Don van Ravenzwaaij

Action video games do not improve the speed of information processing in simple perceptual tasks.

Previous research suggests that playing action video games improves performance on sensory, perceptual, and attentional tasks. For instance, Green, Pouget, and Bavelier (2010) used the diffusion model to decompose data from a motion detection task and estimate the contribution of several underlying psychological processes. Their analysis indicated that playing action video games leads to faster information processing, reduced response caution, and no difference in motor responding. Because perceptual learning is generally thought to be highly context-specific, this transfer from gaming is surprising and warrants corroborative evidence from a large-scale training study. We conducted 2 experiments in which participants practiced either an action video game or a cognitive game in 5 separate, supervised sessions. Prior to each session and following the last session, participants performed a perceptual discrimination task. In the second experiment, we included a third condition in which no video games were played at all. Behavioral data and diffusion model parameters showed similar practice effects for the action gamers, the cognitive gamers, and the nongamers and suggest that, in contrast to earlier reports, playing action video games does not improve the speed of information processing in simple perceptual tasks.

Individual differences in conflict-monitoring: testing means and covariance hypothesis about the Simon and the Eriksen Flanker task

Conflict and context slow-down have been proposed as indicators of a conflict-monitoring system that initiates cognitive control to resolve conflicts in information processing. We investigated individual differences in conflict-monitoring and their associations with working memory (WM) and impulsivity. A total of 150 adults completed a Simon and an Eriksen flanker task, together with measures of WM and impulsivity. On both tasks, responses were slower and less accurate on incompatible than on compatible trials (conflict effect), and the conflict effect was larger when the preceding trial was compatible than when it was incompatible (context effect). Stimulus repetition did not explain the context effect. Individual differences could be attributed to three separable factors for each task: general speeded performance, conflict effect, and context effect. Evidence for across-task generality of these factors was sparse. Associations of these factors with impulsivity were weak at best. WM was correlated with general speed, and also with some but not all factors reflecting conflict-related processes.

Does the Name-Race Implicit Association Test Measure Racial Prejudice?

Research using the Implicit Association Test (IAT) has shown that names labeled as Caucasian elicit more positive associations than names labeled as non-Caucasian. One interpretation of this result is that the IAT measures latent racial prejudice. An alternative explanation is that the result is due to differences in in-group/out-group membership. In this study, we conducted three different IATs: one with same-race Dutch names versus racially charged Moroccan names; one with same-race Dutch names versus racially neutral Finnish names; and one with Moroccan names versus Finnish names. Results showed equivalent effects for the Dutch-Moroccan and Dutch-Finnish IATs, but no effect for the Finnish-Moroccan IAT. This suggests that the name-race IAT-effect is not due to racial prejudice. A diffusion model decomposition indicated that the IAT-effects were caused by changes in speed of information accumulation, response conservativeness, and non-decision time.

Do the dynamics of prior information depend on task context? An analysis of optimal performance and an empirical test

In speeded two-choice tasks, optimal performance is prescribed by the drift diffusion model. In this model, prior information or advance knowledge about the correct response can manifest itself as a shift in starting point or as a shift in drift rate criterion. These two mechanisms lead to qualitatively different choice behavior. Analyses of optimal performance (i.e., Bogacz et al., 2006; Hanks et al., 2011) have suggested that bias should manifest itself in starting point when difficulty is fixed over trials, whereas bias should (additionally) manifest itself in drift rate criterion when difficulty is variable over trials. In this article, we challenge the claim that a shift in drift criterion is necessary to perform optimally in a biased decision environment with variable stimulus difficulty. This paper consists of two parts. Firstly, we demonstrate that optimal behavior for biased decision problems is prescribed by a shift in starting point, irrespective of variability in stimulus difficulty. Secondly, we present empirical data which show that decision makers do not adopt different strategies when dealing with bias in conditions of fixed or variable across-trial stimulus difficulty. We also perform a test of specific influence for drift rate variability.

Optimal decision making in neural inhibition models

In their influential Psychological Review article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the feedforward inhibition model (FFI), can mimic the DDM and accomplish optimal decision making. Here we show that these conclusions depend on how the models handle negative activation values and (for the LCA) across-trial variability in response conservativeness. Negative neural activations are undesirable for both neurophysiological and mathematical reasons. However, when negative activations are truncated to 0, the equivalence to the DDM is lost. Simulations show that this concern has practical ramifications: the DDM generally outperforms truncated versions of the LCA and the FFI, and the parameter estimates from the neural models can no longer be mapped onto those of the DDM in a simple fashion. We show that for both models, truncation may be avoided by assuming a baseline activity for each accumulator. This solution allows the LCA to approximate the DDM and the FFI to be identical to the DDM.

A simple introduction to Markov Chain Monte-Carlo sampling.

Markov Chain Monte–Carlo (MCMC) is an increasingly popular method for obtaining information about distributions, especially for estimating posterior distributions in Bayesian inference. This article provides a very basic introduction to MCMC sampling. It describes what MCMC is, and what it can be used for, with simple illustrative examples. Highlighted are some of the benefits and limitations of MCMC sampling, as well as different approaches to circumventing the limitations most likely to trouble cognitive scientists.

The EZ diffusion model provides a powerful test of simple empirical effects

Over the last four decades, sequential accumulation models for choice response times have spread through cognitive psychology like wildfire. The most popular style of accumulator model is the diffusion model (Ratcliff Psychological Review, 85, 59–108, 1978), which has been shown to account for data from a wide range of paradigms, including perceptual discrimination, letter identification, lexical decision, recognition memory, and signal detection. Since its original inception, the model has become increasingly complex in order to account for subtle, but reliable, data patterns. The additional complexity of the diffusion model renders it a tool that is only for experts. In response, Wagenmakers et al. (Psychonomic Bulletin & Review, 14, 3–22, 2007) proposed that researchers could use a more basic version of the diffusion model, the EZ diffusion. Here, we simulate experimental effects on data generated from the full diffusion model and compare the power of the full diffusion model and EZ diffusion to detect those effects. We show that the EZ diffusion model, by virtue of its relative simplicity, will be sometimes better able to detect experimental effects than the data–generating full diffusion model.

Probability matching in risky choice: The interplay of feedback and strategy availability

Probability matching in sequential decision making is a striking violation of rational choice that has been observed in hundreds of experiments. Recent studies have demonstrated that matching persists even in described tasks in which all the information required for identifying a superior alternative strategy—maximizing—is present before the first choice is made. These studies have also indicated that maximizing increases when (1) the asymmetry in the availability of matching and maximizing strategies is reduced and (2) normatively irrelevant outcome feedback is provided. In the two experiments reported here, we examined the joint influences of these factors, revealing that strategy availability and outcome feedback operate on different time courses. Both behavioral and modeling results showed that while availability of the maximizing strategy increases the choice of maximizing early during the task, feedback appears to act more slowly to erode misconceptions about the task and to reinforce optimal responding. The results illuminate the interplay between “top-down” identification of choice strategies and “bottom-up” discovery of those strategies via feedback.

Metastudies for robust tests of theory.

We describe and demonstrate an empirical strategy useful for discovering and replicating empirical effects in psychological science. The method involves the design of a metastudy, in which many independent experimental variables—that may be moderators of an empirical effect—are indiscriminately randomized. Radical randomization yields rich datasets that can be used to test the robustness of an empirical claim to some of the vagaries and idiosyncrasies of experimental protocols and enhances the generalizability of these claims. The strategy is made feasible by advances in hierarchical Bayesian modeling that allow for the pooling of information across unlike experiments and designs and is proposed here as a gold standard for replication research and exploratory research. The practical feasibility of the strategy is demonstrated with a replication of a study on subliminal priming.

A simulation study of the strength of evidence in the recommendation of medications based on two trials with statistically significant results

A typical rule that has been used for the endorsement of new medications by the Food and Drug Administration is to have two trials, each convincing on its own, demonstrating effectiveness. “Convincing” may be subjectively interpreted, but the use of p-values and the focus on statistical significance (in particular with p < .05 being coined significant) is pervasive in clinical research. Therefore, in this paper, we calculate with simulations what it means to have exactly two trials, each with p < .05, in terms of the actual strength of evidence quantified by Bayes factors. Our results show that different cases where two trials have a p-value below .05 have wildly differing Bayes factors. Bayes factors of at least 20 in favor of the alternative hypothesis are not necessarily achieved and they fail to be reached in a large proportion of cases, in particular when the true effect size is small (0.2 standard deviations) or zero. In a non-trivial number of cases, evidence actually points to the null hypothesis, in particular when the true effect size is zero, when the number of trials is large, and when the number of participants in both groups is low. We recommend use of Bayes factors as a routine tool to assess endorsement of new medications, because Bayes factors consistently quantify strength of evidence. Use of p-values may lead to paradoxical and spurious decision-making regarding the use of new medications.