Brain Inspired
BI 225 Henk De Regt: Understanding in Machines and Humans
Brain Inspired
•November 19, 2025•1h 43m
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Brain Inspired•Nov 19, 2025
Why It Matters
A skill‑based definition of understanding offers a concrete metric for evaluating scientific competence, crucial for advancing AI accountability and improving science communication across audiences.
Key Takeaways
- •Understanding requires theory-based explanations and relevant scientific skills
- •Distinguishes from mere factual accumulation or subjective insight
- •Enables benchmarking AI and human scientific comprehension
- •Highlights role of metaphors and abstraction in science communication
- •Connects to realism debates and idealization in scientific models
Pulse Analysis
Henk de Regt’s philosophy of science reframes understanding as an active, skill‑driven capacity rather than a passive feeling of clarity. According to his framework, a researcher truly understands a phenomenon when they can construct a theory‑grounded explanation and make qualitative predictions without resorting to detailed calculations. This contextual approach separates genuine comprehension from simply amassing facts or experiencing an intuitive “aha” moment, positioning understanding as a measurable competency within scientific practice.
The practical implications of de Regt’s model are especially salient for artificial intelligence. By defining clear criteria for scientific understanding, researchers can devise benchmarks that test whether AI systems possess the same explanatory and predictive abilities as human experts. Such benchmarks promise to move AI evaluation beyond pattern recognition toward genuine reasoning, offering a pathway to certify machine understanding in domains ranging from physics to biology. This shift could reshape funding decisions, regulatory standards, and public trust in AI‑driven research.
Beyond AI, the discussion touches on how metaphors, abstraction, and idealization shape both expert and public comprehension of science. De Regt argues that effective communication leverages metaphorical tools to bridge complex theories and everyday intuition, while still preserving rigorous explanatory power. Recognizing the skill‑based nature of understanding helps educators and policymakers design curricula and outreach that cultivate the necessary analytical abilities, ultimately narrowing the gap between scientific communities and broader society.
Episode Description
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Henk de Regt is a professor of Philosophy of Science and the director of the Institute for Science in Society at Radboud University. Henk wrote the book on Understanding. Literally, he wrote what has become a classic in philosophy of science, Understanding Scientific Understanding.
Henks' account of understanding goes roughly like this, but you can learn more in his book and other writings. To claim you understand something in science requires that you can produce a theory-based explanation of whatever you claim to understand, and it depends on you having the right scientific skills to be able to work productively with that theory - for example, making qualitative predictions about it without performing calculations. So understanding is contextual and depends on the skills of the understander.
There's more nuance to it, so like I said you should read the book, but this account of understanding distinguishes it from explanation itself, and distinguishes it from other accounts of understanding, which take understanding to be either a personal subjective sense - that feeling of something clicking in your mind - or simply the addition of more facts about something.
In this conversation, we revisit Henk's work on understanding, and how it touches on many other topics, like realism, the use of metaphors, how public understanding differs from expert understanding, idealization and abstraction in science, and so on.
And, because Henk's kind of understanding doesn't depend on subjective awareness or things being true, he and his cohorts have begun working on whether there could be a benchmark for degrees of understanding, to possibly asses whether AI demonstrates understanding, and to use as a common benchmark for humans and machines.
Google Scholar page
Social: @henkderegt.bsky.social;
Book:
Understanding Scientific Understanding.
Related papers
Towards a benchmark for scientific understanding in humans and machines
Metaphors as tools for understanding in science communication among experts and to the public
Two scientific perspectives on nerve signal propagation: how incompatible approaches jointly promote progress in explanatory understanding
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