UC Merced

Autism is a developmental disorder characterized by a diverse set of behavioral characteristics, including social difficulties, seizures, motor abnormalities, executive dysfunction, and problems of inflexibility and overselectivity in learning. The breadth of this behavioral profile has made identifying the underlying neural mechanisms difficult for researchers seeking an explanation. An examination of the various roles played by the dopamine (DA) system in learning, attention, and cognitive control uncovers a surprising connection between DA and the symptoms of autism. DA abnormalities are associated with motor problems, repetitive behaviors, seizures, poor implicit learning, learning to follow eye gaze, and executive dysfunction. Led by these facts, I hypothesize that impaired interactions between DA and the prefrontal cortex (PFC) can explain many of the behavioral patterns observed in autism. Under my account, the PFC actively maintains context information that modulates processing in other brain areas so as to produce behavior appropriate for the current setting or situation. The DA system provides a mechanism for learning when PFC contents should be updated to support shifting task contingencies. I hypothesize that inflexibility in the updating of PFC contents, caused by dysfunctional DA/PFC interactions, is at the heart of many behaviors seen in autism. In this document I demonstrate the viability of this hypothesis by perturbing the updating of PFC in five computational models of healthy cognition, covering five distinct behavioral domains, producing autistic patterns of performance in all five cases through this common biological deficit. Specifically, I show how abnormal DA/PFC interactions can explain executive dysfunction, differences during the learning of category structures, impaired implicit learning, difficulties utilizing contextual information to disambiguate homographs, and overselective behavior in people with autism. Thus, I offer a unifying biological account of phenomena that have previously been treated separately in the autism literature and demonstrate the usefulness of the tools of computational modeling in this endeavour.