Towards a Formal Specification of the Need for Competence

Computational modelling offers a powerful tool for formalising psychological theories, making them more transparent, testable, and applicable in digital contexts. Yet, the question often remains: how should one computationally model a theory? We provide a demonstration of how formalisms taken from artificial intelligence, specifically reinforcement learning (RL), can offer a fertile starting point. In our ongoing research, we address inconsistencies in the characterisation of the "need for competence", postulated as a basic psychological need within Self-Determination Theory (SDT)—arguably the most influential framework for intrinsic motivation (IM) in psychology. In prior work (https://osf.io/preprints/psyarxiv/n6x8s v1), through conceptual analysis of canonic SDT texts, we have identified multiple distinct facets of competence: effectance, skill use, task performance, and capacity growth. Here, we draw on the computational IM literature in RL to suggest that different existing formalisms may be appropriate for modelling these different facets. Using these formalisms, we reveal underlying preconditions that SDT fails to make explicit, demonstrating how computational models can improve our understanding of biological IM. More generally, our work can support a cycle of theory development by inspiring new computational models, which can then be tested empirically to refine the theory. Thus, we provide a foundation for advancing competence-related theory in SDT and motivational psychology more broadly. The latest write-up of this work is available at https://arxiv.org/abs/2502.07423.