Assessing FTD and SSDs with NLP methods
Advancements in computational systems have introduced innovative methods for automated FTD assessment. A seminal approach by Foltz et al. ( 1998 ) utilizes distributional similarity, specifically measuring semantic relatedness between consecutive text segments using latent semantic analysis (LSA) (Landauer and Dumais, 1997 ) to measure coherence, providing a proxy that was later used to quantify the degree FTD. This method’s diagnostic utility was demonstrated by Elvevåg et al. ( 2007 ) , who found significant differences in automated coherence metrics when comparing individuals with schizophrenia to healthy controls, as well as among patients with varying levels of thought disorder.

Building on this work, a subsequent study integrated LSA-based coherence metrics into a machine learning classifier that accurately predicted psychosis onset in a small sample of at-risk youth, achieving perfect leave-one-out cross-validation accuracy (Bedi et al., 2015 ) . An adapted version maintained 83% accuracy in predicting psychosis onset in a larger, independent dataset (Corcoran et al., 2018 ) . More recently, neural word embeddings (Mikolov et al., 2013 ) , which represent words as vectors derived from neural networks trained to predict nearby words, have been explored as an alternative to LSA for coherence analysis. Similarity metrics from these embeddings showed promising results in aligning with clinical assessments of thought disorder (Just et al., 2019 , 2020 ) .

With advances in NLP methods, recent studies have used sentence embeddings from BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019 ) to identify coherence differences between transcripts from individuals with SSDs and those from healthy controls (Tang et al., 2021 ) . As a transformer-based model, BERT generates context-specific representations of tokens by dynamically incorporating information from surrounding words, unlike LSA or neural word embeddings, which rely on static word representations derived from all of the contexts a word is observed in during training. Prior research also introduced methods for assessing global coherence – estimating the relationship between a unit of text and the overarching theme of a text – using these methods to improve coherence evaluation in automatic speech recognition by extracting time series features for machine learning (Xu et al., 2021 , 2022 ) .

With the emergence of autoregressive LMs, some recent studies (Palaniyappan et al., 2023 ; Fradkin et al., 2023 ; Sharpe et al., 2024 ) have examined the assessment of psychosis using such models to demonstrate that LMs can be utilized with in-silico experimental research to gain better understanding of the linguistic manifestation of FTD. In contrast to BERT, which is a bidirectional model that utilizes tokens on both sides of a target token for prediction, autoregressive LMs are designed to predict only the next token in a sequence. While BERT-derived representations are highly effective for estimating semantic relatedness, autoregressive LMs are specifically optimized for generating coherent and fluent sequences of text, offering potential for developing alternative approaches to FTD evaluation. However, these approaches have primarily relied on such models without exploring how model size and granular context windows affect sensitivity to linguistic manifestations of FTD.