Uncovering mental structure 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 we address by examining individual differences across an unprecedented range of behavioral tasks, self-report surveys, and real-world outcomes. We derive a cognitive ontology 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 reveals opportunities for theoretic synthesis and identifies stable individual traits. Additionally, surveys predict self-reported real-world outcomes while tasks largely do not. We conclude that data-driven ontologies lay the groundwork for a cumulative psychological science.