What Predicts Stroop Performance? A Conditional Random Forest Approach

37 Pages Posted: 7 Jul 2016 Last revised: 13 May 2017

See all articles by Hans IJzerman

Hans IJzerman

Université Grenoble Alpes

Thomas Pollet

VU University Amsterdam

Charlie Ebersole

University of Virginia

David Kun

Functional Finances Ltd

Date Written: July 5, 2016

Abstract

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.

Keywords: Stroop performance, exploration, machine learning, psychology

Suggested Citation

IJzerman, Hans and Pollet, Thomas and Ebersole, Charlie and Kun, David, What Predicts Stroop Performance? A Conditional Random Forest Approach (July 5, 2016). Available at SSRN: https://ssrn.com/abstract=2805205 or http://dx.doi.org/10.2139/ssrn.2805205

Hans IJzerman (Contact Author)

Université Grenoble Alpes ( email )

Grenoble
France

Thomas Pollet

VU University Amsterdam ( email )

De Boelelaan 1105
Amsterdam, ND North Holland 1081 HV
Netherlands

Charlie Ebersole

University of Virginia ( email )

1400 University Ave
Charlottesville, VA 22903
United States

David Kun

Functional Finances Ltd ( email )

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