Forms of prediction in the nervous system

The idea that predictions shape how we perceive and comprehend the world has become increasingly influential in the field of systems neuroscience. It also forms an important framework for understanding neuropsychiatric disorders, which are proposed to be the result of disturbances in the mechanisms through which prior information influences perception and belief, leading to the production of suboptimal models of the world. There is a widespread tendency to conceptualize the influence of predictions exclusively in terms of ‘top-down’ processes, whereby predictions generated in higher-level areas exert their influence on lower-level areas within an information processing hierarchy. However, this excludes from consideration the predictive information embedded in the ‘bottom-up’ stream of information processing. We describe evidence for the importance of this distinction and argue that it is critical for the development of the predictive processing framework and, ultimately, for an understanding of the perturbations that drive the emergence of neuropsychiatric symptoms and experiences

Sensory neuroscience: Linking dopamine, expectation and hallucinations

Psychosis is associated with a dysregulation of the brain’s dopamine-mediated neurotransmitter system. Yet, specific mechanisms underlying psychotic symptoms are not well understood. A new study has now uncovered a dopamine-dependent mechanism that explains why psychotic patients experience hallucination

Anomalous Perceptions and Beliefs Are Associated With Shifts Toward Different Types of Prior Knowledge in Perceptual Inference.

Psychotic phenomena manifest in healthy and clinical populations as complex patterns of aberrant perceptions (hallucinations) and tenacious, irrational beliefs ( delusions). According to predictive processing accounts, hallucinations and delusions arise from atypicalities in the integration of prior knowledge with incoming sensory information. However, the computational details of these atypicalities and their specific phenomenological manifestations are not well characterized. We tested the hypothesis that hallucination-proneness arises from increased reliance on overly general application of prior knowledge in perceptual inference, generating percepts that readily capture the gist of the environment but inaccurately render its details. We separately probed the use of prior knowledge to perceive the gist vs the details of ambiguous images in a healthy population with varying degrees of hallucination- and delusion-proneness. We found that the use of prior knowledge varied with psychotic phenomena and their composition in terms of aberrant percepts vs aberrant beliefs. Consistent with previous findings, hallucination-proneness conferred an advantage using prior knowledge to perceive image gist but, contrary to predictions, did not confer disadvantage perceiving image details. Predominant hallucination-proneness actually conferred advantages perceiving both image gist and details, consistent with reliance on highly detailed perceptual knowledge. Delusion-proneness, and especially predominance of delusion-proneness over hallucination-proneness, conferred disadvantage perceiving image details but not image gist, though evidence of specific impairment of detail perception was preliminary. We suggest this is consistent with reliance on abstract, belief-like knowledge. We posit that phenomenological variability in psychotic experiences may be driven by variability in the type of knowledge observers rely upon to resolve perceptual ambiguity

The promises and pitfalls of applying computational models to neurological and psychiatric disorders

Computational models have become an integral part of basic neuroscience and have facilitated some of the major advances in the field. More recently, such models have also been applied to the understanding of disruptions in brain function. In this review, using examples and a simple analogy, we discuss the potential for computational models to inform our understanding of brain function and dysfunction. We argue that they may provide, in unprecedented detail, an understanding of the neurobiological and mental basis of brain disorders and that such insights will be key to progress in diagnosis and treatment. However, there are also potential problems attending this approach. We highlight these and identify simple principles that should always govern the use of computational models in clinical neuroscience, noting especially the importance of a clear specification of a model’s purpose and of the mapping between mathematical concepts and reality

Evidence of absence: no relationship between behaviourally measured prediction error response and schizotypy

Introduction: The predictive processing framework has attracted much interest in the field of schizophrenia research in recent years, with an increasing number of studies also carried out in healthy individuals with nonclinical psychosis-like experiences. The current research adopted a continuum approach to psychosis and aimed to investigate different types of prediction error responses in relation to psychometrically defined schizotypy. Methods: 102 healthy volunteers underwent a battery of behavioural tasks including a) a force-matching task, b) a Kamin blocking task, and c) a reversal learning task together with three questionnaires measuring domains of schizotypy from different approaches. Results: Neither frequentist nor Bayesian statistical methods supported the notion that alterations in prediction error responses were related to schizotypal traits in any of the three tasks. Conclusions: These null results suggest that deficits in predictive processing associated with clinical states of psychosis are not always present in healthy individuals with schizotypal traits

Evidence of absence: no relationship between behaviourally measured prediction error response and schizotypy

Introduction: The predictive processing framework has attracted much interest in the field of schizophrenia research in recent years, with an increasing number of studies also carried out in healthy individuals with nonclinical psychosis-like experiences. The current research adopted a continuum approach to psychosis and aimed to investigate different types of prediction error responses in relation to psychometrically defined schizotypy. Methods: One hundred and two healthy volunteers underwent a battery of behavioural tasks including (a) a force-matching task, (b) a Kamin blocking task, and (c) a reversal learning task together with three questionnaires measuring domains of schizotypy from different approaches. Results: Neither frequentist nor Bayesian statistical methods supported the notion that alterations in prediction error responses were related to schizotypal traits in any of the three tasks. Conclusions: These null results suggest that deficits in predictive processing associated with clinical states of psychosis are not always present in healthy individuals with schizotypal traits

The role of priors in Bayesian models of perception

In a recent opinion article, Pellicano and Burr (2012) speculate about how a Bayesian architecture might explain many features of autism ranging from stereotypical movement to atypical phenomenological experience. We share the view of other commentators on this paper (Brock, 2012; Friston et al., 2013; Van Boxtel and Lu, 2013) that applying computational methods to psychiatric disorders is valuable (Montague et al., 2012). However, we argue that in this instance there are fundamental technical and conceptual problems which must be addressed if such a perspective is to become useful. Based on the Bayesian observer model (Figure 1), Pellicano and Burr speculate that perceptual abnormalities in autism can be explained by differences in how beliefs about the world are formed, or combined with sensory information, and that sensory processing itself is unaffected (although, confusingly, they also speak of sensory atypicalities in autism). In computational terms, the authors are suggesting that the likelihood function is unaltered in autism and that the posterior is atypical either because of differences in the prior, or because of the way in which prior and likelihood are combined. The latter statement is problematic because within the framework of probability theory, the combination of these two components is fixed as determined by Bayes' theorem: they are multiplied. Put simply, a mathematically consistent Bayesian model cannot accommodate a perceptual abnormality in autism that is due to the way in which belief and sensory information, i.e., prior and likelihood, are combined. Furthermore, if sensory processing is mathematically represented as a likelihood function (as it typically is within Bayesian models), then changes in the prior cannot lead to changes in sensation, as the authors claim (they can only lead to changes in perception)