A novel method for assessing rival models of recognition memory

A general comparison is made between the multinomial processing tree (MPT) approach and a strength-based approach for modeling recognition memory measurement. Strength models include the signal-detection model and the dual-process model. Existing MPT models for recognition memory and a new generic MPT model, called the Multistate (MS) model, are contrasted with the strength models. Although the ROC curves for the MS model and strength model are similar, there is a critical difference between existing strength models and MPT models that goes beyond the assessment of the ROC. This difference concerns the question of stochastic mixtures for foil test trials. The hazard function and the reverse hazard function are powerful methods for detecting the presence of a probabilistic mixture. Several new theorems establish a novel method for obtaining information about the hazard function and reverse hazard function for the latent continuous distributions that are assumed in the strength approach to recognition memory. Evidence is provided that foil test trials involve a stochastic mixture. This finding occurred for both short-term memory procedures, such as the Brown–Peterson task, and long-term list-learning procedures, such as the paired-associate task. The effect of mixtures on foil trials is problematic for existing strength models but can be readily handled by MPT models such as the MS model. Other phenomena, such as the mirror effect and the effect of target-foil similarity, are also predicted accurately by the MPT modeling framework.     

Signal detection models for the same–different task

Signal detection models for the same–different task are presented. In contrast to the standard approach that only considers the proportion correct, the models apply to the full four by two same–different table. The approach allows one to consider models that recognize bias and other effects. Two basic signal detection models, associated with different decision rules, are presented. A version of the covert decisions rule is introduced that directly allows for same–different bias, in contrast to earlier versions. It is shown how to fit the models with standard software for nonlinear mixed models. The models are applied to data from a recent same–different study.     

(Non)Issues of Infinite Regress in Modeling Motor Behavior

In the past, infinite regress criticisms that have been raised about models of motor behavior have been reserved for executive-type models (e.g., Beek & Meijer, 1988). On the basis of Gödel's (1930/1986) proof that an algorithm cannot prove its own validity, the authors reason that executive- as well as self-organized-type explanatory models of motor behavior have infinite regress difficulties. The conclusion offered in the present article is that judgments on a model's theoretical importance should be based not on issues of infinite regress but on other relevant characteristics, such as its propensity for falsification (Popper, 1959).     

Integrating Human and Natural Systems in Community Psychology: An Ecological Model of Stewardship Behavior

Community psychology (CP) research on the natural environment lacks a theoretical framework for analyzing the complex relationship between human systems and the natural world. We introduce other academic fields concerned with the interactions between humans and the natural environment, including environmental sociology and coupled human and natural systems. To demonstrate how the natural environment can be included within CP’s ecological framework, we propose an ecological model of urban forest stewardship action. Although ecological models of behavior in CP have previously modeled health behaviors, we argue that these frameworks are also applicable to actions that positively influence the natural environment. We chose the environmental action of urban forest stewardship because cities across the United States are planting millions of trees and increased citizen participation in urban tree planting and stewardship will be needed to sustain the benefits provided by urban trees. We used the framework of an ecological model of behavior to illustrate multiple levels of factors that may promote or hinder involvement in urban forest stewardship actions. The implications of our model for the development of multi-level ecological interventions to foster stewardship actions are discussed, as well as directions for future research to further test and refine the model.     

Using Generalizability Theory to Disattenuate Correlation Coefficients for Multiple Sources of Measurement Error

Over the years, research in the social sciences has been dominated by reporting of reliability coefficients that fail to account for key sources of measurement error. Use of these coefficients, in turn, to correct for measurement error can hinder scientific progress by misrepresenting true relationships among the underlying constructs being investigated. In the research reported here, we addressed these issues using generalizability theory (G-theory) in both traditional and new ways to account for the three key sources of measurement error (random-response, specific-factor, and transient) that affect scores from objectively scored measures. Results from 20 widely used measures of personality, self-concept, and socially desirable responding showed that conventional indices consistently misrepresented reliability and relationships among psychological constructs by failing to account for key sources of measurement error and correlated transient errors within occasions. The results further revealed that G-theory served as an effective framework for remedying these problems. We discuss possible extensions in future research and provide code from the computer package R in an online supplement to enable readers to apply the procedures we demonstrate to their own research.     

Models for the Detection of Deviations from the Expected Processing Strategy in Completing the Items of Cognitive Measures

This paper presents confirmatory factor models with fixed factor loadings that enable the identification of deviations from the expected processing strategy. The instructions usually define the expected processing strategy to a considerable degree. Simplification is a deviation from instructions that is likely to occur in complex cognitive measures. Since simplification impairs the validity of the measure, its identification is important. Models representing simplicity and instruction-based processing strategies were considered in investigating the data of 345 participants obtained by a working memory measure in order to find out whether and how the use of these strategies influences model-data fit. As expected, the consideration of simplicity strategies improved the model-data fit achieved for the instruction-based strategy.     

Choosing the Appropriate Design and Analysis of a Research Project

It is extremely important that the proper mental planning of a research project is considered before the data collection ever begins. Otherwise, problems may be encountered after the data have been collected and it may then be too late to correct them. This paper will explain some of the important considerations necessary in choosing an adequate design and analysis for a research project.