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.

A two-factor hypothesis is provided for the spacing effect in free recall of twoitem clusters, (a) The formation and storage of clusters is more likely with small within-category spacing, and (b) the retrieval of clusters is more likely with large within-category spacing. The hypothesis is studied on three levels of increasing theoretical sophistication. Central to all the analyses is the representation of pair-clustering data in terms of basic events in an underlying sample space. At the lowest level, descriptive measures of category recall are shown to vary with spacing in a complex manner that is consistent with, but in no way demonstrative of, the two-factor hypothesis. Next, a simple statistical model is formulated that permits measurement of hypothetical storage and retrieval contributions to pair clustering. A study of these two quantities provides direct support for the two-factor hypothesis. Finally, the two-factor hypothesis is embedded in a multilevel Markov model for pair clustering that postulates separate short-term and long-term memory states. The model provides a good account of trial-to-trial changes in the basic events of pair clustering.

This article demonstrates how multinomial processing tree (MPT) models can be used as assessment tools to measure the source of cognitive deficits in clinical populations. The application of this type of modeling is illustrated with a model developed by Batchelder and Riefer (1980, Psychological Review) that uses the free recall of category pairs to separately measure storage and retrieval processes in memory. A special version of the model is described that incorporates order constraints in the model's parameters over repeated trails. Computer simulations of the model can be used to address such issues as bias and standard error in the parameter estimates, and how these are affected by individual differences. The article discusses the utility of conducting validity tests of MPT models when used for clinical assessment, and this is illustrated for the pair-clustering model in two experiments. Experiment 1 shows that presentation rate during study affects the storage of clusters but not their retrievability, while Experiment 2 shows that part-list cuing during recall hurts the retrieval of clusters, but does not affect their storage. Experiment 3 and 4 apply the model to two clinical populations: schizophrenics and alcoholics with organic brain damage. The model's analysis reveals that each clinical group suffers deficits in both storage and retrieval compared to a control group. The results suggest that the retrieval deficits are stronger and occur more consistently over trails, whereas storage deficits become significant only on later trails. In addition, the organic alcoholics exhibit no improvement in retrieval over trails, although their storage improves over trails at the same rate as that for the control group.