Theoretical considerations and psycholinguistic studies have alternatively provided criticism and support for the proposal that semantic and grammatical functions are distinct subprocesses within the language domain. Neurobiological evidence concerning this hypothesis was sought by (1) comparing, in normal adults, event-related brain potentials (ERPs) elicited by words that provide primarily semantic information (open class) and grammatical information (closed class) and (2) comparing the effects of the altered early language experience of congenitally deaf subjects on ERPs to open and closed class words. In normal-hearing adults, the different word types elicited qualitatively different ERPs that were compatible with the hypothesized different roles of the word classes in language processing. In addition, whereas ERP indices of semantic processing were virtually identical in deaf and hearing subjects, those linked to grammatical processes were markedly different in deaf and hearing subjects. The results suggest that nonidentical neural systems with different developmental vulnerabilities mediate these different aspects of language. More generally, these results provide neurobiological support for the distinction between semantic and grammatical functions.
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http://dx.doi.org/10.1093/cercor/2.3.244 | DOI Listing |
Cerebellum
January 2025
Center for Language and Cognition, University of Groningen, PO box 716, 9700 AS, Groningen, the Netherlands.
Pediatric cerebellar tumor survivors may present with spontaneous language impairments following treatment, but the nature of these impairments is still largely unclear. A recent study by Svaldi et al. (Cerebellum.
View Article and Find Full Text PDFOpen Mind (Camb)
December 2024
Universidade de Lisboa, Centro de Linguística, Lisbon, Portugal.
Most natural languages have more than one linguistic form available to express disjunction. One of these forms is often reported by native speakers to be more exclusive than the other(s) and, in recent years, it has been claimed that some languages may in fact have dedicated exclusive disjunctions. In this paper, we report on a series of experiments testing this claim across five languages of primary interest.
View Article and Find Full Text PDFSignal Transduct Target Ther
December 2024
School of Basic Medical Science, Tsinghua University, 30 Shuangqing Rd., Haidian District, Beijing, 100084, China.
Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail.
View Article and Find Full Text PDFJ Clin Med
November 2024
Department of Neurosurgery, College of Medicine, The University of Tennessee Health Sciences, Memphis, TN 38163, USA.
Lumbar spinal stenosis (LSS) is a major cause of chronic lower back and leg pain, and is traditionally diagnosed through labor-intensive analysis of magnetic resonance imaging (MRI) scans by radiologists. This study aims to streamline the diagnostic process by developing an automated radiology report generation (ARRG) system using a vision-language (VL) model. We utilized a Generative Image-to-Text (GIT) model, originally designed for visual question answering (VQA) and image captioning.
View Article and Find Full Text PDFNeural Netw
November 2024
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, PR China. Electronic address:
In black-box scenarios, adversarial attacks against text classification models face challenges in ensuring highly available adversarial samples, especially a high number of invalid queries under long texts. The existing methods select distractors by comparing the confidence vector differences obtained before and after deleting words, and the query increases linearly with the length of the text, making it difficult to apply to attack scenarios with limited queries. Generating adversarial samples based on a thesaurus can lead to semantic inconsistencies and even grammatical errors, making it easy for the target model to recognize adversarial samples and resulting in a low success rate of attacks.
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