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A Subsymbolic Model of Language Pathology in Schizophrenia

A Subsymbolic Model of Language Pathology in Schizophrenia Uli Grasemann Risto Miikkulainen Department of Computer Sciences, The University of Texas at Austin {uli, risto}@cs.utexas.edu Ralph Hoffman Department of Psychiatry, Yale University School of Medicine ralph.hoffman@yale.edu Abstract schizophrenia based on dysfunctional inhibition and prim- ing mechanisms. Cohen and colleagues (Cohen & Servan- Schreiber, 1992; Braver, Barch, & Cohen, 1999) modeled the interactions between prefrontal cortex and the DA system to explain impaired processing of context in schizophrenia. In a study more closely related to the present paper, Hoff- man & McGlashan (1997, 2006) used simple recurrent net- works (Harris & Elman, 1989) to simulate speech percep- tion in order to understand the mechanisms underlying hal- lucinated speech, which is one characteristic symptom of schizophrenia. Excessive pruning of recurrent connections caused spontaneous speech percepts to be generated, thus em- ulating hallucinated speech. This result is particularly inter- esting because significant reductions in cortical connectivity occur ordinarily during adolescence. That schizophrenia usu- ally first emerges during or shortly after this developmental period suggests that abnormal neurodevelopmental pruning may indeed be involved in the genesis of this disorder. The study reported in this paper extends this hypothesis to three other critical manifestations of schizophrenia, specifi- cally those that disrupt understanding and recall of stories. First, patients with delusions often discover spurious plots around them due to a tendency to confuse the actors in their personal stories with those of the shared stories of their cul- ture. Secondly, patients with language disorganization tend to insert extraneous language material that derails story re- call (Hoffman RE, Watts A, Varanko M, Lane D, Quinlan D, unpublished data). Finally, after an initial active stage of the disorder, patients tend to compensate for these positive symp- toms, showing reduced symptoms at the cost of curtailed lan- guage output (Ventura et al., 2004). The goal of this paper is to demonstrate that pathologies hypothesized to underlie schizophrenia can produce the ab- normalities described above in a computational model of hu- man story processing. The model used is an expanded ver- sion of DISCERN (Miikkulainen, 1993), a system that sim- ulates human story understanding and recall using a multi- modular neural network. DISCERN has been previously ex- tended to include a memory architecture that permits the sys- tem to store and recall complex stories composed of multiple story segments, or scripts (Fidelman, Miikkulainen, & Hoff- man, 2005). Further extensions of of the model include the capacity to process emotional content and an output filtering mechanism that allows the system to discard distorted lan- guage. This paper reports first results of a simulation of language pathology in schizophrenia. Using DISCERN, a subsymbolic model of story understanding and recall, the impact of differ- ent simulated lesions hypothesized to underlie schizophrenia is investigated. In response to excessive connection pruning, the model reproduces symptoms of delusions and disorganized language seen in schizophrenia, as well as the reduced out- put seen in compensated later states of the disorder. The ef- fects of other lesions are less consistent with the symptoms of schizophrenia. The model therefore forms a promising ba- sis for future computational investigations into the underlying causes of schizophrenia. Keywords: Cognitive Modeling; Schizophrenia; Neural Net- works; Natural Language Processing. Introduction Schizophrenia is a disabling psychiatric disorder character- ized by complex alterations of language and thought. Estab- lishing the physiological basis of its symptoms would greatly enhance our understanding of this disorder, possibly leading to more effective treatments. Yet after decades of intensive research, underlying brain processes remain uncertain (Ka- pur, 2003; Pantelis et al., 2005). Competing theories include reduced connectivity within and between cortical networks, drop-out of certain types of neurons, and disturbances in- volving subcortical dopamine (DA) neurons that affect corti- cal systems. Unfortunately currently available neuroimaging, pharmacological, and postmortem methods remain limited in characterizing neural substrates of schizophrenia. Thus, the schizophrenic brain remains a vexing “black-box” where out- put deviations challenge researchers to infer underlying pro- cesses. It is not surprising therefore that schizophrenia has become an important focus in the emerging field of computa- tional models of psychopathology. During the last two decades, researchers have tried to cap- ture aspects of schizophrenia and many other disorders us- ing connectionist models, with the goal of informing clinical research and resolving competing hypotheses about underly- ing brain mechanisms. In one of the earliest models of psy- chopathology, Hoffman (1987) investigated the emergence of “parasitic” stable states in attractor networks (Hopfield, 1982) to model delusions and hallucinations seen in schizophrenia. More recently, Ruppin, Reggia, and Horn (1996) used attrac- tor networks in a computational model of Stevens’s (1992) theory of the pathogenesis of schizophrenia. Spitzer (1997) and Silberman, Bentin, and Miikkulainen (2007) used self- organizing feature maps to model impaired lexical access in