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Reclaiming AI as a Theoretical Tool for Cognitive Science
Iris van Rooij1,2,3, Olivia Guest1,2, Federico Adolfi4,5
1Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands.
Computational Brain & Behavior
|July 16, 2026
Summary
Human cognition as computation is a useful idea, but current Artificial Intelligence (AI) overestimates its practical feasibility. Creating human-level AI is computationally intractable, leading to flawed self-understanding.
Area of Science:
- Cognitive Science
- Artificial Intelligence
- Computational Theory
Background:
- The concept of human cognition as computation was foundational to cognitive science and Artificial Intelligence (AI).
- AI historically served as a provider of computational tools for cognitive science theory-building.
- Contemporary AI shifts focus from theoretical possibility to practical realization of human-level cognition.
Purpose of the Study:
- To formally prove the computational intractability of creating human-level cognitive systems.
- To critique the current trajectory of AI and its impact on cognitive science.
- To propose a return to AI as a theoretical tool for understanding cognition.
Main Methods:
- Formal proof of computational intractability.
- Analysis of the theoretical implications of AI's practical pursuits.
- Conceptual critique of AI's current role in cognitive science.
Main Results:
- Achieving human-level cognition in AI systems is intrinsically computationally intractable.
- Current AI systems are decoys that distort our understanding of human cognition.
- AI practice is currently deteriorating, not advancing, cognitive science.
Conclusions:
- The pursuit of practical human-level AI is theoretically flawed and detrimental to cognitive science.
- Reorienting AI as a theoretical tool is crucial for advancing cognitive understanding.
- Avoiding past conceptual errors is necessary for a healthier AI-cognitive science relationship.
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