TESTING MODELS OF DECISION-MAKING USING CONFIDENCE RATINGS IN CLASSIFICATION

Classification implies decision making (or response selection) of some kind. Studying the decision process using a traditional signal detect ion 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-crite rion models, can mimic each other's predictions about performance leve l. In this article, the authors show that by analyzing certain distrib utional properties of confidence ratings, a researcher can determine w hether the decision process is optimal, without knowing the form of th e encoding distributions. Empirical results are reported for three typ es 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.