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Category: Project

Description: An experimental science relies on solid and replicable results. The last few years have seen a rich discussion on the reliability and validity of psychological science and whether our experimental findings can falsify our existing theoretical models. Yet, concerns have also arisen that this movement may impede new theoretical developments. In this article, we re- analyze the data from a crowdsourced replication project that concluded that lab site did not matter as predictor for Stroop performance, and, therefore, that there were no “hidden moderators” (i.e., context was likely to matter little in predicting the outcome of the Stroop task). The authors challenge this conclusion via a new analytical method– supervised machine learning - that “allows the data to speak.” The authors apply this approach to the results from a Stroop task to illustrate the utility of machine learning and to propose moderators for future (confirmatory) testing. The authors discuss differences with some conclusions of the original article, which variables need to be controlled for in future inhibitory control tasks, and why psychologists can use machine learning to find surprising, yet solid, results in their own data.