJ, Ohmuro N, Allen P, Moseley P, Kempton MJ, Modinos G, Sachs G, van der Gaag M, de Haan L, Gadelha A, Bressan R, Barrantes-Vidal N, Ruhrmann S, Catalan A, McGuire P. Speech Illusions in People at Clinical High Risk for Psychosis Linked to Clinical Outcome. Schizophr Bull 2023; 49:339-349. [PMID: 36516396 PMCID: PMC10016413 DOI: 10.1093/schbul/sbac163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND HYPOTHESIS Around 20% of people at clinical high risk (CHR) for psychosis later develop a psychotic disorder, but it is difficult to predict who this will be. We assessed the incidence of hearing speech (termed speech illusions [SIs]) in noise in CHR participants and examined whether this was associated with adverse clinical outcomes. STUDY DESIGN At baseline, 344 CHR participants and 67 healthy controls were presented with a computerized white noise task and asked whether they heard speech, and whether speech was neutral, affective, or whether they were uncertain about its valence. After 2 years, we assessed whether participants transitioned to psychosis, or remitted from the CHR state, and their functioning. STUDY RESULTS CHR participants had a lower sensitivity to the task. Logistic regression revealed that a bias towards hearing targets in stimuli was associated with remission status (OR = 0.21, P = 042). Conversely, hearing SIs with uncertain valence at baseline was associated with reduced likelihood of remission (OR = 7.72. P = .007). When we assessed only participants who did not take antipsychotic medication at baseline, the association between hearing SIs with uncertain valence at baseline and remission likelihood remained (OR = 7.61, P = .043) and this variable was additionally associated with a greater likelihood of transition to psychosis (OR = 5.34, P = .029). CONCLUSIONS In CHR individuals, a tendency to hear speech in noise, and uncertainty about the affective valence of this speech, is associated with adverse outcomes. This task could be used in a battery of cognitive markers to stratify CHR participants according to subsequent outcomes.
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Affiliation(s)
- Emily J Hird
- To whom correspondence should be addressed; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, 16 De Crespigny Park, SE5 8AF, London, UK; e-mail:
| | | | - Paul Allen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- School of Psychology, Whitelands College, University of Roehampton, Holybourne Ave, London, SW15 4JD, UK
| | - Peter Moseley
- Psychology Department, Northumbria University, College Lane, Newcastle-Upon-Tyne, NE1 8ST, UK
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Gemma Modinos
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Gabriele Sachs
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Mark van der Gaag
- Faculty of Behavioural and Movement Sciences, Department of Clinical Psychology, VU University, van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
- EMGO Institute for Health and Care Research, VU University, van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
- Department of Psychosis Research, Parnassia Psychiatric Institute, Zoutkeetsingel 40, 2512 HN The Hague, The Netherlands
| | - Lieuwe de Haan
- Department Early Psychosis, AMC, Academic Psychiatric Centre, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
- Arkin, Amsterdam, The Netherlands
| | - Ary Gadelha
- LiNC - Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo – UNIFESP, São Paulo, Brazil
| | - Rodrigo Bressan
- LiNC - Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo – UNIFESP, São Paulo, Brazil
| | - Neus Barrantes-Vidal
- Departament de Psicologia Clínica i de la Salut (Universitat Autònoma de Barcelona), Fundació Sanitària Sant Pere Claver (Spain), Spanish Mental Health Research Network (CIBERSAM), Barcelona, Spain
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany
| | - Ana Catalan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Psychiatry Department, Biocruces Bizkaia Health Research Institute, OSI Bilbao-Basurto, Facultad de Medicina y Odontología, University of the Basque Country UPV/EHU, Centro de Investigación en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Plaza de Cruces 12, 48903 Barakaldo, Bizkaia, Spain
| | - EU-GEI High Risk Study
McGuirePhilipDepartment of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, Denmark 458 Hill, London, SE5 8AF, UKValmaggiaLucia RDepartment of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, Denmark Hill, 456, London, SE5 8AF, UKKemptonMatthew JDepartment of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, Denmark 458 Hill, London, SE5 8AF, UKCalemMariaDepartment of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, Denmark 458 Hill, London, SE5 8AF, UKTogninStefaniaDepartment of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, Denmark 458 Hill, London, SE5 8AF, UKModinosGemmaDepartment of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, Denmark 458 Hill, London, SE5 8AF, UKde HaanLieuweDepartment Early Psychosis, AMC, Academic Psychiatric Centre, Meibergdreef 5, 1105 AZ Amsterdam, The NetherlandsArkin, Amsterdam, The Netherlandsvan der GaagMarkFaculty of Behavioural and Movement Sciences, Department of Clinical Psychology and EMGO Institute for Health and Care Research, VU University, van der Boechorststraat 1, 1081 BT Amsterdam, The NetherlandsDepartment of Psychosis Research, Parnassia Psychiatric Institute, Zoutkeetsingel 40, 2512 HN The Hague, The NetherlandsVelthorstEvaDepartment Early Psychosis, AMC, Academic Psychiatric Centre, Meibergdreef 5, 1105 AZ Amsterdam, The NetherlandsDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY 10029, USAKraanTamar CDepartment Early Psychosis, AMC, Academic Psychiatric Centre, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlandsvan DamDaniella SDepartment Early Psychosis, AMC, Academic Psychiatric Centre, Meibergdreef 5, 1105 AZ Amsterdam, The NetherlandsBurgerNadineDepartment of Psychosis Research, Parnassia Psychiatric Institute, Zoutkeetsingel 