Proceedings of the Annual Meeting of the Cognitive Science Society

A Self-Organizing Map (SOM) is a type of artificial neural network. Artificial neurons in the SOM form local assembliesthat become specialized in responding to categories of stimuli. Assemblies emerge through competition and cooperationbetween artificial neurons. Here we present a SOM aimed to model autism by means of increasing cooperation betweenneurons in the map. Descriptions of local hyperconnectivity in neuronal circuits in ASD make our implementation bio-logically sound. Remarkably, the change in low-level processing of our model, led to high level atypicalities mirroringASD behavior. Increasing cooperation produced deficient organization of neuronal assemblies accounting for fragmentedrepresentations of perceptual categories, idiosyncratic use of word labels, and atypical shape bias in lexical development.The results of our model successfully matched the behavioral performance of children with ASD in a categorization task,and shed light on how to understand the atypical development of the neurocognitive profile of ASD.