Philosophical Transactions of the Royal Society B Biological Sciences · 2018 · 131 citations · 36 references
Social SciencesAnthropologyPsychologyAnthropological LinguisticsLinguistic AnthropologySociolinguisticsEmotion PerceptionEnvironmental PerceptionOlfactory PhysiologyOlfactory ProcessingOlfactory SystemsAbstract DomainsCross-cultural Data CollectionLinguistic CategorizationOlfactory AnthropologyOlfactory EthnographyOlfactory Perception Of EnvironmentsSensory Evaluation Of OdorsSmell Perception
Olfaction presents a particularly interesting arena to explore abstraction in language. Like other abstract domains, such as time, odours can be difficult to conceptualize. An odour cannot be seen or held, it can be difficult to locate in space, and for most people odours are difficult to verbalize. On the other hand, odours give rise to primary sensory experiences. Every time we inhale we are using olfaction to make sense of our environment. We present new experimental data from 30 Jahai hunter-gatherers from the Malay Peninsula and 30 matched Dutch participants from the Netherlands in an odour naming experiment. Participants smelled monomolecular odorants and named odours while reaction times, odour descriptors and facial expressions were measured. We show that while Dutch speakers relied on concrete descriptors, i.e. they referred to odour sources (e.g. smells like lemon ), the Jahai used abstract vocabulary to name the same odours (e.g. musty ). Despite this differential linguistic categorization, analysis of facial expressions showed that the two groups, nevertheless, had the same initial emotional reactions to odours. Critically, these cross-cultural data present a challenge for how to think about abstraction in language. This article is part of the theme issue ‘Varieties of abstract concepts: development, use and representation in the brain’.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text
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