Papers

BACKGROUND: Formal thought disorder (FTD) is a highly disabling transdiagnostic feature that impedes communication and social ties. Progress in understanding and treating FTD has been hampered by the uncertainties in its assessment.

AIMS: We examined if a short 3-5min assessment of transcribed speech can capture the latent dimensions and network structure of FTD and predict functional outcomes.

METHOD: In a transdiagnostic sample (N = 666) with a single longitudinal follow-up over 3-12 months (n = 244), we administered the short form of the Thought and Language Index to measure eight individual features of FTD. We determined the baseline factor structure of FTD, its temporal invariance at follow-up, and the predictive validity of FTD dimensions on the global single-item Social and Occupational Functioning Assessment Scale scores at baseline and follow-up. We identified the most influential and putative primary phenomena within the FTD syndrome, using network analysis.

RESULTS: Factor analyses revealed a stable three-factor model of FTD: impoverishment (poverty of speech, weakening of goal), loosening (looseness, illogicality) and peculiarities (peculiar words, peculiar sentences), with excellent fit (Comparative Fit Index: 0.997, root mean square error of approximation: 0.040) and metric invariance over time. Impoverishment and peculiarities predicted functioning at baseline and 3-12 months later (cross-sectional: β = -0.196, p < 0.001 and β = -0.298, p = 0.001, respectively; longitudinal: β = -0.201, p = 0.037 and β = -0.336, p = 0.042, respectively). Looseness and poverty of speech were putative primary features influencing other FTD phenomena. Weakening of goal and peculiar sentences were the most connected phenomena.

CONCLUSIONS: By integrating latent variable and network approaches, we provide a unified, empirically grounded framework to interpret FTD assessed using a brief speech task. We report a replicable three-dimensional structure, identify central symptoms that may maintain the FTD syndrome, and the specific dimensions that influence functional disability. These findings clarify the prognostically valuable features of FTD for future mechanistic and interventional research.

BACKGROUND: Thought disorder (TD) is a core feature of severe mental illnesses such as schizophrenia, characterized by disruptions in speech, language, and communication. People with TD face unique barriers that hinder their involvement in research, both as participants and as partners. Their systematic underrepresentation in psychiatric research is driven by pervasive assumptions about their decisional capacity, willingness to participate, and ability to engage in research. This perpetuates a biased evidence base, likely hindering the therapeutic progress toward addressing this core problem.

METHODS: This review, informed by professional (clinical and research) and lived (bottom-up and phenomenological) experience of TD, examines how flawed assumptions regarding capacity, engagement, and participatory abilities serve as active barriers to inclusion.

RESULTS: We argue for a shift toward supported inclusion through tailored capacity assessments, enhanced informed consent procedures, targeted training of research personnel, and systemic institutional practices. Incorporating lived experiences of those with TD as research partners is integral to this approach, fostering co-production of research that is more valid, inclusive, and applicable.

CONCLUSIONS: Without these inclusion-focused changes, the development of treatments for TD is likely to have very slow progress and a critical segment of the severely unwell population will continue to be underrepresented from the scientific process, undermining both the utility and generalizability of psychiatric research.

During language comprehension, the larger neural response to unexpected versus expected inputs is often taken as evidence for predictive coding—a specific computational architecture and optimization algorithm proposed to approximate probabilistic inference in the brain. However, other predictive processing frameworks can also account for this effect, leaving the unique claims of predictive coding untested. In this study, we used MEG to examine both univariate and multivariate neural activity in response to expected and unexpected inputs during word-by-word reading comprehension. We further simulated this activity using an implemented predictive coding model that infers the meaning of words from their orthographic form. Consistent with previous findings, the univariate analysis showed that, between 300 and 500 ms, unexpected words produced a larger evoked response than expected words within a left ventromedial temporal region that supports the mapping of orthographic word-forms onto lexical and conceptual representations. Our model explained this larger evoked response as the enhanced lexico-semantic prediction error produced when prior top-down predictions failed to suppress activity within lexical and semantic “error units”. Critically, our simulations showed that despite producing minimal prediction error, expected inputs nonetheless reinstated top-down predictions within the model's lexical and semantic “state” units. Two types of multivariate analyses provided evidence for this functional distinction between state and error units within the ventromedial temporal region. First, within each trial, the same individual voxels that produced a larger response to unexpected inputs between 300 and 500 ms produced unique temporal patterns to expected inputs that resembled the patterns produced within a pre-activation time window. Second, across trials, and again within the same 300–500 ms time window and left ventromedial temporal region, pairs of expected words produced spatial patterns that were more similar to one another than the spatial patterns produced by pairs of expected and unexpected words, regardless of specific item. Together, these findings provide compelling evidence that the left ventromedial temporal lobe employs predictive coding to infer the meaning of incoming words from their orthographic form during reading comprehension.

