人工语法学习中的重编码和表示。
Recoding and representation in artificial grammar learning.
机构信息
Department of Psychology, University of Manitoba, Winnipeg, MB, Canada, R3T 2N2.
出版信息
Behav Res Methods. 2013 Jun;45(2):470-9. doi: 10.3758/s13428-012-0253-6.
We apply an exemplar model of memory to explain performance in the artificial grammar task. The model blends the convolution-based method for representation developed in Jones and Mewhort's BEAGLE model of semantic memory (Psychological Review 114:1-37, 2007) with the storage and retrieval assumptions in Hintzman's MINERVA 2 model of episodic memory (Behavior Research Methods, Instruments, and Computers, 16:96-101, 1984). The model captures differences in encoding to fit data from two experiments that document the influence of encoding on implicit learning. We provide code so that researchers can adapt the model and techniques to their own experiments.
我们应用记忆的范例模型来解释人工语法任务中的表现。该模型融合了 Jones 和 Mewhort 的 BEAGLE 语义记忆模型（《心理评论》114：1-37，2007）中基于卷积的表示方法，以及 Hintzman 的 MINERVA 2 情节记忆模型（《行为研究方法、仪器和计算机》，16：96-101，1984）中的存储和检索假设。该模型捕捉到了编码的差异，以适应两个记录编码对内隐学习影响的实验数据。我们提供了代码，以便研究人员可以将模型和技术应用于自己的实验。
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