A recent meta-analysis by Au et al. Psychonomic Bulletin & Review, 22, 366-377, (2015) reviewed the n-back training paradigm for working memory (WM) and evaluated whether (when aggregating across existing studies) there was evidence that gains obtained for training tasks transferred to gains in fluid intelligence (Gf). Their results revealed an overall effect size of g = 0.24 for the effect of n-back training on Gf. We reexamine the data through a Bayesian lens, to evaluate the relative strength of the evidence for the alternative versus null hypotheses, contingent on the type of control condition used. We find that studies using a noncontact (passive) control group strongly favor the alternative hypothesis that training leads to transfer but that studies using active-control groups show modest evidence in favor of the null. We discuss these findings in the context of placebo effects.

The question of whether computerized cognitive training leads to generalized improvements of intellectual abilities has been a popular, yet contentious, topic within both the psychological and neurocognitive literatures. Evidence for the effective transfer of cognitive training to nontrained measures of cognitive abilities is mixed, with some studies showing apparent successful transfer, while others have failed to obtain this effect. At the same time, several authors have made claims about both successful and unsuccessful transfer effects on the basis of a form of responder analysis, an analysis technique that shows that those who gain the most on training show the greatest gains on transfer tasks. Through a series of Monte Carlo experiments and mathematical analyses, we demonstrate that the apparent transfer effects observed through responder analysis are illusory and are independent of the effectiveness of cognitive training. We argue that responder analysis can be used neither to support nor to refute hypotheses related to whether cognitive training is a useful intervention to obtain generalized cognitive benefits. We end by discussing several proposed alternative analysis techniques that incorporate training gain scores and argue that none of these methods are appropriate for testing hypotheses regarding the effectiveness of cognitive training.

We argue that the mismatch between data and analytical methods, along with common practices for dealing with “messy” data, can lead to inaccurate conclusions. Specifically, using previously published data on racial bias and culture of honor, we show that manifest effects, and therefore theoretical conclusions, are highly dependent on how researchers decide to handle extreme scores and nonlinearities when data are analyzed with traditional approaches. Within LS approaches, statistical effects appeared or disappeared on the basis of the inclusion or exclusion of as little as 1.5% (3 of 198) of the data, and highly predictive variables were masked by nonlinearities. We then demonstrate a new statistical modeling technique called the general monotone model (GeMM) and show that it has a number of desirable properties that may make it more appropriate for modeling messy data: It is more robust to extreme scores, less affected by outlier analyses, and more robust to violations of linearity on both the response and predictor variables compared with a variety of well-established statistical algorithms and frequently possesses greater statistical power. We argue that using procedures that make fewer assumptions about the data, such as GeMM, can lessen the need for researchers to use data-editing strategies (e.g., to apply transformations or to engage outlier analyses) on their data to satisfy often unrealistic statistical assumptions, leading to more consistent and accurate conclusions about data than traditional approaches of data analysis.

Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order-constrained linear least-squares (OCLO) optimization algorithm that maximizes the linear least-squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out-of-sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat-tailed distributions), OCLO shows less bias and variance, and substantially better out-of-sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data.

Sources of bias in confidence and probability judgments, for example conservatism, overconfidence, and subadditivity, are some of the most important and rigorously researched topics within judgment and decision making. However, despite the seemingly obvious importance of memory processes on these types of judgments, much of this research has focused on external factors independent of memory processes, such as the effects of various types of elicitation format. In this chapter, we review the research relevant to commonly observed effects related to confidence and probability judgment, and then provide a memory-process account of these phenomena based on two models: Minerva-DM, a multiple-trace memory model; and HyGene, an extension of Minerva-DM that incorporates hypothesis generation. We contend that accounting for the dependence of judgments on memory provides a unifying theoretical framework for these various phenomena, as well as cognitive models that accurately reflect real-world behavior.