Predicting consumer liking and preference based on emotional responses and sensory perception: A study with basic taste solutions

Samant, Shilpa S.; Chapko, Matthew J.; Seo, Han‐Seok

doi:10.1016/j.foodres.2017.07.021

Cited by 65 publications

(57 citation statements)

References 28 publications

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“…For humans, this negative experience, coupled with the negative emotional response elicited when tasting the bitter flavor, reinforces that they should avoid certain foods; the expression of disgust in response to the bitter taste also communicates this experience to others. The reverse can be seen for reinforcement behaviors associated with sweet foods [27,28].…”

Section: Relationship Between Emotion and Facial Expressionmentioning

confidence: 95%

“…Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart used to conduct the systematic literature review [31]. elements, determining the relationship between taste, autonomic nervous system (ANS) responses, and emotions, analyzing the response of various sweeteners, examining how food composition influences the relationship between consumption and food emotion, or determining how facial reactions respond to basic tastes [26,27,[32][33][34][35][36][37][38][39][40]. While techniques using AFEA to assess product development are still in their infancy, researchers have built a foundation for how best to employ these technologies to achieve a wide range of product development-related research goals.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

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The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

2020

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In the late 1970s, analysis of facial expressions to unveil emotional states began to grow and flourish along with new technologies and software advances. Researchers have always been able to document what consumers do, but understanding how consumers feel at a specific moment in time is an important part of the product development puzzle. Because of this, biometric testing methods have been used in numerous studies, as researchers have worked to develop a more comprehensive understanding of consumers. Despite the many articles on automated facial expression analysis (AFEA), literature is limited in regard to food and beverage studies. There are no standards to guide researchers in setting up materials, processing data, or conducting a study, and there are few, if any, compilations of the studies that have been performed to determine whether any methodologies work better than others or what trends have been found. Through a systematic Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) review, 38 articles were found that were relevant to the research goals. The authors identified AFEA study methods that have worked and those that have not been as successful and noted any trends of particular importance. Key takeaways include a listing of commercial AFEA software, experimental methods used within the PRISMA analysis, and a comprehensive explanation of the critical methods and practices of the studies analyzed. Key information was analyzed and compared to determine effects on the study outcomes. Through analyzing the various studies, suggestions and guidance for conducting and analyzing data from AFEA experiments are discussed.

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Section: Relationship Between Emotion and Facial Expressionmentioning

confidence: 95%

Section: Systematic Literature Reviewmentioning

confidence: 99%

The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

2020

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“…More specifically, participants were asked to check all appropriate terms, listed on either emotion check-allthat-apply (CATA) question or sensory CATA question. This method was found to be suitable for characterizing product temperature-dependent sensory-attribute variations in foods and beverages (Chapko and Seo, 2017;Pramudya and Seo, 2018). The emotion CATA question included 39 emotion terms from the EsSense Profile R (King and Meiselman, 2010).…”

Section: Cata Questions Of Emotion and Sensory Tests For Coffee Beveragementioning

confidence: 99%

“…First, food-evoked emotions play an important role in food acceptance and choice (King and Meiselman, 2010;Dalenberg et al, 2014;Gutjar et al, 2015). Furthermore, measuring both evoked emotions and sensory perception has been found to yield better understanding of consumer acceptance and preference toward foods or beverages (Samant et al, 2017). Second, thermal sensation (physical warmth or coldness) has been found to evoke emotional responses in humans (Kanosue et al, 2002;Sung et al, 2007;Williams and Bargh, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Finally, both sensory attributes and emotional responses have been found to play an important role in consumer acceptance of food or beverage products (Seo et al, 2009c;King and Meiselman, 2010;Dalenberg et al, 2014;Gutjar et al, 2015;Samant et al, 2017). It was therefore to be determined whether the impacts of sensory attributes and emotional responses on liking of coffee or green tea beverages would vary as a function of sample temperature (Research proposition 4a) and gender (Research proposition 4b).…”

Section: Introductionmentioning

confidence: 99%

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Multisensory Human-Food Interaction

Velasco

Nijholt²,

Karunanayaka³

2018

Frontiers Research Topics

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Personality traits affect the influences of intensity perception and emotional responses on hedonic rating and preference rank toward basic taste solutions

Samant

Seo

2018

J of Neuroscience Research

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This study aimed at determining, based on independent predictors of taste intensity and emotional response, whether individual personality traits could affect prediction models of overall liking and preference rank toward basic taste solutions. Sixty-seven participants rated taste intensities (TI) of four basic-taste solutions at both low and high concentrations, and of plain water. Emotional responses toward each sample were measured using a self-reported emotion questionnaire (SE), facial expressions (FE), and/or autonomic nervous system responses (ANS). Participants rated overall liking of the samples and ranked their preferences. Based on the results of a hierarchical cluster analysis of five personality traits measured using the Big Five Inventory, participants were classified into two clusters: cluster N (high neuroticism) and cluster E (high extraversion). Results showed that the SE measure for both clusters N and E was better than the TI, FE, and ANS measures in explaining variances of overall liking or preference rank. A measurement of effect size found that using FE and/or taste intensity measures, along with self-reported emotion measure, could enhance model predictability of overall liking or preference rank toward taste samples for cluster N, while the contribution to the prediction model for cluster E was minimal. ANS measures showed little contribution to the prediction model of overall liking for either cluster. In conclusion, this study shows that personality traits, in particular traits of extraversion and neuroticism, affect not only optimum measures of emotional responses, but also modulate predicting overall liking and preference rank toward basic taste solutions.

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Predicting consumer liking and preference based on emotional responses and sensory perception: A study with basic taste solutions

Cited by 65 publications

References 28 publications

The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages

Multisensory Human-Food Interaction

Personality traits affect the influences of intensity perception and emotional responses on hedonic rating and preference rank toward basic taste solutions

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