The linguistics of schizophrenia: thought disturbance as language pathology across positive symptoms

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech

Background

Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosis, capturing symptoms like thought disorder and flattened affect. Recent advancements in Natural Language Processing (NLP) methodologies enable the automated extraction of informative speech features, which has been leveraged for early psychosis detection and assessment of symptomology. However, critical gaps persist, including the absence of standardized sample collection protocols, small sample sizes, and a lack of multi-illness classification, limiting clinical applicability. Our study aimed to (1) identify an optimal assessment approach for the online and remote collection of speech, in the context of assessing the psychosis spectrum and evaluate whether a fully automated, speech-based machine learning (ML) pipeline can discriminate among different conditions on the schizophrenia-bipolar spectrum (SSD-BD-SPE), help-seeking comparison subjects (MDD), and healthy controls (HC) at varying layers of analysis and diagnostic complexity.

Methods

We adopted online data collection methods to collect 20 minutes of speech and demographic information from individuals. Participants were categorized as "healthy" help-seekers (HC), having a schizophrenia-spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), or being on the psychosis spectrum with sub-clinical psychotic experiences (SPE). SPE status was determined based on self-reported clinical diagnosis and responses to the PHQ-8 and PQ-16 screening questionnaires, while other diagnoses were determined based on self-report from participants. Linguistic and paralinguistic features were extracted and ensemble learning algorithms (e.g., XGBoost) were used to train models. A 70%-30% train-test split and 30-fold cross-validation was used to validate the model performance.

Results

The final analysis sample included 1140 individuals and 22,650 minutes of speech. Using 5-minutes of speech, our model could discriminate between HC and those with a serious mental illness (SSD or BD) with 86% accuracy (AUC = 0.91, Recall = 0.7, Precision = 0.98). Furthermore, our model could discern among HC, SPE, BD and SSD groups with 86% accuracy (F1 macro = 0.855, Recall Macro = 0.86, Precision Macro = 0.86). Finally, in a 5-class discrimination task including individuals with MDD, our model had 76% accuracy (F1 macro = 0.757, Recall Macro = 0.758, Precision Macro = 0.766).

Conclusion

Our ML pipeline demonstrated disorder-specific learning, achieving excellent or good accuracy across several classification tasks. We demonstrated that the screening of mental disorders is possible via a fully automated, remote speech assessment pipeline. We tested our model on relatively high number conditions (5 classes) in the literature and in a stratified sample of psychosis spectrum, including HC, SPE, SSD and BD (4 classes). We tested our model on a large sample (N = 1150) and demonstrated best-in-class accuracy with remotely collected speech data in the psychosis spectrum, however, further clinical validation is needed to test the reliability of model performance.

Natural Language Processing and Schizophrenia: A Scoping Review of Uses and Challenges

(1) Background: Approximately 1% of the global population is affected by schizophrenia, a disorder marked by cognitive deficits, delusions, hallucinations, and language issues. It is associated with genetic, neurological, and environmental factors, and linked to dopaminergic hyperactivity and neurotransmitter imbalances. Recent research reveals that patients exhibit significant language impairments, such as reduced verbal output and fluency. Advances in machine learning and natural language processing show potential for early diagnosis and personalized treatments, but additional research is required for the practical application and interpretation of such technology. The objective of this study is to explore the applications of natural language processing in patients diagnosed with schizophrenia. (2) Methods: A scoping review was conducted across multiple electronic databases, including Medline, PubMed, Embase, and PsycInfo. The search strategy utilized a combination of text words and subject headings, focusing on schizophrenia and natural language processing. Systematically extracted information included authors, population, primary uses of the natural language processing algorithms, main outcomes, and limitations. The quality of the identified studies was assessed. (3) Results: A total of 516 eligible articles were identified, from which 478 studies were excluded based on the first analysis of titles and abstracts. Of the remaining 38 studies, 18 were selected as part of this scoping review. The following six main uses of natural language processing were identified: diagnostic and predictive modeling, followed by specific linguistic phenomena, speech and communication analysis, social media and online content analysis, clinical and cognitive assessment, and linguistic feature analysis. (4) Conclusions: This review highlights the main uses of natural language processing in the field of schizophrenia and the need for more studies to validate the effectiveness of natural language processing in diagnosing and treating schizophrenia.

