Towards a formal theory of the need for competence via computational intrinsic motivation
File(s)
Published version
Author(s)
Lintunen, Erik Matias
Ady, Nadia M
Deterding, Sebastian
Guckelsberger, Christian
Type
Conference Paper
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.
Editor(s)
Barner, D
Bramley, NR
Walker, CM
Date Issued
2025-07-30
Date Acceptance
2025-04-04
Citation
Proceedings of the Annual Meeting of the Cognitive Science Society, 2025, 47, pp.2175-2183
Start Page
2175
End Page
2183
Journal / Book Title
Proceedings of the Annual Meeting of the Cognitive Science Society
Volume
47
Copyright Statement
© 2025 by the author(s). This work is made available under the terms of a Creative Commons Attribution License, available at https://creativecommons.org/licenses/by/4.0/
License URL
Source
CogSci 2025
Publication Status
Published
Start Date
2025-07-30
Finish Date
2025-08-02
Coverage Spatial
San Francisco, CA, USA