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Editorial: AI and neuroscience: integrating knowledge, reasoning, and theory of mind
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Bochao Zou,
Rob Lowe,
Andrew Owens
Frontiers in Computational Neuroscience, Volume: 20
Swansea University Author:
Darren Edwards
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© 2026 Edwards, Zou, Lowe and Owens. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
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DOI (Published version): 10.3389/fncom.2026.1859797
Abstract
Artificial intelligence and neuroscience have been converging on a shared problem about how to explain and ultimately build systems (biological or synthetic) that can acquire knowledge from experience, reason under uncertainty, and coordinate perspectives from self-model representations to other min...
| Published in: | Frontiers in Computational Neuroscience |
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| ISSN: | 1662-5188 |
| Published: |
Frontiers Media SA
2026
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71947 |
| Abstract: |
Artificial intelligence and neuroscience have been converging on a shared problem about how to explain and ultimately build systems (biological or synthetic) that can acquire knowledge from experience, reason under uncertainty, and coordinate perspectives from self-model representations to other minds (Hassabis et al., 2017; Langley et al., 2022; Limanowski and Blankenburg, 2013; Nawaz et al., 2025). The “AI and neuroscience: integrating knowledge, reasoning, and theory of mind” theme captures this convergence by explicitly highlighting a broad body of research linking accounts of neural information processing with computational architectures that can learn, generalize, and remain interpretable. At a broad level, the contributions in this Research Topic can be read as collectively operating across three complementary levels. First, they investigate biological substrates of computation and the representational constraints that come with real neural tissue. Second, they advance architectures and modeling frameworks that treat cognition as an evolving repertoire of learned competencies rather than a set of isolated tasks. Third, they address human–AI coupling, i.e., how AI systems can extend cognition without displacing the very internal knowledge structures that make reasoning and perspective-taking possible in the first place. |
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| Item Description: |
Editorial |
| College: |
Faculty of Medicine, Health and Life Sciences |