The Relation Between Valence and Arousal in Subjective Odor Experience

Abstract

IntroductionThe main purpose of this study was to investigate the overall relation between the mean (at the nomothetic or group level) subjective valence and arousal ratings for odors. Although well established in other sensory modalities (e.g., visual, auditory, gustatory, tactile), this relation has not previously been investigated for odors covering a large range of the valence dimension. In addition, we evaluated the EmojiGrid (a recently introduced intuitive graphical affective self-report tool) for the affective appraisal of odors. Methods Young and healthy participants (N = 56, 32 females) used the EmojiGrid to rate the perceived valence and arousal for 40 different and randomly presented odors, ranging in valence from unpleasant to pleasant. ResultsThe overall relation between mean valence and arousal can be described by a U-shaped (quadratic) form; odors scoring near neutral on mean valence have the lowest mean arousal ratings, while odors scoring either high (pleasant) or low (unpleasant) on mean valence show higher mean arousal ratings. The results for odors that were also used in previous studies in the literature agree with their earlier reported values. Conclusion Mean arousal ratings increase with (positive or negative) emotional valence. Participants intuitively used the EmojiGrid to report their affective appraisal of odors without any verbal labels or written instructions. Implications The current findings are relevant for various applications and environments (e.g., public, retail, entertainment) where odors are used to induce desired emotional states (e.g., relaxation, arousal) and behaviors. The EmojiGrid can efficiently be applied to assess whether specific odors evoke the desired subjective affective experiences.

An Immersive Self-Report Tool for the Affective Appraisal of 360° VR Videos

“…They are also indebted to Prof Duncan Brumby (University College London) for his thoughtful suggestions and helpful comments on an earlier draft of this paper. Part of this work has previously been presented as a poster at the EuroVR 2019 conference in Tallin, Estland, 23-25 October, 2019 (Toet et al, 2019a).…”

Sequential dependency for affective appraisal of food images

“…In previous studies we found that users were intuitively able to use the EmojiGrid to report their food-evoked emotions (Kaneko et al, 2018;Toet et al, 2018). The EmojiGrid has been extensively validated with different age groups, nationalities, and ethnicities for a range of different affective stimuli (Kaneko et al, 2018;Toet et al, 2018Toet et al, , 2019aToet and Van Erp, 2019).…”

“…The full set of stimuli is described in more detail elsewhere (Kaneko et al, 2018;Toet et al, 2018) and is also available online (https://osf.io/cyqg7/download). The set of food images were selected such that their associated mean valence (as determined in earlier studies, see Toet et al, 2018Toet et al, , 2019a are distributed along the entire scale (ranging from very low valence for rotten, moulded or contaminated food, via neutral for raw onions, boiled eggs or potatoes, to very high valence for fresh fruit, chocolates and pastries). The 10 additional food images from the validated FRIDa database were selected such that their associated valence scores (as reported in their accompanying data file, see Foroni et al, 2013) also cover a large part of the scale.…”

Closed-Loop Cognitive Stress Regulation Using Fuzzy Control in Wearable-Machine Interface Architectures

“…One future application of the proposed architecture could be incorporating potential non-invasive actuation effective in regulating arousal state in the real-life human-in-the-loop scenarios. The examples of these actuation are listening to music [85]- [88], adjusting the light in workplaces [89]- [91], smelling fragrances [92]- [94], and drinking beverages such as coffee, tea, and water [95]. Using different actuators and wearable devices, one may perform system identification and model the actuation dynamics [96]- [100].…”