Balakrishnan J D, Ratcliff R
Department of Psychological Sciences, Purdue University, West Lafayette, Indiana 47907, USA.
J Exp Psychol Hum Percept Perform. 1996 Jun;22(3):615-33. doi: 10.1037//0096-1523.22.3.615.
Classification implies decision making (or response selection) of some kind. Studying the decision process using a traditional signal detection theory analysis is difficult for two reasons: (a) The model makes a strong assumption about the encoding process (normal noise), and (b) the two most popular decision models, optimal and distance-from-criterion models, can mimic each other's predictions about performance level. In this article, the authors show that by analyzing certain distributional properties of confidence ratings, a researcher can determine whether the decision process is optimal, without knowing the form of the encoding distributions. Empirical results are reported for three types of experiments: recognition memory, perceptual discrimination, and perceptual categorization. In each case, the data strongly favored the distance-from-criterion model over the optimal model.
分类意味着某种形式的决策（或反应选择）。使用传统的信号检测理论分析来研究决策过程存在两方面困难：（a）该模型对编码过程（正态噪声）做出了强有力的假设；（b）两种最流行的决策模型，即最优模型和偏离标准模型，在关于表现水平的预测上能够相互模仿。在本文中，作者表明，通过分析信心评级的某些分布特性，研究人员可以在不知道编码分布形式的情况下确定决策过程是否是最优的。文中报告了针对三种类型实验的实证结果：识别记忆、知觉辨别和知觉分类。在每种情况下，数据都强烈支持偏离标准模型而非最优模型。
