Prediction Error Processing Research Articles

Abnormal Reward System Activation in Mania

Transmission of reward signals is a function of dopamine, a neurotransmitter known to be involved in the mechanism of psychosis. Using functional magnetic resonance imaging (fMRI), we investigated how expectation and receipt of monetary rewards modulate brain activation in patients with bipolar mania and schizophrenia. We studied 12 acutely manic patients with a history of bipolar disorder, 12 patients with a current episode of schizoaffective disorder or schizophrenia and 12 healthy subjects. All patients were treated with dopamine antagonists at the time of the study. Subjects performed a delayed incentive paradigm with monetary reward in the scanner that allowed for investigating effects of expectation, receipt, and omission of rewards. Patients with schizophrenia and healthy control subjects showed the expected activation of dopaminergic brain areas, that is, ventral tegmentum activation upon expectation of monetary rewards and nucleus accumbens activation during receipt vs omission of rewards. In manic patients, however, we did not find a similar pattern of brain activation and the differential signal in the nucleus accumbens upon receipt vs omission of rewards was significantly lower compared to the healthy control subjects. Our findings provide evidence for abnormal function of the dopamine system during receipt or omission of expected rewards in bipolar disorder. These deficits in prediction error processing in acute mania may help to explain symptoms of disinhibition and abnormal goal pursuit regulation.

Disrupted prediction-error signal in psychosis: evidence for an associative account of delusions

Delusions are maladaptive beliefs about the world. Based upon experimental evidence that prediction error-a mismatch between expectancy and outcome--drives belief formation, this study examined the possibility that delusions form because of disrupted prediction--error processing. We used fMRI to determine prediction-error-related brain responses in 12 healthy subjects and 12 individuals (7 males) with delusional beliefs. Frontal cortex responses in the patient group were suggestive of disrupted prediction-error processing. Furthermore, across subjects, the extent of disruption was significantly related to an individual's propensity to delusion formation. Our results support a neurobiological theory of delusion formation that implicates aberrant prediction-error signalling, disrupted attentional allocation and associative learning in the formation of delusional beliefs.

Ageing and early-stage Parkinson's disease affect separable neural mechanisms of mesolimbic reward processing

The ability to learn stimulus-reward associations on the basis of reward prediction errors critically depends on the mesolimbic dopaminergic system including the dopaminergic midbrain and the ventral striatum. It is known that healthy elderly and patients with Parkinson's disease are less proficient than healthy young adults in learning stimulus-reward contingencies, but it is unclear whether this is due to dysfunctional mesolimbic reward prediction or due to deficiency in processing the rewards per se. We used a well-established event-related fMRI reward-prediction paradigm to address this question. Young adults showed the well-replicated pattern of midbrain and ventral striatal activation for stimuli that predicted monetary reward when compared with stimuli that predicted neutral feedback. Also, as expected, the predicted reward feedback itself did not elicit a mesolimbic response. Healthy elderly subjects and unmedicated early-stage idiopathic Parkinson's disease patients showed the opposite pattern with an absent mesolimbic reward prediction response, but mesolimbic activation to the reward feedback itself. This suggests that the healthy elderly and Parkinson's disease patients were less proficient in learning the predictive value of the reward cues despite preserved mesolimbic processing of reward prediction errors. Parkinson's disease patients additionally displayed a relatively increased response of the anterior cingulate during reward feedback processing and diminished functional connectivity of the midbrain and ventral striatum. Our results are compatible with existing behavioural evidence that both groups exhibit a particularly pronounced deficit in learning from positive feedback and support the view that a tendency to underestimate expected values of reward cues might underlie this deficit. Furthermore, alterations in reward processing in Parkinson's disease extend beyond accelerated ageing effects and include altered connectivity within the mesolimbic system.

Activity in human ventral striatum locked to errors of reward prediction.

The mesolimbic dopaminergic system has long been known to be involved in the processing of rewarding stimuli, although recent evidence from animal research has suggested a more specific role of signaling errors in the prediction of rewards. We tested this hypothesis in humans, using functional magnetic resonance imaging (fMRI) and an operant conditioning paradigm for the discrete delivery of small quantities of fruit juice, along with a control experiment in which juice was substituted with a neutral visual stimulus. A local estimation of the activity in the ventral striatum showed a significant differentiation when the juice was withheld at the expected time of delivery; this finding was not replicated in the case of visual stimulation, providing evidence for time-locked processing of reward prediction errors in human ventral striatum.

Convergence of the projection method for an autoregressive process and a matched DPCM code

The key step in the analysis of differential pulse code modulation (DPCM) is to find the steady-state probability distribution of a random process in terms of which the code distortion can be evaluated. For the case of an autoregressive source and a matched DPCM code, a well-known approximation technique has been used for the evaluation of the steady-state distribution of the prediction error process. However, the validity of this approximation method has not been justified before. A framework in which this approximation technique can be viewed as the projection method for the solution of integral equations is established. Sufficient conditions under which the approximation method can be rigorously justified are obtained.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Performance contours of autoregressive estimates

Given a pth-order AR (autoregressive) process, the authors obtain an explicit formula that relates the true parameters and their estimates (or distorted versions) to the variance of the prediction error process. For a second-order AR model they determine contours within which the parameter estimates must lie, corresponding to a set of prediction error variance values. Conversely, for a given set of parameter estimates, they obtain contours within which the true parameters would lie, corresponding to a set of prediction error variance values. These results provide some interesting insights about AR modeling.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>