The effects of virtual reality immersion on the content and structure of the narrative discourse of healthy adults

BACKGROUND

Narrative discourse is central to effective participation in conversations. When discourse is assessed in people with communication disability, structured tasks (e.g., picture descriptions) provide experimental control, while unstructured tasks (e.g., personal narratives) represent more natural communication. Immersive virtual reality (VR) technology may provide a solution by creating standardized experiences for narrative retell, therefore balancing ecological validity and experimental control in discourse assessment. Research is needed to understand how VR immersion affects narrative retell, first for adults with no communication disability, before application with adults with aphasia or related communication disability.

AIMS

To assess (1) the effects of VR immersion on the linguistic content and structure of narrative retells in a healthy adult population; and (2) whether VR immersion can influence the way a narrative is retold so that the speaker conveys their own experience, rather than the experience of the characters they are watching.

METHODS & PROCEDURES

In this pilot cohort study, 13 healthy adult participants with no reported communication disability watched an animated short film and a comparable immersive VR short film in a randomized order. Participants were asked to retell the events of the story after each condition in as much detail as possible.

OUTCOMES & RESULTS

Mean length of utterance (in morphemes) was significantly higher in the video condition compared with the VR condition. Significantly more first-person pronouns were used in the VR condition compared with the video condition. No other measures of linguistic content or structure were significantly different between the VR and video conditions.

CONCLUSIONS & IMPLICATIONS

Increased morpho-syntactic length and complexity in the video condition may suggest effects of elicitation stimulus on the narrative produced. The larger number of first-person pronouns in the VR condition may reflect that participants experienced a sense of presence in the virtual environment, and therefore were able to retell their communication experience rather than narrating the experiences of characters from an external perspective. Given the increasing need for more functional assessment of discourse in people with communication disability, further research is needed to validate these findings.

WHAT THIS PAPER ADDS

What is already known on this subject As an ecologically valid tool, discourse analysis is often used to assess daily communicative exchanges in adults with acquired communication disability. Clinicians and researchers using narrative discourse assessment must balance the experimental control and diagnostic reference sample capabilities of structured tasks with the ecological validity and real-life transferability of unstructured personal narratives. What this study adds to existing knowledge This study explores the use of immersive VR technologies to create standardized, replicable, immersive experiences as a foundation for narrative discourse assessment. It highlights how the 'sense of presence' in a virtual world can prompt healthy adult speakers to retell a narrative of a personal experience that can be replicated for many different participants. The results suggest that immersive VR narrative assessment for adults with communication disability may balance ecological validity with measurement reliability in discourse assessment. What are the potential or actual clinical observations of this work? Immersion in VR resulted in the production of narratives with morpho-syntactic features that aligned with typical narrative generation, rather than retell. Participants used more first-person pronouns, suggesting retelling of personal experience. Though further study is needed, these preliminary findings suggest clinicians can use immersive VR stimuli to generate structured story generations that balance experimental and diagnostic control with ecological validity in narrative discourse assessment for adults with communication disability.

Agency of Subjects and Eye Movements in Schizophrenia Spectrum Disorders

People with schizophrenia spectrum disorders (SSD) show anomalies in language processing with respect to "who is doing what" in an action. This linguistic behavior is suggestive of an atypical representation of the formal concepts of "Agent" in the lexical representation of a verb, i.e., its thematic grid. To test this hypothesis, we administered a silent-reading task with sentences including a semantic violation of the animacy trait of the grammatical subject to 30 people with SSD and 30 healthy control participants (HCs). When the anomalous grammatical subject was the Agent of the event, a significant increase of Gaze Duration was observed in HCs, but not in SSDs. Conversely, when the anomalous subject was a Theme, SSDs displayed an increased probability of go-back movements, unlike HCs. These results are suggestive of a higher tolerability for anomalous Agents in SSD compared to the normal population. The fact that SSD participants did not show a similar tolerability for anomalous Themes rules out the issue of an attention deficit. We suggest that general communication abilities in SSD might benefit from explicit training on deep linguistic structures.

