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, Russell A. Poldrack

Research output: Contribution to journalArticlepeer-review

192 Scopus citations


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.

Original languageEnglish (US)
Article number2319
JournalNature communications
Issue number1
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • General
  • Physics and Astronomy(all)


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