Pitt Mark A, Myung Jay I, Altieri Nicholas
Department of Psychology, Ohio State University, Columbus, Ohio 43210, USA.
Psychon Bull Rev. 2007 Jun;14(3):442-8. doi: 10.3758/bf03194086.
Vitevitch and Luce (1998) showed that the probability with which phonemes co-occur in the language (phonotactic probability) affects the speed with which words and nonwords are named. Words with high phonotactic probabilities between phonemes were named more slowly than words with low probabilities, whereas with nonwords, just the opposite was found. To reproduce this reversal in performance, a model would seem to require not merely sublexical representations, but sublexical representations that are relatively independent of lexical representations. ARTphone (Grossberg, Boardman, & Cohen, 1997) is designed to meet these requirements. In this study, we used a technique called parameter space partitioning to analyze ARTphone's behavior and to learn if it can mimic human behavior and, if so, to understand how. To perform best, differences in sublexical node probabilities must be amplified relative to lexical node probabilities to offset the additional source of inhibition (from top-down masking) that is found at the sublexical level.
维特维奇和卢斯（1998年）表明，音素在语言中共同出现的概率（音位结构概率）会影响单词和非单词的命名速度。音素之间音位结构概率高的单词比概率低的单词命名速度更慢，而对于非单词，结果则相反。为了重现这种表现上的反转，一个模型似乎不仅需要次词汇表征，还需要相对独立于词汇表征的次词汇表征。ARTphone（格罗斯伯格、博德曼和科恩，1997年）就是为满足这些要求而设计的。在本研究中，我们使用了一种称为参数空间划分的技术来分析ARTphone的行为，并了解它是否能够模仿人类行为，如果可以，还要了解其方式。为了达到最佳性能，相对于词汇节点概率，次词汇节点概率的差异必须被放大，以抵消在次词汇层面发现的额外抑制源（来自自上而下的掩蔽）。
