The Information Bottleneck idea has been influential because it captures a tidy balance: languages keep signals compact while preserving what matters. New work questions one of the framework’s complexity measures by showing it leads to odd predictions for color naming. The authors compare several definitions, introduce a method that rates how much each one explains real-world patterns, and test whether these pressures combine or compete as languages evolve.
Understanding which pressures truly shape vocabularies matters beyond linguistics. It affects how we build fair language technologies, how we support linguistic diversity, and how we think about human learning and cooperation. Follow the link to see how empirical data can arbitrate between competing ideas and what that tells us about the forces guiding human communication and potential.
Abstract
Human languages balance communicative informativity with complexity, conveying as much as needed through the simplest means required to do so. Yet, these concepts—informativity and complexity—have been operationalized in various ways, and it remains unclear which definitions best capture empirical linguistic patterns. A particularly successful operationalization is that offered by the Information Bottleneck framework, which suggests a balance between complexity and informativity across domains like color, kinship, and number. However, we show that the notion of complexity employed by this framework has some counterintuitive consequences. Focusing on color terms, we then study to what extent this and other notions of complexity play a role in explaining cross-linguistic regularity. We propose a method to assess their explanatory contributions; and to probe whether they enter in a joint optimization or in a trade-off competition. This offers a more general framework to study language change and the forces that shape it, where instead of showing that a given model is compatible with existing data, the data is used to adjudicate between candidate measures.
