associative agreement as a predictor of naming ability in alzheimer's disease: a case for the semantic nature of associative links

Clicks: 232
ID: 131544
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #174 of 220 articles by views in lasers in manufacturing and materials processing

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 220 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
We aimed to address the long-standing issue of the nature of the relationships that link a cue word to words associated with it. In keeping with a recently proposed neuropsychological model of semantic memory (Zannino et al., 2015), we provide support for the hypothesis that associative links are semantic in nature and not lexical. In support of this hypothesis, we demonstrate a relationship in healthy subjects between the probability of producing word X in response to cue word Y in a free association task and the probability of using word X to describe the meaning of word Y. Furthermore, we provide evidence that associative measures are altered in people suffering from Alzheimer's disease (AD) and predict their level of performance in a picture-naming task. We provide a parsimonious account of the experimental data gathered form these different sources of evidence according to the hypothesis that the links between a cue word and its associates can be viewed as binding a concept (the cue) to pieces of information regarding its meaning (the associates).
Reference Key
zannino2018frontiersassociative Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Gian Daniele Zannino;Roberta Perri;Alice Teghil;Alice Teghil;Carlo Caltagirone;Carlo Caltagirone;Giovanni A. Carlesimo;Giovanni A. Carlesimo
Journal lasers in manufacturing and materials processing
Year 2018
DOI
10.3389/fnbeh.2017.00261
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.