Abstract
Research on food experience is typically challenged by the way questions are worded. We therefore developed the EmojiGrid: a graphical (language-independent) intuitive self-report tool to measure food-related valence and arousal. In a first experiment participants rated the valence and the arousing quality of 60 food images, using either the EmojiGrid or two independent visual analog scales (VAS). The valence ratings obtained with both tools strongly agree. However, the arousal ratings only agree for pleasant food items, but not for unpleasant ones. Furthermore, the results obtained with the EmojiGrid show the typical universal U-shaped relation between the mean valence and arousal that is commonly observed for a wide range of (visual, auditory, tactile, olfactory) affective stimuli, while the VAS tool yields a positive linear association between valence and arousal. We hypothesized that this disagreement reflects a lack of proper understanding of the arousal concept in the VAS condition. In a second experiment we attempted to clarify the arousal concept by asking participants to rate the valence and intensity of the taste associated with the perceived food items. After this adjustment the VAS and EmojiGrid yielded similar valence and arousal ratings (both showing the universal U-shaped relation between the valence and arousal). A comparison with the results from the first experiment showed that VAS arousal ratings strongly depended on the actual wording used, while EmojiGrid ratings were not affected by the framing of the associated question. This suggests that the EmojiGrid is largely self-explaining and intuitive. To test this hypothesis, we performed a third experiment in which participants rated food images using the EmojiGrid without an associated question, and we compared the results to those of the first two experiments. The EmojiGrid ratings obtained in all three experiments closely agree. We conclude that the EmojiGrid appears to be a valid and intuitive affective self-report tool that does not rely on written instructions and that can efficiently be used to measure food-related emotions.
Highlights
BackgroundBesides the sensory characteristics of food, food-evoked emotion is a crucial factor in predicting consumer’s food preference and in developing new products (Dalenberg et al, 2014; Gutjar et al, 2015)
This validation study shows that the individual emoji reliably convey the intended degrees of valence and arousal, and that their arrangement along the boundaries of the EmojiGrid appears intuitive
For each image and for both self-assessment tools (EmojiGrid and visual analog scales (VAS)) we computed the mean response across all participants
Summary
Besides the sensory characteristics of food, food-evoked emotion is a crucial factor in predicting consumer’s food preference and in developing new products (Dalenberg et al, 2014; Gutjar et al, 2015). Verbal tools enable users to report their current affective state by selecting or rating words that best express their feelings They are the most commonly used techniques to measure emotional responses to food, due to their ease of application, cost-effectiveness, and discriminative power (Churchill and Behan, 2010; Dorado et al, 2016). Since graphical self-report tools do not rely on verbal descriptions of emotions, they may be useful for cross-cultural studies since they eliminate the need for translation and the problems associated therewith (e.g., Curia et al, 2001; van Zyl and Meiselman, 2015) They may be more effective to measure and express mixed (complex) emotions that are hard to verbalize (Elder, 2018). We first give a brief overview of existing affect self-report measurement tools, focusing in particular on pictorial scales, and we discuss their limitations as tools to measure food-related emotional experiences
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