Advancing Self-Determination Theory With Computational Intrinsic Motivation: The Case of Competence

Computational models are powerful tools to formalise psychological theories, render them severely testable, and embed them into digital applications. Yet they have seen little uptake in Self-Determination Theory (SDT). Here we demonstrate how SDT's conception of competence and optimal challenge can benefit from computational modelling informed by AI research on computational intrinsic motivation (IM). We surface underspecification in present verbal definitions, challenging the construct validity of common operationalisations and impeding computational implementations. We identify four separate verbal facets of competence and match them to distinct computational IM formalisms. These leverage formal accounts of novelty, diversity, or progress to drive skill acquisition, yielding optimal challenge-seeking behaviour and other competence dynamics postulated by SDT. We argue that these accounts specifically and computational IM more widely highlight inconsistencies within, and can concretise SDT's verbal articulations. We gently introduce motivation researchers to computational modelling and IM, complement model intuition with formal detail, and provide practical pointers for their use in psychological research and application.