This paper presents and tests a formal mathematical model for the analysis of informant responses to systematic interview questions. We assume a situation in which the ethnographer does not know how much each informant knows about the cultural domain under consideration nor the answers to the questions. The model simultaneously provides an estimate of the cultural competence or knowledge of each informant and an estimate of the correct answer to each question asked of the informant. The model currently handles true‐false, multiple‐choice, andfill‐in‐the‐blank type question formats. In familiar cultural domains the model produces good results from as few as four informants. The paper includes a table showing the number of informants needed to provide stated levels of confidence given the mean level of knowledge among the informants. Implications are discussed.

This article presents a family of processing models for the source-monitoring paradigm in human memory. Source monitoring and the special case of reality monitoring have become very popular as paradigms to assess memory deficits in various subject populations. The paradigm provides categorical data that satisfy product-multinomial constraints, and this lends it nicely to multinomial modeling with processing-tree structures as described in Riefer and Batchelder (1988). The models developed herein are based on ideas from high-threshold signal-detection models, and they involve item-detection parameters, source-identification parameters, and various parameters reflecting guessing biases. The purpose of the models is to provide separate, theoretically based measures of old-item detection and source discrimination. The models may strengthen traditional analyses that are based on ad hoc statistics, as well as avoid flawed interpretations that the traditional analyses may produce. The usefulness of the models is revealed by analyzing published data sets from the areas of reality monitoring and bilingual memory.

We review a current and popular class of cognitive models called multinomial processing tree (MPT) models. MPTmodels are simple, substantively motivated statistical models that can be applied to categorical data. They are useful as data-analysis tools for measuring underlying or latent cognitive capacities and as simple models for representing and testing competing psychological theories. Weformally describe the cognitive structure and parametric properties of the class of MPT models and provide an inferential statistical analysis for the entire class. Following this, we provide a comprehensive review of over 80 applications of MPTmodels to a variety of substantive areas in cognitive psychology, including various types of human memory, visual and auditory perception, and logical reasoning. We then address a number of theoretical issues relevant to the creation and evaluation of MPTmodels, including model development, model validity, discrete-state assumptions, statistical issues, and the relation between MPT models and other mathematical models. In the conclusion, we consider the current role of MPT models in psychological research and possible future directions.This article presents a detailed review of a current and popular class of cognitive models called multinomial processing tree (MPT) models. MPT models have been described formally in Riefer and Batchelder (1988) and in Hu and Batchelder (1994b), although models of this type have been around well before the class was first formalized in 1988 (e.g., Batchelder & Riefer, 1980;Chechile & Meyer, 1976;Greeno, James, DaPolito, & Polson, 1978;Humphreys & Bowyer, 1980;B. H. Ross & Bower, 1981). However, the last 10 years have witnessed a deeper understanding and an accelerated use of these models within psychology. This increased popularity of MPT models has resulted not only in the application of these models to new areas in psychology but has also led to a variety of new statistical techniques and a certain amount of theoretical debate. Because of these developments, a review article on this class ofmodels seems timely both for researchers already working in this area and for others who might benefit from using this type of modeling.MPT models are simple, substantively motivated statistical models that can be used to measure underlying or latent cognitive capacities. Psychological data often result from multiple, interacting processes, and operationally 57 defined statistics are quite limited in determining which of these processes are involved in a particular experimental paradigm. One primary use ofMPT models is as dataanalysis tools, capable of disentangling and measuring the separate contribution of different cognitive processes underlying observed data. This approach can be helpful in settling theoretical issues, because psychological theories often focus on one process or another as the fundamental cause of a particular psychological phenomenon. The structural simplicity of the class ofMPT models also makes it a useful framework for developing and testing...

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.

EM algorithm, multinomial models, processing trees, power divergence family,