ArticleNature human behaviour2020
Cultural influences on word meanings revealed through large-scale semantic alignment.
Article in Nature human behaviour, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
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- Review
- Development, Normative Data, and Psychometric Properties of the Turkish Semantic Association Test (SEMAT) for Middle-Aged and Older Adults.Brain and behavior · 2026Article
- When languages favour complex words.Nature human behaviour · 2026Article
- Extracting prototypes from lexical feature norms for settlement concepts.Memory & cognition · 2026Article
- Semantic diversity in Chinese words: Exploring its role in word recognition, lexical ambiguity, character complexity, and semantic alignment.Behavior research methods · 2026Article
- Centroid analysis: Inferring concept representations from open-ended word responses.Behavior research methods · 2026Article
- Semantic similarity across languages reflects neurocognitive dimensions shaped by climate.Nature communications · 2026Article
- Reading experience reveals shared and idiosyncratic neural patterns during text comprehension.NPJ science of learning · 2025Article
- A Standardized Semantic Feature Norm for 954 Cantonese Concepts.Scientific data · 2025Article
- Quebec French Version of the Hayling Sentence Completion Test: Error Scoring Guidelines, Normative Data for Adults and the Elderly and Validation Study in Mild Cognitive Impairment and Alzheimer's Disease.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025Article
- Water Is and Is Not HCognitive science · 2025Article
- Semantic alignment: A measure to quantify the degree of semantic equivalence for English-Chinese translation equivalents based on distributional semantics.Behavior research methods · 2025Article
- Pathogen stress heightens sensorimotor dimensions in the human collective semantic space.Communications psychology · 2025Article
- Semantic Anchors Facilitate Task Encoding in Continual Learning.Open mind : discoveries in cognitive science · 2025Article
- Invariant representations in abstract concept grounding - the physical world in grounded cognition.Psychonomic bulletin & review · 2024Review
- Word reuse and combination support efficient communication of emerging concepts.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Neural Representations of Concreteness and Concrete Concepts Are Specific to the Individual.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Cognitive entailments among "the true, the good, the beautiful": a mainland Chinese sample.Cognitive processing · 2024Article
- TACO: A Turkish database for abstract concepts.Behavior research methods · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
If the structure of language vocabularies mirrors the structure of natural divisions that are universally perceived, then the meanings of words in different languages should closely align. By contrast, if shared word meanings are a product of shared culture, history and geography, they may differ between languages in substantial but predictable ways. Here, we analysed the semantic neighbourhoods of 1,010 meanings in 41 languages. The most-aligned words were from semantic domains with high internal structure (number, quantity and kinship). Words denoting natural kinds, common actions and artefacts aligned much less well. Languages that are more geographically proximate, more historically related and/or spoken by more-similar cultures had more aligned word meanings. These results provide evidence that the meanings of common words vary in ways that reflect the culture, history and geography of their users.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.