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.
Highlights
Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge
We focus on the psychological construct of self-regulation, which refers to the ability to regulate behavior in service of longer-term goals
We evaluate the ability of psychological measurements to predict a range of self-reported real-world outcomes, and unpack the predictive success in terms of the ontology
Summary
To address this goal, we evaluated the association between task and survey DVs. Neither measurement category could predict DVs from the other category, and correlations between measurement categories were weak (Supplementary Fig. 1). We proceeded by constructing two psychological spaces Future reconciliation of these two spaces may be possible, but would require the addition of spanning DVs that correlate with both task and survey DVs. We defined task and the survey psychological spaces using exploratory factor analysis (EFA; Fig. 1c; Methods). The robustness of the factor models was assessed using two methods: confidence intervals for factor loadings were created by bootstrapping, and EFA was rerun dropping out each individual measure (and all constituent DVs) to assess convergence of the factor solutions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
