In the past 30 years there has been a growing body of research using different methods (behavioural, electrophysiological, neuropsychological, TMS and imaging studies) asking whether processing words from different grammatical classes (especially nouns and verbs) engage different neural systems. To date, however, each line of investigation has provided conflicting results. Here we present a review of this literature, showing that once we take into account the confounding in most studies between semantic distinctions (objects vs. actions) and grammatical distinction (nouns vs. verbs), and the conflation between studies concerned with mechanisms of single word processing and those studies concerned with sentence integration, the emerging picture is relatively clear-cut: clear neural separability is observed between the processing of object words (nouns) and action words (typically verbs), grammatical class effects emerge or become stronger for tasks and languages imposing greater processing demands. These findings indicate that grammatical class per se is not an organisational principle of knowledge in the brain; rather, all the findings we review are compatible with two general principles described by typological linguistics as underlying grammatical class membership across languages: semantic/pragmatic, and distributional cues in language that distinguish nouns from verbs. These two general principles are incorporated within an emergentist view which takes these constraints into account.
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http://dx.doi.org/10.1016/j.neubiorev.2010.04.007 | DOI Listing |
Alzheimers Dement
December 2024
Cognitive Neuroscience Centre, University of San Andres, Victoria, Buenos Aires, Argentina.
Background: Dementia impacts the way individuals perceive and describe everyday events. Alzheimer's disease (AD) notably affects processing of entities manifested by nouns, while behavioral variant frontotemporal dementia (bvFTD) often presents a detached, third-person perspective. Yet, the potential of natural language processing tools (NLP) to detect these variations in spontaneous speech remains explored.
View Article and Find Full Text PDFBackground: Identifying language variation in healthy aging speakers is important for understanding normal cognitive aging. Setting a baseline of normal aging languages in the first place is necessary for the evaluation of language performances of old adults. Lexical concreteness, a well-studied psycholinguistic parameter, has been used to detect semantic memory-related deficits.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
National University of Singapore, Singapore, Singapore.
Comput Inform Nurs
December 2024
Author Affiliations: Data Driven WV, John Chambers College of Business and Economics (Ms Bailey), and School of Nursing, West Virginia University (Dr Carter-Templeton), Morgantown; School of Library and Information Sciences, North Carolina Central University, Durham (Dr Peterson); Duke University School of Nursing, Durham, NC (Dr Oermann); Dwight Schar College of Nursing and Health Sciences, Ashland University, OH (Dr Owens).
All disciplines, including nursing, may be experiencing significant changes with the advent of free, publicly available generative artificial intelligence tools. Recent research has shown the difficulty in distinguishing artificial intelligence-generated text from content that is written by humans, thereby increasing the probability for unverified information shared in scholarly works. The purpose of this study was to determine the extent of generative artificial intelligence usage in published nursing articles.
View Article and Find Full Text PDFBehav Res Methods
December 2024
Department of Psychology, University of Milano-Bicocca, P.zza dell'Ateneo Nuovo, 1, 20126, Milano, Italy.
Despite being largely spoken and studied by language and cognitive scientists, Italian lacks large resources of language processing data. The Italian Crowdsourcing Project (ICP) is a dataset of word recognition times and accuracy including responses to 130,465 words, which makes it the largest dataset of its kind item-wise. The data were collected in an online word knowledge task in which over 156,000 native speakers of Italian took part.
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