A central goal of linguistics is to understand how words evolve. Past research has found that macro-level factors such as frequency of word usage and population size explain the pace of lexical evolution. Here we focus on cognitive and affective factors, testing whether valence (positivity-negativity) explains lexical evolution rates. Using estimates of cognate replacement rates for 200 concepts on an Indo-European language tree spanning six to ten millennia, we find that negative valence correlates with faster cognate replacement. This association holds when controlling for frequency of use, and follow-up analyses show that it is most robust for adjectives ('dirty' versus 'clean'; 'bad' versus 'good'); it does not consistently reach statistical significance for verbs, and never reaches significance for nouns. We also present experiments showing that individuals are more likely to replace words for negative versus positive concepts. Our findings suggest that emotional valence affects micro-level guided variation, which drives macro-level valence-dependent mutation in adjectives.
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http://dx.doi.org/10.1038/s41562-022-01483-8 | DOI Listing |
Psychol Rep
December 2024
Department of Psychology, University of Cambridge, Cambridge, UK.
Historically, debates over relationships between spoken lexical form and meaning have been dominated by views of arbitrariness. However more recent research revealed a different perspective, in which non-arbitrary mappings play an important role in the makeup of a lexicon. It is now clear that phoneme-sound symbolism - along with other types of form-to-meaning mappings - contributes to non-arbitrariness (iconicity) of spoken words, which is present in many forms and degrees in different languages.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
December 2024
Department of Computer Science, Sapienza University of Rome, Roma 00161, Italy.
Front Microbiol
November 2024
Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
Introduction: The integration of artificial intelligence (AI) in pathogenic microbiology has accelerated research and innovation. This study aims to explore the evolution and trends of AI applications in this domain, providing insights into how AI is transforming research and practice in pathogenic microbiology.
Methods: We employed bibliometric analysis and topic modeling to examine 27,420 publications from the Web of Science Core Collection, covering the period from 2010 to 2024.
Stud Health Technol Inform
November 2024
Medical University of Vienna, Center for Medical Data Science, Institute of Artificial Intelligence, Spitalgasse 23, 1090 Vienna, Austria.
The Arden Syntax is a language designed for the encoding of medical knowledge into clinical decision support systems. Its evolution is overseen by Health Level 7. A significant enhancement in its new version 3.
View Article and Find Full Text PDFSentence production is the uniquely human ability to transform complex thoughts into strings of words. Despite the importance of this process, language production research has primarily focused on single words. It remains an untested assumption that insights from this literature generalize to more naturalistic utterances like sentences.
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