mlsanityData to 14 Oct 2026

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

arXiv 2502.07423 ↗First posted 11 Feb 2025cs.AIcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 21 of its 64 references that are in the map. First posted on arXiv 11 Feb 2025.

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21 of 64 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Diversity Progress for Goal Selection in Discriminability-Motivated RL3 Nov
  2. 2024No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery27 Aug

Other references · 19

  1. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
  2. 2018Diversity is All You Need: Learning Skills without a Reward Functioncited by 752
  3. 2018Large-Scale Study of Curiosity-Driven Learningcited by 280
  4. 2016Variational Intrinsic Controlcited by 267
  5. 2017Automatic Goal Generation for Reinforcement Learning Agentscited by 259
  6. 2018Visual Reinforcement Learning with Imagined Goalscited by 251
  7. 2019Skew-Fit: State-Covering Self-Supervised Reinforcement Learningcited by 174
  8. 2022Goal-Conditioned Reinforcement Learning: Problems and Solutionscited by 158
  9. 2020Automatic Curriculum Learning For Deep RL: A Short Surveycited by 150
  10. 2023Getting aligned on representational alignmentcited by 143
  11. 2020RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environmentscited by 135
  12. 2020Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Surveycited by 85
7 more
  1. 2018CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learningcited by 79
  2. 2024OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Codecited by 74
  3. 2013Empowerment -- an Introductioncited by 68
  4. 2022An information-theoretic perspective on intrinsic motivation in reinforcement learning: a surveycited by 28
  5. 2024Goals as Reward-Producing Programscited by 14
  6. 2019Intrinsic motivations and open-ended learningcited by 6
  7. 2018Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loopcited by 4

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  1. 2024The intrinsic motivation of reinforcement and imitation learning for sequential tasks
  2. 2026Psychological Competence as a Missing Dimension in AI Evaluation
  3. 2023Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review
  4. 2025Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments
  5. 2025Truly Self-Improving Agents Require Intrinsic Metacognitive Learning
  6. 2025From Curiosity to Competence: How World Models Interact with the Dynamics of Exploration
  7. 2026Intrinsic Motivation in Reinforcement Learning: A Research Agenda for Adaptive Self-Organisation
  8. 2026Human Cognition in Machines: A Unified Perspective of World Models