Semantic processing abnormalities have been observed across the schizophrenia spectrum. However, it is unclear whether associations between semantic processing measures and schizotypal traits are stable over time. The current study aimed to explore the temporal stability of semantic processing measures and their correlations with schizotypal traits. In this study, we used the Schizotypal Personality Questionnaire (SPQ) to assess schizotypal traits and explored the association between schizotypal traits and semantic processing measures (i.e., N400- a large negativity with a broad scalp distribution, peaking around 400 ms after the presentation of any potentially meaningful stimulus) at baseline (Time 1; n = 63) and 3 months later (Time 2; n = 44). Repeated-measure ANOVA was conducted to examine the stability of the semantic processing measures; the intraclass correlation coefficient (ICC) was used to examine test-retest reliability; Pearson's r was calculated to explore associations between schizotypal traits and semantic processing measures. Results showed that both behavioral (reaction times) and N400 measures showed high reliability but low temporal stability. N400 latency for semantically unrelated stimuli was correlated with the cognitive-perceptual and the disorganized dimensions of schizotypal traits at Time 2. In conclusion, semantic processing measures generally showed good reliability. Schizotypal traits were correlated with N400 latencies in the current sample, but further studies are needed to examine whether this association is stable.
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http://dx.doi.org/10.1016/j.ijpsycho.2022.06.002 | DOI Listing |
PLoS One
January 2025
Department of Computer Science, GC Women University Sialkot, Sialkot, Pakistan.
Modern dialogue systems rely on emotion recognition in conversation (ERC) as a core element enabling empathetic and human-like interactions. However, the weak correlation between emotions and semantics poses significant challenges to emotion recognition in dialogue. Semantically similar utterances can express different types of emotions, depending on the context or speaker.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare, No. 1188 Wuzhong Avenue, Wuzhong District Suzhou, Suzhou 215004, China.
The automatic and accurate extraction of diverse biomedical relations from literature constitutes the core elements of medical knowledge graphs, which are indispensable for healthcare artificial intelligence. Currently, fine-tuning through stacking various neural networks on pre-trained language models (PLMs) represents a common framework for end-to-end resolution of the biomedical relation extraction (RE) problem. Nevertheless, sequence-based PLMs, to a certain extent, fail to fully exploit the connections between semantics and the topological features formed by these connections.
View Article and Find Full Text PDFJ Intell
January 2025
School of Social Sciences, Tsinghua University, Beijing 100084, China.
As psychological research progresses, the issue of concept overlap becomes increasing evident, adding to participant burden and complicating data interpretation. This study introduces an Embedding-based Semantic Analysis Approach (ESAA) for detecting redundancy in psychological concepts, which are operationalized through their respective scales, using natural language processing techniques. The ESAA utilizes OpenAI's text-embedding-3-large model to generate high-dimensional semantic vectors (i.
View Article and Find Full Text PDFJ Imaging
January 2025
RCAM Laboratory, Telecommunications Department, Sidi Bel Abbes University, Sidi Bel Abbes 22000, Algeria.
In recent years, deep-network-based hashing has gained prominence in image retrieval for its ability to generate compact and efficient binary representations. However, most existing methods predominantly focus on high-level semantic features extracted from the final layers of networks, often neglecting structural details that are crucial for capturing spatial relationships within images. Achieving a balance between preserving structural information and maximizing retrieval accuracy is the key to effective image hashing and retrieval.
View Article and Find Full Text PDFJ Pers Med
January 2025
Department of Ophthalmology, Mayo Clinic, Rochester, MN 55905, USA.
: Augmented reality (AR) may allow vitreoretinal surgeons to leverage microscope-integrated digital imaging systems to analyze and highlight key retinal anatomic features in real time, possibly improving safety and precision during surgery. By employing convolutional neural networks (CNNs) for retina vessel segmentation, a retinal coordinate system can be created that allows pre-operative images of capillary non-perfusion or retinal breaks to be digitally aligned and overlayed upon the surgical field in real time. Such technology may be useful in assuring thorough laser treatment of capillary non-perfusion or in using pre-operative optical coherence tomography (OCT) to guide macular surgery when microscope-integrated OCT (MIOCT) is not available.
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