Rapid-REM: A MATLAB simulator of the replaced-elements model

A recent proposal for an elemental account of associative learning phenomena is the replaced-elements model (REM) put forward by Wagner (2003). Although the ideas underlying this model are comparatively simple, implementation of the model is rather complex. In this article, we present Rapid-REM, a MATLAB simulator of Wagner's model. Rapid-REM features a graphical user interface for manipulating all essential parameter values and for control of the simulation process, graphical visualization of the simulation course and the results, and the alternative possibility of simulating the replaced-elements model as it was originally proposed (Wagner & Brandon, 2001). Rapid-REM is available free of charge from www.staff.uni-marburg.de/(tilde)lachnit/Rapid-REM/. This simulator makes it easy to derive predictions for REM and evaluate them, and it will therefore facilitate insights into the mechanisms of associative learning.     

Charting the process of change: A primer on survival analysis

Survival analysis is a powerful and useful technique for understanding qualitative change. This article provides a practical, nontechnical introduction to the use of survival analysis for social scientists. Important issues in using survival analysis are discussed, including research design, data preparation and management, and data analysis. Attendance data from a self-helf organization are used to illustrate common survival analysis tasks such as describing the overall survival and hazard functions, examining covariate effects, and modeling the form of the hazard function over time. An appendix that discusses the strengths and weaknesses of current survival analysis computer programs is included. Editor's note: Edward Seidman served as action editor for this article while serving as Associate Editor for Methodology.I thank William Davidson, Susan Englund, Bruce Rapkin, Kurt Ribisl, and three anonymous reviewers for their helpful comments on earlier drafts. The example data presented here were collected with the support of an NIMH grant (MH37390) awarded to Julian Rappaport and Ed Seidman.     

How to use the diffusion model: Parameter recovery of three methods: EZ, fast-dm, and DMAT

Parameter recovery of three different implementations of the Ratcliff diffusion model was investigated: the EZ model (Wagenmakers, van der Maas, & Grasman, 2007), fast-dm (Voss & Voss, 2007), and DMAT (Vandekerckhove & Tuerlinckx, 2007). Their capacity to recover both the mean structure and individual differences in parameter values was explored. The three methods were applied to simulated data generated by the diffusion model, by the leaky, competing accumulator (LCA) model (Usher & McClelland, 2001) and by the linear ballistic accumulator (LBA) model (Brown & Heathcote, 2008). Results show that EZ and DMAT are better capable than fast-dm in recovering experimental effects on parameters. EZ was best in recovering individual differences in parameter values. When data were generated by the LCA model, the diffusion model estimates obtained with all three methods correlated well with corresponding LCA model parameters. No such one-on-one correspondence could be established between parameters of the LBA model and the diffusion model.     

HMS: A MATLAB simulator of the Harris model of associative learning

Harris (2006) recently proposed a new elemental model of the processes involved in associative learning. Although Harris explicated all relevant mathematical and conceptual details of the model in his article, implementing a computer simulation of his model requires considerable programming expertise and work. We therefore present the Harris model simulator (HMS), a MATLAB simulator of Harris's model. HMS provides a graphical user interface for manipulating all essential parameter values and for controlling the simulation process, the graphical visualization of the simulation course, and the numerical results. HMS is available free of charge from www.staff.uni-marburg.de/(tilde)lachnit/harris/. HMS allows researchers to easily derive and evaluate predictions for the Harris model, and it will therefore facilitate insights into the mechanisms of associative learning.     

A network learning model with GERT analysis

Graphical Evaluation and Review Technique (GERT) can be used to analyze the teaching-learning process. By representing the teaching-learning process with a stochastic network, it is possible to use a topology equation for a closed network to obtain some parameters for the total teaching-learning process. To illustrate the techniques, two examples concerning a basic calculus course are given. In addition, the relation to the familiar Markovian learning models is indicated.     

