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

Abstract

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.