Age differences in storage and retrieval: A multinomial modeling analysis

The Development of Clustering in Episodic Memory: A Cognitive-Modeling Approach

Younger children's free recall from episodic memory is typically less organized than recall by older children. To investigate if and how repeated learning opportunities help children use organizational strategies that improve recall, the authors analyzed category clustering across four study-test cycles. Seven-year-olds, 10-year-olds, and young adults (N = 150) studied categorically related words for a free-recall task. The cognitive processes underlying recall and clustering were measured with a multinomial model. The modeling revealed that developmental differences emerged particularly in the rate of learning to encode words as categorical clusters. The learning curves showed a common pattern across age groups, indicating developmental invariance. Memory for individual items also contributed to developmental differences and was the only factor driving 7-year-olds' moderate improvements in recall.

The Item-Specific Deficit Approach to evaluating verbal memory dysfunction: rationale, psychometrics, and application

In the current study, we introduce the Item-Specific Deficit Approach (ISDA), a novel method for characterizing memory process deficits in list-learning data. To meet this objective, we applied the ISDA to California Verbal Learning Test (CVLT) data collected from a sample of 132 participants (53 healthy participants and 79 neurologically compromised participants). Overall, the ISDA indices measuring encoding, consolidation, and retrieval deficits demonstrated advantages over some traditional indices and indicated acceptable reliability and validity. Currently, the ISDA is intended for experimental use, although further research may support its utility for characterizing memory impairments in clinical assessments.

Multinomial Processing Tree Models

Multinomial processing tree (MPT) models have become popular in cognitive psychology in the past two decades. In contrast to general-purpose data analysis techniques, such as log-linear models or other generalized linear models, MPT models are substantively motivated stochastic models for categorical data. They are best described as tools (a) for measuring the cognitive processes that underlie human behavior in various tasks and (b) for testing the psychological assumptions on which these models are based. The present article provides a review of MPT models and their applications in psychology, focusing on recent trends and developments in the past 10 years. Our review is nontechnical in nature and primarily aims at informing readers about the scope and utility of MPT models in different branches of cognitive psychology.

Cognitive psychometrics: assessing storage and retrieval deficits in special populations with multinomial processing tree models

This article demonstrates how multinomial processing tree models can be used as assessment tools to measure cognitive deficits in clinical populations. This is illustrated with a model developed by W. H. Batchelder and D. M. Riefer (1980) that separately measures storage and retrieval processes in memory. The validity of the model is tested in 2 experiments, which show that presentation rate affects the storage of items (Experiment 1) and part-list cuing hurts item retrieval (Experiment 2). Experiments 3 and 4 examine 2 clinical populations: schizophrenics and alcoholics with organic brain damage. The model reveals that each group exhibits deficits in storage and retrieval, with the retrieval deficits being stronger and occurring more consistently over trials. Also, the alcoholics with organic brain damage show no improvement in retrieval over trials, although their storage improves at the same rate as a control group.

Extending General Processing Tree Models to Analyze Reaction Time Experiments

General processing tree (GPT) models are usually used to analyze categorical data collected in psychological experiments. Such models assume functional relations between probabilities of the observed behavior categories and the unobservable choice probabilities involved in a cognitive task. This paper extends GPT models for categorical data to the analysis of continuous data in a class of response time (RT) experiments in cognitive psychology. Suppose that a cognitive task involves several discrete processing stages and both accuracy (categorical) and latency (continuous) measures are obtained for each of the response categories. Furthermore, suppose that the task can be modeled by a GPT model that assumes serialization among the stages. The observed latencies of the response categories are functions of the choice probabilities and processing times (PT) at each of the processing stages. The functional relations are determined by the processing structure of the task. A general framework is presented and it is applied to a set of data obtained from a source monitoring experiment. Copyright 2001 Academic Press.

GPT.EXE: a powerful tool for the visualization and analysis of general processing tree models

This paper introduces GPT.EXE, a computer program for designing and implementing general processing tree (GPT) models. First, designing and building GPT models using this program is discussed. The second major emphasis is a description of various statistical procedures that can be carried out with GPT.EXE. There is also a brief section on the on-line documentation of this program. Throughout the text, pictures of windows from the program are displayed to help explain the procedures being described by the text.

Theoretical and empirical review of multinomial process tree modeling

We review a current and popular class of cognitive models called multinomial processing tree (MPT) models. MPT models 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. We formally 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 MPT models 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 MPT models, 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.

A multinomial modeling analysis of the recognition-failure paradigm

The recognition-failure paradigm has received much theoretical consideration, especially the Tulving-Wiseman function and its exceptions. We show that the Tulving-Wiseman function does a poor job of accounting for the data, both when its fit is measured with a model-based, goodness-of-fit statistic and when a logically equivalent reformulation of the function is compared with data. We then present a simple multinomial model based on retrieval-independence theory that is capable of measuring storage and retrieval processes in recognition failure. The model is used to conduct a meta-analysis of the recognition-failure paradigm, and shows that violations of the Tulving-Wiseman function occur under conditions in which weak storage is coupled with strong retrieval. In addition, if storage and retrieval are assumed to be positively correlated across conditions, the model produces a theoretically motivated, alternative equation to the Tulving-Wiseman function that provides a virtually identical fit to the data.

A multinomial modeling analysis of the mnemonic benefits of bizarre imagery

A series of experiments was conducted to explore the cognitive processes that mediate the bizarreness effect, that is, the finding that bizarre or unusual imagery is recalled better than common imagery. In all experiments, subjects were presented with noun pairs that were embedded within bizarre or common sentences in a mixed-list design. None of the experiments produced a bizarreness effect for cued recall; however, for two of the experiments, the bizarre noun pairs were remembered significantly better than the common pairs for free recall. To determine if these differences were due to the storage or retrieval of the items, a multinomial model for the analysis of imagery mediation in paired-associate learning was developed and applied to the data from the experiments. The model revealed that bizarre sentences benefited the retrieval of the noun pairs but not their storage within memory. The empirical and modeling results are discussed relative to previous findings and theories on the bizarreness effect.