Towards a Formal Theory of the Need for Competence via Computational Intrinsic Motivation

TL;DR

The paper tackles the lack of formal computational models for the need for competence in Self-Determination Theory by mapping four facets—effectance, skill use, task performance, and capacity growth—onto reinforcement-learning formalisms from intrinsically motivated AI. It demonstrates concrete correspondences, showing how each facet can be captured by distinct mechanisms such as curiosity-driven exploration, empowerment, discriminative goal signaling, and goal-based performance metrics. The work highlights the explicit preconditions these models impose and the resources they unlock (models, simulators, evaluation metrics) for hypothesis testing and theory refinement. Overall, it provides a foundation for integrating computational models into SDT research, enabling rigorous testing and iterative theory development in motivational psychology.

Abstract

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 can offer a fertile starting point. Specifically, we focus on the "need for competence", postulated as a key basic psychological need within Self-Determination Theory (SDT) -- arguably the most influential framework for intrinsic motivation (IM) in psychology. Recent research has identified multiple distinct facets of competence in key SDT texts: effectance, skill use, task performance, and capacity growth. We draw on the computational IM literature in reinforcement learning 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 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.