Predicting the crossmodal correspondences of odors using an electronic nose

Ward, Ryan J ORCID: 0000-0002-9850-5191, Rahman, Shammi, Wuerger, Sophie ORCID: 0000-0003-0080-5813 and Marshall, Alan ORCID: 0000-0002-8058-5242 (2022) Predicting the crossmodal correspondences of odors using an electronic nose Heliyon, 8 (4). e09284-e09284. ISSN 2405-8440, 2405-8440

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Abstract

When designing multisensorial experiences, robustly predicting the crossmodal perception of olfactory stimuli is a critical factor. We investigate the possibility of predicting olfactory crossmodal correspondences using the underlying physicochemical features. An electronic nose was tuned to the crossmodal perceptual axis of olfaction and was used to foretell people's crossmodal correspondences between odors and the angularity of shapes, smoothness of texture, perceived pleasantness, pitch, and colors. We found that the underlying physicochemical features of odors could be used to predict people's crossmodal correspondences. The human-machine perceptual dimensions that correlated well are the angularity of shapes (r = 0.71), the smoothness of texture (r = 0.82), pitch (r = 0.70), and the lightness of color (r = 0.59). The human-machine perceptual dimensions that did not correlate well (r < 0.50) are the perceived pleasantness (r = 0.20) and the hue of the color (r = 0.42 & 0.44). All perceptual dimensions except for the perceived pleasantness could be robustly predicted (p-values < 0.0001) including the hue of color. While it is recognized that olfactory perception is strongly shaped by learning and experience, our findings suggest that there is a systematic and predictable link between the physicochemical features of odorous stimuli and crossmodal correspondences. These findings may provide a crucial building block towards the digital transmission of smell and enhancing multisensorial experiences with better designs as well as more engaging, and enriched experiences.

Item Type:	Article
Uncontrolled Keywords:	Crossmodal correspondences, Crossmodal associations, Machine learning, Electronic nose, Regression, Odors
Divisions:	Faculty of Health & Life Sciences Faculty of Health & Life Sciences > Inst. Population Health Faculty of Science & Engineering > IDEAS
Depositing User:	Symplectic Admin
Date Deposited:	25 Apr 2022 07:39
Last Modified:	01 Mar 2026 11:18
DOI:	10.1016/j.heliyon.2022.e09284
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3153725
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