Individual differences in emotion word processing: A diffusion model analysis

The exploratory study investigated individual differences in implicit processing of emotional words in a lexical decision task. A processing advantage for positive words was observed, and differences between happy and fear-related words in response times were predicted by individual differences in specific variables of emotion processing: Whereas more pronounced goal-directed behavior was related to a specific slowdown in processing of fear-related words, the rate of spontaneous eye blinks (indexing brain dopamine levels) was associated with a processing advantage of happy words. Estimating diffusion model parameters revealed that the drift rate (rate of information accumulation) captures unique variance of processing differences between happy and fear-related words, with highest drift rates observed for happy words. Overall emotion recognition ability predicted individual differences in drift rates between happy and fear-related words. The findings emphasize that a significant amount of variance in emotion processing is explained by individual differences in behavioral data.     

A FORTRAN 77 program for a nonparametric item response model: The Mokken scale analysis

A nonparametric item response theory model—the Mokken scale analysis (a stochastic elaboration of the deterministic Guttman scale)—and a computer program that performs this analysis are described. Three procedures of scaling are distinguished: a search procedure, an evaluation of the whole set of items, and an extension of an existing scale. All procedures provide a coefficient of scalability for all items that meet the criteria of the Mokken model and an item coefficient of scalability for every item. Four different types of reliability coefficient are computed both for the entire set of items and for the scalable items. A test of robustness of the found scale can be performed to analyze whether the scale is invariant across different subgroups or samples. This robustness test serves as a goodness of fit test for the established scale. The program is written in FORTRAN 77. Two versions are available, an SPSS-X procedure program (which can be used with the SPSS-X mainframe package) and a stand-alone program suitable for both mainframe and microcomputers.     

FACTOR: A computer program to fit the exploratory factor analysis model

Exploratory factor analysis (EFA) is one of the most widely used statistical procedures in psychological research. It is a classic technique, but statistical research into EFA is still quite active, and various new developments and methods have been presented in recent years. The authors of the most popular statistical packages, however, do not seem very interested in incorporating these new advances. We present the program FACTOR, which was designed as a general, user-friendly program for computing EFA. It implements traditional procedures and indices and incorporates the benefits of some more recent developments. Two of the traditional procedures implemented are polychoric correlations and parallel analysis, the latter of which is considered to be one of the best methods for determining the number of factors or components to be retained. Good examples of the most recent developments implemented in our program are (1) minimum rank factor analysis, which is the only factor method that allows one to compute the proportion of variance explained by each factor, and (2) the simplimax rotation method, which has proved to be the most powerful rotation method available. Of these methods, only polychoric correlations are available in some commercial programs. A copy of the software, a demo, and a short manual can be obtained free of charge from the first author.     

A stochastic model for free recall

A statistical model for verbal learning is presented and tested against experimental data. The model describes a Markov process with a realizable absorbing state, allowing complete learning on some finite trial as well as imperfect retention prior to this trial.This work was carried out while the author was at Lincoln Laboratory, Massachusetts Institute of Technology.Operated with support from the U. S. Army, Navy, and Air Force.     

A diffusion model of early visual search: Theoretical analysis and experimental results

Summary Meinecke (1989, Exp. 1, cond. HO) showed that the detectability of a visual target embedded in a linear noise array decreases with increasing retinal eccentricity, while the reaction time (RT) of the hits increases. One of the most interesting features of her results was that the RT of the correct rejections is consistently larger than the RT for signals presented near the fovea. This finding suggests that initially visual attention is concentrated near the fixation point and then diffuses across the stimulus array to perform a serial, exhaustive search. We present a diffusion model of early visual-search processes that quantitatively describes this evolution of attention in time and space; in contrast to most previous conceptions, it is based on a genuine relation between the spatial and temporal dimensions of the search processes performed. The model predicts quantitatively both detection performance and RT. We conducted an experiment similar to that of Meinecke (1989), but with an additional variation of the presentation time. All the main features of the 189 predictions could be explained by the model. The interpretation of the four model's parameters is discussed in some detail and compared with previous estimates of the microscopic search speed derived from alternative models. Finally, we consider some possible modifications related to results of Kehrer (1987, 1989), and some generalizations to multi target detection and two-dimensional stimulus arrays.     

ILAB: A program for postexperimental eye movement analysis

The recording and analysis of eye movements are fundamental to a diverse set of research applications, including studies in which reading, visual search, and both overt and covert visuospatial attention are examined. Software tools supplied with commonly available eye-tracking equipment have generally been limited in functionality and nonextensible. Because of this dearth of available software, ILAB was createdto provide an extensible frame workfor analyzing various aspects of eye movements. The program consists of a series of open-source MATLAB functions. The program’s data structures keep raw data, analysis preferences, and analyzed data separate, thus maintaining data fidelity and promoting extensibility.     

Effects of implicit fear of failure on cognitive processing: A diffusion model analysis

Whereas previous studies suggest that individuals with high implicit fear of failure (FF) perform worse on various indicators of general performance, the underlying mechanisms of this effect have not yet been understood. In our experimental study, 280 participants worked on a binary color discrimination task. Half of the participants were frustrated by means of negative performance feedback, while the control group received mainly positive feedback. We employed a diffusion model analysis (Ratcliff in Psychol Rev 85(2):59–108, 1978) to disentangle the different components involved in the execution of the task. Results revealed that participants in the frustration condition adopted more conservative decision settings (threshold separation parameter of the diffusion model). Besides, high implicit FF was related to slow information accumulation (drift), and this relation was stronger in the frustration condition. Participants with higher FF further showed reduced learning rates during the task. Task related intrusive thoughts are discussed as mechanism for reduced performance of high FF individuals. We conclude that diffusion model analyses can contribute to a better understanding of the mechanisms underlying the effects of psychological motives.     

Statistical power analysis for one-way analysis of variance: A computer program

To facilitate the computation of statistical power for analysis of variance, Cohen developed the index of effect sizef, defined as theSD between groups divided by theSD within groups. A microcomputer program for statistical power allows the user to compute the value off in any of several ways: by specifying the mean andSD for every cell in the ANOVA; by specifying the mean value for the two extreme cells and the pattern of dispersion for the remaining cells; by estimating the proportion of variance in the dependent variable that will be explained by group membership; and/or with reference to conventions for small, medium, and large effects. The program will compute power for any single set of parameters; it will also allow the user to generate tables and graphs showing how power will vary as a function of effect size, sample size, andα.     

A diffusion model for discrimination of temporal numerosity

This paper presents a model for sequential discrimination between the temporal frequencies of two discrete events. The model results from adjoining a theory for continuous estimation of temporal numerosity to a mathematical representation of the response criterion. A simple set of assumptions is used to describe the time evolution of numerosity estimates by a stochastic diffusion process. The predictions of the model are extensively compared to a body of results presented in a previous report, with an emphasis on individual performances. It is shown that a number of response strategies, observed in actual experiments, can be predicted by generalizing the classical notion of a single-valued response threshold to a stochastic, time-varying process.