Purpose: Speed and accuracy of lexical access change with healthy ageing and neurodegeneration. While a word's immediate phonological neighbourhood density (i.e. words differing by a single phoneme) influences access, connectivity to all words in the phonological network (i.e. closeness centrality) may influence processing. This study aimed to investigate the effect of closeness centrality on speed and accuracy of lexical processing pre- and post- a single word-training session in healthy younger and older adults, and adults with logopenic primary progressive aphasia (lvPPA), which affects phonological processing.
Method: Participants included 29 young and 17 older healthy controls, and 10 adults with lvPPA. Participants received one session of word-training on words with high or low closeness centrality, using a picture-word verification task. Changes in lexical decision reaction times (RT) and accuracy were measured.
Result: Baseline RT was unaffected by age and accuracy was at ceiling for controls. Post-training, only young adults' RT were significantly faster. Adults with lvPPA were slower and less accurate than controls at baseline, with no training effect. Closeness centrality did not influence performance.
Conclusion: Absence of training effect for older adults suggests higher threshold to induce priming, possibly associated with insufficient dosage or fatigue. Implications for word-finding interventions with older adults are discussed.
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http://dx.doi.org/10.1080/17549507.2022.2141323 | DOI Listing |
Ecol Evol
January 2025
Department of Environmental Systems Science ETH Zürich Switzerland.
Scavenging is a widespread feeding strategy involving a diversity of taxa from different trophic levels, from apex predators to obligate scavengers. Scavenger species play a crucial role in ecosystem functioning by removing carcasses, recycling nutrients and preventing disease spread. Understanding the trophic roles of scavenger species can help identify specialized species with unique roles and species that may be more vulnerable to ecological changes.
View Article and Find Full Text PDFArq Gastroenterol
January 2025
Editorial Department, The Japanese Society of Internal Medicine, Tokyo, Japan.
Background: This study aims to analyze the structural dynamics of research collaboration in hepatology over a 30-year period (1994-2023), focusing on co-authorship networks. By examining data from the Web of Science Core Collection, the study explores key metrics such as network density, clustering coefficient, and centrality measures, providing insights into how collaborative efforts have shaped the field of hepatology.
Methods: Using Python (Version 3.
Arq Gastroenterol
January 2025
Editorial Department, The Japanese Society of Internal Medicine, Tokyo, Japan.
Background: This study aims to analyze the co-authorship network in Gastroenterology research, focusing on publications from 2000 to 2023, to understand the collaborative relationships among researchers and identify key contributors in the field.
Methods: Using data from the Web of Science (WoS), I examined 18,855 Gastroenterology-related articles published between 2000 and 2023. The analysis was conducted using Python within the PyCharm environment.
Entropy (Basel)
December 2024
Electronic Engineering Institute, National University of Defense Technology, Hefei 230037, China.
Correctly identifying influential nodes in a complex network and implementing targeted protection measures can significantly enhance the overall security of the network. Currently, indicators such as degree centrality, closeness centrality, betweenness centrality, H-index, and K-shell are commonly used to measure node influence. Although these indicators can identify critical nodes to some extent, they often consider node attributes from a narrow perspective and have certain limitations.
View Article and Find Full Text PDFEntropy (Basel)
November 2024
Sony Computer Science Laboratories, Tokyo 141-0022, Japan.
The symmetric Kullback-Leibler centroid, also called the Jeffreys centroid, of a set of mutually absolutely continuous probability distributions on a measure space provides a notion of centrality which has proven useful in many tasks, including information retrieval, information fusion, and clustering. However, the Jeffreys centroid is not available in closed form for sets of categorical or multivariate normal distributions, two widely used statistical models, and thus needs to be approximated numerically in practice. In this paper, we first propose the new Jeffreys-Fisher-Rao center defined as the Fisher-Rao midpoint of the sided Kullback-Leibler centroids as a plug-in replacement of the Jeffreys centroid.
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