A DISTRIBUTED-MEMORY MODEL OF SEMANTIC PRIMING
A DISTRIBUTED-MEMORY MODEL OF SEMANTIC PRIMING
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DOI：
10.1037/0278-7393.21.1.3
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发表时间：
1995-01-01
影响因子：
2.6
通讯作者：
MASSON, MEJ
中科院分区：
文献类型：
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作者：
MASSON, MEJ
描述了词汇知识表示和访问的语义网络模型的替代方案，其中关于单词的知识被表示为跨处理单元集合的激活模式。 在这种分布式记忆模型中，语义启动效应自然地源于表示相关启动项和目标的激活模式的相似性。 启动效应可以通过在目标出现之前改变激活模式的干预刺激来减少。 这个过程是通过单词命名任务来实证证明的。 分布式内存模型的实现版本用于模拟这些结果，并且还模拟了先前研究的结果，其中参与者对介入素数和目标之间的项目进行了公开的反应。 讨论了启动的语义网络和复合提示模型的比较。
An alternative to semantic network models of lexical knowledge representation and access is described, in which knowledge about a word is represented as a pattern of activation across a collection of processing units. In this distributed memory model, semantic priming effects arise naturally from the similarity of the patterns of activation that represent a related prime and target. Priming effects can be reduced by an intervening stimulus that modifies the pattern of activation before the target appears. This process is demonstrated empirically with a word naming task. An implemented version of the distributed memory model is used to simulate these results, and results from previous research in which participants overtly responded to the item that intervened between a prime and target are also simulated. Comparisons with semantic network and compound cue models of priming are discussed.