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How Does Schizophrenia Affect A Person's Life

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How Does Schizophrenia Affect A Person's Life – 1 Clinical Science Program, Department of Psychology, Virginia Tech, Blacksburg, Virginia 24061, 2 Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland 20892, and

Information about sensations, actions, consequences and their associations are often unknown, inaccurate or subject to change. Bayesian learning theories suggest that to reduce uncertainty and maintain adaptive control, the brain optimally integrates new sensory input with past experience, in order to learn (form beliefs about) of the causes and consequences of sensations and actions (Stephan and Mathys, 2014). The extent to which perceptions are updated to support the exploitation of prior knowledge or sensitivity to new sensory information depends on the strength of that information and associated confidence.

How Does Schizophrenia Affect A Person's Life

Hallucinations (false beliefs) and delusions (persistent false beliefs; ie, positive symptoms) are the main symptoms of schizophrenia and are accompanied by frequent motivational/affective impairments (negative symptoms) and cognitive domain (Fig. 1; American Psychiatric Association, 2013) . Using a Bayesian framework, it is theorized that positive symptoms of schizophrenia may arise from impaired belief updating (Stephan and Mathys, 2014). Some hypothesize that these impairments are caused by overexcitability at the sensory level, as irrelevant or noisy sensory input erroneously enters consciousness and affects belief (for a review, see Walton et al., 2017). Others have argued that atypical belief updating is due to higher levels of metacognitive impairments that lead to an imbalance of confidence related to new sensory input and past experiences (Frith and Friston , 2013; Walton et al., 2017). Recently, Adams et al. (2018), proposed a Bayesian “attraction-like” model that parsimoniously accounts for both types of inference errors.

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Left, An original diagram of the term “schizophrenia” in a word cloud created using text from the “Signs and Symptoms” section of a website published by the National Institute of Mental Health (2016) in schizophrenia. Word clouds are created using the online tool at www.WordClouds.com. Right, examples of symptoms that fall within the positive, negative and cognitive symptom domains of schizophrenia (Rolls et al., 2008; American Psychiatric Association, 2013). Positive symptoms are thoughts, behaviors or sensory perceptions that are common to patients and outside of socio-cultural norms. Negative symptoms are thoughts, feelings or behaviors that are absent/impaired in patients, but common to most individuals. Cognitive symptoms are unwanted changes in various cognitive domains, including memory, attention and reasoning. For these symptoms to be clinically relevant, they must be persistent (at least 1 month or more) and interfere with multiple domains of life (eg, social relationships, work, self-care, etc. ).

The prefrontal attractor network is a class of biologically plausible and mechanistic models of cognitive phenomena, including working memory and decision making (for a review, see Rolls et al., 2008). Repeated experience of stimuli leads to learning by adjusting synaptic weights. Over time, this results in the formation of relatively low-energy, repetitive firing patterns (steady state) that are more easily activated by inputs that match preexisting weights. Adams and others. (2018) claim that impaired belief updating in schizophrenia is due to unstable persuasion networks. They hypothesized that the cortical networks in schizophrenia are easily disrupted from internal or external “noise” and therefore, instead of forming stable recurrent states, the networks more easily jump between states. . Increased susceptibility to noise in turn reduces the likelihood of stabilization in any condition (Rolls et al., 2008).

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 has more red beads while the other has more blue beads. The urns are then hidden, and the beads are taken in order from one of the urns (with a replacement). Participants rated the subjective probability that each presented bead was taken from one of the urns (continuous rating from 0 to 100 in data set 1 and Likert rating from 1 to 7 in data set 2). The patients in Dataset 1 suffered from delusions, but not all were diagnosed with schizophrenia (a minority had bipolar/schizoaffective disorder). To test the specificity of the findings on positive symptoms, Dataset 1 also included a non-psychotic clinical control group (with mood/anxiety disorder) from the same inpatient unit, in addition to a healthy control group. Data for all groups in dataset 1 were collected at two time points (before and after remission). Dataset 2 includes data from a medically stable patient and healthy control group for a single time point. All patients in Dataset 2 met the diagnostic criteria for schizophrenia, but it was not known whether they suffered primarily from hallucinations, delusions, or a heterogeneous mixture of these symptoms.

