Modeling Incoherent Discourse in Non-Affective Psychosis

Sleep and dream disturbances associated with dissociative experiences

Some dissociative experiences may be related, in part, to REM intrusion into waking consciousness. If so, some aspects of dream content may be associated with daytime dissociative experiences. We tested the hypothesis that some types of dream content would predict daytime dissociative symptomology. As part of a longitudinal study of the impact of dreams on everyday behavior we administered a battery of survey instruments to 219 volunteers. Assessments included the Dissociative Experiences Scale (DES), along with other measures known to be related to either REM intrusion effects or dissociative experiences. We also collected dream reports and sleep measures across a two-week period from a subgroup of the individuals in the baseline group. Of this subgroup we analyzed two different subsamples; 24 individuals with dream recall for at least half the nights in the two-week period; and 30 individuals who wore the DREEM Headband which captured measures of sleep architecture. In addition to using multiple regression analyses to quantify associations between DES and REM intrusion and dream content variables we used a split half procedure to create high vs low DES groups and then compared groups across all measures. Participants in the high DES group evidenced significantly greater nightmare distress scores, REM Behavior Disorder scores, paranormal beliefs, lucid dreams, and sleep onset times. Validated measures of dreamed first person perspective and overall dream coherence in a time series significantly predicted overall DES score accounting for 26% of the variance in dissociation. Dream phenomenology and coherence of the dreamed self significantly predicts dissociative symptomology as an individual trait. REM intrusion may be one source of dissociative experiences. Attempts to ameliorate dissociative symptoms or to treat nightmare distress should consider the stability of dream content as a viable indicator of dissociative tendencies.

Level of structural integration in people with schizophrenia and schizoaffective disorders - applicability and associations with clinical parameters

Introduction

The psychic structure of people with psychosis has been the subject of theoretical and qualitative considerations. However, it has not been sufficiently studied quantitatively. Therefore, the aim of this study was to explore the structural abilities of people diagnosed with schizophrenia and schizoaffective psychosis using the Levels of Structural Integration Axis of the Operationalized Psychodynamic Diagnosis System (OPD-2-LSIA). The study aimed to determine possible associations between the OPD-2-LSIA and central parameters of illness. Additionally, possible structural differences between people diagnosed with schizophrenia and schizoaffective psychosis were tested.

Methods

This cross-sectional study included 129 outpatients with schizophrenia or schizoaffective disorders. Measures of structural integration, symptom load, severity of illness, cognition, and social functioning were obtained. Descriptive statistics were used to analyze the overall structural level and the structural dimensions. Correlation coefficients were computed to measure the associations between OPD-2-LSIA and variables regarding the severity of illness and psychosocial functioning. Regression models were used to measure the influence of illness-related variables on OPD-2-LSIA, and the influence of OPD-2-LSIA on psychosocial functioning. Participants diagnosed with schizophrenia and schizoaffective disorders were examined with regard to possible group differences.

Results

The results of the OPD-2-LSIA showed that the overall structural level was between 'moderate to low' and 'low level of structural integration'. Significant correlations were found between OPD-2-LSIA and psychotic symptoms (but not depressive symptoms), as well as between OPD-2-LSIA and psychosocial functioning. It was found that variables related to severity of illness had a significant impact on OPD-2-LSIA, with psychotic, but not depressive symptoms being significant predictors. OPD-2-LSIA was found to predict psychosocial functioning beyond symptoms and cognition. No significant differences were found between participants with schizophrenia and schizoaffective psychosis. There was also no correlation found between OPD-2-LSIA and depressive symptomatology (except for the subdimension Internal communication).

Discussion

Contrary to theoretical assumptions, the results of the study show a heterogenous picture of the psychic structure of people with psychosis. The associations between OPD-2-LSIA and severity of illness, particularly psychotic symptomatology, as well as the influence of OPD-2-LSIA on psychosocial functioning, are discussed.

Speech based natural language profile before, during and after the onset of psychosis: A cluster analysis

BACKGROUND AND HYPOTHESIS

Speech markers are digitally acquired, computationally derived, quantifiable set of measures that reflect the state of neurocognitive processes relevant for social functioning. "Oddities" in language and communication have historically been seen as a core feature of schizophrenia. The application of natural language processing (NLP) to speech samples can elucidate even the most subtle deviations in language. We aim to determine if NLP based profiles that are distinctive of schizophrenia can be observed across the various clinical phases of psychosis.

