Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations
Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations
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DOI：
10.3758/s13428-012-0260-7
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发表时间：
2013-06-01
影响因子：
5.4
通讯作者：
Storms, Gert
中科院分区：
文献类型：
--
作者：
De Deyne, Simon;Navarro, Daniel J.;Storms, Gert
在本文中，我们描述了迄今为止收集的最广泛的单词关联集。该数据库包含超过 12,000 个提示词，超过 70,000 名参与者在多响应自由联想任务中生成了三个响应。本研究的目标是（1）创建一个覆盖大部分人类词典的语义网络，（2）通过派生加权有向网络来研究多重响应程序的含义，以及（3）展示从该网络派生的中心性和相关性测量如何预测词汇决策任务中的词汇访问和相似性判断任务中的语义相关性。首先，我们的结果表明，多重响应过程会产生更加异构的响应集，这比单响应过程能够更好地预测词汇访问和语义相关性。其次，网络的有向性质导致中心性分解，主要取决于每个节点的传入链接数量或入度，而不是其集合大小或传出链接数量。两项研究都表明，从单词关联中得出的适当的表示格式和足够丰富的数据代表了词汇和语义处理中有价值的信息类型。
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.