Estimates of utility function parameters from signal detection experiments.

Abstract

A theory is developed to make it possible to estimate the slope parameter of a presumed power law utility function from an analysis of data from signal detection experiments. The theory can be applied to detection data analyzed either according to constant likelihood ratio models or according to linear learning models of bias. Experiments to test the consequences of the theory are described. A linear learning model is found to give the best account of the data. The utility function obtained from this model is a power function with exponent equal to .48.Currently, there is a dualism between the behaviorist tradition in human experimental psychology, concerned exclusively with performance, and other more "cognitive" approaches that include the observers' introspective reports. The scope of theory should therefore be widened to produce models that account for performance and verbal report within a common framework.It is with such a goal in mind that we propose to develop a single coherent theory of the perception of "utility" that will describe not only the results of scaling experiments of both utility and number judgments but also the way people actually use number in such diverse tasks as adding and comparing numbers, gambling, decision making, and signal detection. Some of the earliest systematic attempts to investigate the perception of number and utility are in the domain of scaling. Attneave (1962) has suggested that magnitude esti-This research was supported in part by the Engineering Psychology Programs, Office of Naval Research. This publication replaces Psychophysics Laboratory Report PLR-32. The authors are indebted to the referees for their thoughtful and helpful comments.

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