The roles of culture and language in designing emotion lists: Comparing the same language in different English and Spanish speaking countries

Zyl, Hannelize van; Meiselman, Herbert L.

doi:10.1016/j.foodqual.2014.12.003

Cited by 74 publications

(33 citation statements)

References 14 publications

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“…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 ). However, they have several shortcomings: (1) affect and emotions (especially mixed or complex ones) are difficult to verbalize and the labels used to describe them are inherently ambiguous ( Scherer, 2005 ; Köster and Mojet, 2015 ) and (2) the “affective” or “emotional” lexicon varies across cultures and languages, particularly when it comes to foods (e.g., Curia et al, 2001 ; Gutjar et al, 2015 ; van Zyl and Meiselman, 2015 ). Also, verbal tools are demanding for the user since they require cognitive effort (interpretation) and a significant amount of time to fill them out.…”

Section: Introductionmentioning

confidence: 99%

“…It has therefore been suggested to replace the subjective linguistic increments on rating scales by iconic facial expressions ( Kaye et al, 2017 ). Since graphical self-report tools do not rely on verbal descriptions of emotions, they may also 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 ). Also, they may be more effective to measure and express mixed (complex) emotions that are hard to verbalize ( Elder, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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EmojiGrid: A 2D Pictorial Scale for the Assessment of Food Elicited Emotions

et al. 2018

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EmojiGrid: A 2D Pictorial Scale for the Assessment of Food Elicited Emotions

et al. 2018

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“…Shortly after, van Zyl and Meiselman (2015) presented a procedure to ensure that emotional lists were fully composed by emotions. The present research was developed to improve and test the lexicon developed by Chaya et al (2015) following the approach proposed by van Zyl and Meiselman (2015). The proposed procedure allowed an easy filtering of terms for the study of emotional response.…”

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confidence: 99%

Improvement of an emotional lexicon for the evaluation of beers

Mora

Giussani

Pagliarini

et al. 2019

Food Quality and Preference

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Emotional response has been the subject of many studies during the last years. Many studies have shown the importance of using consumers to generate emotional lexicons. Chaya et al. (2015) developed a consumer defined (CD) lexicon to assess emotional response elicited by beer products. Shortly after, van Zyl and Meiselman (2015) presented a procedure to ensure that emotional lists were fully composed by emotions. The present research was developed to improve and test the lexicon developed by Chaya et al. (2015) following the approach proposed by van Zyl and Meiselman (2015). The proposed procedure allowed an easy filtering of terms for the study of emotional response. As a consequence, the test was shorter, clearer, and easier to understand and to complete by consumers. The improved emotional lexicon of beer favoured 1) the efficiency of the research in terms of discrimination among samples, 2) the simplicity of use by the consumers.

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“…Since it does not seem to be clear what verbal emotion-assessment tools actually measure, the focus should be more on measures of core affect, such as valence and arousal [12]. However, questionnaires have several shortcomings: (1) emotions are sometimes hard to express in words, and the words describing the emotions are typically ambiguous [13], (2) both the number and connotation of emotional words vary between languages and cultures and [14][15][16], and (3) individuals vary widely in their vocabulary and general language skills [12]. Consequently, the description of emotions may be differently interpreted by people from different cultures and languages [17], and differences in emotion intensity, context, and other semantics among cultures may be lost in translation [18].…”

Section: Introductionmentioning

confidence: 99%

“…Emoji-based ratings scales have, for instance, been used to evaluate online training simulations [35], and the user experience of electronic questionnaires [36]. Since emoji-based self-report tools do not need verbal labels, they do not require translation [15,16]. While verbal labels trigger analytical and rational responses, emoji afford a more intuitive and affective response.…”

Section: Introductionmentioning

confidence: 99%

The EmojiGrid as a Tool to Assess Experienced and Perceived Emotions

Toet

Erp

2019

Psych

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In a recent study on food-evoked emotions, we observed that people often misunderstood the currently available affective self-report tools. We, therefore, developed a new intuitive and language-independent self-report instrument called the EmojiGrid: a rectangular response grid labeled with facial icons (emoji) that express different degrees of valence and arousal. We found that participants intuitively and reliably reported their affective appraisal of food by clicking on the EmojiGrid, even without verbal instructions. In this study, we investigated whether the EmojiGrid can also serve as a tool to assess one’s own (experienced) emotions and perceived emotions of others. In the first experiment, participants (N = 90) used the EmojiGrid to report their own emotions, evoked by affective images from a database with corresponding normative ratings (obtained with a 9-point self-assessment mannikin scale). In the second experiment, participants (N = 61) used the EmojiGrid to report the perceived emotional state of persons shown in different affective situations, in pictures from a database with corresponding normative ratings (obtained with a 7-point Likert scale). For both experiments, the affective (valence and arousal) ratings obtained with the EmojiGrid show excellent agreement with the data provided in the literature (intraclass correlations of at least 0.90). Also, the relation between valence and arousal shows the classic U-shape at the group level. Thus, the EmojiGrid appears to be a useful graphical self-report instrument for the assessment of evoked and perceived emotions.

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The roles of culture and language in designing emotion lists: Comparing the same language in different English and Spanish speaking countries

Cited by 74 publications

References 14 publications

EmojiGrid: A 2D Pictorial Scale for the Assessment of Food Elicited Emotions

EmojiGrid: A 2D Pictorial Scale for the Assessment of Food Elicited Emotions

Improvement of an emotional lexicon for the evaluation of beers

The EmojiGrid as a Tool to Assess Experienced and Perceived Emotions

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