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Delusions, a core symptom of psychosis, are false beliefs that are rigidly held with strong conviction despite contradictory evidence. Alterations in inferential processes have long been proposed to underlie delusional pathology, but previous attempts to show this have failed to yield compelling evidence for a specific relationship between inferential abnormalities and delusional severity in schizophrenia. Using a novel, incentivized information-sampling task (a modified version of the beads task), alongside well-characterized decision-making tasks, we sought a mechanistic understanding of delusions in a sample of medicated and unmedicated patients with schizophrenia who exhibited a wide range of delusion severity. In this novel task, participants chose whether to draw beads from one of two hidden jars or to guess the identity of the hidden jar, in order to minimize financial loss from a monetary endowment, and concurrently reported their probability estimates for the hidden jar. We found that patients with higher delusion severity exhibited increased information seeking (i.e. increased draws-to-decision behaviour). This increase was highly specific to delusion severity as compared to the severity of other psychotic symptoms, working-memory capacity, and other clinical and socio-demographic characteristics. Delusion-related increases in information seeking were present in unmedicated patients, indicating that they were unlikely due to antipsychotic medication. In addition, after adjusting for delusion severity, patients as a whole exhibited decreased information seeking relative to healthy individuals, a decrease that correlated with lower socioeconomic status. Computational analyses of reported probability estimates further showed that more delusional patients exhibited abnormal belief updating characterized by stronger reliance on prior beliefs formed early in the inferential process, a feature that correlated with increased information seeking in patients. Other decision-making parameters that could have theoretically explained the delusion effects, such as those related to subjective valuation, were uncorrelated with both delusional severity and information seeking among the patients. In turn, we found some preliminary evidence that subjective valuation (rather than belief updating) may explain group differences in information seeking unrelated to delusions. Together, these results suggest that abnormalities in belief updating, characterized by stronger reliance on prior beliefs formed by incorporating information presented earlier in the inferential process, may be a core computational mechanism of delusional ideation in psychosis. Our results thus provide direct empirical support for an inferential mechanism that naturally captures the characteristic rigidity associated with delusional beliefs., (© The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Recent findings demonstrate that patients with schizophrenia are worse at learning to predict rewards than losses, suggesting that motivational context modulates learning in this disease. However, these findings derive from studies in patients treated with antipsychotic medications, D2 receptor antagonists that may interfere with the neural systems that underlie motivation and learning. Thus, it remains unknown how motivational context affects learning in schizophrenia, separate from the effects of medication., Methods: To examine the impact of motivational context on learning in schizophrenia, we tested 16 unmedicated patients with schizophrenia and 23 matched controls on a probabilistic learning task while they underwent functional magnetic resonance imaging (fMRI) under 2 conditions: one in which they pursued rewards, and one in which they avoided losses. Computational models were used to derive trial-by-trial prediction error responses to feedback., Results: Patients performed worse than controls on the learning task overall, but there were no behavioral effects of condition. FMRI revealed an attenuated prediction error response in patients in the medial prefrontal cortex, striatum, and medial temporal lobe when learning to predict rewards, but not when learning to avoid losses., Conclusions: Patients with schizophrenia showed differences in learning-related brain activity when learning to predict rewards, but not when learning to avoid losses. Together with prior work, these results suggest that motivational deficits related to learning in schizophrenia are characteristic of the disease and not solely a result of antipsychotic treatment., (© The Author 2016. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Patients with loss of dopamine due to Parkinson's disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from 'model-free' learning. The other, 'model-based' learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson's disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson's disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson's disease may be related to an inability to pursue reward based on complete representations of the environment., (© The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modeling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target-detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modeled incidental learning of these associations using a Rescorla-Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory to visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticity.