Joachim Vandekerckhove

Fitting the Ratcliff diffusion model to experimental data.

Many experiments in psychology yield both reaction time and accuracy data. However, no off-the-shelf methods yet exist for the statistical analysis of such data. One particularly successful model has been the diffusion process, but using it is difficult in practice because of numerical, statistical, and software problems. We present a general method for performing diffusion model analyses on experimental data. By implementing design matrices, a wide range of across-condition restrictions can be imposed on model parameters, in a flexible way. It becomes possible to fit models with parameters regressed onto predictors. Moreover, data analytical tools are discussed that can be used to handle various types of outliers and contaminants. We briefly present an easy-touse software tool that helps perform diffusion model analyses.

Diffusion model analysis with MATLAB: A DMAT primer

The Ratcliff diffusion model has proved to be a useful tool in reaction time analysis. However, its use has been limited by the practical difficulty of estimating the parameters. We present a software tool, the Diffusion Model Analysis Toolbox (DMAT), intended to make the Ratcliff diffusion model for reaction time and accuracy data more accessible to experimental psychologists. The tool takes the form of a MATLAB toolbox and can be freely downloaded from ppw.kuleuven.be/okp/dmatoolbox. Using the program does not require a background in mathematics, nor any advanced programming experience (but familiarity with MATLAB is useful). We demonstrate the basic use of DMAT with two examples.

Testing theories of post-error slowing

People tend to slow down after they make an error. This phenomenon, generally referred to as post-error slowing, has been hypothesized to reflect perceptual distraction, time wasted on irrelevant processes, an a priori bias against the response made in error, increased variability in a priori bias, or an increase in response caution. Although the response caution interpretation has dominated the empirical literature, little research has attempted to test this interpretation in the context of a formal process model. Here, we used the drift diffusion model to isolate and identify the psychological processes responsible for post-error slowing. In a very large lexical decision data set, we found that post-error slowing was associated with an increase in response caution and—to a lesser extent—a change in response bias. In the present data set, we found no evidence that post-error slowing is caused by perceptual distraction or time wasted on irrelevant processes. These results support a response-monitoring account of post-error slowing.

Hierarchical diffusion models for two-choice response times

Two-choice response times are a common type of data, and much research has been devoted to the development of process models for such data. However, the practical application of these models is notoriously complicated, and flexible methods are largely nonexistent. We combine a popular model for choice response times-the Wiener diffusion process-with techniques from psychometrics in order to construct a hierarchical diffusion model. Chief among these techniques is the application of random effects, with which we allow for unexplained variability among participants, items, or other experimental units. These techniques lead to a modeling framework that is highly flexible and easy to work with. Among the many novel models this statistical framework provides are a multilevel diffusion model, regression diffusion models, and a large family of explanatory diffusion models. We provide examples and the necessary computer code.

A diffusion model decomposition of the practice effect

When people repeatedly perform the same cognitive task, their mean response times (RTs) invariably decrease. The mathematical function that best describes this decrease has been the subject of intense debate. Here, we seek a deeper understanding of the practice effect by simultaneously taking into account the changes in accuracy and in RT distributions with practice, both for correct and error responses. To this end, we used the Ratcliff diffusion model, a successful model of two-choice RTs that decomposes the effect of practice into its constituent psychological processes. Analyses of data from a 10,000-trial lexical decision task demonstrate that practice not only affects the speed of information processing, but also response caution, response bias, and peripheral processing time. We conclude that the practice effect consists of multiple subcomponents, and that it may be hazardous to abstract the interactive combination of these subcomponents in terms of a single output measure such as mean RT for correct responses. Supplemental materials may be downloaded from http://pbr .psychonomic-journals.org/content/supplemental.

A Bayesian perspective on the Reproducibility Project: Psychology

We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors—a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis—for a large subset (N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor < 10). The majority of the studies (64%) did not provide strong evidence for either the null or the alternative hypothesis in either the original or the replication, and no replication attempts provided strong evidence in favor of the null. In all cases where the original paper provided strong evidence but the replication did not (15%), the sample size in the replication was smaller than the original. Where the replication provided strong evidence but the original did not (10%), the replication sample size was larger. We conclude that the apparent failure of the Reproducibility Project to replicate many target effects can be adequately explained by overestimation of effect sizes (or overestimation of evidence against the null hypothesis) due to small sample sizes and publication bias in the psychological literature. We further conclude that traditional sample sizes are insufficient and that a more widespread adoption of Bayesian methods is desirable.

Meta-analyses are no substitute for registered replications: a skeptical perspective on religious priming

According to a recent meta-analysis, religious priming has a positive effect on prosocial behavior (Shariff et al., 2015). We first argue that this meta-analysis suffers from a number of methodological shortcomings that limit the conclusions that can be drawn about the potential benefits of religious priming. Next we present a re-analysis of the religious priming data using two different meta-analytic techniques. A Precision-Effect Testing–Precision-Effect-Estimate with Standard Error (PET-PEESE) meta-analysis suggests that the effect of religious priming is driven solely by publication bias. In contrast, an analysis using Bayesian bias correction suggests the presence of a religious priming effect, even after controlling for publication bias. These contradictory statistical results demonstrate that meta-analytic techniques alone may not be sufficiently robust to firmly establish the presence or absence of an effect. We argue that a conclusive resolution of the debate about the effect of religious priming on prosocial behavior – and about theoretically disputed effects more generally – requires a large-scale, preregistered replication project, which we consider to be the sole remedy for the adverse effects of experimenter bias and publication bias.

A Hierarchical Latent Stochastic Differential Equation Model for Affective Dynamics.

In this article a continuous-time stochastic model (the Ornstein-Uhlenbeck process) is presented to model the perpetually altering states of the core affect, which is a 2-dimensional concept underlying all our affective experiences. The process model that we propose can account for the temporal changes in core affect on the latent level. The key parameters of the model are the average position (also called home base), the variances and covariances of the process, and the regulatory mechanisms that keep the process in the vicinity of the average position. To account for individual differences, the model is extended hierarchically. A particularly novel contribution is that in principle all parameters of the stochastic process (not only the mean but also its variance and the regulatory parameters) are allowed to differ between individuals. In this way, the aim is to understand the affective dynamics of single individuals and at the same time investigate how these individuals differ from one another. The final model is a continuous-time state-space model for repeated measurement data taken at possibly irregular time points. Both time-invariant and time-varying covariates can be included to investigate sources of individual differences. As an illustration, the model is applied to a diary study measuring core affect repeatedly for several individuals (thereby generating intensive longitudinal data).

A diffusion model account of the relationship between the emotional flanker task and rumination and depression

Although there exists a consensus that depression is characterized by preferential processing of negative information, empirical findings to support the association between depression and rumination on the one hand and selective attention for negative stimuli on the other hand have been elusive. We argue that one of the reasons for the inconsistent findings may be the use of aggregate measures of response times and accuracies to measure attentional bias. Diffusion model analysis allows to partial out the information processing component from other components that comprise the decision-making process. In this study, we applied a diffusion model to an emotional flanker task. Results revealed that when focusing on a negative target, both rumination and depression were associated with facilitated processing due to negative distracters, whereas only rumination was associated with less interference by positive distracters. After controlling for depression scores, rumination still predicted attentional bias for negative information, but depression scores were no longer predictive after controlling for rumination. Consistent with elusive findings in the literature, we did not find this pattern of results when using accuracy scores or mean response times. Our results suggest that rumination accounts for the attentional bias for negative information found in depression.

Identification of everyday objects on the basis of fragmented outline versions

Although Attneave (1954 Psychological Review 61 183–193) and Biederman (1987 Psychological Review 94 115–147) have argued that curved contour segments are most important in shape perception, Kennedy and Domander (1985 Perception 14 367–370) showed that fragmented object contours are better identifiable when straight segments are shown. We used the set of line drawings published by Snodgrass and Vanderwart (1980 Journal of Experimental Psychology: Human Learning and Memory 6 174–215), to make outline versions that could be used to investigate this issue with a larger and more heterogeneous stimulus set. Fragments were placed either around the ‘salient’ points or around the midpoints (points midway between two salient points), creating curved versus relatively straight fragments when the original outline was fragmented (experiment 1), or angular and straight fragments when straight-line versions were fragmented (experiment 2). We manipulated fragment length in each experiment except the last one, in which we presented only selected points (experiment 3). While fragmented versions were on average more identifiable when straight fragments were shown, certain objects were more identifiable when the curved segments or the angles were shown. A tentative explanation of these results is presented in terms of an advantage for straight segments during grouping processes for outlines with high part salience, and an advantage for curved segments during matching processes for outlines with low part salience.