Forty years ago, the Kaiser Research group pioneered an approach t o interpersonal assessment based on a structural model of personality, the interpersonal circle. With renewed interest in circular models, issues about the measurement, analysis, and application of these kinds of individual data reemerge. This article is an introduction t o and revision of the b u i c principles of circular profile analysis. Using the recent circumplex of interpersonal problems, we define the model and its data, explain the structural summary, and discuss the descriptive and possible clinical significance of profile indices. Future researchers should seek t o expand understanding of how circular profile variables relate to clinical assessment and treatment, and also apply the analytic methods t o test key aspects of interpersonal theory.(2) The variables ofapersonality system should be designed to measure-on the same continuum-the normal or "adjustive" aspects ofbehavior as well as abnormal or pathological extremes. (3) Measurement of interpersonal behavior requires a broad collection of simple, specijc variables which are systematically related to each other and which are applicable to the study of adjustive or maladjustive responses. Correspondence regarding this article can be directed to either author: Michael B. G u m , Psychology Department, University ofWisconsin-Parkside, Kenosha. WI 53141; orJ. D. Balakrishnan, Department of Psychological Sciences, Purdue University, West Lafayette, IN 47907. Electronic mail may be sent via Internet to gurtman@uwp.edu.

A new approach to studying decision making in discrimination tasks is described that does not depend on the technical assumptions of signal detection theory (e.g., normality of the encoding distributions). In 3 different experiments, results of these new distribution-free tests converge on a single, surprising conclusion: response biases had substantial effects on the encoding distributions but no effect on the decision rule, which was uniformly unbiased in equal and unequal base rate conditions and in symmetric and asymmetric payoff conditions. This seemingly paradoxical result is fundamentally inconsistent with the entire family of signal detection theory models, raising some important questions about the significance of many published results in the human performance literature.

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.

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Operators' performance in a vigilance task is often assumed to depend on their choice of a detection criterion. When the signal rate is low this criterion is set high, causing the hit and false alarm rates to be low. With increasing time on task the criterion presumably tends to increase even further, thereby further decreasing the hit and false alarm rates. Virtually all of the empirical evidence for this simple interpretation is based on estimates of the bias measure beta from signal detection theory. In this article, I describe a new approach to studying decision making that does not require the technical assumptions of signal detection theory. The results of this new analysis suggest that the detection criterion is never biased toward either response, even when the signal rate is low and the time on task is long. Two modifications of the signal detection theory framework are considered to account for this seemingly paradoxical result. The first assumes that the signal rate affects the relative sizes of the variances of the information distributions; the second assumes that the signal rate affects the logic of the operator's stopping rule. Actual or potential applications of this research include the improved training and performance assessment of operators in areas such as product quality control, air traffic control, and medical and clinical diagnosis.