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.
In Proceedings of the 33rd Annual Meeting of the Cognitive Science Society 2011.

Uli Grasemann Postdoctoral Alumni uli [at] cs utexas edu
Ralph E. Hoffman Collaborator ralph hoffman [at] yale edu
Risto Miikkulainen Faculty risto [at] cs utexas edu
DISCERN DISCERN is a large, modular neural network system for reading, paraphrasing and answering questions about stereotypical ... 1993