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.

The reliability of published research findings in psychology has been a topic of rising concern. Publication bias, or treating positive findings differently from negative findings, is a contributing factor to this "crisis of confidence," in that it likely inflates the number of false positive effects in the literature. We demonstrate a Bayesian model averaging approach that takes into account the possibility of publication bias and allows for a better estimate of true underlying effect size. Accounting for the possibility of bias leads to a more conservative interpretation of published studies as well as metaanalyses. We provide mathematical details of the method and examples.

There are many ways to measure how people manage risk when they make decisions. A standard approach is to measure risk propensity using self-report questionnaires. An alternative approach is to use decision-making tasks that involve risk and uncertainty, and apply cognitive models of task behavior to infer parameters that measure people’s risk propensity. We report the results of a within-participants experiment that used three questionnaires and four decision-making tasks. The questionnaires are the Risk Propensity Scale, the Risk Taking Index, and the DomainSpecific Risk Taking Scale. The decision-making tasks are the Balloon Analogue Risk Task, the preferential choice gambling task, the optimal stopping problem, and the bandit problem. We analyze the relationships between the risk measures and cognitive parameters using Bayesian inferences about the patterns of correlation, and using a novel cognitive latent variable modeling approach. The results show that people’s risk propensity is generally consistent within different conditions for each of the decision-making tasks. There is, however, little evidence that the way people manage risk generalizes across the tasks, or that it corresponds to the questionnaire measures.

This study investigated the abilities of listeners to classify various sorts of musical stimuli as major vs minor. All stimuli combined four pure tones: low and high tonics (G5 and G6), dominant (D), and either a major third (B) or a minor third (B[symbol: see text]). Especially interesting results were obtained using tone-scrambles, randomly ordered sequences of pure tones presented at ≈15 per second. All tone-scrambles tested comprised 16 G's (G5's + G6's), 8 D's, and either 8 B's or 8 B[symbol: see text]'s. The distribution of proportion correct across 275 listeners tested over the course of three experiments was strikingly bimodal, with one mode very close to chance performance, and the other very close to perfect performance. Testing with tone-scrambles thus sorts listeners fairly cleanly into two subpopulations. Listeners in subpopulation 1 are sufficiently sensitive to major vs minor to classify tone-scrambles nearly perfectly; listeners in subpopulation 2 (comprising roughly 70% of the population) have very little sensitivity to major vs minor. Skill in classifying major vs minor tone-scrambles shows a modest correlation of around 0.5 with years of musical training.

In optimal stopping problems, people are asked to choose the best option out of a sequence of alternatives, under the constraint that they cannot return to an earlier option once it is rejected. We examine human performance on variations of the optimal stopping problem, with different environments and with different goals. Specifically, we consider environments that have relatively high or low numbers, under the goals of choosing the maximum or the minimum. A natural consequence of this design is that we study the decisions that people make in both congruent situations, in which the environment and goal align, and incongruent situations, in which the environment and goal differ. First, we present empirical evidence that people adapt to both high and low environments as well as to both maximum and minimum goal frames, and that they make decisions consistent with using threshold-based models. Second, we apply a previously developed threshold model of individual performance to our data, inferring the thresholds people use. Lastly, we use Bayes factors to test whether people are sensitive to environments, goals, and congruency in optimal stopping problems. Overall, our results suggest that there are psychological similarities in congruent situations, pointing toward invariances and rationalities in the way people solve optimal stopping problems as the environments and goals change, without contaminating framing effects.