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Efficient compression in color naming and its evolution.


ABSTRACT: We derive a principled information-theoretic account of cross-language semantic variation. Specifically, we argue that languages efficiently compress ideas into words by optimizing the information bottleneck (IB) trade-off between the complexity and accuracy of the lexicon. We test this proposal in the domain of color naming and show that (i) color-naming systems across languages achieve near-optimal compression; (ii) small changes in a single trade-off parameter account to a large extent for observed cross-language variation; (iii) efficient IB color-naming systems exhibit soft rather than hard category boundaries and often leave large regions of color space inconsistently named, both of which phenomena are found empirically; and (iv) these IB systems evolve through a sequence of structural phase transitions, in a single process that captures key ideas associated with different accounts of color category evolution. These results suggest that a drive for information-theoretic efficiency may shape color-naming systems across languages. This principle is not specific to color, and so it may also apply to cross-language variation in other semantic domains.

SUBMITTER: Zaslavsky N 

PROVIDER: S-EPMC6077716 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Efficient compression in color naming and its evolution.

Zaslavsky Noga N   Kemp Charles C   Regier Terry T   Tishby Naftali N  

Proceedings of the National Academy of Sciences of the United States of America 20180718 31


We derive a principled information-theoretic account of cross-language semantic variation. Specifically, we argue that languages efficiently compress ideas into words by optimizing the information bottleneck (IB) trade-off between the complexity and accuracy of the lexicon. We test this proposal in the domain of color naming and show that (<i>i</i>) color-naming systems across languages achieve near-optimal compression; (<i>ii</i>) small changes in a single trade-off parameter account to a large  ...[more]

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