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Reduced model-based decision-making in schizophrenia.

ABSTRACT: Individuals with schizophrenia have a diminished ability to use reward history to adaptively guide behavior. However, tasks traditionally used to assess such deficits often rely on multiple cognitive and neural processes, leaving etiology unresolved. In the current study, we adopted recent computational formalisms of reinforcement learning to distinguish between model-based and model-free decision-making in hopes of specifying mechanisms associated with reinforcement-learning dysfunction in schizophrenia. Under this framework, decision-making is model-free to the extent that it relies solely on prior reward history, and model-based if it relies on prospective information such as motivational state, future consequences, and the likelihood of obtaining various outcomes. Model-based and model-free decision-making was assessed in 33 schizophrenia patients and 30 controls using a 2-stage 2-alternative forced choice task previously demonstrated to discern individual differences in reliance on the 2 forms of reinforcement-learning. We show that, compared with controls, schizophrenia patients demonstrate decreased reliance on model-based decision-making. Further, parameter estimates of model-based behavior correlate positively with IQ and working memory measures, suggesting that model-based deficits seen in schizophrenia may be partially explained by higher-order cognitive deficits. These findings demonstrate specific reinforcement-learning and decision-making deficits and thereby provide valuable insights for understanding disordered behavior in schizophrenia. (PsycINFO Database Record

SUBMITTER: Culbreth AJ

PROVIDER: S-EPMC4980177 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Reduced model-based decision-making in schizophrenia.

Culbreth Adam J AJ Westbrook Andrew A Daw Nathaniel D ND Botvinick Matthew M Barch Deanna M DM

Journal of abnormal psychology 20160512 6

Individuals with schizophrenia have a diminished ability to use reward history to adaptively guide behavior. However, tasks traditionally used to assess such deficits often rely on multiple cognitive and neural processes, leaving etiology unresolved. In the current study, we adopted recent computational formalisms of reinforcement learning to distinguish between model-based and model-free decision-making in hopes of specifying mechanisms associated with reinforcement-learning dysfunction in schi ...[more]

PMID: 27175984

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Project description:ObjectiveNumerous studies have identified impaired decision making (DM) under both ambiguity and risk in adult patients with schizophrenia. However, the assessment of DM in patients with adolescent-onset schizophrenia (AOS) has been challenging as a result of the instability and heterogeneity of manifestations. The Iowa Gambling Task (IGT) and Game of Dice Task (GDT), which are frequently used to evaluate DM respectively under ambiguity and risk, are sensitive to adolescents and neuropsychiatric patients. Our research intended to examine the performance of DM in a relatively large sample of patients with AOS using the above-mentioned two tasks. We also aimed to take a closer look at the relationship between DM and symptom severity of schizophrenia.MethodsWe compared the performance of DM in 71 patients with AOS and 53 well-matched healthy controls using IGT for DM under ambiguity and GDT for DM under risk through net scores, total scores and feedback ration. Neuropsychological tests were conducted in all participants. Clinical symptoms were evaluated by using Positive and Negative Syndrome Scale (PANSS) in 71 patients with AOS. Pearson's correlation revealed the relationship among total score of DM and clinical and neuropsychological data.ResultsCompared to healthy controls, patients with AOS failed to show learning effect and had a significant difference on the 5th block in IGT and conducted more disadvantageous choices as well as exhibited worse negative feedback rate in GDT. Apart from DM impairment under risk, diminished DM abilities under ambiguity were found related to poor executive function in AOS in the present study.ConclusionsOur findings unveiled the abnormal pattern of DM in AOS, mainly reflected under the risky condition, extending the knowledge on the performance of DM under ambiguity and risk in AOS. Inefficient DM under risk may account for the lagging impulse control and the combined effects of developmental disease. In addition, our study demonstrated that the performance on IGT was related to executive function in AOS.

Project description:BackgroundDual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease.MethodsWe developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice.ResultsWe show that physician's beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker's threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice.ConclusionsWe have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the large extent dominated by expected utility theory. The model also provides a platform for reconciling two groups of competing dual processing theories (parallel competitive with default-interventionalist theories).

Dataset Information

Reduced model-based decision-making in schizophrenia.

Publications

Reduced model-based decision-making in schizophrenia.

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