Multinomial Modeling and the Measurement of Cognitive Processes

This article presents a detailed discussion and application of a methodology, called multinomial modeling, that can be used to measure and study cognitive processes. Multinomial modeling is a statistically based technique that involves estimating hypothetical parameters that represent the probabilities of unobservable cognitive events. Models in this class provide a statistical methodology that is compatible with computational theories of cognition. Multinomial models are relatively uncomplicated, do not require advanced mathematical techniques, and have certain advantages over other, more traditional methods for studying cognitive processes. The statistical methodology behind multinomial modeling is briefly discussed, including procedures for data collection, model development, parameter estimation, and hypothesis testing. Three substantive examples of multinomial modeling are presented. Each example, taken from a different area within the field of human memory, involves the development ofa multinomial model and its application to a specific experiment. It is shown how multinomial models facilitate the interpretation of the experiments. The conclusion discusses the general advantages of multinomial models and their potential application as research tools for the study of cognitive processes.