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bioRxiv · 10.1101/2025.11.12.688012

Low-Dimensional and Optimised Representations of High-Level Information in the Expert Brain

Costantino, A. I.·Platonov, A.·Fontana Vieira, F.·Van Hove, E.·Bilalic, M.·Op de Beeck, H.
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Abstract

What transforms a novice into an expert? Decades of research show that expertise relies on domain-specific knowledge, but a neural account of this transformation has remained fragmentary: we lack an understanding of what information expert representations encode, how they are structured for efficient use, and where in the brain they reside. Using chess as a model system, we combine neuroimaging with multivariate pattern analysis to reveal three principles of the expert brain. Expertise drives a shift in representational content, from surface visual features to high-level, relational information. It is accompanied by a structural change to low-dimensional, optimised representation: codes become more compact and better organised, yet retain the detail needed for precise evaluation. Finally, the representational load shifts from sensory-specific cortices to domain-general frontoparietal networks. The expert brain packs more into less, concentrating richer knowledge into fewer, better-organised representations that support the rapid, flexible decisions of mastery.

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Costantino, A. I., Platonov, A., Fontana Vieira, F., Van Hove, E., Bilalic, M., Op de Beeck, H.. 2025-11-12. Low-Dimensional and Optimised Representations of High-Level Information in the Expert Brain. https://doi.org/10.1101/2025.11.12.688012

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