No current laboratory test can reliably identify patients with schizophrenia. Instead,
key symptoms are observed via language, including derailment, where patients cannot follow
a coherent storyline, and delusions, where false beliefs are repeated as fact. Brain
processes underlying these and other symptoms remain unclear, and characterizing them
would greatly enhance our understanding of schizophrenia. In this situation, computational
models can be valuable tools to formulate testable hypotheses and to complement clinical
research. This dissertation aims to capture the link between biology and schizophrenic
symptoms using DISCERN, a connectionist model of human story processing. Competing
illness mechanisms proposed to underlie schizophrenia are simulated in DISCERN,
and are evaluated at the level of narrative language, the same level used to diagnose patients.
The result is the first simulation of a speaker with schizophrenia. Of all illness
models, hyperlearning, a model of overly intense memory consolidation, produced the best
fit to patient data, as well as compelling models of delusions and derailments. If validated
experimentally, the hyperlearning hypothesis could advance the current understanding of
schizophrenia, and provide a platform for simulating the effects of future treatments. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2010-12-2589 |
| Date | 07 February 2011 |
| Creators | Grasemann, Hans Ulrich |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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