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The language of smell: Connecting linguistic and psychophysical properties of odor descriptors
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). (Computational Brain Science Laboratory, KTH)ORCID iD: 0000-0001-6553-823X
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). (Computational Brain Science Laboratory, KTH)ORCID iD: 0000-0002-2358-7815
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS. Gavagai, Slussplan 9, Stockholm, Sweden. (Human Language Technology Group,)ORCID iD: 0000-0003-4042-4919
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2018 (English)In: Cognition, ISSN 0010-0277, E-ISSN 1873-7838, Vol. 178, p. 37-49Article in journal (Refereed) Published
Abstract [en]

The olfactory sense is a particularly challenging domain for cognitive science investigations of perception, memory, and language. Although many studies show that odors often are difficult to describe verbally, little is known about the associations between olfactory percepts and the words that describe them. Quantitative models of how odor experiences are described in natural language are therefore needed to understand how odors are perceived and communicated. In this study, we develop a computational method to characterize the olfaction-related semantic content of words in a large text corpus of internet sites in English. We introduce two new metrics: olfactory association index (OAI, how strongly a word is associated with olfaction) and olfactory specificity index (OSI, how specific a word is in its description of odors). We validate the OAI and OSI metrics using psychophysical datasets by showing that terms with high OAI have high ratings of perceived olfactory association and are used to describe highly familiar odors. In contrast, terms with high OSI have high inter-individual consistency in how they are applied to odors. Finally, we analyze Dravnieks's (1985) dataset of odor ratings in terms of OAI and OSI. This analysis reveals that terms that are used broadly (applied often but with moderate ratings) tend to be olfaction-unrelated and abstract (e.g., “heavy” or “light”; low OAI and low OSI) while descriptors that are used selectively (applied seldom but with high ratings) tend to be olfaction-related (e.g., “vanilla” or “licorice”; high OAI). Thus, OAI and OSI provide behaviorally meaningful information about olfactory language. These statistical tools are useful for future studies of olfactory perception and cognition, and might help integrate research on odor perception, neuroimaging, and corpus-based linguistic models of semantic organization.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 178, p. 37-49
Keywords [en]
Computational linguistics, Distributional semantics, Odour identification, Odour naming, Sensory lexicon, Sensory-semantic integration
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-228700DOI: 10.1016/j.cognition.2018.05.007Scopus ID: 2-s2.0-85047188460OAI: oai:DiVA.org:kth-228700DiVA, id: diva2:1211058
Note

QC 20180530

Available from: 2018-05-30 Created: 2018-05-30 Last updated: 2018-05-30Bibliographically approved

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Herman, PawelLansner, AndersKarlgren, Jussi

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