DESIGN

Our sample consisted of 147 participants and included 39 healthy controls (HC), 72 with first-episode psychosis (FEP), 18 in a clinical high-risk state (CHR), 18 with schizophrenia (SZ). A structured task elicited 3 minutes of speech, which was then transformed into quantitative measures on 12 linguistic variables (lexical, syntactic, and semantic). Cluster analysis that leveraged healthy variations was then applied to determine language-based subgroups.

RESULTS

We observed a three-cluster solution. The largest cluster included most HC and the majority of patients, indicating a 'typical linguistic profile (TLP)'. One of the atypical clusters had notably high semantic similarity in word choices with less perceptual words, lower cohesion and analytical structure; this cluster was almost entirely composed of patients in early stages of psychosis (EPP - early phase profile). The second atypical cluster had more patients with established schizophrenia (SPP - stable phase profile), with more perceptual but less cognitive/emotional word classes, simpler syntactic structure, and a lack of sufficient reference to prior information (reduced givenness).

CONCLUSION

The patterns of speech deviations in early and established stages of schizophrenia are distinguishable from each other and detectable when lexical, semantic and syntactic aspects are assessed in the pursuit of 'formal thought disorder'.

Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities

Preventing relapse in schizophrenia improves long-term health outcomes. Repeated episodes of psychotic symptoms shape the trajectory of this illness and can be a detriment to functional recovery. Despite early intervention programs, high relapse rates persist, calling for alternative approaches in relapse prevention. Predicting imminent relapse at an individual level is critical for effective intervention. While clinical profiles are often used to foresee relapse, they lack the specificity and sensitivity needed for timely prediction. Here, we review the use of speech through Natural Language Processing (NLP) to predict a recurrent psychotic episode. Recent advancements in NLP of speech have shown the ability to detect linguistic markers related to thought disorder and other language disruptions within 2-4 weeks preceding a relapse. This approach has shown to be able to capture individual speech patterns, showing promise in its use as a prediction tool. We outline current developments in remote monitoring for psychotic relapses, discuss the challenges and limitations and present the speech-NLP based approach as an alternative to detect relapses with sufficient accuracy, construct validity and lead time to generate clinical actions towards prevention.

Natural language processing for mental health interventions: a systematic review and research framework

Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP's potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients' clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers' characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.

Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling

BACKGROUND

Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in the automatic detection of speech disorganization in formal thought disorder (FTD). However, we lack an understanding of precisely what common NLP metrics measure and how they relate to theoretical accounts of FTD. We propose tackling these questions by using deep generative language models to simulate FTD-like narratives by perturbing computational parameters instantiating theory-based mechanisms of FTD.

METHODS

We simulated FTD-like narratives using Generative-Pretrained-Transformer-2 by either increasing word selection stochasticity or limiting the model's memory span. We then examined the sensitivity of common NLP measures of derailment (semantic distance between consecutive words or sentences) and tangentiality (how quickly meaning drifts away from the topic) in detecting and dissociating the 2 underlying impairments.

RESULTS

Both parameters led to narratives characterized by greater semantic distance between consecutive sentences. Conversely, semantic distance between words was increased by increasing stochasticity, but decreased by limiting memory span. An NLP measure of tangentiality was uniquely predicted by limited memory span. The effects of limited memory span were nonmonotonic in that forgetting the global context resulted in sentences that were semantically closer to their local, intermediate context. Finally, different methods for encoding the meaning of sentences varied dramatically in performance.

CONCLUSIONS

This work validates a simulation-based approach as a valuable tool for hypothesis generation and mechanistic analysis of NLP markers in psychiatry. To facilitate dissemination of this approach, we accompany the paper with a hands-on Python tutorial.

