Attractor-like dynamics in belief updating in schizophrenia
Open accessAbstract
Subjects with a diagnosis of schizophrenia (Scz) overweight unexpected evidence in probabilistic inference: such evidence becomes “aberrantly salient.” A neurobiological explanation for this effect is that diminished synaptic gain (e.g., hypofunction of cortical NMDARs) in Scz destabilizes quasi-stable neuronal network states (or “attractors”). This attractor instability account predicts that (1) Scz would overweight unexpected evidence but underweight consistent evidence, (2) belief updating would be more vulnerable to stochastic fluctuations in neural activity, and (3) these effects would correlate. Hierarchical Bayesian belief updating models were tested in two independent datasets (n= 80 male andn= 167 female) comprising human subjects with Scz, and both clinical and nonclinical controls (some tested when unwell and on recovery) performing the “probability estimates” version of the beads task (a probabilistic inference task). Models with a standard learning rate, or including a parameter increasing updating to “disconfirmatory evidence,” or a parameter encoding belief instability were formally compared. The “belief instability” model (based on the principles of attractor dynamics) had most evidence in all groups in both datasets. Two of four parameters differed between Scz and nonclinical controls in each dataset: belief instability and response stochasticity. These parameters correlated in both datasets. Furthermore, the clinical controls showed similar parameter distributions to Scz when unwell, but were no different from controls once recovered. These findings are consistent with the hypothesis that attractor network instability contributes to belief updating abnormalities in Scz, and suggest that similar changes may exist during acute illness in other psychiatric conditions. SIGNIFICANCE STATEMENTSubjects with a diagnosis of schizophrenia (Scz) make large adjustments to their beliefs following unexpected evidence, but also smaller adjustments than controls following consistent evidence. This has previously been construed as a bias toward “disconfirmatory” information, but a more mechanistic explanation may be that in Scz, neural firing patterns (“attractor states”) are less stable and hence easily altered in response to both new evidence and stochastic neural firing. We model belief updating in Scz and controls in two independent datasets using a hierarchical Bayesian model, and show that all subjects are best fit by a model containing a belief instability parameter. Both this and a response stochasticity parameter are consistently altered in Scz, as the unstable attractor hypothesis predicts.
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- Authors
- Adams, R. A., Napier, G., Roiser, J. P., Mathys, C., & Gilleen, J.
- Affiliations
- University College London (GB)
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- King's College London (GB)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2018-09-05
- Volume
- 38
- Issue
- 44
- Pages
- 9471-9485
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6705994
- OpenAlex
- W2889886504
- Cited by
- 77
- References
- 61
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 15 · 2023: 7 · 2022: 9 · 2021: 8 · 2020: 13 · 2019: 6
- FWCI
- 5.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- SDL (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection (cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Institute for Health and Care Research (CL-2013-18-003)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- schizophrenia, belief updating, attractor dynamics, probabilistic inference, NMDA receptor hypofunction, synaptic gain, Bayesian hierarchical model, beads task, neural network stability
- MeSH terms
- Adult, Culture, Female, Humans, Male, Middle Aged, Models, Neurological, Nerve Net, Probability Learning, Psychomotor Performance, Schizophrenia, Schizophrenic Psychology, Young Adult
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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