[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-458198-105":59,"doc-detail-458198-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","lexical-meaning-is-lower-dimensional-in-psychosis","Lexical meaning is lower dimensional in psychosis","","Diverse language models map how meaning is organized in speech, and this process appears altered in psychosis. The study tests whether the vector-space dimensionality defined by LM embeddings of speech samples is more easily reduced in psychosis than in typical populations. Principal component analysis provides reducibility proxies, including components needed for 90% variance and cumulative variance from the first two components, complemented by intrinsic dimensionality. Results across datasets in three languages show significantly higher reducibility in psychosis, suggesting an underlying intrinsic geometry of semantic associations.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/lexical-meaning-is-lower-dimensional-in-psychosis/458198/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/lexical-meaning-is-lower-dimensional-in-psychosis/458198.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does the study investigate about psychosis and language?","Question",{"text":112,"@type":113},"It examines whether the dimensionality of semantic representations from speech embeddings becomes more reducible in psychosis than in other groups.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which methods are used to measure semantic space reducibility?",{"text":117,"@type":113},"The study uses principal component analysis (PCA) to compute metrics such as components needed for 90% variance and cumulative variance from the first two components, and it also estimates intrinsic dimensionality (ID).",{"name":119,"@type":110,"acceptedAnswer":120},"What do the results suggest about semantic associations in psychosis?",{"text":121,"@type":113},"Findings consistent across datasets in three languages indicate a significantly higher reducibility of the semantic space, pointing to an intrinsic geometry underlying semantic associations in speech.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},458198,1792211184,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41,"preview_page_count":4},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nLexical meaning is lower dimensional in psychosis  \nClaudio Palominos1􀀍, Frederike Stein2,3, Tilo Kircher2,3, Rosa Ayesa-Arriola4,5, Lena Palaniyappan6,7,8,9, Philipp Homan10,11, Iris E. Sommer12 & Wolfram Hinzen1,13  \nDiverse language models (LMs), including large language models (LLMs) based on deep neural networks, allow us to chart how people organize meanings in speech and how this process breaks down in conditions. Recent evidence has pointed to higher mean semantic similarities between words in people with psychosis, conceptualized as a ‘shrunk’(more compressed) semantic space. Based on this, we hypothesized that the dimensionality of the vector spaces as defined by the embeddings of speech samples from LMs would also be easier to reduce in psychosis. To test this, we used principal component analysis (PCA) to calculate different metrics serving as proxies for reducibility, including the number of components needed to reach 90% of variance, and the cumulative variance explained by the first two components. For further exploration, intrinsic dimensionality (ID) was also estimated. Results consistent over datasets in three languages confirmed significantly higher reducibility of the semantic space in psychosis. This result points to the existence of an underlying intrinsic geometry of the space of semantic associations in speech, which may underlie more surface-level measurements such as semantic similarity. It also offers a new foundational approach to speech in mental disorders.  \nKeywords Schizophrenia, Psychosis, Language models, Semantics, Word embeddings  \nWhen communicating, we draw concepts from our semantic memory into structured speech, using grammar to connect different words and phrases to convey meaning. Through this process, we metaphorically navigate a semantic space, traversing different regions in a trajectory that as such gives meaning to the discourse as a whole. This notion aligns with the framework of distributional semantics in language models (LMs), where (sub)words, sentences, or even larger linguistic units are represented as vector embeddings in a high-dimensional vector space. Metrics such as cosine similarity capture the relationships between vectors: the smaller the angle between them, the more semantically related the words, sentences, or texts are, as they tend to occur in similar contextsin the data used to train these models.  \nSeveral semantic similarity metrics have been employed to analyze spontaneous speech of people with schizophrenia spectrum disorder (SSD), who exhibit distinct navigational patterns through semantic space compared to healthy controls1–5. These differences have informed the development of classification models aimed at distinguishing clinical groups like people at high-risk who will/will not transition to psychosis and individuals with/without SSD6,7. At the core of this research program lies the question of whether major psychiatric disorders such as SSD disrupt the underlying organization of the semantic space and how this manifests in natural speech8. Yet despite the growing use of semantic variables in recent studies, little is known about how these variables relate to each other, or whether they reflect overlapping or independent aspects of semantic organization. This lack of integration complicates efforts to interpret findings across studies or to identify robust, generalizable features of semantic disruption in psychosis. One attempt to address this was made in9, by capturing the contributions of over a hundred semantic variables derived from language models jointly, in a single ‘composite  \n1Department of Translation & Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain. 2Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany. 3Center for Mind, Brain and Behavior (CMBB), Marburg, Germany. 4Department of Psychiatry, Marqués deValdecilla Unive","cbCaihHjn5ZXeRk4","https://ap.wps.com/l/cbCaihHjn5ZXeRk4","pdf",2658269,12,"English","# Lexical meaning and semantic space in psychosis\n## Embeddings, distributional semantics, and semantic similarity\n## Reducibility measures: PCA and intrinsic dimensionality\n## Results across datasets and implications for mental disorders","[{\"question\":\"What does the study investigate about psychosis and language?\",\"answer\":\"It examines whether the dimensionality of semantic representations from speech embeddings becomes more reducible in psychosis than in other groups.\"},{\"question\":\"Which methods are used to measure semantic space reducibility?\",\"answer\":\"The study uses principal component analysis (PCA) to compute metrics such as components needed for 90% variance and cumulative variance from the first two components, and it also estimates intrinsic dimensionality (ID).\"},{\"question\":\"What do the results suggest about semantic associations in psychosis?\",\"answer\":\"Findings consistent across datasets in three languages indicate a significantly higher reducibility of the semantic space, pointing to an intrinsic geometry underlying semantic associations in speech.\"}]","Lexical meaning is lower dimensional in psychosis | PDF",1790752056]