Automatic language analysis identifies and predicts schizophrenia in first-episode of psychosis

Automated language analysis of speech has been shown to distinguish healthy control (HC) vs chronic schizophrenia (SZ) groups, yet the predictive power on first-episode psychosis patients (FEP) and the generalization to non-English speakers remain unclear. We performed a cross-sectional and longitudinal (18 months) automated language analysis in 133 Spanish-speaking subjects from three groups: healthy control or HC (n = 49), FEP (n = 40), and chronic SZ (n = 44). Interviews were manually transcribed, and the analysis included 30 language features (4 verbal fluency; 20 verbal productivity; 6 semantic coherence). Our cross-sectional analysis showed that using the top ten ranked and decorrelated language features, an automated HC vs SZ classification achieved 85.9% accuracy. In our longitudinal analysis, 28 FEP patients were diagnosed with SZ at the end of the study. Here, combining demographics, PANSS, and language information, the prediction accuracy reached 77.5% mainly driven by semantic coherence information. Overall, we showed that language features from Spanish-speaking clinical interviews can distinguish HC vs chronic SZ, and predict SZ diagnosis in FEP patients.

Assessing Schizophrenia Patients through Linguistic and Acoustic Features using Deep Learning Techniques

Thought, language, and communication disorders are among the salient characteristics of schizophrenia. Such impairments are often exhibited in patients' conversations. Researches have shown that assessments of thought disorder are crucial for tracking the clinical patients' conditions and early detection of clinical high-risks. Detecting such symptoms require a trained clinician's expertise, which is prohibitive due to cost and the high patient-to-clinician ratio. In this paper, we propose a machine learning method using Transformer-based model to help automate the assessment of the severity of the thought disorder of schizophrenia. The proposed model uses both textual and acoustic speech between occupational therapists or psychiatric nurses and schizophrenia patients to predict the level of their thought disorder. Experimental results show that the proposed model has the ability to closely predict the results of assessments for Schizophrenia patients base on the extracted semantic, syntactic and acoustic features. Thus, we believe our model can be a helpful tool to doctors when they are assessing schizophrenia patients.

Construct Validity of the Pictogram Test for Diagnosis of Schizophrenia

Objectives: To examine construct validity of the Pictogram Test (PT) which assesses disturbances in thinking in individuals with schizophrenia. The PT was developed in Russia; it was found to be applicable for the English-speaking population of the USA. The variables of the PT were correlated with Minnesota Multiphasic Personality Inventory (MMPI-2).

Method: Russian-and English-speaking participants completed the PT and MMPI-2 in their native languages.

Results: The PT variables that reflected attribute selection choice of intermediate concepts for memorization and the variables that were geometrical shapes had significant correlations with MMPI-2 scales linked to schizophrenia. This represents evidence of convergent validity. The same PT indices did not significantly correlate with most of the MMPI-2 scales that are not elevated in schizophrenia, representing some discriminant validity of the PT.

Conclusions: The less often the participants were able to connect target words with economical intermediate concepts, the higher were the elevations of schizophrenia-related scales. Also, the more abstract and remote their intermediate concepts, the less often they recalled targets. These findings give evidence of the validity of the PT in assessing the thinking of individuals with schizophrenia and related conditions.

