An adaptive approach to human decision making: Learning theory, decision theory, and human performance.

Abstract

This article describes a general model of decision rule learning, the rule competition model, composed of 2 parts: an adaptive network model that describes how individuals learn to predict the payoffs produced by applying each decision rule for any given situation and a hill-climbing model that describes how individuals learn to fine tune each rule by adjusting its parameters. The model was tested and compared with other models in 3 experiments on probabilistic categorization. The first experiment was designed to test the adaptive network model using a probability learning task, the second was designed to test the parameter search process using a criterion learning task, and the third was designed to test both parts of the model simultaneously by using a task that required learning both category rules and cutoff criteria. Probabilistic categorization is an important class of decision problems in which stimuli are sampled from a number of categories and the decision maker must decide the category from which each stimulus was sampled. Payoffs depend on both the true category membership and the decision maker's response for each stimulus. Examples are found in all areas of psychology: In perception, auditory or visual stimuli are categorized as signal or noise, and in memory recognition, verbal items are categorized as old or new. In cognition, exemplar patterns are assigned to conceptual categories, and in industrial psychology, job applicants are categorized as acceptable or unacceptable. Finally, in clinical psychology, patient symptom patterns are assigned to disease categories. For the past 35 years, the general theory of signal detection (Peterson, Birdsall, & Fox, 1954) has served as the most prominent model of probabilistic categorization. It has been successfully applied to all of the areas of psychology mentioned (see Green & Swets, 1966; for perception; Bernbach, 1967, and Wickelgren & Norman, 1966, for memory recognition; Ashby & Gott, 1988, for conceptual categorization; Cronbach & Gleser, 1965, for industrial psychology; and Swets & Pickett, 1982, for medical diagnosis). The core idea is that (a) each stimulus is represented as a point within a multidimensional stimulus space, (b) this multidimensional space is partitioned into response regions, and (c) a stimulus is categorized according to the region within which it lies. Simple decision rules are normally used to describe how the stimulus space is partitioned.' For example, unidimensional stimuli can be divided into two categories by either a cutoff rule (all points above a cutoff go into one category) or by an interval rule (all points inside an interval go into one category). Two-dimensional stimuli can be partitioned into