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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ian W. Eisenberg & Patrick G. Bissett & A. Zeynep Enkavi & Jamie Li & David P. MacKinnon & Lisa A. Marsch & Russell A. Poldrack, 2019. "Uncovering the structure of self-regulation through data-driven ontology discovery," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10301-1
    DOI: 10.1038/s41467-019-10301-1
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    Cited by:

    1. Kpegli, Yao Thibaut & Corgnet, Brice & Zylbersztejn, Adam, 2023. "All at once! A comprehensive and tractable semi-parametric method to elicit prospect theory components," Journal of Mathematical Economics, Elsevier, vol. 104(C).
    2. Junjiao Feng & Liang Zhang & Chunhui Chen & Jintao Sheng & Zhifang Ye & Kanyin Feng & Jing Liu & Ying Cai & Bi Zhu & Zhaoxia Yu & Chuansheng Chen & Qi Dong & Gui Xue, 2022. "A cognitive neurogenetic approach to uncovering the structure of executive functions," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    3. Strömbäck, Camilla & Skagerlund, Kenny & Västfjäll, Daniel & Tinghög, Gustav, 2020. "Subjective self-control but not objective measures of executive functions predicts financial behavior and well-being," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    4. Acerbi, Alberto & Sacco, Pier Luigi, 2022. "The self-control vs. self-indulgence dilemma: A culturomic analysis of 20th century trends," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 101(C).
    5. D. Jones & V. Lowe & J. Graff-Radford & H. Botha & L. Barnard & D. Wiepert & M. C. Murphy & M. Murray & M. Senjem & J. Gunter & H. Wiste & B. Boeve & D. Knopman & R. Petersen & C. Jack, 2022. "A computational model of neurodegeneration in Alzheimer’s disease," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Bu, Di & Hanspal, Tobin & Liao, Yin & Liu, Yong, 2020. "Financial literacy and self-control in FinTech: Evidence from a field experiment on online consumer borrowing," SAFE Working Paper Series 273, Leibniz Institute for Financial Research SAFE.

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