Commentary: Perceptual learning in autism: over-specificity and possible remedies

Understanding Sensory-Motor Disorders in Autism Spectrum Disorders by Extending Hebbian Theory: Formation of a Rigid-Autonomous Phase Sequence

Autism spectrum disorder is a neuropsychiatric disorder characterized by persistent deficits in social communication and social interaction and restricted, repetitive patterns of behavior, interests, or activities. The symptoms invariably appear in early childhood and cause significant impairment in social, occupational, and other important functions. Various abnormalities in the genetic, neurological, and endocrine systems of patients with autism spectrum disorder have been reported as the etiology; however, no clear factor leading to the onset of the disease has been identified. Additionally, higher order cognitive dysfunctions, which are represented by a lack of theory of mind, sensorimotor disorders, and memory-related disorders (e.g., flashbacks), have been reported in recent years, but no theoretical framework has been proposed to explain these behavioral abnormalities. In this study, we extended Hebb's biopsychology theory to provide a theoretical framework that comprehensively explains the various behavioral abnormalities observed in autism spectrum disorder. Specifically, we propose that a wide range of symptoms in autism spectrum disorder may be caused by the formation of a rigid-autonomous phase sequence (RAPS) in the brain. Using the RAPS formation theory, we propose a biopsychological mechanism that could be a target for the treatment of autism spectrum disorders.

Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism

Children with autism spectrum disorders (ASD) often display atypical learning styles, however little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. Critically, while learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.

Towards a whole brain model of Perceptual Learning

A hallmark of modern Perceptual Learning (PL) is the extent to which learning is specific to the trained stimuli. Such specificity to orientation, spatial location and even eye of training has been used as psychophysical evidence of the neural basis of learning. This argument that specificity of PL implies regionalization of brain plasticity implicitly assumes that examination of a singular locus of PL is an appropriate approach to understand learning. However, recent research shows that learning effects once thought to be specific depend on subtleties of the training paradigm and that within even a simple training procedure there are multiple aspects of the task and stimuli that are learned simultaneously. Here, we suggest that learning on any task involves a broad network of brain regions undergoing changes in representations, read-out weights, decision rules, attention and feedback processes as well as oculomotor changes. However, importantly, the distribution of learning across the neural system depends upon the details of the training procedure and the characterstics of the individual being trained. We propose that to advance our understanding of PL, the field must move towards understanding how distributed brain processes jointly contribute to behavioral learning effects.