To test whether attraction-like dynamics account for individual differences in belief updating, the authors (Adams et al., 2018) define six biologically plausible Bayesian learning models. [hierarchical Gaussian filter (HGF); Mathys et al., 2011]. HGF models allow individual estimation of higher-level influences (e.g., how prior experience shapes sensory expectations) and lower-level inferences (e.g., how errors in sensory input / prediction affects beliefs) in learning (Mathys et al., 2011). They then used Bayesian model selection (Rigoux et al., 2014) to determine which model best fit the data in each data set and each group (ie, patient and control) separately. Two of these models estimate a “belief-stability” parameter, which at high values ​​produces simulated behavior consistent with the assumptions of unstable attractor states (i.e., more shifting between states and reducing the probability of stabilization in any state). For more details on parameters included/estimated in alternative models, see Adams et al. (2018), their Table 3.

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To estimate individual-level parameters, the authors developed a task model (i.e., perceptual model), followed by a model of how trial-by-trial predictions were mapped onto the present behavior (ie, response model). The model that best fit the data for all groups in both datasets estimated the following parameters from the perceptual model: overall learning rate (trial-to-trial difference in perceptions of urn), initial belief difference and belief strength (strength to update belief to new sensory input when the individual is uncertain compared to some of their predictions). To account for alternative sources of noise (eg, random neuronal firing that is not captured by environmental variability), the authors also estimated the response stochasticity parameter for all response models (inverse variability in the trial-to-trial mapping of prediction of responses).

Statistical tests on Dataset 1 showed that both delusional patients and clinical controls showed greater belief instability and response to stochasticity than healthy controls, with no difference between the patients and clinical controls. After remission, delusional patients had greater belief instability and response stochasticity than controls, but clinical controls did not. The results were replicated in dataset 2, with higher belief instability and response stochasticity in patients with schizophrenia than controls. In dataset 2, the initial belief difference differed between patients and controls, but this difference was no longer statistically significant after comparing patients to a subset of control participants who were better matched to age and gender. The belief and response stochasticity parameters are moderately and reliably correlated in both datasets and in the simulations, suggesting that higher belief strength is associated with a weaker link between expectations and responses. .

Behavioral findings reported by Adams et al. (2018) largely replicated the existing literature on probabilistic decision-making and learning in schizophrenia (Peters and Garety, 2006; Corlett et al., 2009; Averbeck et al., 2011; Jardri et al., 2017). Compared to controls, patients with schizophrenia overweighted unexpected evidence, underestimated expected evidence, and were more confident when sensory evidence was limited (ie, jumped to conclusions). Computational modeling results provide support for unstable dynamics such as attraction as a plausible mechanism for this spectrum of impairments in belief updating. In particular, under high uncertainty, patients with schizophrenia are more likely to change beliefs when observing new sensory input (high belief instability) and are less likely to stabilize on the beliefs to either urn as the evidence increases (high response stochasticity). Importantly, this model partially accounts for both metacognitive and sensory interpretations of these impairments. The authors also partially rule out other possible explanations (for example, no differences in initial belief differences or general learning rates), suggesting that unstable attractor-like dynamics account for of behavioral differences between patients and controls.

The existing literature largely assumes that belief updating impairments in schizophrenia are specific markers of acute positive symptoms or vulnerability to delusions (Garety and Freeman, 2013). For example, Woodward et al. (2009) showed that in patients with schizophrenia, the reduction of delusion-related symptoms from precognitive to postcognitive behavioral therapy corresponds to an increased accumulation of evidence (less jumping to conclusions). Notably, this effect does not depend on the requirements of working memory or the value of the reward of a correct answer, which led the authors to conclude their determination of symptoms related to deception (Woodward et al. , 2009; Garety and Freeman, 2013). Adams and others. (2018) did not test for within-group differences in belief instability or response stochasticity parameters before and after remission in dataset 1, and the correlated symptoms before remission of the parameter estimates from that time. This makes it difficult to determine whether the parameters of the unstable attractor-like model reflect change linked to specific symptom domains. Indeed, the results of the study (Adams et al., 2018) raise questions about whether impairments in belief updating and unstable persuasion dynamics uniquely underlie positive symptoms (i.e. , hallucinations and/or delusions). The authors (Adams et al., 2018)

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