Terry Regier’s research while affiliated with University of California, Berkeley and other places

Claims about lexical elaboration (e.g. Mongolian has many horse-related terms) are widespread in the scholarly and popular literature. Here, we show that computational analyses of bilingual dictionaries can be used to test claims about lexical elaboration at scale. We validate our approach by introducing BILA, a dataset including 1,574 bilingual dictionaries, and showing that it confirms 147 out of 163 previous claims from the literature. We then identify previously unreported examples of lexical elaboration, and analyze how lexical elaboration is influenced by ecological and cultural variables. Claims about lexical elaboration are sometimes dismissed as either obvious or fanciful, but our work suggests that large-scale computational approaches to the topic can produce nonobvious and well-grounded insights into language and culture.

Claims about lexical elaboration (e.g. Mongolian has many horse-related terms) are widespread in the scholarly and popular literature. Here we show that computational analyses of bilingual dictionaries can be used to test claims about lexical elaboration at scale. We validate our approach by introducing BILA, a data set including 1574 bilingual dictionaries, and showing that it confirms 147 out of 163 previous claims from the literature. We then identify previously unreported examples of lexical elaboration, and analyze how lexical elaboration is influenced by ecological and cultural variables. Claims about lexical elaboration are sometimes dismissed as either obvious or fanciful, but our work suggests that large-scale computational approaches to the topic can produce non-obvious and well-grounded insights about language and culture.

It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that some other proposals such as iterated learning alone, communication alone, or the greater learnability of convex categories, do not yield the same outcome as clearly. We conclude that the combination of iterated learning and communication provides a plausible means by which human semantic systems become efficient.

Claims about lexical elaboration (e.g. Mongolian has many horse-related terms) are widespread in the scholarly and popular literature. Here we introduce BILA, a dataset including 1606 bilingual dictionaries, and show how it can be used to study lexical elaboration at scale. We first validate our approach by showing that it confirms 146 out of 163 previous claims from the literature. We then identify previously unreported examples of lexical elaboration, and analyze how lexical elaboration relates to ecological and cultural variables. Claims about lexical elaboration are sometimes dismissed as either obvious or fanciful, but our work suggests that large-scale computational approaches to the topic can produce non-obvious and well-grounded insights about language and culture.

Claims about lexical elaboration (e.g. Mongolian has many horse-related terms) are widespread in the scholarly and popular literature. Here we show that computational analyses of bilingual dictionaries can be used to test claims about lexical elaboration at scale. We validate our approach by introducing BILA, a data set including 1574 bilingual dictionaries, and showing that it confirms 147 out of 163 previous claims from the literature. We then identify previously unreported examples of lexical elaboration, and analyze how lexical elaboration is influenced by ecological and cultural variables. Claims about lexical elaboration are sometimes dismissed as either obvious or fanciful, but our work suggests that large-scale computational approaches to the topic can produce non-obvious and well-grounded insights about language and culture.

Communicative efficiency is a prominent theory in linguistics and cognitive science. While numerous studies have shown how the pressure to save energy is reflected in the form of spoken languages, few have explored this phenomenon in signed languages. In this paper, we show how handshapes in American Sign Language (ASL) reflect these efficiency pressures and we present new evidence of communicative efficiency in the visual-gestural modality. We focus on handshapes that are used in both native ASL signs and signs borrowed from English to compare efficiency pressures from both ASL and English. First, we design new methodologies to quantify the articulatory effort required to produce handshapes as well as the perceptual effort needed to recognize them. Then, we compare correlations between communicative effort and usage statistics in ASL and English. Our findings reveal that frequent ASL handshapes are easier to produce and that pressures for communicative efficiency mostly come from ASL usage, not from English lexical borrowing.

Distinctive aspects of a culture are often reflected in the meaning and usage of words in the language spoken by bearers of that culture. Keywords such as душа (soul) in Russian, hati (heart) in Indonesian and Malay, and gezellig (convivial/cosy/fun) in Dutch are held to be especially culturally revealing, and scholars have identified a number of such keywords using careful linguistic analyses (Peeters, 2020b; Wierzbicka, 1990). Because keywords are expected to have different statistical properties than related words in other languages, we argue that a quantitative comparison of word usage across languages can help to identify cultural keywords. To support this claim, we describe a computational method that compares word frequencies across languages, and apply it to both linguistic corpora and word association data. The method identifies culturally specific words that range from “obvious” examples, such as Amsterdam in Dutch, to non‐obvious yet independently proposed examples, such as hati (heart) in Indonesian. We show in addition that linguistic corpora and word association data provide converging evidence about culturally specific words. Our results therefore show how computational analyses and behavioral experiments can supplement the methods previously used by linguists to identify culturally salient words across languages.

It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that iterated learning alone, and communication alone, do not yield the same outcome as clearly.