Natural Language Processing markers in first episode psychosis and people at clinical high-risk

Recent work has suggested that disorganised speech might be a powerful predictor of later psychotic illness in clinical high risk subjects. To that end, several automated measures to quantify disorganisation of transcribed speech have been proposed. However, it remains unclear which measures are most strongly associated with psychosis, how different measures are related to each other and what the best strategies are to collect speech data from participants. Here, we assessed whether twelve automated Natural Language Processing markers could differentiate transcribed speech excerpts from subjects at clinical high risk for psychosis, first episode psychosis patients and healthy control subjects (total N = 54). In-line with previous work, several measures showed significant differences between groups, including semantic coherence, speech graph connectivity and a measure of whether speech was on-topic, the latter of which outperformed the related measure of tangentiality. Most NLP measures examined were only weakly related to each other, suggesting they provide complementary information. Finally, we compared the ability of transcribed speech generated using different tasks to differentiate the groups. Speech generated from picture descriptions of the Thematic Apperception Test and a story re-telling task outperformed free speech, suggesting that choice of speech generation method may be an important consideration. Overall, quantitative speech markers represent a promising direction for future clinical applications.

Neurocognitive Impairment in Severe Mental Illness. Comparative study with Spanish Speaking Patients

BACKGROUND

Serious mental illness (SMI) represents a category of psychiatric disorders characterized by specific difficulties of personal and social functioning, derived from suffering severe and persistent mental health problems.

AIMS

We wanted to look into differences in cognitive performance among different SMI patients.

METHODS

Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) screening was applied in one sample of SMI patients (n = 149) and another of healthy comparison participants (n = 35). Within the SMI sample, three different subsamples were formed: one with 97 patients with schizophrenia, a second with 29 patients with mood disorders, and a third with 23 patients with personality disorder. We performed a comparative study within and between groups.

RESULTS

Analysis of covariance was performed. Significant differences were found for cognitive functioning including attention and memory.

CONCLUSIONS

RBANS can be recommended for the detection of neurocognitive deficits in psychiatric disorders, especially in Schizophrenia.

Differences in fractal patterns and characteristic periodicities between word salads and normal sentences: Interference of meaning and sound

Fractal dimensions and characteristic periodicities were evaluated in normal sentences, computer-generated word salads, and word salads from schizophrenia patients, in both Japanese and English, using the random walk patterns of vowels.　In normal sentences, the walking curves were smooth with gentle undulations, whereas computer-generated word salads were rugged with mechanical repetitions, and word salads from patients with schizophrenia were unreasonably winding with meaningless repetitive patterns or even artistic cohesion. These tendencies were similar in both languages. Fractal dimensions between normal sentences and word salads of schizophrenia were significantly different in Japanese [1.19 ± 0.09 (n = 90) and 1.15 ± 0.08 (n = 45), respectively] and English [1.20 ± 0.08 (n = 91), and 1.16 ± 0.08 (n = 42)] (p < 0.05 for both). Differences in long-range (>10) periodicities between normal sentences and word salads from schizophrenia patients were predominantly observed at 25.6 (p < 0.01) in Japanese and 10.7 (p < 0.01) in English. The differences in fractal dimension and characteristic periodicities of relatively long-range (>10) presented here are sensitive to discriminate between schizophrenia and healthy mental state, and could be implemented in social robots to assess the mental state of people in care.

Small Words That Matter: Linguistic Style and Conceptual Disorganization in Untreated First-Episode Schizophrenia

This study aimed to shed light on the linguistic style affecting the communication discourse in first-episode schizophrenia (FES) by investigating the analytic thinking index in relation to clinical scores of conceptual and thought disorganization (Positive and Negative Syndrome Scale, PANSS-P2 and Thought and Language Index, TLI). Using robust Bayesian modeling, we report three major findings: (1) FES subjects showed reduced analytic thinking, exhibiting a less categorical linguistic style than healthy control (HC) subjects (Bayes factor, BF10 > 1000), despite using the same proportion of function and content words as HCs; (2) the lower the analytic thinking score, the higher the symptoms scores of conceptual disorganization (PANSS-P2, BF = 22.66) and global disorganization of thinking (TLI, BF10 = 112.73); (3) the linguistic style is a better predictor of conceptual disorganization than the cognitive measure of processing speed in schizophrenia (SZ). These findings provide an objectively detectable linguistic style with a focus on Natural Language Processing Analytics of transcribed speech samples of patients with SZ that require no clinical judgment. These findings also offer a crucial insight into the primacy of linguistic structural disruption in clinically ascertained disorganized thinking in SZ. Our work contributes to an emerging body of literature on the psychopathology of SZ using a first-order lexeme-level analysis and a hypothesis-driven approach. At a utilitarian level, this has implications for improving educational and social outcomes in patients with SZ.

