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

What transforms a novice into an expert? While decades of research show that expertise relies on domain-specific knowledge, 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. Here, using chess as a model system for outstanding performance, we combine neuroimaging with multivariate pattern analysis to reveal three principles of the expert brain. We show that expertise drives a shift in representational content, from surface visual features to high-level, relational information. This is accompanied by a structural change, to low-dimensional, optimised representation. Neural codes become more compact and better organised for rapid use, yet retain the details needed for precise evaluation. Finally, we find the representational load shifts from sensory-specific cortices to domain-general frontoparietal networks. These principles show how the expert brain packs more into less, concentrating richer knowledge into fewer, better-organised representations that support rapid, flexible decision-making that defines mastery.