Skip to main content


eCommons@Cornell

eCommons@Cornell >
Faculty of Computing and Information Science >
Computing and Information Science >
Computing and Information Science Technical Reports >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/10178
Title: A Data-Acquisition Model for Learning and Cognitive Development and Its Implications for Autism
Authors: Lotem, Arnon
Halpern, Joseph Y.
Keywords: autism
learning
Issue Date: 19-Mar-2008
Abstract: A data-driven model of learning is proposed, where a network of nodes and links is constructed that represents what has been heard and observed. Autism is viewed as the consequence of a disorder in the data-acquisition component of the model---essentially, it is the result of getting an ``inappropriate'' distribution of data. The inappropriate data distribution leads to problems in data segmentation, which, in turn leads to a poor network representation. It is shown how the model, given inappropriate data distributions, can reproduce the main cognitive deficits associated with autism, including weak central coherence, impaired theory of mind, and executive dysfunction. In addition, it is shown how the model itself can explain the inappropriate data distribution as the result of an inappropriate initial network. Finally, we discuss the relationships between our model and existing neurological models of autism, and the possible implications of our model for treatment.
URI: http://hdl.handle.net/1813/10178
Appears in Collections:Computing and Information Science Technical Reports

Files in This Item:

File Description SizeFormat
autism.pdf360.04 kBAdobe PDFView/Open

Refworks Export

Items in eCommons are protected by copyright, with all rights reserved, unless otherwise indicated.

 

© 2014 Cornell University Library Contact Us