40, 2512 HN The Hague, The NetherlandsNelsonBarnabyCentre for Youth Mental Health, University of Melbourne, 35 Poplar Road (Locked Bag 10), Parkville, Victoria 485 3052, AustraliaMcGorryPatrickCentre for Youth Mental Health, University of Melbourne, 35 Poplar Road (Locked Bag 10), Parkville, Victoria 485 3052, AustraliaPaul AmmingerGünterCentre for Youth Mental Health, University of Melbourne, 35 Poplar Road (Locked Bag 10), Parkville, Victoria 485 3052, AustraliaPantelisChristosCentre for Youth Mental Health, University of Melbourne, 35 Poplar Road (Locked Bag 10), Parkville, Victoria 485 3052, AustraliaPolitisAthenaCentre for Youth Mental Health, University of Melbourne, 35 Poplar Road (Locked Bag 10), Parkville, Victoria 485 3052, AustraliaGoodallJoanneCentre for Youth Mental Health, University of Melbourne, 35 Poplar Road (Locked Bag 10), Parkville, Victoria 485 3052, AustraliaRiecher-RösslerAnitaUniversity Psychiatric Hospital, Wilhelm Klein-Strasse 27, CH-4002 Basel, SwitzerlandBorgwardtStefanUniversity Psychiatric Hospital, Wilhelm Klein-Strasse 27, CH-4002 Basel, SwitzerlandRappCharlotteUniversity Psychiatric Hospital, Wilhelm Klein-Strasse 27, CH-4002 Basel, SwitzerlandIttigSarahUniversity Psychiatric Hospital, Wilhelm Klein-Strasse 27, CH-4002 Basel, SwitzerlandStuderusErichUniversity Psychiatric Hospital, Wilhelm Klein-Strasse 27, CH-4002 Basel, SwitzerlandSmieskovaRenataUniversity Psychiatric Hospital, Wilhelm Klein-Strasse 27, CH-4002 Basel, SwitzerlandBressanRodrigoLiNC - Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo – UNIFESP, São Paulo, BrazilGadelhaAryLiNC - Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo – UNIFESP, São Paulo, BrazilBrietzkeElisaDepto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo – UNIFESP, São Paulo, BrazilAsevedoGraccielleLiNC - Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo – UNIFESP, São Paulo, BrazilAsevedoElsonLiNC - Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo – UNIFESP, São Paulo, BrazilZugmanAndreLiNC - Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo – UNIFESP, São Paulo, BrazilBarrantes-VidalNeusDepartament de Psicologia Clínica i de la Salut (Universitat Autònoma de Barcelona), Fundació Sanitària Sant Pere Claver (Spain), Spanish Mental Health Research Network (CIBERSAM), Barcelona, SpainDomínguez-MartínezTecelliCONACYT-Dirección de Investigaciones Epidemiológicas y Psicosociales, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz (México), Mexico City, MexicoTorrecillaPilarDepartament de Psicologia Clínica i de la Salut (Universitat Autònoma de Barcelona), Barcelona, SpainKwapilThomas RDepartment of Psychology, University of Illinois at Urbana-Champaign, IL, USAMonsonetManelDepartament de Psicologia Clínica i de la Salut (Universitat Autònoma de Barcelona), Barcelona, SpainHinojosaLídiaDepartament de Psicologia Clínica i de la Salut (Universitat Autònoma de Barcelona), Barcelona, SpainKazesMathildeUniversity Paris Descartes, Hôpital Sainte-Anne, C’JAAD, Service HospitaloUniversitaire, Inserm U894, Institut de Psychiatrie (CNRS 3557) Paris, FranceDabanClaireUniversity Paris Descartes, Hôpital Sainte-Anne, C’JAAD, Service HospitaloUniversitaire, Inserm U894, Institut de Psychiatrie (CNRS 3557) Paris, FranceBourginJulieUniversity Paris Descartes, Hôpital Sainte-Anne, C’JAAD, Service HospitaloUniversitaire, Inserm U894, Institut de Psychiatrie (CNRS 3557) Paris, FranceGayOlivierUniversity Paris Descartes, Hôpital Sainte-Anne, C’JAAD, Service HospitaloUniversitaire, Inserm U894, Institut de Psychiatrie (CNRS 3557) Paris, FranceMam-Lam-FookCéliaUniversity Paris Descartes, Hôpital Sainte-Anne, C’JAAD, Service HospitaloUniversitaire, Inserm U894, Institut de Psychiatrie (CNRS 3557) Paris, FranceKrebsMarie-OdileUniversity Paris Descartes, Hôpital Sainte-Anne, C’JAAD, Service HospitaloUniversitaire, Inserm U894, Institut de Psychiatrie (CNRS 3557) Paris, FranceNordholmDorteMental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University of Copenhagen, Copenhagen, DenmarkRandersLasseMental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University of Copenhagen, Copenhagen, DenmarkKrakauerKristineMental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University of Copenhagen, Copenhagen, DenmarkGlenthøjLouiseMental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University of Copenhagen, Copenhagen, DenmarkGlenthøjBirteCentre for Neuropsychiatric Schizophrenia Research (CNSR) & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, DenmarkNordentoftMereteMental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University of Copenhagen, Copenhagen, DenmarkRuhrmannStephanDepartment of Psychiatry and Psychotherapy, University of Cologne, Cologne, GermanyGebhardDominikaDepartment of Psychiatry and Psychotherapy, University of Cologne, Cologne, GermanyArnholdJuliaPsyberlin, Berlin, GermanyKlosterkötterJoachimDepartment of Psychiatry and Psychotherapy, University of Cologne, Cologne, GermanySachsGabrieleDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, AustriaLasserIrisDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, AustriaWinklbaurBernadetteDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, AustriaDelespaulPhilippe ADepartment of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, PO Box 616, 6200 MD 464 Maastricht, The NetherlandsMondriaan Mental Health Trust, PO Box 4436 CX Heerlen, The NetherlandsRuttenBart PDepartment of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, PO Box 616, 6200 MD 464 Maastricht, The Netherlandsvan Os1JimDepartment of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, PO Box 616, 6200 MD 464 Maastricht, The Netherlands
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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Hein TP, Gong Z, Ivanova M, Fedele T, Nikulin V, Herrojo Ruiz M. Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans. Commun Biol 2023; 6:271. [PMID: 36922553 PMCID: PMC10017780 DOI: 10.1038/s42003-023-04628-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.
