A cognitive multiplex network approach to investigate mental navigation and predict high-level cognition

High-level cognition, such as intelligence and creativity, are considered the hallmark of human cognition; however, their complexity hinders the identification of underlying common mechanisms. We focus on one such likely mechanism-mental navigation. We utilize converging computational methods to demonstrate how mental navigation-operationalized via verbal fluency tasks-predicts individual differences in creativity, intelligence, and openness to experience (the personality trait most closely related to them). Participants' (N = 479) responses to two tasks-a 2-min animal fluency task and a 2-min generating synonyms of the word "hot" fluency task-were modeled over a multidimensional model (a cognitive multiplex network) of the mental lexicon. Quantitative measures of their mental navigation were used to build regression models that significantly predicted their assessed high-level cognition (replicating across both fluency tasks). Finally, we developed an online tool that capitalizes on our approach-the High-level Cognitive Prediction tool. Overall, we show how converging computational tools can elucidate the complexity of high-level cognition.