Abstract
Fueled by developments in computational neuroscience, there has been increasing interest in the underlying neurocomputational mechanisms of psychosis. One successful approach involves predictive coding and Bayesian inference. Here, inferences regarding the current state of the world are made by combining prior beliefs with incoming sensory signals. Mismatches between prior beliefs and incoming signals constitute prediction errors that drive new learning. Psychosis has been suggested to result from a decreased precision in the encoding of prior beliefs relative to the sensory data, thereby garnering maladaptive inferences. Here, we review the current evidence for aberrant predictive coding and discuss challenges for this canonical predictive coding account of psychosis. For example, hallucinations and delusions may relate to distinct alterations in predictive coding, despite their common co-occurrence. More broadly, some studies implicate weakened prior beliefs in psychosis, and others find stronger priors. These challenges might be answered with a more nuanced view of predictive coding. Different priors may be specified for different sensory modalities and their integration, and deficits in each modality need not be uniform. Furthermore, hierarchical organization may be critical. Altered processes at lower levels of a hierarchy need not be linearly related to processes at higher levels (and vice versa). Finally, canonical theories do not highlight active inference—the process through which the effects of our actions on our sensations are anticipated and minimized. It is possible that conflicting findings might be reconciled by considering these complexities, portending a framework for psychosis more equipped to deal with its many manifestations.
Highlights
Fueled by developments in computational neuroscience, there has been increasing interest in the underlying neurocomputational mechanisms of psychosis
Predictive coding conceives of the brain as a hierarchy whose goal is to maximize the evidence for its model of the world by comparing prior beliefs with sensory data, and using the resultant prediction errors (PEs) to update the model (Figure 1)
Hierarchical Bayesian inference entails modeling ourselves as agents who change the world: in this scheme, experiences such as agency and selfhood are inferred from the consequences of our own actions [13]
Summary
In Bayesian predictive coding schemes, the PE is affected by the precision of the sensory data: if it is high, the precision-weighted PE in case of a mismatch will be greater, and vice versa (Figure 1A)—just as in classical statistical inference, the t statistic is greater if the standard error of the data is smaller. One view has linked hallucinations to a failure to attenuate sensory precision, including the sensory consequences of inner speech, analogous to the mechanism that is thought to underlie delusions of control [58,77,78,79,80] This would correspond to the notion of low precision of priors relative to a disproportionately high precision of neural signals that encode inner speech in auditory cortex, akin to the canonical predictive coding account. An increased influence of learned high-level beliefs in relation to psychotic symptoms was reported for the perception of images with impoverished sensory information where perceptual inference relies strongly on priors [107]. These asymmetries can be explained with a Bayesian model, if we allow agents to derive utility from their beliefs [112]
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