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Affiliation(s)
- Thomas P Hein
- Goldsmiths, University of London, Psychology Department, Whitehead Building New Cross, London, SE14 6NW, UK
| | - Zheng Gong
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Marina Ivanova
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Tommaso Fedele
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Maria Herrojo Ruiz
- Goldsmiths, University of London, Psychology Department, Whitehead Building New Cross, London, SE14 6NW, UK.
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31
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Tecilla M, Großbach M, Gentile G, Holland P, Sporn S, Antonini A, Herrojo Ruiz M. Modulation of Motor Vigor by Expectation of Reward Probability Trial-by-Trial Is Preserved in Healthy Ageing and Parkinson's Disease Patients. J Neurosci 2023; 43:1757-1777. [PMID: 36732072 PMCID: PMC10010462 DOI: 10.1523/jneurosci.1583-22.2022] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 12/13/2022] [Accepted: 12/31/2022] [Indexed: 02/04/2023] Open
Abstract
Motor improvements, such as faster movement times or increased velocity, have been associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigor dynamically remains undetermined. We investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial. We conducted three studies that coupled a reversal learning paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. In Study 1, we tested healthy younger [HYA; 37 (24 females)] and older adults [HOA; 37 (17 females)], and medicated Parkinson's disease (PD) patients [20 (7 females)]. We showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo, commensurate with movement time, on a trial-by-trial basis without robustly modulating reaction time (RT). Using Bayesian linear mixed models, we demonstrated a similar invigoration effect on performance tempo in HYA, HOA, and PD, despite HOA and PD being slower than HYA. In Study 2 [HYA, 39 (29 females)], we additionally showed that retrospective subjective inference about credit assignment did not contribute to differences in motor vigor effects. Last, Study 3 [HYA, 33 (27 females)] revealed that explicit beliefs about the reward tendency (confidence ratings) modulated performance tempo trial-by-trial. Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster tempo. We also provide robust evidence for a preserved sensitivity of motor vigor to inferences about the action-reward mapping in aging and medicated PD.SIGNIFICANCE STATEMENT Navigating a world rich in uncertainty relies on updating beliefs about the probability that our actions lead to reward. Here, we investigated how inferring the action-reward contingencies in a volatile environment modulated motor vigor trial-by-trial in healthy younger and older adults, and in Parkinson's disease (PD) patients on medication. We found an association between trial-by-trial predictions about the tendency of the action-reward contingency and performance tempo, with stronger expectations speeding the movement. We additionally provided evidence for a similar sensitivity of performance tempo to the strength of these predictions in all groups. Thus, dynamic beliefs about the changing relationship between actions and their outcome enhanced motor vigor. This positive bias was not compromised by age or Parkinson's disease.
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Affiliation(s)
- Margherita Tecilla
- Department of Psychology, Goldsmiths, University of London, London SE146NW, United Kingdom
| | - Michael Großbach
- Institute of Music Physiology and Musicians' Medicine, Hannover University of Music Drama and Media, Hannover 30175, Germany
| | - Giovanni Gentile
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua 35131, Italy
| | - Peter Holland
- Department of Psychology, Goldsmiths, University of London, London SE146NW, United Kingdom
| | - Sebastian Sporn
- Department of Clinical and Movement Neuroscience, Queen Square Institute of Neurology, University College London, London WC1N3BG, United Kingdom
| | - Angelo Antonini
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua 35131, Italy
| | - Maria Herrojo Ruiz
- Department of Psychology, Goldsmiths, University of London, London SE146NW, United Kingdom
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Ramadan R, Randell A, Lavoie S, Gao CX, Manrique PC, Anderson R, McDowell C, Zbukvic I. Empirical evidence for climate concerns, negative emotions and climate-related mental ill-health in young people: A scoping review. Early Interv Psychiatry 2023. [PMID: 36641809 DOI: 10.1111/eip.13374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 10/18/2022] [Accepted: 01/01/2023] [Indexed: 01/16/2023]
Abstract
BACKGROUND Climate change represents a serious threat to human health, including mental health, due to both the traumatizing effects of extreme climate events and the psychological effect of worry about climate change. OBJECTIVES This scoping review aims to search the literature for original research investigating mental health and climate-related negative emotions in young people. Findings will help to understand the current landscape, gaps in the literature, and provide recommendations for future youth mental health research and practice. METHODS A scoping review of the literature examining negative emotions associated with climate change in young people was undertaken. Studies published prior to March 2022 were included if they examined mental ill-health (e.g., symptoms of depression or anxiety) or negative emotions (e.g., distress, worry, concern) associated with the threat of climate change. RESULTS Of 3939 articles screened, 26 met the inclusion criteria. Together, studies show that young people are worried about climate change. Negative emotions about climate change were correlated with symptoms of mental ill-health. Studies also explored coping strategies young people use to manage their distress caused by climate change. DISCUSSION The limited literature in this area indicates a key gap in youth mental health research. While available evidence suggests that young people are concerned about climate change, more research is needed on the relationship between climate-related negative emotions and mental ill-health. Clinicians should assess for and address climate-related negative emotions in young people. Mental health leaders are urged to advocate for actions to mitigate the mental health impact of climate change.
