The emergence of autism in the Bayesian brain
2017-03-02T04:19:18Z (GMT) by
The brain represents the state of the world around us based upon a stream of ambiguous sensory data. This requires the integration of different sources of information, including information received from different sense modalities, and information drawn from past experience. Bayesian theories of perception provide an approach to characterising how systematic differences in the probabilistic integration of information in the brain may underlie the differences that occur between individuals in their perceptual experience. The present thesis explores how perceptual, motor and social aspects of autism spectrum disorder (ASD) may emerge from variation in the neurocognitive processes described by Bayesian theories. A focus is on the predictive processing model of brain function, which links Bayesian theories of perception to neural mechanisms. The thesis includes empirical studies that examine perceptual, sensorimotor and cognitive aspects of ASD. ASD and nonclinical autistic traits are first examined in the context of body perception; concerning, specifically, how the brain represents the state of the limbs based on visual, tactile and proprioceptive information, and how this sensory information is integrated with prior expectations regarding the body. These processes are investigated using a multisensory perceptual illusion: the rubber hand illusion. ASD is associated with a largely typical perceptual experience of this illusion, indicating intact multisensory integration in the context of body representation. A reduced influence of the illusion on motor function, however, supports a difference in ASD in the integration of expectations for limb position (influenced by the illusion) with conflicting sensory (proprioceptive) signals. The integration of incoming information with existing expectations about the environment is also examined in the context of statistical learning. A computerised task is developed to assess the integration of iterative feedback with prior information as participants predict the location of a noisy set of visual markers. The hypothesis tested is that features of ASD are associated with a persistently higher weighting of incoming information in driving inference, at the expense of expectations developed from recent experience; in this more cognitive context, these data provide evidence against this hypothesis, suggesting that a more context-dependent atypicality in information processing in ASD is more likely than that initially hypothesised. The thesis also develops theoretical and philosophically-relevant treatments of how the symptoms of ASD may emerge from differences in brain function characterised within a Bayesian framework. This includes drawing on recent theoretical developments regarding the role of volatility processing and action in maintaining optimal inference on the environment. In addition, models of Bayesian inference in the brain can be extended to the social domain. For instance, the process of representing the mental states of other people (i.e., theory of mind) can be cast in terms of implicit inference on the external causes of sensory signals. In light of these expanded models of predictive processing, the differences in information processing hypothesised to occur in ASD have implications not just for perception, but also for motor behaviours, social cognition and social interaction. The overall view that emerges is that diverse aspects of ASD may be captured in terms of how incoming sensory signals are integrated, in a probabilistic manner, with the brain’s hierarchical and multimodal model of its environment. A promising direction for this field is in developing this idea in the context of action (i.e., active inference) and more recent models of how the brain estimates the optimal weighting of sensory information. This area of research has the potential to provide a nuanced perspective on the neurocognitive basis of ASD and the relationship between sensory mechanisms and autistic behaviours.