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.

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PhD Thesis, Department of Computer Science, The University of Texas at Austin. 147 pages. Technical report TR-11-11.

@phdthesis{grasemann:phd10, title={A Computational Model of Language Pathology in Schizophrenia}, author={Uli Grasemann}, month={December}, school={Department of Computer Science, The University of Texas at Austin}, address={Austin, TX}, pages={147 pages}, note={Technical report TR-11-11}, url="http://www.cs.utexas.edu/users/ai-lab?grasemann:phd10", year={2010} }