Abstract
Predictive coding potentially provides an explanatory model for understanding the neurocognitive mechanisms of psychosis. It proposes that cognitive processes, such as perception and inference, are implemented by a hierarchical system, with the influence of each level being a function of the estimated precision of beliefs at that level. However, predictive coding models of psychosis are insufficiently constrained—any phenomenon can be explained in multiple ways by postulating different changes to precision at different levels of processing. One reason for the lack of constraint in these models is that the core processes are thought to be implemented by the function of specific cortical layers, and the technology to measure layer specific neural activity in humans has until recently been lacking. As a result, our ability to constrain the models with empirical data has been limited. In this review we provide a brief overview of predictive processing models of psychosis and then describe the potential for newly developed, layer specific neuroimaging techniques to test and thus constrain these models. We conclude by discussing the most promising avenues for this research as well as the technical and conceptual challenges which may limit its application.
Highlights
Symptoms of psychosis including hallucinations and delusions are associated with various psychiatric and neurological disorders, including schizophrenia, bipolar disorder, Parkinson's and Alzheimer's disease
We have described how laminar fMRI can directly test the hypotheses that psychosis is associated with increased/decreased signalling of prior expectations or whether alternative mechanisms are responsible
Experimental paradigms that manipulate whether expectations are low-level, like serial dependence, or high-level, as when perceptual biases are induced by learned cues, in combination with laminar fMRI to investigate whether the observed effects should be attributed to altered feedback or feedforward signalling, will provide important evidence that predictive coding deficits in psychosis are hierarchical in nature
Summary
Symptoms of psychosis including hallucinations and delusions are associated with various psychiatric and neurological disorders, including schizophrenia, bipolar disorder, Parkinson's and Alzheimer's disease. (Fletcher and Frith, 2009) This theory postulates that the brain forms a hierarchical model of its environment, where each hierarchical level maintains a belief about the most likely cause of its inputs, which is updated by discrepancies between the level's prior belief and its inputs (prediction errors). According to this view, hallucinations and delusions result from aberrant neural signalling of prior beliefs, sensory input and/ or prediction errors (Friston, 2005; Stephan et al, 2006; Fletcher and Frith, 2009; Adams et al, 2013; Sterzer et al, 2018). We will describe how this technique might help us constrain predictive coding models of psychosis, in particular by its ability to disentangle bottom-up and top-down signals, as well as describing potential limitations to the utility of this technique
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