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Affiliation(s)
- Reem Ramadan
- Orygen, Parkville, Victoria, Australia.,Orygen, North Western Mental Health, Melbourne Health, Parkville, Victoria, Australia
| | - Alicia Randell
- Orygen, Parkville, Victoria, Australia.,Headspace National Youth Mental Health Foundation, Melbourne, Victoria, Australia.,Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Suzie Lavoie
- Orygen, Parkville, Victoria, Australia.,Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Caroline X Gao
- Orygen, Parkville, Victoria, Australia.,Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia.,Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Paula Cruz Manrique
- Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Rebekah Anderson
- Orygen, Parkville, Victoria, Australia.,Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Caitlin McDowell
- Orygen, Parkville, Victoria, Australia.,Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Isabel Zbukvic
- Orygen, Parkville, Victoria, Australia.,Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
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Fromm S, Katthagen T, Deserno L, Heinz A, Kaminski J, Schlagenhauf F. Belief Updating in Subclinical and Clinical Delusions. SCHIZOPHRENIA BULLETIN OPEN 2023; 4:sgac074. [PMID: 39145350 PMCID: PMC11207849 DOI: 10.1093/schizbullopen/sgac074] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Background and Hypothesis Current frameworks propose that delusions result from aberrant belief updating due to altered prediction error (PE) signaling and misestimation of environmental volatility. We aimed to investigate whether behavioral and neural signatures of belief updating are specifically related to the presence of delusions or generally associated with manifest schizophrenia. Methods Our cross-sectional design includes human participants (n[female/male] = 66[25/41]), stratified into four groups: healthy participants with minimal (n = 22) or strong delusional-like ideation (n = 18), and participants with diagnosed schizophrenia with minimal (n = 13) or strong delusions (n = 13), resulting in a 2 × 2 design, which allows to test for the effects of delusion and diagnosis. Participants performed a reversal learning task with stable and volatile task contingencies during fMRI scanning. We formalized learning with a hierarchical Gaussian filter model and conducted model-based fMRI analysis regarding beliefs of outcome uncertainty and volatility, precision-weighted PEs of the outcome- and the volatility-belief. Results Patients with schizophrenia as compared to healthy controls showed lower accuracy and heightened choice switching, while delusional ideation did not affect these measures. Participants with delusions showed increased precision-weighted PE-related neural activation in fronto-striatal regions. People with diagnosed schizophrenia overestimated environmental volatility and showed an attenuated neural representation of volatility in the anterior insula, medial frontal and angular gyrus. Conclusions Delusional beliefs are associated with altered striatal PE-signals. Juxtaposing, the potentially unsettling belief that the environment is constantly changing and weaker neural encoding of this subjective volatility seems to be associated with manifest schizophrenia, but not with the presence of delusional ideation.
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Affiliation(s)
- Sophie Fromm
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health CCM, Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Department of Psychology, Humboldt-Universität zu Berlin, Germany
| | - Teresa Katthagen
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health CCM, Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin, Germany
| | - Lorenz Deserno
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Würzburg, Würzburg, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Psychiatry and Psychotherapy, Technische Universität, Dresden, Germany
| | - Andreas Heinz
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health CCM, Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Jakob Kaminski
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health CCM, Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin, Germany
| | - Florian Schlagenhauf
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health CCM, Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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34
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Kazinka R. The Specificity of Paranoia and Uncertainty. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:1053-1054. [DOI: 10.1016/j.bpsc.2022.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 09/24/2022] [Indexed: 11/06/2022]
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35
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Sheffield JM, Suthaharan P, Leptourgos P, Corlett PR. Belief Updating and Paranoia in Individuals With Schizophrenia. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:1149-1157. [PMID: 35430406 PMCID: PMC9827723 DOI: 10.1016/j.bpsc.2022.03.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/10/2022] [Accepted: 03/31/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Persecutory delusions are among the most common delusions in schizophrenia and represent the extreme end of the paranoia continuum. Paranoia is accompanied by significant worry and distress. Identifying cognitive mechanisms underlying paranoia is critical for advancing treatment. We hypothesized that aberrant belief updating, which is related to paranoia in human and animal models, would also contribute to persecutory beliefs in individuals with schizophrenia. METHODS Belief updating was assessed in 42 participants with schizophrenia and 44 healthy control participants using a 3-option probabilistic reversal learning task. Hierarchical Gaussian Filter was used to estimate computational parameters of belief updating. Paranoia was measured using the Positive and Negative Syndrome Scale and the revised Green et al. Paranoid Thoughts Scale. Unusual thought content was measured with the Psychosis Symptom Rating Scale and the Peters et al. Delusions Inventory. Worry was measured using the Dunn Worry Questionnaire. RESULTS Paranoia was significantly associated with elevated win-switch rate and prior beliefs about volatility both in schizophrenia and across the whole sample. These relationships were specific to paranoia and did not extend to unusual thought content or measures of anxiety. We observed a significant indirect effect of paranoia on the relationship between prior beliefs about volatility and worry. CONCLUSIONS This work provides evidence that relationships between belief updating parameters and paranoia extend to schizophrenia, may be specific to persecutory beliefs, and contribute to theoretical models implicating worry in the maintenance of persecutory delusions.
