Motivational Deficits in Schizophrenia and the Representation of Expected Value

Waltz, James A.; Gold, James M.

doi:10.1007/7854_2015_385

Cited by 65 publications

(50 citation statements)

References 149 publications

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“…[35][36][37] However, problems in schizophrenia arise on tasks such as PRL tasks, in which participants are required to learn complex (eg, probabilistic) reinforcement contingencies by maintaining and updating representations of the stimulus and action values over time. Our findings are consistent with recent proposals [1][2][3][4] that individuals with schizophrenia have a deficit in the ability to use feedback valence and prediction errors to update value representations and guide choice. These proposals are supported by recent fMRI findings 4,8 that poor PRL performance in schizophrenia is associated with disturbances that extend beyond the striatum to cognitive control network regions, including dorsolateral prefrontal cortex, anterior cingulate cortex, and dorsal parietal cortex.…”

Section: F Reddy Et Alsupporting

confidence: 82%

Probabilistic Reversal Learning in Schizophrenia: Stability of Deficits and Potential Causal Mechanisms

Reddy¹,

Waltz

Green³

et al. 2016

SCHBUL

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Although individuals with schizophrenia show impaired feedback-driven learning on probabilistic reversal learning (PRL) tasks, the specific factors that contribute to these deficits remain unknown. Recent work has suggested several potential causes including neurocognitive impairments, clinical symptoms, and specific types of feedback-related errors. To examine this issue, we administered a PRL task to 126 stable schizophrenia outpatients and 72 matched controls, and patients were retested 4 weeks later. The task involved an initial probabilistic discrimination learning phase and subsequent reversal phases in which subjects had to adjust their responses to sudden shifts in the reinforcement contingencies. Patients showed poorer performance than controls for both the initial discrimination and reversal learning phases of the task, and performance overall showed good test-retest reliability among patients. A subgroup analysis of patients (n = 64) and controls (n = 49) with good initial discrimination learning revealed no between-group differences in reversal learning, indicating that the patients who were able to achieve all of the initial probabilistic discriminations were not impaired in reversal learning. Regarding potential contributors to impaired discrimination learning, several factors were associated with poor PRL, including higher levels of neurocognitive impairment, poor learning from both positive and negative feedback, and higher levels of indiscriminate response shifting. The results suggest that poor PRL performance in schizophrenia can be the product of multiple mechanisms.

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Section: F Reddy Et Alsupporting

confidence: 82%

Probabilistic Reversal Learning in Schizophrenia: Stability of Deficits and Potential Causal Mechanisms

Reddy¹,

Waltz

Green³

et al. 2016

SCHBUL

Self Cite

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“…In primates, considerable evidence accumulated in the past 15 years indicates that these operations involve the orbitofrontal cortex (OFC). In humans, OFC neurodegeneration or dysfunction is associated with abnormal decision making (Barch et al, 2016;Bechara et al, 1994;Camille et al, 2011;Fellows, 2011;Hodges, 2001;Rahman et al, 1999;Waltz and Gold, 2016;Yu et al, 2018). Furthermore, neural signals recorded in OFC during choices correlate with subjective values (Arana et al, 2003;Chaudhry et al, 2009;Gottfried et al, 2003;Hare et al, 2008;Howard et al, 2015;Howard and Kahnt, 2017;Klein-Flugge et al, 2013;Peters and Buchel, 2009).…”

Section: Introductionmentioning

confidence: 99%

Neural Mechanisms of Economic Choices in Mice

Kuwabara

Holy

Padoa-Schioppa

2019

Preprint

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Economic choices entail computing and comparing subjective values. Evidence from primates indicates that this behavior relies on the orbitofrontal cortex. Conversely, previous work in rodents provided conflicting results. Here we present a mouse model of economic choice behavior, and we show that the lateral orbital (LO) area is intimately related to the decision process. In the experiments, mice chose between different juices offered in variable amounts. Choice patterns closely resembled those measured in primates. Optogenetic inactivation of LO dramatically disrupted choices by inducing erratic changes of relative value and by increasing choice variability. Neuronal recordings revealed that different groups of cells encoded the values of individual options, the binary choice outcome and the chosen value. These groups match those previously identified in primates, except that the neuronal representation in mice is spatial (in monkeys it is good-based). Our results lay the foundations for a circuit-level analysis of economic decisions.

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“…For example, it could emerge from abnormal representations of the expected value of actions, or from disrupted signaling of mismatches between expected and obtained outcomes, reward prediction errors (RPEs) (2-3). Reinforcement learning is powerful framework for quantifying and linking such mechanisms to underlying biology (4).…”

Section: Introductionmentioning

confidence: 99%

Intact Ventral Striatal Prediction Error Signaling in Medicated Schizophrenia Patients

Culbreth

Westbrook

et al. 2016

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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Background Midbrain dopaminergic neurons code a computational quantity, reward prediction error (RPE), which has been causally related to learning. Recently, this insight has been leveraged to link phenomenological and biological levels of understanding in psychiatric disorders, such as schizophrenia. However, results have been mixed, possibly due to small sample sizes. Here we present results from two studies with relatively large Ns to assess VS RPE in schizophrenia. Methods In the current study we analyzed data from two independent studies, involving a total of 87 chronic medicated schizophrenia patients and 61 controls. Subjects completed a probabilistic reinforcement-learning task in conjunction with fMRI scanning. We fit each participant's choice behavior to a Q-learning model and derived trial-wise RPEs. We then modeled BOLD signal data with parametric regressor functions using these values to determine whether patient and control groups differed in prediction-error-related BOLD signal modulations. Results Both groups demonstrated robust VS RPE BOLD activations. Interestingly, these BOLD activation patterns did not differ between groups in either study. This was true when we included all participants in the analysis, as well as when we excluded participants whose data was not sufficiently fit by the models. Conclusions These data demonstrate the utility of computational methods in isolating/testing underlying mechanisms of interest in psychiatric disorders. Importantly, similar VS RPE signal encoding across groups suggests that this mechanism does not drive task deficits in these patients. Deficits may instead stem from aberrant prefrontal/parietal circuits associated with maintenance and selection of goal-relevant information.

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Motivational Deficits in Schizophrenia and the Representation of Expected Value

Cited by 65 publications

References 149 publications

Probabilistic Reversal Learning in Schizophrenia: Stability of Deficits and Potential Causal Mechanisms

Probabilistic Reversal Learning in Schizophrenia: Stability of Deficits and Potential Causal Mechanisms

Neural Mechanisms of Economic Choices in Mice

Intact Ventral Striatal Prediction Error Signaling in Medicated Schizophrenia Patients

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