Computerized text and voice analysis of patients with chronic schizophrenia in art therapy

This explorative study of patients with chronic schizophrenia aimed to clarify whether group art therapy followed by a therapist-guided picture review could influence patients' communication behaviour. Data on voice and speech characteristics were obtained via objective technological instruments, and these characteristics were selected as indicators of communication behaviour. Seven patients were recruited to participate in weekly group art therapy over a period of 6 months. Three days after each group meeting, they talked about their last picture during a standardized interview that was digitally recorded. The audio recordings were evaluated using validated computer-assisted procedures, the transcribed texts were evaluated using the German version of the LIWC2015 program, and the voice recordings were evaluated using the audio analysis software VocEmoApI. The dual methodological approach was intended to form an internal control of the study results. An exploratory factor analysis of the complete sets of output parameters was carried out with the expectation of obtaining typical speech and voice characteristics that map barriers to communication in patients with schizophrenia. The parameters of both methods were thus processed into five factors each, i.e., into a quantitative digitized classification of the texts and voices. The factor scores were subjected to a linear regression analysis to capture possible process-related changes. Most patients continued to participate in the study. This resulted in high-quality datasets for statistical analysis. To answer the study question, two results were summarized: First, text analysis factor called Presence proved to be a potential surrogate parameter for positive language development. Second, quantitative changes in vocal emotional factors were detected, demonstrating differentiated activation patterns of emotions. These results can be interpreted as an expression of a cathartic healing process. The methods presented in this study make a potentially significant contribution to quantitative research into the effectiveness and mode of action of art therapy.

Assessing coherence through linguistic connectives: Analysis of speech in patients with schizophrenia-spectrum disorders

BACKGROUND

Incoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD.

METHODS

Connectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups.

RESULTS

SSD-patients used less contingency (e.g., because) (p = .008) and multiclass connectives (e.g., as) (p < .001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p = .04) and temporality connectives (e.g., after) (adj-p < .001), narrower similarity-range of expansion (e.g., and) (adj-p = .002) and multiclass connectives (adj-p = .04), and lower maximum similarity of expansion connectives (adj-p = .005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85 % accuracy.

DISCUSSION

Our results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD.

Towards a temporospatial framework for measurements of disorganization in speech using semantic vectors

Incoherent speech in schizophrenia has long been described as the mind making "leaps" of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual "leaps" in spoken word meanings have used language-based embedding spaces. An embedding space represents meaning of words as numerical vectors where a greater proximity between word vectors represents more shared meaning. However, there are limitations with word vector-based operationalizations of coherence which can limit their appeal and utility in clinical practice. First, the use of esoteric word embeddings can be conceptually hard to grasp, and this is complicated by several different operationalizations of incoherent speech. This problem can be overcome by a better visualization of methods. Second, temporal information from the act of speaking has been largely neglected since models have been built using written text, yet speech is spoken in real time. This issue can be resolved by leveraging time stamped transcripts of speech. Third, contextual information - namely the situation of where something is spoken - has often only been inferred and never explicitly modeled. Addressing this situational issue opens up new possibilities for models with increased temporal resolution and contextual relevance. In this paper, direct visualizations of semantic distances are used to enable the inspection of examples of incoherent speech. Some common operationalizations of incoherence are illustrated, and suggestions are made for how temporal and spatial contextual information can be integrated in future implementations of measures of incoherence.

Speech disturbances in schizophrenia: Assessing cross-linguistic generalizability of NLP automated measures of coherence

INTRODUCTION

Language disorders - disorganized and incoherent speech in particular - are distinctive features of schizophrenia. Natural language processing (NLP) offers automated measures of incoherent speech as promising markers for schizophrenia. However, the scientific and clinical impact of NLP markers depends on their generalizability across contexts, samples, and languages, which we systematically assessed in the present study relying on a large, novel, cross-linguistic corpus.

METHODS

We collected a Danish (DK), German (GE), and Chinese (CH) cross-linguistic dataset involving transcripts from 187 participants with schizophrenia (111DK, 25GE, 51CH) and 200 matched controls (129DK, 29GE, 42CH) performing the Animated Triangles Task. Fourteen previously published NLP coherence measures were calculated, and between-groups differences and association with symptoms were tested for cross-linguistic generalizability.