Theory of Mind, Executive Functions, and Syntax in Bilingual Children with Autism Spectrum Disorder

Impairments in Theory of Mind (ToM) are a core feature of Autism Spectrum Disorder (ASD). ToM may be enhanced by various factors, including bilingualism, executive functions (EF), and complex syntax. This work investigates the language-cognition interface in ASD by exploring whether ToM can be enhanced by bilingualism, whether such ToM boosts would be due to EF or syntax, and whether routes to mentalizing would differ between bilinguals and monolinguals on the spectrum. Twenty-seven monolingual Greek-speaking and twenty-nine bilingual Albanian-Greek children with ASD were tested on ToM reasoning in verbal and low-verbal ToM tasks, an executive function 2-back task, and a sentence repetition task. Results revealed that bilingual children with ASD performed better than monolinguals with ASD in the low-verbal ToM and the 2-back tasks. In the sentence repetition task, bilinguals scored higher than monolinguals in complex sentences, and specifically in adverbials and relatives. Regarding the relations between ToM, EF, and sentence repetition, the monolingual group’s performance in the verbal ToM tasks was associated with complement syntax, whereas, for the bilingual children with ASD, performance in both verbal and low-verbal ToM tasks was associated with EF and adverbial clause repetition. The overall pattern of results suggests that mentalizing may follow distinct pathways across the two groups.

[Executive control of language production in schizophrenia: a pilot neuropsychological study]

OBJECTIVE

To test the general hypothesis about executive deficits in language production in schizophrenia as well as more specific hypothesis that this deficit would be more pronounced in the case of higher demand on executive functions.

MATERIAL AND METHODS

Twenty-five patients with schizophrenia and twenty-seven healthy controls were asked to tell a story based on a series of pictures and then to give an oral composition on the given topic.

RESULTS AND CONCLUSION

Schizophrenia patients, compared to controls, demonstrated poorer programming as well as shorter text and phrase length in both tasks. Oral composition on the given topic in patients was characterized by the presence of agrammatism, need for leading questions due to the difficulties of story plot generation as well as higher variance in syntactic complexity and text length. Therefore, the authors revealed executive deficit in language production, more pronounced in the task with less numerous external cues for planning and sequential text explication, in schizophrenia patients.

Wernicke-Kleist-Leonhard phenotypes of endogenous psychoses: a review of their validity

While the ICD-DSM paradigm has been a major advance in clinical psychiatry, its usefulness for biological psychiatry is debated. By defining consensus-based disorders rather than empirically driven phenotypes, consensus classifications were not an implementation of the biomedical paradigm. In the field of endogenous psychoses, the Wernicke-Kleist-Leonhard (WKL) pathway has optimized the descriptions of 35 major phenotypes using common medical heuristics on lifelong diachronic observations. Regarding their construct validity, WKL phenotypes have good reliability and predictive and face validity. WKL phenotypes come with remarkable evidence for differential validity on age of onset, familiality, pregnancy complications, precipitating factors, and treatment response. Most impressive is the replicated separation of high- and low-familiality phenotypes. Created in the purest tradition of the biomedical paradigm, the WKL phenotypes deserve to be contrasted as credible alternatives with other approaches currently under discussion.
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Modeling Incoherent Discourse in Non-Affective Psychosis

BACKGROUND

Computational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis.

METHODS

Speech samples were obtained from patients with a diagnosis of schizophrenia or schizoaffective disorder, who were divided into two groups of n = 20 subjects each, based on different clinical ratings of positive formal thought disorder, and n = 20 healthy control subjects.

