Autism Spectrum Disorder (ASD) affects about 78 million individuals worldwide, but the core mechanisms underlying this neurodevelopmental condition remain relatively unknown. ASD is defined by persistent deficits in social interactions and communication, and by restricted and repetitive behaviors. Recent predictive coding theories offer potential accounts of ASD. The predictive coding framework assumes that the brain constantly generates predictions about its environment. In ASD, the way these predictions are integrated with sensory information to produce a percept may be atypical. My research mainly focuses on the predictive coding theories of ASD, using behavioral tasks, questionnaires, computational models and various neuroimaging methods. I characterized the predictive abilities and sensory peculiarities of autistic individuals and identified their neural and molecular correlates. Altogether, my research project contributes to a better understanding of the mechanisms underlying ASD.

Difficulties encountered by autistic individuals could be due to suboptimal statistical learning. The main aim of this research project is to identify non-invasive ways to enhance statistical learning in autistic adults. Studies in neurotypicals showed that when a specific frontal region of the brain is less activated, learning is enhanced. This decreased activation was observed in NT using a non-invasive brain stimulation method or simply using music background while learning a task. In this project, we will investigate whether these methods can also decrease the activity level in this brain region in ASD, and most importantly, whether they can improve statistical learning in ASD.