Uncovering the structure of self-regulation through data-driven ontology discovery

Ian W. Eisenberg (), Patrick G. Bissett, A. Zeynep Enkavi, Jamie Li, David P. MacKinnon, Lisa A. Marsch and Russell A. Poldrack
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Ian W. Eisenberg: Stanford University
Patrick G. Bissett: Stanford University
A. Zeynep Enkavi: Stanford University
Jamie Li: Stanford University
David P. MacKinnon: Arizona State University
Lisa A. Marsch: Geisel School of Medicine at Dartmouth
Russell A. Poldrack: Stanford University

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: Abstract Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction, two issues that are particularly damaging for the study of multifaceted constructs like self-regulation. Here, we derive a psychological ontology from a study of individual differences across a broad range of behavioral tasks, self-report surveys, and self-reported real-world outcomes associated with self-regulation. Though both tasks and surveys putatively measure self-regulation, they show little empirical relationship. Within tasks and surveys, however, the ontology identifies reliable individual traits and reveals opportunities for theoretic synthesis. We then evaluate predictive power of the psychological measurements and find that while surveys modestly and heterogeneously predict real-world outcomes, tasks largely do not. We conclude that self-regulation lacks coherence as a construct, and that data-driven ontologies lay the groundwork for a cumulative psychological science.

Date: 2019
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DOI: 10.1038/s41467-019-10301-1

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