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Affiliation(s)
- Julia M Sheffield
- Department of Psychiatry & Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
| | - Praveen Suthaharan
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, Connecticut
| | - Pantelis Leptourgos
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, Connecticut
| | - Philip R Corlett
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, Connecticut
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36
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Herzog P, Kube T, Fassbinder E. How childhood maltreatment alters perception and cognition - the predictive processing account of borderline personality disorder. Psychol Med 2022; 52:2899-2916. [PMID: 35979924 PMCID: PMC9693729 DOI: 10.1017/s0033291722002458] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 06/24/2022] [Accepted: 07/18/2022] [Indexed: 01/05/2023]
Abstract
Borderline personality disorder (BPD) is a severe mental disorder, comprised of heterogeneous psychological and neurobiological pathologies. Here, we propose a predictive processing (PP) account of BPD to integrate these seemingly unrelated pathologies. In particular, we argue that the experience of childhood maltreatment, which is highly prevalent in BPD, leaves a developmental legacy with two facets: first, a coarse-grained, alexithymic model of self and others - leading to a rigidity and inflexibility concerning beliefs about self and others. Second, this developmental legacy leads to a loss of confidence or precision afforded beliefs about the consequences of social behavior. This results in an over reliance on sensory evidence and social feedback, with concomitant lability, impulsivity and hypersensitivity. In terms of PP, people with BPD show a distorted belief updating in response to new information with two opposing manifestations: rapid changes in beliefs and a lack of belief updating despite disconfirmatory evidence. This account of distorted information processing has the potential to explain both the instability (of affect, self-image, and interpersonal relationships) and the rigidity (of beliefs about self and others) which is typical of BPD. At the neurobiological level, we propose that enhanced levels of dopamine are associated with the increased integration of negative social feedback, and we also discuss the hypothesis of an impaired inhibitory control of the prefrontal cortex in the processing of negative social information. Our account may provide a new understanding not only of the clinical aspects of BPD, but also a unifying theory of the corresponding neurobiological pathologies. We conclude by outlining some directions for future research on the behavioral, neurobiological, and computational underpinnings of this model, and point to some clinical implications of it.
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Affiliation(s)
- Philipp Herzog
- Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, D-23562 Lübeck, Germany
- Department of Psychiatry and Psychotherapy, Christian-Albrechts-University of Kiel, Niemannsweg 147, D-24105 Kiel, Germany
- Department of Psychology, University of Koblenz-Landau, Ostbahnstr. 10, 76829 Landau, Germany
| | - Tobias Kube
- Department of Psychology, University of Koblenz-Landau, Ostbahnstr. 10, 76829 Landau, Germany
| | - Eva Fassbinder
- Department of Psychiatry and Psychotherapy, Christian-Albrechts-University of Kiel, Niemannsweg 147, D-24105 Kiel, Germany
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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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Rethinking delusions: A selective review of delusion research through a computational lens. Schizophr Res 2022; 245:23-41. [PMID: 33676820 PMCID: PMC8413395 DOI: 10.1016/j.schres.2021.01.023] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 01/27/2021] [Accepted: 01/29/2021] [Indexed: 02/06/2023]
Abstract
Delusions are rigid beliefs held with high certainty despite contradictory evidence. Notwithstanding decades of research, we still have a limited understanding of the computational and neurobiological alterations giving rise to delusions. In this review, we highlight a selection of recent work in computational psychiatry aimed at developing quantitative models of inference and its alterations, with the goal of providing an explanatory account for the form of delusional beliefs in psychosis. First, we assess and evaluate the experimental paradigms most often used to study inferential alterations in delusions. Based on our review of the literature and theoretical considerations, we contend that classic draws-to-decision paradigms are not well-suited to isolate inferential processes, further arguing that the commonly cited 'jumping-to-conclusion' bias may reflect neither delusion-specific nor inferential alterations. Second, we discuss several enhancements to standard paradigms that show promise in more effectively isolating inferential processes and delusion-related alterations therein. We further draw on our recent work to build an argument for a specific failure mode for delusions consisting of prior overweighting in high-level causal inferences about partially observable hidden states. Finally, we assess plausible neurobiological implementations for this candidate failure mode of delusional beliefs and outline promising future directions in this area.
