Norris Dennis
Medical Research Council Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge, United Kingdom.
Psychol Rev. 2006 Apr;113(2):327-57. doi: 10.1037/0033-295X.113.2.327.
This article presents a theory of visual word recognition that assumes that, in the tasks of word identification, lexical decision, and semantic categorization, human readers behave as optimal Bayesian decision makers. This leads to the development of a computational model of word recognition, the Bayesian reader. The Bayesian reader successfully simulates some of the most significant data on human reading. The model accounts for the nature of the function relating word frequency to reaction time and identification threshold, the effects of neighborhood density and its interaction with frequency, and the variation in the pattern of neighborhood density effects seen in different experimental tasks. Both the general behavior of the model and the way the model predicts different patterns of results in different tasks follow entirely from the assumption that human readers approximate optimal Bayesian decision makers.
本文提出了一种视觉单词识别理论，该理论假定，在单词识别、词汇判断和语义分类任务中，人类读者的行为如同最优贝叶斯决策者。这促成了一种单词识别计算模型——贝叶斯阅读者的开发。贝叶斯阅读者成功模拟了一些关于人类阅读的最重要数据。该模型解释了单词频率与反应时间及识别阈值之间函数关系的本质、邻域密度的影响及其与频率的相互作用，以及在不同实验任务中观察到的邻域密度效应模式的变化。该模型的一般行为以及它在不同任务中预测不同结果模式的方式，完全源自人类读者近似于最优贝叶斯决策者这一假设。
