Apprehending schizophrenic discourse: A structural analysis of the Listener's task

doi:10.1016/0093-934X(82)90057-8

Brain and Language

Volume 15, Issue 2, March 1982, Pages 207-233

https://doi.org/10.1016/0093-934X(82)90057-8 Get rights and content

Abstract

Characterizing aberrances specific to schizophrenic utterances, either purely in experiential terms or simply as features intrinsic to the language material itself, has demonstrated important limitations. Consequently, a model of listener interaction with text was pursued whereby constituent statements of discourse are organized on interpretation into hierarchal structures (or “bases”) according to their mutual presupposition relationships. “Coherence” was taken as the experiential signifier of a text base realization whose geometry conforms to a set of five conditions that collectively define “strong hierarchy” (SH). Violations of particular axioms yielded a typology of textual incoherence, referred to collectively as “non-strong-hierarchal” (NSH) discourse. In the experimental portion of the paper, determination of NSH discourse was found to classify speech samples randomly selected from a psychiatric population into schizophrenic and nonschizophrenic groups with a high degree of accurracy. Clinician responses to these same samples were studied. Classificatory accuracy of individual descriptive indices of “thought disorder” was found to be poor. Discriminant analyses for individual clinicians improved classificatory accuracy considerably. The two clinicians who retrospectively could be turned into the most powerful discriminators for schizophrenia also demonstrated the higher canonical correlations with the discourse analysis. These data suggest that clinician competence to a certain extent is modeled by the discourse analysis. The implications of these findings in terms of what is in the mind of the schizophrenic speaker were discussed, and a program for future research in this area was outlined.