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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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40
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Corlett PR, Mollick JA, Kober H. Meta-analysis of human prediction error for incentives, perception, cognition, and action. Neuropsychopharmacology 2022; 47:1339-1349. [PMID: 35017672 PMCID: PMC9117315 DOI: 10.1038/s41386-021-01264-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 12/23/2021] [Accepted: 12/28/2021] [Indexed: 12/30/2022]
Abstract
Prediction errors (PEs) are a keystone for computational neuroscience. Their association with midbrain neural firing has been confirmed across species and has inspired the construction of artificial intelligence that can outperform humans. However, there is still much to learn. Here, we leverage the wealth of human PE data acquired in the functional neuroimaging setting in service of a deeper understanding, using an MKDA (multi-level kernel-based density) meta-analysis. Studies were identified with Google Scholar, and we included studies with healthy adult participants that reported activation coordinates corresponding to PEs published between 1999-2018. Across 264 PE studies that have focused on reward, punishment, action, cognition, and perception, consistent with domain-general theoretical models of prediction error we found midbrain PE signals during cognitive and reward learning tasks, and an insula PE signal for perceptual, social, cognitive, and reward prediction errors. There was evidence for domain-specific error signals--in the visual hierarchy during visual perception, and the dorsomedial prefrontal cortex during social inference. We assessed bias following prior neuroimaging meta-analyses and used family-wise error correction for multiple comparisons. This organization of computation by region will be invaluable in building and testing mechanistic models of cognitive function and dysfunction in machines, humans, and other animals. Limitations include small sample sizes and ROI masking in some included studies, which we addressed by weighting each study by sample size, and directly comparing whole brain vs. ROI-based results.
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Affiliation(s)
| | | | - Hedy Kober
- Department of Psychiatry, Yale University, New Haven, CT, USA.
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41
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Hauke DJ, Roth V, Karvelis P, Adams RA, Moritz S, Borgwardt S, Diaconescu AO, Andreou C. Increased Belief Instability in Psychotic Disorders Predicts Treatment Response to Metacognitive Training. Schizophr Bull 2022; 48:826-838. [PMID: 35639557 PMCID: PMC9212107 DOI: 10.1093/schbul/sbac029] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
BACKGROUND AND HYPOTHESIS In a complex world, gathering information and adjusting our beliefs about the world is of paramount importance. The literature suggests that patients with psychotic disorders display a tendency to draw early conclusions based on limited evidence, referred to as the jumping-to-conclusions bias, but few studies have examined the computational mechanisms underlying this and related belief-updating biases. Here, we employ a computational approach to understand the relationship between jumping-to-conclusions, psychotic disorders, and delusions. STUDY DESIGN We modeled probabilistic reasoning of 261 patients with psychotic disorders and 56 healthy controls during an information sampling task-the fish task-with the Hierarchical Gaussian Filter. Subsequently, we examined the clinical utility of this computational approach by testing whether computational parameters, obtained from fitting the model to each individual's behavior, could predict treatment response to Metacognitive Training using machine learning. STUDY RESULTS We observed differences in probabilistic reasoning between patients with psychotic disorders and healthy controls, participants with and without jumping-to-conclusions bias, but not between patients with low and high current delusions. The computational analysis suggested that belief instability was increased in patients with psychotic disorders. Jumping-to-conclusions was associated with both increased belief instability and greater prior uncertainty. Lastly, belief instability predicted treatment response to Metacognitive Training at the individual level. CONCLUSIONS Our results point towards increased belief instability as a key computational mechanism underlying probabilistic reasoning in psychotic disorders. We provide a proof-of-concept that this computational approach may be useful to help identify suitable treatments for individual patients with psychotic disorders.
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Affiliation(s)
- D J Hauke
- To whom correspondence should be addressed; 250 College St., 12th Floor, Toronto, ON M5T 1R8, Canada; tel: +1 (416) 535-8501 ext. 30585, fax: +1 416-583-1207, e-mail:
| | - V Roth
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - P Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - R A Adams
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK,Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - S Moritz
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - S Borgwardt
- Department of Psychiatry and Psychotherapy, Translational Psychiatry Unit, University of Lübeck, Lübeck, Germany,Center of Brain, Behaviour and Metabolism, University of Lübeck, Lübeck, Germany
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42
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Abstract
Conflicting evidence about how the brain processes social and individual learning stems from which type of information is presented as the primary source of knowledge during experiments.
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Affiliation(s)
- Orit Nafcha
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Uri Hertz
- Department of Cognitive Sciences, University of Haifa, Haifa, Israel
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43
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Groman SM, Thompson SL, Lee D, Taylor JR. Reinforcement learning detuned in addiction: integrative and translational approaches. Trends Neurosci 2022; 45:96-105. [PMID: 34920884 PMCID: PMC8770604 DOI: 10.1016/j.tins.2021.11.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/04/2021] [Accepted: 11/19/2021] [Indexed: 02/03/2023]
Abstract
Suboptimal decision-making strategies have been proposed to contribute to the pathophysiology of addiction. Decision-making, however, arises from a collection of computational components that can independently influence behavior. Disruptions in these different components can lead to decision-making deficits that appear similar behaviorally, but differ at the computational, and likely the neurobiological, level. Here, we discuss recent studies that have used computational approaches to investigate the decision-making processes underlying addiction. Studies in animal models have found that value updating following positive, but not negative, outcomes is predictive of drug use, whereas value updating following negative, but not positive, outcomes is disrupted following drug self-administration. We contextualize these findings with studies on the circuit and biological mechanisms of decision-making to develop a framework for revealing the biobehavioral mechanisms of addiction.