RESULTS

Coherence metrics that were automatically derived from interview transcripts significantly predicted clinical ratings of thought disorder. Significant results from multinomial regression analysis revealed that group membership (controls vs. patients with vs. without formal thought disorder) could be predicted based on automated coherence analysis when bias was considered. Further improvement of the regression model was reached by including variables that psychiatric research has shown to inform clinical diagnostics of positive formal thought disorder.

CONCLUSIONS

Automated coherence analysis may capture different features of incoherent speech than clinical ratings of formal thought disorder. Models of incoherence in non-affective psychosis should include automatically derived coherence metrics as well as lexical and syntactic features that influence the comprehensibility of speech.

Language in schizophrenia and aphasia: the relationship with non-verbal cognition and thought disorder

Objective: To determine the relationship between language abnormalities and broader cognitive impairment and thought disorder by examining language and cognition in schizophrenia and aphasia (a primary language disorder).Methods: Cognitive and linguistic profiles were measured with a battery of standardised tests, and compared in a clinical population of n = 50 (n = 30 with schizophrenia and n = 20 with aphasia) and n = 61 non-clinical comparisons (n = 45 healthy controls and n = 16 non-affected first-degree relatives of patients with schizophrenia).Results: Both clinical groups showed linguistic deficits. Verbal impairment was more severe in participants with aphasia, whereas non-verbal performance was more affected in participants with schizophrenia. In schizophrenia, but not in aphasia, verbal and non-verbal performance were associated. Formal thought disorder was associated with impairment in executive function and in grammatical, but not naming, tasks.Conclusion: While patients with schizophrenia and aphasia showed language impairments, the nature and cognitive basis of these impairments may be different; language performance disassociates from broader cognitive functioning in aphasia but may be an intrinsic expression of a broader cognitive impairment in schizophrenia. Thought disorder may represent a core malfunction of grammatical processing. Results suggests that communicative ability may be a valid target in cognitive remediation strategies in schizophrenia.

Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook

Although most patients who experience a first-episode of psychosis achieve remission of positive psychotic symptoms, relapse is common. Existing relapse evaluation strategies are limited by their reliance on direct and timely contact with professionals, and accurate reporting of symptoms. A method by which to objectively identify early relapse warning signs could facilitate swift intervention. We collected 52,815 Facebook posts across 51 participants with recent onset psychosis (mean age = 23.96 years; 70.58% male) and applied anomaly detection to explore linguistic and behavioral changes associated with psychotic relapse. We built a one-class classification model that makes patient-specific personalized predictions on risk to relapse. Significant differences were identified in the words posted to Facebook in the month preceding a relapse hospitalization compared to periods of relative health, including increased usage of words belonging to the swear (p < 0.0001, Wilcoxon signed rank test), anger (p < 0.001), and death (p < 0.0001) categories, decreased usage of words belonging to work (p = 0.00579), friends (p < 0.0001), and health (p < 0.0001) categories, as well as a significantly increased use of first (p < 0.0001) and second-person (p  < 0.001) pronouns. We additionally observed a significant increase in co-tagging (p < 0.001) and friending (p < 0.0001) behaviors in the month before a relapse hospitalization. Our classifier achieved a specificity of 0.71 in predicting relapse. Results indicate that social media activity captures objective linguistic and behavioral markers of psychotic relapse in young individuals with recent onset psychosis. Machine-learning models were capable of making personalized predictions of imminent relapse hospitalizations at the patient-specific level.

Deictic Navigation Network: Linguistic Viewpoint Disturbances in Schizophrenia

This paper introduces the Deictic Navigation Network, a cognitive-linguistic framework to analyze and clarify the nature of viewpoint disturbances in language, applied to schizophrenia. We argue that such disturbances have linguistic counterparts in the use of deixis: linguistic elements of which the interpretation relies on the situational context of the discourse and their connection to a subject-bound perspective. The DNN connects such linguistic phenomena to three viewpoint disturbances, which can manifest in different degrees of extremity: (i) the reduced capacity to recognize one's own subjective perspective and the subjective perspectives of others; (ii) the reduced capacity to separate present perspectives from distinct past, future, and hypothetical perspectives; and (iii) the reduced capacity to integrate projected viewpoint structures into the actual here-and-now. We explain how application of the DNN to language in schizophrenia enables the localization of perspectivization disturbances and helps to clarify the nature of disturbances in the ability to build complex viewpoint structures in language as well as cognition.

