Uncovering the structure of self-regulation through data-driven ontology discovery
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Description: 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 we address by examining individual differences across a broad range of behavioral tasks, self-report surveys, and self-reported real-world outcomes. We derive a psychological ontology embedded in an interpretable psychological space and evaluate the predictive power of many psychological measurements related to 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. Additionally, surveys modestly relate to real-world outcomes while tasks largely do not. We conclude that data-driven ontologies lay the groundwork for a cumulative psychological science.