Models with discrete latent variables for analysis of categorical data: A framework and a MATLAB MDLV toolbox

Studies in the social and behavioral sciences often involve categorical data, such as ratings, and define latent constructs underlying the research issues as being discrete. In this article, models with discrete latent variables (MDLV) for the analysis of categorical data are grouped into four families, defined in terms of two dimensions (time and sampling) of the data structure. A MATLAB toolbox (referred to as the “MDLV toolbox”) was developed for applying these models in practical studies. For each family of models, model representations and the statistical assumptions underlying the models are discussed. The functions of the toolbox are demonstrated by fitting these models to empirical data from the European Values Study. The purpose of this article is to offer a framework of discrete latent variable models for data analysis, and to develop the MDLV toolbox for use in estimating each model under this framework. With this accessible tool, the application of data modeling with discrete latent variables becomes feasible for a broad range of empirical studies.     

Investigating latent constructs with item response models: A MATLAB IRTm toolbox

Item response theory (IRT) models are the central tools in modern measurement and advanced psychometrics. We offer a MATLAB IRT modeling (IRTm) toolbox that is freely available and that follows an explicit design matrix approach, giving the end user control and flexibility in building a model that goes beyond standard models, such as the Rasch model (Rasch, 1960) and the two-parameter logistic model. As such, IRTm allows for a large variety of unidimensional IRT models for binary responses, the incorporation of additional person and item information, and deviations from common model assumptions. An exclusive key feature of the toolbox is the inclusion of copula IRT models to handle local item dependencies. Two appendixes for this report, containing example code and information on the general copula IRT in IRTm, may be downloaded from brm.psychonomic-journals.org/content/supplemental.     

Diffusion model drift rates can be influenced by decision processes: An analysis of the strength-based mirror effect

Improving memory for studied items (targets) often helps participants reject nonstudied items (lures), a pattern referred to as the strength-based mirror effect (SBME). Criss (2010) demonstrated the SBME in diffusion model drift rates; that is, the target drift rate was higher and the lure drift rate was lower for lists of words studied 5 times versus lists of words studied once. She interpreted the drift rate effect for lures as evidence for the differentiation process, whereby strong memory traces produce a poorer match to lure items than do weak memory traces. However, she noted that strength may have also affected a model parameter called the drift criterion-a participant-controlled decision parameter that defines the zero point in drift rate. We directly contrasted the differentiation and drift-criterion accounts by manipulating list strength either at both encoding and retrieval (which produces a differentiation difference in the studied traces) or at retrieval only (which equates differentiation from the study list but provides the opportunity to change decision processes based on strength). Across 3 experiments, results showed that drift rates for lures were lower on strong tests than on weak tests, and this effect was observed even when strength was varied at retrieval alone. Therefore, results provided evidence that the SBME is produced by changes in decision processes, not by differentiation of memory traces. (PsycINFO Database Record (c) 2012 APA, all rights reserved).     

A Selective Service primer for counselors

This article summarizes the major provisions of the draft law as they pertain to the new registrant. It explains the importance of the registrant's being as cognizant as possible of his rights and obligations under the law. Finally, it describes how a counselor may help a young man obtain all the benefits to which he is entitled.     

Conducting and interpreting canonical correlation analysis in personality research: a user-friendly primer

The purpose of this article is to reduce potential statistical barriers and open doors to canonical correlation analysis (CCA) for applied behavioral scientists and personality researchers. CCA was selected for discussion, as it represents the highest level of the general linear model (GLM) and can be rather easily conceptualized as a method closely linked with the more widely understood Pearson r correlation coefficient. An understanding of CCA can lead to a more global appreciation of other univariate and multivariate methods in the GLM. We attempt to demonstrate CCA with basic language, using technical terminology only when necessary for understanding and use of the method. We present an entire example of a CCA analysis using SPSS (Version 11.0) with personality data.     

