The Spatial Coding Model of Visual Word Identification
The Spatial Coding Model of Visual Word Identification
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DOI：
10.1037/a0019738
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发表时间：
2010-07-01
影响因子：
5.4
通讯作者：
Davis, Cohn J.
中科院分区：
文献类型：
--
作者：
Davis, Cohn J.
视觉单词识别要求读者对单词中字母的身份和顺序进行编码，并将此代码与先前学习的代码进行匹配。该词汇匹配过程的当前模型设定了特定于上下文的字母代码，其中字母表示与特定的序列位置或特定的本地上下文（例如，字母簇）。这里描述的空间编码模型采用了不同的方法，字母位置编码和词汇匹配的基础上上下文无关的字母表示。在这个模型中，字母的位置是动态编码的，与一个计划称为空间编码。词汇匹配是通过一种称为叠加匹配的方法来实现的，在这种方法中，输入代码和学习代码根据它们共同字母的相对位置进行匹配。该模型的模拟说明了它的能力，以解释广泛的结果，从掩蔽形式启动文献，以及捕捉基准的发现，从未启动的词汇决策任务。
Visual word identification requires readers to code the identity and order of the letters in a word and match this code against previously learned codes. Current models of this lexical matching process posit context-specific letter codes in which letter representations are tied to either specific serial positions or specific local contexts (e.g., letter clusters). The spatial coding model described here adopts a different approach to letter position coding and lexical matching based on context-independent letter representations. In this model, letter position is coded dynamically, with a scheme called spatial coding. Lexical matching is achieved via a method called superposition matching, in which input codes and learned codes are matched on the basis of the relative positions of their common letters. Simulations of the model illustrate its ability to explain a broad range of results from the masked form priming literature, as well as to capture benchmark findings from the unprimed lexical decision task.