Attractor-like Dynamics in Belief Updating in Schizophrenia

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 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 ( = 80 male and = 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. Subjects 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.

An Integrative and Mechanistic Model of Impaired Belief Updating in Schizophrenia

“…Others posit that atypical belief updating is because of higher-level metacognitive impairments leading to imbalance in confidence associated with new sensory input and past experiences (Frith and Friston, 2013;Valton et al, 2017). Recently, Adams et al (2018), proposed a Bayesian "attractor-like" model that parsimoniously accounts for both types of inferential error.…”

“…Over time, this results in the formation of relatively low-energy, recurring firing patterns (stable states) that are more readily activated by input that matches pre-existing weights. Adams et al (2018) posit that impaired belief updating in schizophrenia results from unstable attractor networks. They hypothesized that cortical networks in schizophrenia are prone to disruption from either internal or external "noise", and thus rather than forming stable recurrent states, the networks are more prone to jumping between states.…”

“…To test for differences in belief updating, Adams et al (2018) used data from a version of the "Beads" task collected in two independent samples of patients and controls. Participants were shown two urns with a combination of red and blue beads; one had more red beads while the other had more blue beads.…”

“…To test whether attractor-like dynamics accounted for individual differences in belief updating, the authors (Adams et al, 2018) defined six biologically plausible Bayesian learning models [hierarchical Gaussian filter (HGF); Mathys et al, 2011]. HGF models allow for individuallevel estimation of higher-level influences (e.g., how past experiences shape sensory expectations) and lower-level inferences (e.g., how sensory input/prediction errors influence beliefs) on learning (Mathys et al, 2011).…”

Distinct Hierarchical Alterations of Intrinsic Neural Timescales Account for Different Manifestations of Psychosis

“…Perceptual-inference models of psychosis suggest that these symptoms result from alterations in the updating of internal models of the environment that are used to make inferences about external sensory events and their causes (1-3). These models are receiving increasing empirical support (4)(5)(6)(7)(8)(9), yet current theories do not provide a satisfactory explanation for how hallucinations and delusions tend to co-occur but sometimes manifest in isolation. This suggests that these psychotic symptoms may share a common neurobiological mechanism and simultaneously depend on symptom-specific pathways.…”