GazeAlyze: a MATLAB toolbox for the analysis of eye movement data

This article presents GazeAlyze, a software package, written as a MATLAB (MathWorks Inc., Natick, MA) toolbox developed for the analysis of eye movement data. GazeAlyze was developed for the batch processing of multiple data files and was designed as a framework with extendable modules. GazeAlyze encompasses the main functions of the entire processing queue of eye movement data to static visual stimuli. This includes detecting and filtering artifacts, detecting events, generating regions of interest, generating spread sheets for further statistical analysis, and providing methods for the visualization of results, such as path plots and fixation heat maps. All functions can be controlled through graphical user interfaces. GazeAlyze includes functions for correcting eye movement data for the displacement of the head relative to the camera after calibration in fixed head mounts. The preprocessing and event detection methods in GazeAlyze are based on the software ILAB 3.6.8 Gitelman (Behav Res Methods Instrum Comput 34(4), 605-612, 2002). GazeAlyze is distributed free of charge under the terms of the GNU public license and allows code modifications to be made so that the program's performance can be adjusted according to a user's scientific requirements.     

Stereotype threat and test performance: A primer for school psychologists

Ethical guidelines require school psychologists to ensure that their assessment practices are nondiscriminatory, but typical discussions on this topic neglect the possible discriminatory effects of cultural stereotypes on assessment results. Recent research on stereotype threat shows that students' knowledge of stereotype-based negative expectations about their test performance can depress their actual test performance. This paper discusses the range of conditions that promote stereotype threat and identifies important moderators and mediators of the phenomenon. Several practical suggestions are offered for school psychologists to consider when interviewing students, interpreting assessment results, and developing programs to increase schoolwide achievement.     

Embryo genetic disorder analysis demonstration project: A model protocol

A demonstration project designed for couples at a minimum 25% risk for having a child with cystic fibrosis is being undertaken using embryo genetic disorder analysis within an organization proficient inin vitro fertilization techniques. Prior to offering the project to patients, issues were identified that needed to be addressed in order to improve the likelihood of success. We then developed a set of protocols that would enable us to introduce this new technology to our staff and to prospective patients in a consistent manner.     

A geometrical analysis of the unfolding model: Nondegenerate solutions

Given the complete setR of rank orders derived from some configuration ofn stimulus points inr dimensions in accordance with the unfolding model, a stimulus configuration which generates just these orders will be described as a solution forR. The space is assumed to be Euclidean. Necessary and sufficient conditions are determined for a nondegenerate configuration to be a solution forR. The geometrical conditions which are necessary and sufficient to determine the subset of pairs of opposite orders are also identified and constitute the constraint system for the ordinal vector model.The present work was completed while the writer was in receipt of an Australian Commonwealth Postgraduate Scholarship under the supervision of Professor J. A. Keats.     

A geometrical analysis of the unfolding model: General solutions

Given the complete setR of rank orders obtained from any configuration ofn stimulus points inr dimensions in accordance with the unfolding model, a configuration from which just these orders may be derived will be described as a solution forR. The space is assumed to be Euclidean. Necessary and sufficient conditions are derived for a configuration to be a solution forR. The geometrical constraints which are necessary and sufficient to determine the subset of pairs of orders and opposites contained inR are also identified and constitute the constraint system for the ordinal vector model. The relationship between the two models is discussed.     

Fast-dm: A free program for efficient diffusion model analysis

In the present article, a flexible and fast computer program, called fast-dm, for diffusion model data analysis is introduced. Fast-dm is free software that can be downloaded from the authors' websites. The program allows estimating all parameters of Ratcliff's (1978) diffusion model from the empirical response time distributions of any binary classification task. Fast-dm is easy to use: it reads input data from simple text files, while program settings are specified by commands in a control file. With fast-dm, complex models can be fitted, where some parameters may vary between experimental conditions, while other parameters are constrained to be equal across conditions. Detailed directions for use of fast-dm are presented, as well as results from three short simulation studies exemplifying the utility of fast-dm.