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It Is What It Isn't: Introducing a Constraint-Based Approach to Structure Learning.

Christoffer Lundbak Olesen1, Nace Mikuš1, Mads Hansen2

  • 1Interacting Minds Centre, Aarhus University, Jens Chr. Skous Vej 4, 8000 Aarhus, Denmark.

Entropy (Basel, Switzerland)
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

This study reframes structure learning in computational cognitive models. A new constraint-based dynamics approach shows how representations emerge from component interactions, offering a distinct foundation for biological systems.

Keywords:
cognitive modellingcomputational psychiatryconstraint-based dynamicsdelusionshierarchical Gaussian filterstructure learning

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Biological cognition relies on learning structured representations, especially in ambiguous environments.
  • Current computational models often overlook temporal dynamics in structure learning, focusing on inference.

Purpose of the Study:

  • To reframe structure learning as an emergent outcome of constraint-based dynamics.
  • To develop and demonstrate a proof-of-concept constraint-based computational cognitive model.

Main Methods:

  • Developed a model where individual learning components are constrained by observations and system-level relations.
  • Formalized constraint satisfaction using Bayesian probability, distinct from epistemic inference.
  • Simulated the model in environments with varying ambiguity levels.

Main Results:

  • The model successfully differentiated observation spaces into stable representational categories.
  • Analyzed how global parameters influence learning trajectories and behavioral alignment.
  • Representational structure emerged dynamically through component interactions, stabilization, and elimination.

Conclusions:

  • A constraint-based approach offers a novel framework for computational cognitive modeling.
  • This approach provides a conceptually distinct foundation for linking computational models to biological systems.
  • Emergent representational structure arises from dynamic constraint satisfaction rather than direct encoding.