title: Testing models of decision making using confidence ratings in classification.
abstract: 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.
authors.affiliation: Department of Psychological Sciences, Purdue University, West Lafayette, Indiana 47907, USA. jdb@psych.purdue.edu
authors.forename: J D
authors.initials: JD
authors.lastname: Balakrishnan
authors.forename: R
authors.initials: R
authors.lastname: Ratcliff
mesh_terms: D000328:Adult; D016001:Confidence Intervals; D003657:Decision Making; D004193:Discrimination Learning; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D011939:Mental Recall; D015233:Models, Statistical; D011337:Probability Learning; D017603:Signal Detection, Psychological; D013269:Stochastic Processes; D014706:Verbal Learning