A Data-Acquisition Model for Learning and Cognitive Development and Its Implications for Autism

摘要

A data-driven model of learning is proposed,where a network of nodes and links is constructed that represents whathas been heard and observed. Autism is viewed as the consequence of a disorder in the data-acquisitioncomponent of the model---essentially, it is the result of getting an``inappropriate'' distribution of data. The inappropriate data distribution leads to problems in datasegmentation, which, in turn leads to a poor network representation.It is shown how the model, giveninappropriate data distributions, can reproduce the main cognitive deficitsassociated with autism, including weak central coherence, impaired theory of mind, and executive dysfunction. In addition, it isshown how the model itself can explain the inappropriate datadistribution as the result of an inappropriate initial network. Finally, we discuss the relationships between our model and existing neurologicalmodels of autism, and the possible implications of our modelfor treatment.