Background: Schizophrenia is a chronic, debilitating disorder characterised by distorted thinking, perceptions, behaviours, and even language impairments. We investigated the linguistic anomalies in Korean schizophrenia patients compared to non-psychotic psychiatric controls to determine whether the linguistic anomalies in English speakers with schizophrenia were replicated in Korean speakers.
Methods: Thirty-four schizophrenia patients and 70 non-psychotic psychiatric controls were included in this study. The SCT was utilised as the text data for analysis. For linguistic analysis, we evaluated texts regarding semantics and syntax. We separately counted the number of semantic or syntactic errors in the written texts of study participants and compared them between patients and controls.
Results: Schizophrenia patients showed significantly more semantic errors ( < .001) and syntactic errors ( < .001) per 1,000 characters than non-psychotic psychiatric controls. Specifically, inappropriate word or syntactic component selection is noticeable in schizophrenia patients. These differences were still significant after adjusting for general intelligence measured by the K-WAIS-IV.
Conclusion: Schizophrenia patients showed both semantic and syntactic errors in written language. Moreover, these errors seemed to be partly independent of general intelligence. Notably, patients showed a noticeable number of syntactic errors. Further investigation into the language of patients with schizophrenia and schizophrenia-spectrum disorders is required.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/13546805.2023.2209313 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Misreading Medicine: Statutory Prohibitions of Abortion for Disability.
J Med Humanit
January 2025
S.J. Quinney College of Law, University of Utah, Salt Lake City, UT, USA.
Abortion prohibitions in some states include carve-outs based on the medical condition of either the mother or the fetus. These carve-outs, however, may be couched in limiting language structured by legislators rather than in language understandable in the context of medical care. In circumstances where legislative bodies fail to adequately incorporate medical professionals in the drafting of medical laws, the resulting vagueness or ambiguity may lead to a lack of utility or viability.
View Article and Find Full Text PDFReconsidering learnable fine-grained text prompts for few-shot anomaly detection in visual-language models.
Neural Netw
February 2025
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China; Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, 250014, China. Electronic address:
Few-Shot Anomaly Detection (FSAD) in industrial images aims to identify abnormalities using only a few normal images, which is crucial for industrial scenarios where sample training is limited. The recent advances in large-scale pre-trained visual-language models have brought significant improvements to the FSAD, which typically requires hundreds of text prompts to be manually crafted through prompt engineering. However, manually designed text prompts cannot accurately match the informative features of different categories across diverse images, and the domain gap between train and test datasets can severely impact the generalization capability of text prompts.
View Article and Find Full Text PDFCrossed Wernicke's Aphasia With Focal Clonic Cluster Seizures With Secondary Generalization Mimicking Faciobrachial Seizures Following a Right Middle-Cerebral-Artery Ischemic Stroke: A Novel Phenotypic Presentation.
Neurohospitalist
October 2024
Department of Neuromedicine, Bangur Institute of Neurosciences, Institute of Post Graduate Medical Education and Research & SSKM Hospital, Kolkata, India.
Crossed Wernicke's aphasia (CWA) following a stroke is a rare clinical phenomenon, particularly when associated with seizures. This case report presents a unique instance of crossed CWA accompanied by focal clonic cluster seizures affecting the left arm and face, with secondary generalization, in a monolingual Bengali-speaking patient following a right middle cerebral artery ischemic stroke. The patient, a 70-year-old male from rural India, presented with acute behavioral abnormalities and language impairment.
View Article and Find Full Text PDFRelevance of Prosodic Focus and Lexical Stress for Discourse Comprehension in Turkish: Evidence from Psychometric and Electrophysiological Data.
J Cogn Neurosci
October 2024
Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
Prosody underpins various linguistic domains ranging from semantics and syntax to discourse. For instance, prosodic information in the form of lexical stress modifies meanings and, as such, syntactic contexts of words as in Turkish kaz-má "pickaxe" (noun) versus káz-ma "do not dig" (imperative). Likewise, prosody indicates the focused constituent of an utterance as the noun phrase filling the wh-spot in a dialogue like What did you eat? I ate----.
View Article and Find Full Text PDFA Modified Deep Semantic Segmentation Model for Analysis of Whole Slide Skin Images.
Sci Rep
October 2024
Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, 44000, Pakistan.
Article Synopsis
- Automated segmentation is crucial for computer-aided diagnosis in identifying abnormalities in biomedical images, but challenges exist due to variations in color, texture, and shape.
- Traditional semantic segmentation methods tend to be complex and slow because they require deeper neural networks, highlighting the need for more efficient techniques.
- This article presents a modified segmentation model using EfficientNet-B3 with UNet, achieving improved accuracy for non-melanoma skin cancer segmentation, increasing average class accuracy from 79% to 83% and overall accuracy from 85% to 94%.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!