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Affiliation(s)
- Stephanie M. Groman
- Department of Neuroscience, University of Minnesota,Department of Psychiatry, Yale University,Correspondence to be directed to: Stephanie Groman, 321 Church Street SE, 4-125 Jackson Hall Minneapolis MN 55455,
| | | | - Daeyeol Lee
- The Zanvyl Krieger Mind/Brain Institute, The Solomon H Snyder Department of Neuroscience, Department of Psychological and Brain Sciences, Kavli Neuroscience Discovery Institute, Johns Hopkins University
| | - Jane R. Taylor
- Department of Psychiatry, Yale University,Department of Neuroscience, Yale University,Department of Psychology, Yale University
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44
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Hein TP, Herrojo Ruiz M. State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning. Neuroimage 2022; 249:118895. [PMID: 35017125 DOI: 10.1016/j.neuroimage.2022.118895] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 10/21/2021] [Accepted: 01/07/2022] [Indexed: 12/20/2022] Open
Abstract
Anxiety influences how the brain estimates and responds to uncertainty. The consequences of these processes on behaviour have been described in theoretical and empirical studies, yet the associated neural correlates remain unclear. Rhythm-based accounts of Bayesian predictive coding propose that predictions in generative models of perception are represented in alpha (8-12 Hz) and beta oscillations (13-30 Hz). Updates to predictions are driven by prediction errors weighted by precision (inverse variance) encoded in gamma oscillations (>30 Hz) and associated with the suppression of beta activity. We tested whether state anxiety alters the neural oscillatory activity associated with predictions and precision-weighted prediction errors (pwPE) during learning. Healthy human participants performed a probabilistic reward-based learning task in a volatile environment. In our previous work, we described learning behaviour in this task using a hierarchical Bayesian model, revealing more precise (biased) beliefs about the tendency of the reward contingency in state anxiety, consistent with reduced learning in this group. The model provided trajectories of predictions and pwPEs for the current study, allowing us to assess their parametric effects on the time-frequency representations of EEG data. Using convolution modelling for oscillatory responses, we found that, relative to a control group, state anxiety increased beta activity in frontal and sensorimotor regions during processing of pwPE, and in fronto-parietal regions during encoding of predictions. No effects of state anxiety on gamma modulation were found. Our findings expand prior evidence on the oscillatory representations of predictions and pwPEs into the reward-based learning domain. The results suggest that state anxiety modulates beta-band oscillatory correlates of pwPE and predictions in generative models, providing insights into the neural processes associated with biased belief updating and poorer learning.
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Affiliation(s)
- Thomas P Hein
- Goldsmiths, Psychology Department, Whitehead Building New Cross, University of London, Lewisham Way, New Cross, London SE14 6NW, United Kingdom.
| | - Maria Herrojo Ruiz
- Goldsmiths, Psychology Department, Whitehead Building New Cross, University of London, Lewisham Way, New Cross, London SE14 6NW, United Kingdom; Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russian Federation.
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45
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Katthagen T, Fromm S, Wieland L, Schlagenhauf F. Models of Dynamic Belief Updating in Psychosis-A Review Across Different Computational Approaches. Front Psychiatry 2022; 13:814111. [PMID: 35492702 PMCID: PMC9039658 DOI: 10.3389/fpsyt.2022.814111] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 02/18/2022] [Indexed: 11/20/2022] Open
Abstract
To understand the dysfunctional mechanisms underlying maladaptive reasoning of psychosis, computational models of decision making have widely been applied over the past decade. Thereby, a particular focus has been on the degree to which beliefs are updated based on new evidence, expressed by the learning rate in computational models. Higher order beliefs about the stability of the environment can determine the attribution of meaningfulness to events that deviate from existing beliefs by interpreting these either as noise or as true systematic changes (volatility). Both, the inappropriate downplaying of important changes as noise (belief update too low) as well as the overly flexible adaptation to random events (belief update too high) were theoretically and empirically linked to symptoms of psychosis. Whereas models with fixed learning rates fail to adjust learning in reaction to dynamic changes, increasingly complex learning models have been adopted in samples with clinical and subclinical psychosis lately. These ranged from advanced reinforcement learning models, over fully Bayesian belief updating models to approximations of fully Bayesian models with hierarchical learning or change point detection algorithms. It remains difficult to draw comparisons across findings of learning alterations in psychosis modeled by different approaches e.g., the Hierarchical Gaussian Filter and change point detection. Therefore, this review aims to summarize and compare computational definitions and findings of dynamic belief updating without perceptual ambiguity in (sub)clinical psychosis across these different mathematical approaches. There was strong heterogeneity in tasks and samples. Overall, individuals with schizophrenia and delusion-proneness showed lower behavioral performance linked to failed differentiation between uninformative noise and environmental change. This was indicated by increased belief updating and an overestimation of volatility, which was associated with cognitive deficits. Correlational evidence for computational mechanisms and positive symptoms is still sparse and might diverge from the group finding of instable beliefs. Based on the reviewed studies, we highlight some aspects to be considered to advance the field with regard to task design, modeling approach, and inclusion of participants across the psychosis spectrum. Taken together, our review shows that computational psychiatry offers powerful tools to advance our mechanistic insights into the cognitive anatomy of psychotic experiences.
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Affiliation(s)
- Teresa Katthagen
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Sophie Fromm
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Einstein Center for Neurosciences, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Lara Wieland
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Einstein Center for Neurosciences, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Florian Schlagenhauf
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Einstein Center for Neurosciences, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
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46
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Piray P, Daw ND. A model for learning based on the joint estimation of stochasticity and volatility. Nat Commun 2021; 12:6587. [PMID: 34782597 PMCID: PMC8592992 DOI: 10.1038/s41467-021-26731-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 10/08/2021] [Indexed: 02/08/2023] Open
Abstract
Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a learning model, in which both factors are learned simultaneously from experience, and use the model to simulate human and animal data across many seemingly disparate neuroscientific and behavioral phenomena. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence complicates and enriches the interpretation of previous results, such as pathological learning in individuals with anxiety and following amygdala damage.