The N400 event-related component has been widely used to investigate the neural mechanisms underlying real-time language comprehension. However, despite decades of research, there is still no unifying theory that can explain both its temporal dynamics and functional properties. In this work, we show that predictive coding – a biologically plausible algorithm for approximating Bayesian inference – offers a promising framework for characterizing the N400. Using an implemented predictive coding computational model, we demonstrate how the N400 can be formalized as the lexico-semantic prediction error produced as the brain infers meaning from the linguistic form of incoming words. We show that the magnitude of lexico-semantic prediction error mirrors the functional sensitivity of the N400 to various lexical variables, priming, contextual effects, as well as their higher-order interactions. We further show that the dynamics of the predictive coding algorithm provides a natural explanation for the temporal dynamics of the N400, and a biologically plausible link to neural activity. Together, these findings directly situate the N400 within the broader context of predictive coding research. More generally, they raise the possibility that the brain may use the same computational mechanism for inference across linguistic and non-linguistic domains.

We used MEG and EEG to examine the effects of Plausibility (anomalous vs. plausible) and Animacy (animate vs. inanimate) on activity to incoming words during language comprehension. We conducted univariate event-related and multivariate spatial similarity analyses on both datasets. The univariate and multivariate results converged in their time course and sensitivity to Plausibility. However, only the spatial similarity analyses detected effects of Animacy. The MEG and EEG findings largely converged between 300–500 ms, but diverged in their univariate and multivariate responses to anomalies between 600–1000 ms. We interpret the full set of results within a predictive coding framework. In addition to the theoretical significance, we discuss the methodological implications of the convergence and divergence between the univariate and multivariate results, as well as between the MEG and EEG results. We argue that a deeper understanding of language processing can be achieved by integrating different analysis approaches and techniques.

During language comprehension, the processing of each incoming word is facilitated in proportion to its predictability. Here, we asked whether anticipated upcoming linguistic information is actually pre-activated before new bottom-up input becomes available, and if so, whether this pre-activation is limited to the level of semantic features, or whether extends to representations of individual word-forms (orthography/phonology). We carried out Representational Similarity Analysis on EEG data while participants read highly constraining sentences. Prior to the onset of the expected target words, sentence pairs predicting semantically-related words (financial “bank” – “loan”) and form-related words (financial “bank” – river “bank”) produced more similar neural patterns than pairs predicting unrelated words (“bank” – “lesson”). This provides direct neural evidence for item-specific semantic and form predictive pre-activation. Moreover, the semantic pre-activation effect preceded the form pre-activation effect, suggesting that top-down pre-activation is propagated from higher to lower levels of the linguistic hierarchy over time.

To comprehend language, we continually use prior context to pre-activate expected upcoming information, resulting in facilitated processing of incoming words that confirm these predictions. But what are the consequences of disconfirming prior predictions? To address this question, most previous studies have examined unpredictable words appearing in contexts that constrain strongly for a single continuation. However, during natural language processing, it is far more common to encounter contexts that constrain for multiple potential continuations, each with some probability. Here, we ask whether and how pre-activating both higher and lower probability alternatives influences the processing of the lower probability incoming word. One possibility is that, similar to language production, there is continuous pressure to select the higher-probability pre-activated alternative through competitive inhibition. During comprehension, this would result in relative costs in processing the lower probability target. A second possibility is that if the two pre-activated alternatives share semantic features, they mutually enhance each other’s pre-activation. This would result in greater facilitation in processing the lower probability target. To distinguish between these accounts, we recorded ERPs as participants read three-sentence scenarios that constrained either for a single word or for two potential continuations – a higher probability expected candidate and a lower probability second-best candidate. We found no evidence that competitive pre-activation between the expected and second-best candidates resulted in costs in processing the second-best target, either during lexico-semantic processing (indexed by the N400) or at later stages of processing (indexed by a later frontal positivity). Instead, we found only benefits of pre-activating multiple alternatives, with evidence of enhanced graded facilitation on lower-probability targets that were semantically related to a higher-probability pre-activated alternative. These findings are consistent with a previous eye-tracking study by Luke and Christianson (2016, Cogn Psychol) using corpus-based materials. They have significant theoretical implications for models of predictive language processing, indicating that routine graded prediction in language comprehension does not operate through the same competitive mechanisms that are engaged in language production. Instead, our results align more closely with hierarchical probabilistic accounts of language comprehension, such as predictive coding.

In people with schizophrenia and related disorders, impairments in communication and social functioning can negatively impact social interactions and quality of life. In the present study, we investigated the cognitive basis of a specific aspect of linguistic communication—lexical alignment— in people with schizophrenia and bipolar disorder. We probed lexical alignment as participants played a collaborative picture-naming game with the experimenter, in which the two players alternated between naming a dual-name picture (e.g., rabbit/bunny) and listening to their partner name a picture. We found evidence of lexical alignment in all three groups, with no differences between the patient groups and the controls. We argue that these typical patterns of lexical alignment in patients were supported by preserved—and in some cases increased—bottom-up mechanisms, which balanced out impairments in top-down perspective-taking.