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

2025-02-11

AI Summary

This paper addresses the conceptual ambiguity and ill-defined boundaries of the “competence need” construct in Self-Determination Theory (SDT). We propose the first computational formalization of SDT competence grounded in reinforcement learning (RL). By systematically mapping SDT’s four-dimensional competence construct—perceived efficacy, skill deployment, task performance, and capacity growth—to core RL mechanisms—including policy optimization, reward shaping, and intrinsic motivation modeling—we uncover implicit cognitive and learning assumptions underlying SDT. Our framework bridges the gap between theoretical abstraction and computational tractability, enabling empirically testable motivational models. It advances SDT toward greater conceptual precision while offering a novel interdisciplinary paradigm for designing autonomous learning mechanisms in AI and for modeling motivation in human–AI collaboration.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Reinforcement Learning
• Humans and AI: Learning Human Values and Preferences

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Computational models offer powerful tools for formalising psychological theories, making them both testable and applicable in digital contexts. However, they remain little used in the study of motivation within psychology. We focus on the"need for competence", postulated as a key basic human need within Self-Determination Theory (SDT) -- arguably the most influential psychological framework for studying intrinsic motivation (IM). The need for competence is treated as a single construct across SDT texts. Yet, recent research has identified multiple, ambiguously defined facets of competence in SDT. We propose that these inconsistencies may be alleviated by drawing on computational models from the field of artificial intelligence, specifically from the domain of reinforcement learning (RL). By aligning the aforementioned facets of competence -- effectance, skill use, task performance, and capacity growth -- with existing RL formalisms, we provide a foundation for advancing competence-related theory in SDT and motivational psychology more broadly. The formalisms reveal underlying preconditions that SDT fails to make explicit, demonstrating how computational models can improve our understanding of IM. Additionally, our work can support a cycle of theory development by inspiring new computational models formalising aspects of the theory, which can then be tested empirically to refine the theory. While our research lays a promising foundation, empirical studies of these models in both humans and machines are needed, inviting collaboration across disciplines.

Problem

• Formalizing need for competence using computational models
• Aligning competence facets with reinforcement learning formalisms
• Enhancing Self-Determination Theory through computational insights

Innovation

• Reinforcement learning models
• Align competence facets
• Improve intrinsic motivation understanding