Pleasantness and trigeminal sensations as salient dimensions in organizing the semantic and physiological spaces of odors

Licón, Carmen C.; Manesse, C.; Dantec, M.; Fournel, Arnaud; Bensafi, Moustafa

doi:10.1038/s41598-018-26510-5

Cited by 45 publications

(37 citation statements)

References 66 publications

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“…One may consequently argue that the trigeminal aspect of the odorants drove the association with an energizing/relaxing dimension. It would be congruent with recent findings showing that the physiological response to odors is partially driven by the trigeminality of the odorants (Licon et al, 2018).…”

Section: Discussionsupporting

confidence: 92%

Measuring automatic associations between relaxing/energizing feelings and odors

Lemercier-Talbot

Coppin

Cereghetti

et al. 2019

Food Quality and Preference

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The extent to which automatic associations exist between relaxing and energizing feelings and odors is unclear. To investigate this question, we used a modified version of the Implicit Association Test. In this task, participants had to make speeded discrimination responses between stimuli and words related to energizing vs. relaxing feelings. These stimuli were either visual stimuli (i.e., vanilla vs. mint labels in Experiment 1) or olfactory (vanillin vs. menthol in Experiment 2, and two fine fragrances in Experiment 3). In compatible blocks, purportedly related items (e.g., vanillin and a label related to relaxing feelings) shared the same response key, while in incompatible blocks they did not (e.g., vanillin and a word related to energizing feelings). In the three experiments, the participants responded significantly faster in the compatible blocks than in the incompatible ones. The stronger the association between purportedly related items, the faster the participant responds in compatible blocks and the slower in incompatible blocks. Consequently, this differential speed of response supports the existence of associations between the stimuli that were considered to be compatible. This argues for the existence of automatic associations between relaxing/energizing feelings and odors, associations that can influence behavior.

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Section: Discussionsupporting

confidence: 92%

Measuring automatic associations between relaxing/energizing feelings and odors

Lemercier-Talbot

Coppin

Cereghetti

et al. 2019

Food Quality and Preference

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“…These databases were from the Dravnieks study [26] (n = 45; i.e. 45 odorants not present in our original dataset could be used), the Boelens & Harding study [30] (n = 56), one set from the Keller et al study [15] (n = 118), and one set from the Licon et al study [31] (n = 19). Within each of these four novel sets, olfactory quality was coded using a continuous variable (Dravnieks: from 0 to 100; Boelens & Haring: from 0 to 9; Keller et al: from 0 to 100; Licon et al: from 0 to 100).…”

Section: Resultsmentioning

confidence: 99%

Chemical features mining provides new descriptive structure-odor relationships

Licón¹,

Bosc²,

Sabri³

et al. 2019

PLoS Comput Biol

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An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants. In other words, why do some odorants smell like fruits and others like flowers? While the so-called stimulus-percept issue was resolved in the field of color vision some time ago, the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day. Although a series of investigations have demonstrated that this relationship exists, the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication. One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality, whereby two very different molecules can evoke a similar odor. Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction. In addition, by automatically extracting new knowledge linking chemistry of odorants and psychology of smells, our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling.

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“…All analyses involved hierarchical clustering and PCA analysis of a descriptor distance matrix that was derived from the semantic vectors of the descriptors at hand. Previous work that has investigated the semantic organization of odor descriptors on the basis of perceptual data (e.g., Dravnieks 1985) have found that odor descriptors primarily are differentiated in terms of pleasantness, but also with respect to edibility (e.g., Kahn et al 2007;Zarzo 2008;Koulakov et al 2011;Licon et al 2018). Further, the distinction between source-based, abstract and evaluative descriptors is highly related to a difference in concreteness, the former descriptors being concrete and the latter abstract.…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, the identified dimensions corresponded either to "relative pleasantness" or to "cues of potential palatibility / nonpalatibility" (Castro et al, 2013: 13). More recently, Licon et al (2018) investigated the semantic space of odors on the basis of PCA analysis of odor-descriptor ratings. Participants rated 105 odors with respect to whether and how well they were described by 24 predefined descriptors 1 , and with regard to their perceptual dimensions in terms of pleasantness, edibility, familiarity and intensity.…”

Section: Introductionmentioning

confidence: 99%

Mapping the semantic organization of the English odor vocabulary using natural language data

Hörberg¹,

Larsson²,

Olofsson³

2020

Preprint

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Olfactory experiences are hard to verbalize, partly because most languages lack devoted odor vocabularies. Yet, there is a need for a standardized odor vocabulary, but no descriptive system for describing the full range of odor experiences has been agreed upon. Many studies of the English odor vocabulary have been based on perceptual data such as odor-descriptor ratings, thereby being limited to a small set of pre-selected descriptors. In the present study, we present a data-driven approach that automatically identifies odor descriptors in English, and then derive their semantic organization on the basis of their distributions in natural texts. Olfactory descriptors are automatically identified on the basis of their degree of olfactory association, and their semantic organization is derived with a distributional-semantic word embedding model. We identify and derive the semantic organization of the descriptors most frequently used to describe odors and flavors in English, both within and across sourcebased, abstract and evaluative descriptor categories. Our method is to a large extent able to capture semantic differences between descriptors related to aroma and flavor qualities, rather than e.g. functional or linguistic aspects, in that it primarily differentiates descriptors with respect to valence and edibility, and the semantic space derived from it is qualitatively similar to a space derived from perceptual data. key words: odor semantics, odor categorization, distributional semantics, word embeddings, corpus linguistics 1 The choice of descriptors was done on the basis of several odor atlases and the comprehensive review of Zarzo and Stanton (2009).

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Pleasantness and trigeminal sensations as salient dimensions in organizing the semantic and physiological spaces of odors

Cited by 45 publications

References 66 publications

Measuring automatic associations between relaxing/energizing feelings and odors

Measuring automatic associations between relaxing/energizing feelings and odors

Chemical features mining provides new descriptive structure-odor relationships

Mapping the semantic organization of the English odor vocabulary using natural language data

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