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Neighbours and relatives: accounting for spatial distribution when testing causal hypotheses in cultural evolution
Many important and interesting hypotheses about cultural evolution are evaluated using cross-cultural correlations: if knowing one particular feature of a culture (e.g. environmental conditions such as temperature, humidity or parasite load) allows you to predict other features (e.g. language featur...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565196/ https://www.ncbi.nlm.nih.gov/pubmed/37829289 http://dx.doi.org/10.1017/ehs.2023.23 |
Sumario: | Many important and interesting hypotheses about cultural evolution are evaluated using cross-cultural correlations: if knowing one particular feature of a culture (e.g. environmental conditions such as temperature, humidity or parasite load) allows you to predict other features (e.g. language features, religious beliefs, cuisine), it is often interpreted as indicating a causal link between the two (e.g. hotter climates carry greater disease risk, which encourages belief in supernatural forces and favours the use of antimicrobial ingredients in food preparation; dry climates make the production of distinct tones more difficult). However, testing such hypotheses from cross-cultural comparisons requires us to take proximity of cultures into account: nearby cultures share many aspects of their environment and are more likely to be similar in many culturally inherited traits. This can generate indirect associations between environment and culture which could be misinterpreted as signals of a direct causal link. Evaluating examples of cross-cultural correlations from the literature, we show that significant correlations interpreted as causal relationships can often be explained as a result of similarity between neighbouring cultures. We discuss some strategies for sorting the explanatory wheat from the co-varying chaff, distinguishing incidental correlations from causal relationships. |
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