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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Andrea I. Costantino,
  • Artem Platonov,
  • Felipe Fontana Vieira,
  • Emily Van Hove,
  • Merim Bilalić,
  • Hans Op de Beeck

摘要

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.