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Attractor-like dynamics in belief updating in schizophrenia

Adams, RA; Napier, G; Roiser, JP; Mathys, C; Gilleen, J; (2018) Attractor-like dynamics in belief updating in schizophrenia. Journal of Neuroscience 10.1523/JNEUROSCI.3163-17.2018. (In press). Green open access

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Abstract

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 N-methyl-D-aspartate receptors) in Scz destabilizes quasi-stable neuronal network states (or 'attractors'). This attractor instability account predicts that i) Scz would overweight unexpected evidence but underweight consistent evidence, ii) belief updating would be more vulnerable to stochastic fluctuations in neural activity, and iii) these effects would correlate.Hierarchical Bayesian belief updating models were tested in two independent datasets (n=80 and n=167, male and female) comprising human subjects with schizophrenia, and both clinical and non-clinical 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 non-clinical 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 to 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 towards '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.

Type: Article
Title: Attractor-like dynamics in belief updating in schizophrenia
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1523/JNEUROSCI.3163-17.2018
Publisher version: https://doi.org/10.1523/JNEUROSCI.3163-17.2018
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Institute of Cognitive Neuroscience
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10056660
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