Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Better explanations of lexical and semantic cognition using networks derived from continued rather than single word associations|
|Author:||De Deyne, S.|
|Citation:||Behavior Research Methods, 2013; 45(2):480-498|
|Simon De Deyne & Daniel J. Navarro & Gert Storms|
|Abstract:||In this article, we describe the most extensive set of word associations collected to date. The database contains over 12,000 cue words for which more than 70,000 participants generated three responses in a multiple-response free association task. The goal of this study was (1) to create a semantic network that covers a large part of the human lexicon, (2) to investigate the implications of a multiple-response procedure by deriving a weighted directed network, and (3) to show how measures of centrality and relatedness derived from this network predict both lexical access in a lexical decision task and semantic relatedness in similarity judgment tasks. First, our results show that the multiple-response procedure results in a more heterogeneous set of responses, which lead to better predictions of lexical access and semantic relatedness than do single-response procedures. Second, the directed nature of the network leads to a decomposition of centrality that primarily depends on the number of incoming links or in-degree of each node, rather than its set size or number of outgoing links. Both studies indicate that adequate representation formats and sufficiently rich data derived from word associations represent a valuable type of information in both lexical and semantic processing.|
|Keywords:||Word associations; Semantic network; Lexical decision; Semantic relatedness; Lexical centrality|
|Rights:||© Psychonomic Society, Inc. 2012|
|Appears in Collections:||Psychology publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.