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Cited by 474

The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.

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

Open access
Eisenberg, Ian W., Bissett, Patrick G., Zeynep Enkavi, A., Li, Jamie, MacKinnon, David P., Marsch, Lisa A., Poldrack, Russell A.·2019·Nature Communications
DOI 10.1038/s41467-019-10301-1
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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.

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Metadata

Authors
Eisenberg, I. W., Bissett, P. G., Zeynep Enkavi, A., Li, J., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-05-24
Volume
10
Issue
1
Pages
2319-2319
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-10301-1
OpenAlex
W2945091780
Cited by
474
References
66
FWCI
54.12
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Uncovering the structure of self-regulation through data-driven ontology discovery. https://app.cochat.ai/papers/10.1038/s41467-019-10301-1
The paper
Eisenberg, I. W., Bissett, P. G., Zeynep Enkavi, A., Li, J., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2019). Uncovering the structure of self-regulation through data-driven ontology discovery. Nature Communications. https://doi.org/10.1038/s41467-019-10301-1

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.