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Probability Matching

Published online by Cambridge University Press:  01 January 2025

M. Frank Norman
Affiliation:
Institute for Mathematical Studies in the Social Sciences Stanford, California
John I. Yellott Jr.
Affiliation:
Institute for Mathematical Studies in the Social Sciences Stanford, California

Abstract

The class of symmetric path-independent models with experimenter-controlled events is considered in conjunction with two-choice probability learning experiments. Various refinements of the notion of probability matching are defined, and the incidence of these properties within this class is studied. It is shown that the linear models are the only models of this class that predict a certain phenomenon that we call stationary probability matching. It is also shown that models within this class that possess an additional property called marginal constancy predict approximate probability matching.

Type
Original Paper
Copyright
Copyright © 1966 Psychometric Society

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Footnotes

*

This research grew out of questions posed by William K. Estes. We are also indebted to Professor Estes for his encouragement and assistance at all stages of this research. During the course of this research J. I. Y. received support from the U. S. Public Health Service (N. I. M. H.). M. F. N.'s present address is the University of Pennsylvania. J. I. Y.'s present address is the University of Minnesota.

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