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

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...

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Bibliographic Details
Published in:Nature communications 2019-05, Vol.10 (1), p.2319-2319, Article 2319
Main Authors: Eisenberg, Ian W., Bissett, Patrick G., Zeynep Enkavi, A., Li, Jamie, MacKinnon, David P., Marsch, Lisa A., Poldrack, Russell A.
Format: Article
Language:English
Subjects:
631/477/2811
706/689/477/2811
Adult
Cognitive ability
Data Science - methods
Datasets as Topic
Humanities and Social Sciences
Humans
Individuality
Knowledge Bases
Knowledge management
multidisciplinary
Ontology
Polls & surveys
Predictions
Predictive Value of Tests
Psychometrics - methods
Science
Science (multidisciplinary)
Self Report - statistics & numerical data
Self-Control - psychology
Traditions
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Summary: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. Scientific progress relies on integrating and building on existing knowledge. Here, the authors propose improving cumulative science by developing data-driven ontologies, and they apply this approach to understanding the construct of self-regulation.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-019-10301-1