On the nature and scope of featural representations of word meaning.
On the nature and scope of featural representations of word meaning.
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DOI：
10.1037//0096-3445.126.2.99
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发表时间：
1997-06
期刊：
影响因子：
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通讯作者：
K. McRae;V. D. de Sa;Mark S. Seidenberg
中科院分区：
文献类型：
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作者：
K. McRae;V. D. de Sa;Mark S. Seidenberg
行为实验和联结主义模型被用来探讨使用的特征表征在计算的词义。研究的重点是特征之间的相关性的作用，以及快速和不定时任务之间的差异，就使用的特征信息。结果表明，特征表征用于词义的初始计算（如在吸引子网络中），特征相关性的模式在人工制品和生物之间不同，并且特征相互关联的程度在语义记忆的组织中起着重要作用。研究还表明，它可能是可能的预测语义启动效应的独立动机的特征理论的语义相关。相关的行为现象，如与阿尔茨海默病（AD）相关的语义障碍的影响进行了讨论。
Behavioral experiments and a connectionist model were used to explore the use of featural representations in the computation of word meaning. The research focused on the role of correlations among features, and differences between speeded and untimed tasks with respect to the use of featural information. The results indicate that featural representations are used in the initial computation of word meaning (as in an attractor network), patterns of feature correlations differ between artifacts and living things, and the degree to which features are intercorrelated plays an important role in the organization of semantic memory. The studies also suggest that it may be possible to predict semantic priming effects from independently motivated featural theories of semantic relatedness. Implications for related behavioral phenomena such as the semantic impairments associated with Alzheimer's disease (AD) are discussed.