Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?

Abstract

Diagnosis and individualized treatment of autism spectrum disorder (ASD) represent major problems for contemporary psychiatry. Tackling these problems requires guidance by a pathophysiological theory. In this paper, we consider recent theories that re-conceptualize ASD from a “Bayesian brain” perspective, which posit that the core abnormality of ASD resides in perceptual aberrations due to a disbalance in the precision of prediction errors (sensory noise) relative to the precision of predictions (prior beliefs). This results in percepts that are dominated by sensory inputs and less guided by top-down regularization and shifts the perceptual focus to detailed aspects of the environment with difficulties in extracting meaning. While these Bayesian theories have inspired ongoing empirical studies, their clinical implications have not yet been carved out. Here, we consider how this Bayesian perspective on disease mechanisms in ASD might contribute to improving clinical care for affected individuals. Specifically, we describe a computational strategy, based on generative (e.g., hierarchical Bayesian) models of behavioral and functional neuroimaging data, for establishing diagnostic tests. These tests could provide estimates of specific cognitive processes underlying ASD and delineate pathophysiological mechanisms with concrete treatment targets. Written with a clinical audience in mind, this article outlines how the development of computational diagnostics applicable to behavioral and functional neuroimaging data in routine clinical practice could not only fundamentally alter our concept of ASD but eventually also transform the clinical management of this disorder.

Obsessive–compulsive disorder as a disorder of attention

“…My argument for a predictive coding account of OCD fits comfortably with related accounts of other neuropsychiatric conditions; ranging from autism (Haker, Schneebeli & Stephan, ), to psychosis (Adams, Stephan, Brown, Frith & Friston, ; Corlett, Frith & Fletcher, ), to hysterical (functional medical) symptoms (Edwards, Adams, Brown, Pareés & Friston, ). The underlying premise here is that nearly all psychiatric symptoms can be understood in terms of false inference.…”

Increased variability but intact integration during visual navigation in Autism Spectrum Disorder

“…An alternative, but not necessarily mutually exclusive explanation, is that the increased variability in path trajectories and end points does not reflect a heightened sensitivity to noise at the stage of encoding, but rather an increase volatility of beliefs. Specifically, it has recently been proposed that the core abnormality in ASD may reside in perceptual aberrations due to an imbalance in predictive coding (Haker et al, 2016;Van de Cruys et al, 2016). According to this hypothesis, individuals with ASD overestimate their sensory prediction errors, such that the world appears more volatile than it is.…”

“…Instead, a broader aberrant learning and inference may be the core component of maladaptive cognition within the condition. Maladaptive inference in ASD can arise from alterations in one of several core components (beyond priors and likelihoods of an extremely simplified Bayesian model) that span a multidimensional computational space (Haker et al, 2016). The hypothesized fundamental mechanism affected in ASD, hierarchical Bayesian learning, is much broader than just the simple equation argued in recent publications (Posterior = Prior x Likelihood).…”

“…In response to its pervasiveness, researchers have recently turned their attention to computational and normative tools attempting to identify canonical computations underlying ASD symptomatology (e.g., Robertson & Baron-Cohen, 2018;Rosenberg et al, 2015). A promising candidate family is that of probabilistic inference (Doya et al, 2007), and indeed a large number of Bayesian accounts of ASD have recently been put forward -positing an anomaly in the strength of Bayesian priors (Pellicano and Burr, 2012;Friston et al, 2013), the abnormal updating of these priors Lieder et al, 2019), the aberrant precision in sensory representations (Brock, 2012;Lawson et al, 2015;Zaidel et al, 2015;Karvelis et al, 2018), and the atypical weighting of sensory prediction error (Friston et al, 2013;Haker et al, 2016;van de Cruys et al, 2016).…”

Line drawings reveal the structure of internal visual models conveyed by cortical feedback.

“…Internal models play an active role in many human behaviours including visual attention 42 , higher cognitive functions such as memory and action planning 43 , decision making 44 and mental time travel 45 . Moreover, internal models are affected in mental disorders including major depression 46 , schizophrenia 47 and autism spectrum disorder 48,49 . Previous work has shown differences in the characteristics of line drawings made by schizophrenic patients 50 and in the ability of autistic children to predict objects from fragmented line drawings 51 .…”