Disturbing the rhythm of thought: Speech pausing patterns in schizophrenia, with and without formal thought disorder

Everyday speech is produced with an intricate timing pattern and rhythm. Speech units follow each other with short interleaving pauses, which can be either bridged by fillers (erm, ah) or empty. Through their syntactic positions, pauses connect to the thoughts expressed. We investigated whether disturbances of thought in schizophrenia are manifest in patterns at this level of linguistic organization, whether these are seen in first degree relatives (FDR) and how specific they are to formal thought disorder (FTD). Spontaneous speech from 15 participants without FTD (SZ-FTD), 15 with FTD (SZ+FTD), 15 FDRs and 15 neurotypical controls (NC) was obtained from a comic strip retelling task and rated for pauses subclassified by syntactic position and duration. SZ-FTD produced significantly more unfilled pauses than NC in utterance-initial positions and before embedded clauses. Unfilled pauses occurring within clausal units did not distinguish any groups. SZ-FTD also differed from SZ+FTD in producing significantly more pauses before embedded clauses. SZ+FTD differed from NC and FDR only in producing longer utterance-initial pauses. FDRs produced significantly fewer fillers than NC. Results reveal that the temporal organization of speech is an important window on disturbances of the thought process and how these relate to language.

A Comprehensive Review of Computational Methods for Automatic Prediction of Schizophrenia With Insight Into Indigenous Populations

Psychiatrists rely on language and speech behavior as one of the main clues in psychiatric diagnosis. Descriptive psychopathology and phenomenology form the basis of a common language used by psychiatrists to describe abnormal mental states. This conventional technique of clinical observation informed early studies on disturbances of thought form, speech, and language observed in psychosis and schizophrenia. These findings resulted in language models that were used as tools in psychosis research that concerned itself with the links between formal thought disorder and language disturbances observed in schizophrenia. The end result was the development of clinical rating scales measuring severity of disturbances in speech, language, and thought form. However, these linguistic measures do not fully capture the richness of human discourse and are time-consuming and subjective when measured against psychometric rating scales. These linguistic measures have not considered the influence of culture on psychopathology. With recent advances in computational sciences, we have seen a re-emergence of novel research using computing methods to analyze free speech for improving prediction and diagnosis of psychosis. Current studies on automated speech analysis examining for semantic incoherence are carried out based on natural language processing and acoustic analysis, which, in some studies, have been combined with machine learning approaches for classification and prediction purposes.

The language profile of formal thought disorder

Formal thought disorder (FTD) is clinically manifested as disorganized speech, but there have been only few investigations of its linguistic properties. We examined how disturbance of thought may relate to the referential function of language as expressed in the use of noun phrases (NPs) and the complexity of sentence structures. We used a comic strip description task to elicit language samples from 30 participants with schizophrenia (SZ), 15 with moderate or severe FTD (SZ + FTD), and 15 minimal or no FTD (SZ−FTD), as well as 15 first-degree relatives of people with SZ (FDRs) and 15 neurotypical controls (NC). We predicted that anomalies in the normal referential use of NPs, sub-divided into definite and indefinite NPs, would identify FTD; and also that FTD would also be linked to reduced linguistic complexity as specifically measured by the number of embedded clauses and of grammatical dependents. Participants with SZ + FTD produced more referential anomalies than NC and produced the fewest definite NPs, while FDRs produced the most and thus also differed from NC. When referential anomalies were classed according to the NP type in which they occurred, the SZ + FTD group produced more anomalies in definite NPs than NC. Syntactic errors did not distinguish groups, but the SZ + FTD group exhibited significantly less syntactic complexity than non-SZ groups. Exploratory regression analyses suggested that production of definite NPs distinguished the two SZ groups. These results demonstrate that FTD can be identified in specific grammatical patterns which provide new targets for detection, intervention, and neurobiological studies.

