Studying psychosis using Natural Language Generation: A review of emerging opportunities

Abstract

Disrupted language in psychotic disorders, such as schizophrenia, can manifest as false contents and formal deviations, often described as thought disorder. These features play a critical role in the social dysfunction associated with psychosis, but we continue to lack insights regarding how these symptoms develop. Natural language Generation (NLG) is a field of computer science that focuses on generating human-like language for various applications. The theory that psychosis is related to the evolution of language in humans suggests that NLG systems that are sufficiently evolved to generate human-like language may also exhibit psychosis-like features. In this conceptual review, we propose using NLG systems that are at various stages of development as in-silico tools to study linguistic features of psychosis. This will allow us to gain a better understanding of the relationship between language and psychosis and potentially pave the way for new therapeutic approaches to address this vexing challenge.

Trajectories through semantic spaces in schizophrenia and the relationship to ripple bursts

“…Here, we use computational modeling of verbal fluency data to derive measures of semantically structured conceptual sampling in patients with a diagnosis of schizophrenia (PScz) and intelligence quotient (IQ)-matched control participants. We leverage advances in Natural Language Processing (NLP) machine learning tools to provide a quantification of semantic associations between words, an approach increasingly applied to the study of natural language and semantic processing both in psychiatry ( 9 , 28 – 32 ) and cognitive neuroscience ( 20 , 33 – 39 ). We then relate model-derived measures of semantic sampling to magnetoencephalography (MEG) signatures of neural replay and replay-associated ripple oscillations in the same participants, measured in a rest session following a separate task.…”