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|Title: ||A Data-Acquisition Model for Learning and Cognitive Development and Its Implications for Autism|
|Authors: ||Lotem, Arnon|
Halpern, Joseph Y.
|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.
we discuss the relationships between our model and existing neurological
models of autism,
and the possible implications of our model
|Appears in Collections:||Computing and Information Science Technical Reports|
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