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Affiliation(s)
- Payam Piray
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA.
| | - Nathaniel D Daw
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
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47
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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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48
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Paranoia and belief updating during the COVID-19 crisis. Nat Hum Behav 2021; 5:1190-1202. [PMID: 34316049 PMCID: PMC8458246 DOI: 10.1038/s41562-021-01176-8] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 07/06/2021] [Indexed: 01/31/2023]
Abstract
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 laboratory task behaviour.
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49
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Abstract
Introduction: Delusions demand an explanation in terms of their neural, psychological, and sociological mechanisms. We must bridge these levels of explanation in order to understand and ultimately treat delusions. To this end, debates continue as to the number of contributing factors, how those factors interact, and their underlying computational mechanisms.Methods: One popular family of models suggests that two separate insults are necessary, a problem with perception and an independent problem with belief. In particular, new work proposes that the belief problem entails a bias against disconfirmatory evidence - yielding the characteristic fixity of delusions. Here, we evaluate that claim, as well as explanations of delusions more broadly.Results: We suggest that such a bias may not explain enough of the variance in belief updating in delusional participants, and, more fundamentally, it might rule out specific accounts of delusions, since, such a bias might prevent them from forming in the first place, under particular assumptions about cognitive architectures.Conclusion: We suggest conceptualising delusions as an evolving uncertainty driven negotiation between beliefs and evidence, in which initial formation is fuelled by unexpected uncertainty, but, once formed, the delusion engenders new expectations about uncertainty that tune down updating but also facilitate the elastic assimilation of contradictory evidence.
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Affiliation(s)
- Philip R Corlett
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
| | - Paul Fletcher
- Department of Psychiatry, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK.,Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
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50
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Silverstein SM, Thompson JL, Gold JM, Schiffman J, Waltz JA, Williams TF, Zinbarg RE, Mittal VA, Ellman LM, Strauss GP, Walker EF, Woods SW, Levin JA, Kafadar E, Kenney J, Smith D, Powers AR, Corlett PR. Increased face detection responses on the mooney faces test in people at clinical high risk for psychosis. NPJ SCHIZOPHRENIA 2021; 7:26. [PMID: 34001909 PMCID: PMC8129098 DOI: 10.1038/s41537-021-00156-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 03/19/2021] [Indexed: 02/03/2023]
Abstract
Identifying state-sensitive measures of perceptual and cognitive processes implicated in psychosis may allow for objective, earlier, and better monitoring of changes in mental status that are predictive of an impending psychotic episode, relative to traditional self-report-based clinical measures. To determine whether a measure of visual perception that has demonstrated sensitivity to the clinical state of schizophrenia in multiple prior studies is sensitive to features of the at-risk mental state, we examined differences between young people identified as being at clinical high risk for psychosis (CHR; n = 37) and non-psychiatric matched controls (n = 29) on the Mooney Faces Test (MFT). On each trial of the MFT, participants report whether they perceive a face in a degraded face image. The CHR group reported perceiving a greater number of faces in both upright and inverted MFT stimuli. Consistent with prior work, males reported more faces on the MFT than females in both conditions. However, the finding of greater reported face perception among CHR subjects was robustly observed in the female CHR group relative to the female control group. Among male CHR participants, greater reported face perception was related to increased perceptual abnormalities. These preliminary results are consistent with a small but growing literature suggesting that heightened perceptual sensitivity may characterize individuals at increased clinical risk for psychosis. Further studies are needed to determine the contributions of specific perceptual, cognitive, and motivational mechanisms to the findings.
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Affiliation(s)
- Steven M. Silverstein
- grid.412750.50000 0004 1936 9166University of Rochester Medical Center, New York, NY USA
| | - Judy L. Thompson
- grid.412750.50000 0004 1936 9166University of Rochester Medical Center, New York, NY USA
| | - James M. Gold
- grid.411024.20000 0001 2175 4264University of Maryland School of Medicine, Baltimore, MD USA
| | - Jason Schiffman
- grid.411024.20000 0001 2175 4264University of Maryland School of Medicine, Baltimore, MD USA ,grid.266093.80000 0001 0668 7243Present Address: University of California, Irvine, CA USA
| | - James A. Waltz
- grid.411024.20000 0001 2175 4264University of Maryland School of Medicine, Baltimore, MD USA
| | - Trevor F. Williams
- grid.16753.360000 0001 2299 3507Northwestern University, Evanston, IL USA
| | - Richard E. Zinbarg
- grid.16753.360000 0001 2299 3507Northwestern University, Evanston, IL USA
| | - Vijay A. Mittal
- grid.16753.360000 0001 2299 3507Northwestern University, Evanston, IL USA
| | - Lauren M. Ellman
- grid.264727.20000 0001 2248 3398Temple University, Philadelphia, PA USA
| | | | - Elaine F. Walker
- grid.189967.80000 0001 0941 6502Emory University, Atlanta, GA USA
| | - Scott W. Woods
- grid.47100.320000000419368710Yale University, New Haven, CT USA
| | - Jason A. Levin
- grid.213876.90000 0004 1936 738XUniversity of Georgia, Athens, GA USA
| | - Eren Kafadar
- grid.47100.320000000419368710Yale University, New Haven, CT USA
| | - Joshua Kenney
- grid.47100.320000000419368710Yale University, New Haven, CT USA
| | - Dillon Smith
- grid.16750.350000 0001 2097 5006Princeton University, Princeton, NJ USA
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