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Disrupted language in psychotic disorders, such as schizophrenia, can manifest as false contents and formal deviations, often described as thought disorder. These features play a critical role in the social dysfunction associated with psychosis, but we continue to lack insights regarding how and why these symptoms develop. Natural language generation (NLG) is a field of computer science that focuses on generating human-like language for various applications. The theory that psychosis is related to the evolution of language in humans suggests that NLG systems that are sufficiently evolved to generate human-like language may also exhibit psychosis-like features. In this conceptual review, we propose using NLG systems that are at various stages of development as in silico tools to study linguistic features of psychosis. We argue that a program of in silico experimental research on the network architecture, function, learning rules, and training of NLG systems can help us understand better why thought disorder occurs in patients. This will allow us to gain a better understanding of the relationship between language and psychosis and potentially pave the way for new therapeutic approaches to address this vexing challenge.
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Of particular interest is the relationship between lexical alignment and positive thought disorder, which is the most obvious clinical manifestation of linguistic communicative difficulties in schizophrenia (American Psychiatric Association, 2013). Some previous studies have linked thought disorder to impairments in taking the listeners' perspective into account (Rochester et al., 1977; Hoffman et al., 1982; Hoffman, 1986; see also Rutter, 1985; Harrow et al., 1989), and to more severe impairments in mentalizing ability (Frith and Corcoran, 1996). There is also preliminary evidence that thought-disordered patients exhibit impaired alignment at other levels of linguistic representation (syntax & description types; Dwyer et al., 2020).
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Beginning in the 1980s, Hoffman and colleagues used theory and modeling to create a body of work on language in schizophrenia that informs the current emergence of automated NLP approaches. First, drawing on the hierarchical nature of language, they operationalized clinician measures of coherence in speech, with tests of classification based on random sampling of speech from a psychiatric population, finding high accuracy for discrimination of speech in schizophrenia from that of other disorders (21). Then, using discourse analysis, they showed that in schizophrenia, basic discourse structure was disrupted, while in mania, decreased coherence comes from increased shifts from one discourse structure to another (22), anticipating findings by Mota et al., who similarly discriminated language in mania from schizophrenia, using speech graph methods (16).
Increasingly, data-driven methods have been implemented to understand psychopathology. Language is the main source of information in psychiatry and represents “big data” at the level of the individual. Language and behavior are amenable to computational natural language processing (NLP) analytics, which may help operationalize the mental status examination. In this review, we highlight the application of NLP to schizophrenia and its risk states as an exemplar of its use, operationalizing tangential and concrete speech as reductions in semantic coherence and syntactic complexity, respectively. Other clinical applications are reviewed, including forecasting suicide risk and detecting intoxication. Challenges and future directions are discussed, including biomarker development, harmonization, and application of NLP more broadly to behavior, including intonation/prosody, facial expression and gesture, and the integration of these in dyads and during discourse. Similar NLP analytics can also be applied beyond humans to behavioral motifs across species, important for modeling psychopathology in animal models. Finally, clinical neuroscience can inform the development of artificial intelligence.
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Disturbance of language and communication is considered hallmark feature of schizophrenia, and has disruptive effects for patients in daily life. Assessment of speech and language dysfunction has traditionally relied on subjective evaluation by clinicians and performance-based clinical neuropsychological measures. These approaches can be imprecise in many respects, and this chapter will describe new automated, computerised approaches for assessing semantic, linguistic, and acoustic features of natural language in people with severe mental illness. These approaches involve the potential for both laboratory and ambulatory assessment across a broad range of contexts. This chapter will also highlight the current state of the technology for computational-based language assessment, and discuss potential obstacles to practical implementations.
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This has direct practical clinical relevance for understanding social dysfunction in psychosis; when a discourse lacks cohesion, listeners must work that much harder to extract meaning from the text or speech (Graesser et al., 2004; Haviland and Clark, 1974; McNamara and Graesser, 2014). Referential cohesion deficits have been consistently reported among schizophrenia patients (Ditman and Kuperberg, 2010; Ditman et al., 2011; Docherty et al., 1996; Hoffman, 1986; Hoffman et al., 1982; Noel-Jorand et al., 1997; Rochester and Martin, 1979). In a landmark study conducted by Rochester and Martin (1979), investigators developed a coding system to examine referential cohesion markers and applied this to transcribed speech of individuals with schizophrenia; the investigators found fewer referential markers used in this group compared to controls.
Schizophrenia and at-risk populations are suggested to exhibit referential cohesion deficits in language production (e.g., producing fewer pronouns or nouns that clearly link to concepts from previous sentences). Much of this work has focused on transcribed speech samples, while no work to our knowledge has examined referential cohesion in written narratives among ultra high risk (UHR) youth using Coh-Metrix, an automated analysis tool. In the present study, written narratives from 84 individuals (UHR = 41, control = 43) were examined. Referential cohesion variables and relationships with symptoms and relevant cognitive variables were also investigated. Findings reveal less word “stem” overlap in narratives produced by UHR youth compared to controls, and correlations with symptom domains and verbal learning. The present study highlights the potential usefulness of automated analysis of written narratives in identifying at-risk youth and these data provide critical information in better understanding the etiology of psychosis. As writing production is commonly elicited in educational contexts, markers of aberrant cohesion in writing represent significant potential for identifying youth who could benefit from further screening, and utilizing software that is easily accessible and free may provide utility in academic and clinical settings.
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Our findings also have social implications. Some researchers have discussed referential impairments in schizophrenia as stemming from a social communicative failure—a failure to take the communicator’s assumptions into account (7,8,72) [see also (73,74)]. In practice, however, given the speed of everyday communication, establishing and drawing on common referents in real time will depend largely on the speed at which both the comprehender and producer can access relevant information from memory [see (75–78)].
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We measured event-related potentials to pronouns in two-clause sentences in 16 people with schizophrenia and 20 demographically matched control participants. Our design crossed the number of potential referents (1-referent, 2-referent) with whether the pronoun matched the gender of its preceding referent(s) (matching, mismatching). This gave rise to four conditions: 1) 1-referent matching (“Edward took courses in accounting but he . . .”); 2) 2-referent matching (“Edward and Phillip took courses but he . . . ”); 3) 1-referent mismatching (“Edward took courses in accounting but she . . .”); and 4) 2-referent mismatching (“Edward and Phillip took courses but she . . .”).
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Apprehending schizophrenic discourse: A structural analysis of the Listener's task

Abstract

Brain and Language

Brain and language

Brain and Language

Journal of Verbal Learning and Verbal Behavior

Brain and Language

Thought, language, and communication disorders: I. Clinical assessment, definition of terms, and evaluation of their reliability

Archives of General Psychiatry

Thought, language, and communication disorders: II. Diagnostic Significance

Archives of General Psychiatry

The intraclass correlation coefficient as a measure of reliability

Psychological Reports

Schizophrenic short-term memory

Journal of Consulting and Clinical Psychology

Interference effects in schizophrenic short-term memory

Journal of Abnormal Psychology

Dementia praecox, or the group of schizophrenias

Schizophrenia, language, and reality

American Psychologist

Referent communication disturbances in acute schizophrenics

Journal of Abnormal Psychology

Ending structures in conversation

Overinclusion and self-editing in schizophrenia

Journal of Abnormal Psychology

Thought into speech

American Scientist

Pauses, prosody, and the demands of production in language

Language and interpretation in psychoanalysis

Mental activity in relaxed wakefulness

Journal of Abnormal Psychology

Effect of talk-induced cognitive operations on comprehension and memory processes