Bayesian modelling of Jumping-to-Conclusions bias in delusional patients
Abstract
INTRODUCTION. When deciding about the cause underlying serially presented events, patients with delusions utilise fewer events than controls, showing a "Jumping-to-Conclusions" bias. This has been widely hypothesised to be because patients expect to incur higher costs if they sample more information. This hypothesis is, however, unconfirmed. METHODS. The hypothesis was tested by analysing patient and control data using two models. The models provided explicit, quantitative variables characterising decision making. One model was based on calculating the potential costs of making a decision; the other compared a measure of certainty to a fixed threshold. RESULTS. Differences between paranoid participants and controls were found, but not in the way that was previously hypothesised. A greater "noise" in decision making (relative to the effective motivation to get the task right), rather than greater perceived costs, best accounted for group differences. Paranoid participants also deviated from ideal Bayesian reasoning more than healthy controls. CONCLUSIONS. The Jumping-to-Conclusions Bias is unlikely to be due to an overestimation of the cost of gathering more information. The analytic approach we used, involving a Bayesian model to estimate the parameters characterising different participant populations, is well suited to testing hypotheses regarding "hidden" variables underpinning observed behaviours.
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Jumping to conclusions may reflect greater cognitive noise in delusional patients, which may speak to reduced precision of higher-level (cognitive) representations and consequently a greater influence of new sensory evidence.
“Bayesian modeling suggests that jumping to conclusions may reflect greater “cognitive noise” in delusional patients (Moutoussis et al., 2011), which may speak to reduced precision of higher level (cognitive) representations and consequently a greater influence of new sensory evidence (Speechley et al., 2010).”
From The Computational Anatomy of Psychosis (2013) · cites this paper as Moutoussis et al., 2011
The JTC bias may arise from altered noise in decision making.
“For example, the JTC bias might arise because of altered noise in decision making.7”
From Computational psychiatry: a Rosetta Stone linking the brain to mental illness (2014) · cites this paper as [7]
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“Bayesian modeling suggests that jumping to conclusions may reflect greater “cognitive noise” in delusional patients (Moutoussis et al., 2011), which may speak to reduced precision of higher level (cognitive) representations and consequently a greater influence of new sensory evidence (Speechley et…”
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- Authors
- Moutoussis, M., Bentall, R. P., El-Deredy, W., & Dayan, P.
- Affiliations
- University of Manchester (GB)
- Bangor University (GB)
- University College London (GB)
- Corresponding author
- Moutoussis, Michael
- Venue
- Cognitive Neuropsychiatry
- Publisher
- Informa UK Limited
- Published
- 2011-09
- Volume
- 16
- Issue
- 5
- Pages
- 422-447
- ISSN
- 1354-6805 (ISSN-L)
- 1464-0619
- OpenAlex
- W2078443730
- Cited by
- 137
- References
- 72
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 5 · 2023: 11 · 2022: 12 · 2021: 8 · 2020: 10 · 2019: 12 · 2018: 11 · 2017: 8 · 2016: 9 · 2015: 11 · 2014: 13 · 2013: 5 · 2012: 4
- FWCI
- 5.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognitive Neuropsychiatry (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Keywords
- jumping to conclusions bias, delusions, paranoia, Bayesian decision making, decision costs, noise in decision making, causal reasoning
- MeSH terms
- Adult, Bayes Theorem, Delusions, Female, Humans, Male, Middle Aged, Paranoid Disorders, Psychotic Disorders, Schizophrenia, Paranoid
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