Low-dimensional and optimised representations of high-level information in the expert brain.

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.