UTCS Artificial Intelligence
courses
talks/events
demos
people
projects
publications
software/data
labs
areas
admin
Modeling Acute and Compensated Language Disturbance in Schizophrenia (2011)
Uli Grasemann
,
Ralph Hoffman
and
Risto Miikkulainen
No current laboratory test can reliably identify patients with schizophrenia. Instead, key symptoms are observed via language, including derailments, 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 work 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, i.e. the same level used to diagnose patients. The result is the first simulation of abnormal storytelling in schizophrenia, both in acute psychotic and compensated stages of the disorder. Of all illness models tested, hyperlearning, a model of overly intense memory consolidation, produced the best fit to the language abnormalities of stable outpatients, as well as compelling models of acute psychotic symptoms. If validated experimentally, the hyperlearning hypothesis could advance the current understanding of schizophrenia, and provide a platform for developing future treatments for this disorder.
View:
PDF
Citation:
In
Proceedings of the 33rd Annual Meeting of the Cognitive Science Society
2011.
Bibtex:
@inproceedings{grasemann:cogsci11, title={Modeling Acute and Compensated Language Disturbance in Schizophrenia}, author={Uli Grasemann and Ralph Hoffman and Risto Miikkulainen}, booktitle={Proceedings of the 33rd Annual Meeting of the Cognitive Science Society}, url="http://www.cs.utexas.edu/users/ai-lab?grasemann:cogsci11", year={2011} }
People
Uli Grasemann
Postdoctoral Alumni
uli [at] cs utexas edu
Ralph E. Hoffman
Formerly affiliated Collaborator
ralph hoffman [at] yale edu
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Projects
Neural Network Models of Schizophrenic Language
2003 - Present
Areas of Interest
Brain and Cognitive Disorders
Cognitive Science
Computational Neuroscience
Memory
Natural Language Processing (Cognitive)
Software/Data
DISCERN
DISCERN is a large, modular neural network system for reading, paraphrasing and answering questions about stereotypical ...
1993
Demos
A Subsymbolic Model of Schizophrenic Language
Uli Grasemann
2010
Labs
Neural Networks