Methods for Evaluating Emotions Evoked by Food Experiences: A Literature Review

• Published in: Frontiers in psychology Vol. 9; p. 911
• Main Authors: Kaneko, Daisuke, Toet, Alexander, Brouwer, Anne-Marie, Kallen, Victor, van Erp, Jan B F
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 08.06.2018
• Subjects: behavioral; cognitive; emotional processing level; food-evoked emotion; physiological; Psychology; toolbox; toolbox; food-evoked emotion; cognitive; behavioral; emotional processing level; physiological
• ISSN: 1664-1078; 1664-1078
• DOI: 10.3389/fpsyg.2018.00911

Besides sensory characteristics of food, food-evoked emotion is a crucial factor in predicting consumer's food preference and therefore in developing new products. Many measures have been developed to assess food-evoked emotions. The aim of this literature review is (i) to give an exhaustive overview of measures used in current research and (ii) to categorize these methods along measurement level (physiological, behavioral, and cognitive) and emotional processing level (unconscious sensory, perceptual/early cognitive, and conscious/decision making) level. This 3 × 3 categorization may help researchers to compile a set of complementary measures ("toolbox") for their studies. We included 101 peer-reviewed articles that evaluate consumer's emotions and were published between 1997 and 2016, providing us with 59 different measures. More than 60% of these measures are based on self-reported, subjective ratings and questionnaires (cognitive measurement level) and assess the conscious/decision-making level of emotional processing. This multitude of measures and their overrepresentation in a single category hinders the comparison of results across studies and building a complete multi-faceted picture of food-evoked emotions. We recommend (1) to use widely applied, validated measures only, (2) to refrain from using (highly correlated) measures from the same category but use measures from different categories instead, preferably covering all three emotional processing levels, and (3) to acquire and share simultaneously collected physiological, behavioral, and cognitive datasets to improve the predictive power of food choice and other models.