RESULTS

One coherence measure, i.e. second-order coherence, robustly generalized across samples and languages. We found several language-specific effects, some of which partially replicated previous findings (lower coherence in German and Chinese patients), while others did not (higher coherence in Danish patients). We found several associations between symptoms and measures of coherence, but the effects were generally inconsistent across languages and rating scales.

CONCLUSIONS

Using a cumulative approach, we have shown that NLP findings of reduced semantic coherence in schizophrenia have limited generalizability across different languages, samples, and measures. We argue that several factors such as sociodemographic and clinical heterogeneity, cross-linguistic variation, and the different NLP measures reflecting different clinical aspects may be responsible for this variability. Future studies should take this variability into account in order to develop effective clinical applications targeting different patient populations.

Validation of natural language processing methods capturing semantic incoherence in the speech of patients with non-affective psychosis

Background

Impairments in speech production are a core symptom of non-affective psychosis (NAP). While traditional clinical ratings of patients' speech involve a subjective human factor, modern methods of natural language processing (NLP) promise an automatic and objective way of analyzing patients' speech. This study aimed to validate NLP methods for analyzing speech production in NAP patients.

Methods

Speech samples from patients with a diagnosis of schizophrenia or schizoaffective disorder were obtained at two measurement points, 6 months apart. Out of N = 71 patients at T1, speech samples were also available for N = 54 patients at T2. Global and local models of semantic coherence as well as different word embeddings (word2vec vs. GloVe) were applied to the transcribed speech samples. They were tested and compared regarding their correlation with clinical ratings and external criteria from cross-sectional and longitudinal measurements.

Results

Results did not show differences for global vs. local coherence models and found more significant correlations between word2vec models and clinically relevant outcome variables than for GloVe models. Exploratory analysis of longitudinal data did not yield significant correlation with coherence scores.

Conclusion

These results indicate that natural language processing methods need to be critically validated in more studies and carefully selected before clinical application.

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC's), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.

Widespread cortical thinning, excessive glutamate and impaired linguistic functioning in schizophrenia: A cluster analytic approach

Introduction

Symptoms of schizophrenia are closely related to aberrant language comprehension and production. Macroscopic brain changes seen in some patients with schizophrenia are suspected to relate to impaired language production, but this is yet to be reliably characterized. Since heterogeneity in language dysfunctions, as well as brain structure, is suspected in schizophrenia, we aimed to first seek patient subgroups with different neurobiological signatures and then quantify linguistic indices that capture the symptoms of "negative formal thought disorder" (i.e., fluency, cohesion, and complexity of language production).

Methods

Atlas-based cortical thickness values (obtained with a 7T MRI scanner) of 66 patients with first-episode psychosis and 36 healthy controls were analyzed with hierarchical clustering algorithms to produce neuroanatomical subtypes. We then examined the generated subtypes and investigated the quantitative differences in MRS-based glutamate levels [in the dorsal anterior cingulate cortex (dACC)] as well as in three aspects of language production features: fluency, syntactic complexity, and lexical cohesion.

Results

Two neuroanatomical subtypes among patients were observed, one with near-normal cortical thickness patterns while the other with widespread cortical thinning. Compared to the subgroup of patients with relatively normal cortical thickness patterns, the subgroup with widespread cortical thinning was older, with higher glutamate concentration in dACC and produced speech with reduced mean length of T-units (complexity) and lower repeats of content words (lexical cohesion), despite being equally fluent (number of words).

Conclusion

We characterized a patient subgroup with thinner cortex in first-episode psychosis. This subgroup, identifiable through macroscopic changes, is also distinguishable in terms of neurochemistry (frontal glutamate) and language behavior (complexity and cohesion of speech). This study supports the hypothesis that glutamate-mediated cortical thinning may contribute to a phenotype that is detectable using the tools of computational linguistics in schizophrenia.

