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1. Why are Some Words More Frequent than Others? New Insights from Network Science

   Liu, Q; De_Deyne, S; Lupyan, G (July 2024, The Evolution of Language: Proceedings of the 15th International Conference (Evolang XV))

   Nölle, J; Raviv, L; Graham, E; Hartmann, S; Jadoul, Y; Josserand, M; Matzinger, T; Mudd, K; Pleyer, M; Slonimska, A (Ed.)

   Why are some words more frequent than others? Surprisingly, the obvious answers to this seemingly simple question, e.g., that frequent words reflect greater communicative needs, are either wrong or incomplete. We show that a word’s frequency is strongly associated with its position in a semantic association network. More centrally located words are more frequent. But is a word’s centrality in a network merely a reflection of inherent centrality of the word’s meaning? Through cross-linguistic comparisons, we found that differences in the frequency of translation-equivalents are predicted by differences in the word’s network structures in the different languages. Specifically, frequency was linked to how many connections a word had and to its capacity to bridge words that are typically not linked. This hints that a word’s frequency (and with it, its meaning) may change as a function of the word’s association with other words. 

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2. Communicative factors in the emergence of phonological dispersion

   Roberts, G; Kuang, J; Clark, R (July 2024, Cognitive Science Society)

   We investigated the emergence of dispersion in phonological systems using an established experimental paradigm in which pairs of participants play a non-linguistic communication game, taking turns to select discrete colors from a continuous underlying space and send them to each other to communicate animal silhouettes. Over time participants established sets of signals made up of combinatorial color units, analogous to the phonemes of natural language. This allowed us to investigate the role of interactive pressures on the emergence of organizational structure in phonological inventories, principally dispersion. We manipulated minimum signal length (as a means of investigating the role of coarticulation) and the presence of probabilistic noise. We also manipulated the nature of the underlying color space. There was an effect of colorspace but not of noise or minimum signal-length. However, dispersion occurred at above-chance levels in all conditions. Our results provide evidence for the role of communicative interaction in the emergence and cultural evolution of phonological structure. 

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3. A data-driven approach to the semantics of iconicity in American Sign Language and English

   https://doi.org/10.1017/langcog.2019.52  

   THOMPSON, BILL; PERLMAN, MARCUS; LUPYAN, GARY; SEVCIKOVA SEHYR, ZED; EMMOREY, KAREN (March 2020, Language and Cognition)

   abstract A growing body of research shows that both signed and spoken languages display regular patterns of iconicity in their vocabularies. We compared iconicity in the lexicons of American Sign Language (ASL) and English by combining previously collected ratings of ASL signs (Caselli, Sevcikova Sehyr, Cohen-Goldberg, & Emmorey, 2017) and English words (Winter, Perlman, Perry, & Lupyan, 2017) with the use of data-driven semantic vectors derived from English. Our analyses show that models of spoken language lexical semantics drawn from large text corpora can be useful for predicting the iconicity of signs as well as words. Compared to English, ASL has a greater number of regions of semantic space with concentrations of highly iconic vocabulary. There was an overall negative relationship between semantic density and the iconicity of both English words and ASL signs. This negative relationship disappeared for highly iconic signs, suggesting that iconic forms may be more easily discriminable in ASL than in English. Our findings contribute to an increasingly detailed picture of how iconicity is distributed across different languages. 

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4. Conceptual categories scaffold verbal semantic structure: A cross-cultural study of child homesign

   https://doi.org/10.12775/3991-1.097  

   Rissman, Lilia; Horton, Laura; Goldin-Meadow, Susan (April 2018, Proceedings of the 12th International Conference on the Evolution of Language)

   In language evolution, formation of conceptual categories preceded formation of linguistic semantic categories (Hurford, 2007). The mapping from concepts to semantics is non-isomorphic, however, as particular languages categorize conceptual space in divergent ways (e.g. English put in is acceptable for both tight-fit and loose-fit relations, while Korean kkita encodes tight-fit relationships only; Choi & Bowerman, 1991). Despite this variation, are there crosslinguistic patterns in how words lexicalize conceptual space? We address this question analyzing how child homesigners from four different cultures describe instrumental events (e.g. cutting bread with a knife). Homesigners are congenitally deaf individuals who have not been taught a signed language. Despite growing up without structured linguistic input, these individuals use a gestural system ("homesign") to communicate (Goldin-Meadow, 2003). We find that homesign descriptions of instrumental events reflect categories present in adult English, Spanish and Mandarin, suggesting crosslinguistic biases for how verbs encode the conceptual space of events, biases which may have been present over the course of language evolution. 

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5. CULEMO: Cultural Lenses on Emotion--Benchmarking LLMs for Cross-Cultural Emotion Understanding

   Belay, T; Ahmed, A; Grissom_II, A; Ameer, I; Sidorov, G; Kolesnikova, O; Yimam, S (July 2025, Association for Computational Linguistics (ACL))

   NLP research has increasingly focused on subjective tasks such as emotion analysis. However, existing emotion benchmarks suffer from two major shortcomings: (1) they largely rely on keyword-based emotion recognition, overlooking crucial cultural dimensions required for deeper emotion understanding, and (2) many are created by translating English-annotated data into other languages, leading to potentially unreliable evaluation. To address these issues, we introduce Cultural Lenses on Emotion (CuLEmo), the first benchmark designed to evaluate culture-aware emotion prediction across six languages: Amharic, Arabic, English, German, Hindi, and Spanish. CuLEmo comprises 400 crafted questions per language, each requiring nuanced cultural reasoning and understanding. We use this benchmark to evaluate several state-of-the-art LLMs on culture-aware emotion prediction and sentiment analysis tasks. Our findings reveal that (1) emotion conceptualizations vary significantly across languages and cultures, (2) LLMs performance likewise varies by language and cultural context, and (3) prompting in English with explicit country context often outperforms in-language prompts for culture-aware emotion and sentiment understanding. The dataset and evaluation code are publicly available. 

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