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<p>According to a recent proposal, natural concepts are those represented by the cells of an optimally partitioned similarity space, where optimality is defined in terms of various design criteria, including the criterion that natural concepts should be easily learnable. While computational studies have shown that, in contexts of individual learning, natural concepts are indeed more easily learned than nonnatural ones, the proposal also suggested that natural concepts should facilitate social learning, helping communities converge on shared meanings through interaction. We test this hypothesis using neural agent-based models to simulate how communities learn conceptual structures through social interaction. Our simulations demonstrate that natural concepts are learned more quickly and with higher accuracy than nonnatural ones in social contexts, thereby providing further support for the optimal partitioning account.</p>

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The Social Learnability of Natural Concepts

  • 期刊论文

  • 发表日期：

    2026年5月19日

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  • Igor Douven

摘要

According to a recent proposal, natural concepts are those represented by the cells of an optimally partitioned similarity space, where optimality is defined in terms of various design criteria, including the criterion that natural concepts should be easily learnable. While computational studies have shown that, in contexts of individual learning, natural concepts are indeed more easily learned than nonnatural ones, the proposal also suggested that natural concepts should facilitate social learning, helping communities converge on shared meanings through interaction. We test this hypothesis using neural agent-based models to simulate how communities learn conceptual structures through social interaction. Our simulations demonstrate that natural concepts are learned more quickly and with higher accuracy than nonnatural ones in social contexts, thereby providing further support for the optimal partitioning account.