Progressive changes in descriptive discourse in First Episode Schizophrenia: a longitudinal computational semantics study

Computational semantics, a branch of computational linguistics, involves automated meaning analysis that relies on how words occur together in natural language. This offers a promising tool to study schizophrenia. At present, we do not know if these word-level choices in speech are sensitive to the illness stage (i.e., acute untreated vs. stable established state), track cognitive deficits in major domains (e.g., cognitive control, processing speed) or relate to established dimensions of formal thought disorder. In this study, we collected samples of descriptive discourse in patients experiencing an untreated first episode of schizophrenia and healthy control subjects (246 samples of 1-minute speech; n = 82, FES = 46, HC = 36) and used a co-occurrence based vector embedding of words to quantify semantic similarity in speech. We obtained six-month follow-up data in a subsample (99 speech samples, n = 33, FES = 20, HC = 13). At baseline, semantic similarity was evidently higher in patients compared to healthy individuals, especially when social functioning was impaired; but this was not related to the severity of clinically ascertained thought disorder in patients. Across the study sample, higher semantic similarity at baseline was related to poorer Stroop performance and processing speed. Over time, while semantic similarity was stable in healthy subjects, it increased in patients, especially when they had an increasing burden of negative symptoms. Disruptions in word-level choices made by patients with schizophrenia during short 1-min descriptions are sensitive to interindividual differences in cognitive and social functioning at first presentation and persist over the early course of the illness.

Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS)

Formal thought disorder (ThD) is a clinical sign of schizophrenia amongst other serious mental health conditions. ThD can be recognized by observing incoherent speech - speech in which it is difficult to perceive connections between successive utterances and lacks a clear global theme. Automated assessment of the coherence of speech in patients with schizophrenia has been an active area of research for over a decade, in an effort to develop an objective and reliable instrument through which to quantify ThD. However, this work has largely been conducted in controlled settings using structured interviews and depended upon manual transcription services to render audio recordings amenable to computational analysis. In this paper, we present an evaluation of such automated methods in the context of a fully automated system using Automated Speech Recognition (ASR) in place of a manual transcription service, with "audio diaries" collected in naturalistic settings from participants experiencing Auditory Verbal Hallucinations (AVH). We show that performance lost due to ASR errors can often be restored through the application of Time-Series Augmented Representations for Detection of Incoherent Speech (TARDIS), a novel approach that involves treating the sequence of coherence scores from a transcript as a time-series, providing features for machine learning. With ASR, TARDIS improves average AUC across coherence metrics for detection of severe ThD by 0.09; average correlation with human-labeled derailment scores by 0.10; and average correlation between coherence estimates from manual and ASR-derived transcripts by 0.29. In addition, TARDIS improves the agreement between coherence estimates from manual transcripts and human judgment and correlation with self-reported estimates of AVH symptom severity. As such, TARDIS eliminates a fundamental barrier to the deployment of automated methods to detect linguistic indicators of ThD to monitor and improve clinical care in serious mental illness.

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.

A word is worth a thousand pictures: A 20-year comparative analysis of aberrant abstraction in schizophrenia, affective psychosis, and non-psychotic depression

thinking is a cognitive process that involves the assimilation of concepts reduced from diffuse sensory input, organized, and interpreted in a manner beyond the obvious. There are multiple facets by which abstraction is measured that include semantic, visual-spatial and social comprehension. This study examined the prevalence and course of abstract and concrete responses to semantic proverbs and aberrant abstraction (composite score of semantic, visual-spatial, and social comprehension) over 20 years in 352 participants diagnosed with schizophrenia, affective psychosis, and unipolar non-psychotic depression. We utilized linear models, two-way ANOVA and contrasts to compare groups and change over time. Linear models with Generalized Estimation Equation (GEE) to determine association. Our findings show that regardless of diagnosis, semantic proverb interpretation improves over time. Participants with schizophrenia give more concrete responses to proverbs when compared to affective psychosis and unipolar depressed without psychosis. We also show that the underlying structure of concretism encompasses increased conceptual overinclusion at index hospitalization and idiosyncratic associations at follow-up; whereas, abstract thinking overtime encompasses increased visual-spatial abstraction at index and rich associations with increased social comprehension scores at follow-up. Regardless of diagnosis, premorbid functioning, descriptive characteristics, and IQ were not associated with aberrant abstraction. Delusions are highly and positively related to aberrant abstraction scores, while hallucinations are mildly and positively related to this score. Lastly, our data point to the importance of examining the underlying interconnected structures of 'established' constructs vis-à-vis mixed methods to provide a description of the rich interior world that may not always map onto current quantitative measures.