Deictic and Propositional Meaning-New Perspectives on Language in Schizophrenia

Emerging linguistic evidence points at disordered language behavior as a defining characteristic of schizophrenia. In this article, we review this literature and demonstrate how a framework focusing on two core functions of language-reference and propositional meaning-can conceptualize schizophrenic symptoms, identify important variables for risk assessment, diagnosis, and treatment, and inform cognitive behavioral therapy and other remedial approaches. We introduce the linguistic phenomena of deictic anchoring and propositional complexity, explain how they relate to schizophrenic symptoms, and show how they can be tracked in language behavior.

Bridging the Gap between Genes and Language Deficits in Schizophrenia: An Oscillopathic Approach

Schizophrenia is characterized by marked language deficits, but it is not clear how these deficits arise from the alteration of genes related to the disease. The goal of this paper is to aid the bridging of the gap between genes and schizophrenia and, ultimately, give support to the view that the abnormal presentation of language in this condition is heavily rooted in the evolutionary processes that brought about modern language. To that end we will focus on how the schizophrenic brain processes language and, particularly, on its distinctive oscillatory profile during language processing. Additionally, we will show that candidate genes for schizophrenia are overrepresented among the set of genes that are believed to be important for the evolution of the human faculty of language. These genes crucially include (and are related to) genes involved in brain rhythmicity. We will claim that this translational effort and the links we uncover may help develop an understanding of language evolution, along with the etiology of schizophrenia, its clinical/linguistic profile, and its high prevalence among modern populations.

Can delusions be understood linguistically?

Delusions are widely believed to reflect disturbed cognitive function, but the nature of this remains elusive. The "un-Cartesian" cognitive-linguistic hypothesis maintains (a) that there is no thought separate from language, that is, there is no distinct mental space removed from language where "thinking" takes place; and (b) that a somewhat broadened concept of grammar is responsible for bestowing meaning on propositions, and this among other things gives them their quality of being true or false. It is argued that a loss of propositional meaning explains why delusions are false, impossible and sometimes fantastic. A closely related abnormality, failure of linguistic embedding, can additionally account for why delusions are held with fixed conviction and are not adequately justified by the patient. The un-Cartesian linguistic approach to delusions has points of contact with Frith's theory that inability to form meta-representations underlies a range of schizophrenic symptoms. It may also be relevant to the nature of the "second factor" in monothematic delusions in neurological disease. Finally, it can inform the current debate about whether or not delusions really are beliefs.

The linguistic roots of natural pedagogy

Natural pedagogy is a human-specific capacity that allows us to acquire cultural information from communication even before the emergence of the first words, encompassing three core elements: (i) a sensitivity to ostensive signals like eye contact that indicate to infants that they are being addressed through communication, (ii) a subsequent referential expectation (satisfied by the use of declarative gestures) and (iii) a biased interpretation of ostensive-referential communication as conveying relevant information about the referent's kind (Csibra and Gergely, 2006, 2009, 2011). Remarkably, the link between natural pedagogy and another human-specific capacity, namely language, has rarely been investigated in detail. We here argue that children's production and comprehension of declarative gestures around 10 months of age are in fact expressions of an evolving faculty of language. Through both declarative gestures and ostensive signals, infants can assign the roles of third, second, and first person, building the 'deictic space' that grounds both natural pedagogy and language use. Secondly, we argue that the emergence of two kinds of linguistic structures (i.e., proto-determiner phrases and proto-sentences) in the one-word period sheds light on the different kinds of information that children can acquire or convey at different stages of development (namely, generic knowledge about kinds and knowledge about particular events/actions/state of affairs, respectively). Furthermore, the development of nominal and temporal reference in speech allows children to cognize information in terms of spatial and temporal relations. In this way, natural pedagogy transpires as an inherent aspect of our faculty of language, rather than as an independent adaptation that pre-dates language in evolution or development (Csibra and Gergely, 2006). This hypothesis is further testable through predictions it makes on the different linguistic profiles of toddlers with developmental disorders.