Influence of Personal Characteristics on the Intensity Rating and Preference of Basic Taste Solutions, Focusing on Emotional Responses and Hedonic Assessment
In a recent study, researchers set out to determine how personality traits, specifically extraversion and neuroticism, impact prediction models of overall liking and preference ranking towards basic taste solutions. The study compared multiple response measures, including self-reported emotions, taste intensity, facial expressions, and autonomic nervous system responses.
The research found that the self-reported emotion questionnaire (SE) was a more effective measure than taste intensities (TI), facial expressions (FE), and autonomic nervous system responses (ANS) in predicting overall liking or preference rank. The contribution to the prediction model for cluster E, high in extraversion, was minimal, while for cluster N, high in neuroticism, incorporating facial expressions and taste intensity measures, along with the self-reported emotion measure, improved the prediction model.
However, ANS measures showed little contribution to the prediction model for either cluster. Sixty-seven participants rated the taste intensities (TI) of four basic-taste solutions at both low and high concentrations, and of plain water. Participants also rated overall liking of the samples and ranked their preferences. Based on the results of a hierarchical cluster analysis, participants were classified into two clusters: cluster N (high neuroticism) and cluster E (high extraversion).
The study revealed that personality traits, particularly neuroticism and extraversion, play a significant role in predicting emotional responses, overall liking, and preference rank towards basic taste solutions. Emotional responses towards taste samples were measured using a self-reported emotion questionnaire, facial expressions, and/or autonomic nervous system responses.
Extraversion, characterized by sociability, higher positive affect, and a tendency to seek stimulation, might result in extraverted individuals reporting stronger or more positive emotional reactions to taste stimuli. Neuroticism, marked by emotional instability and a tendency toward negative affect, anxiety, and sensitivity to stressors, might influence taste perception by heightening sensitivity to unpleasant or intense tastes.
Incorporating personality traits into prediction models could improve their accuracy by accounting for individual differences in emotional reactivity, expressivity, and reporting styles. Self-reported emotion measures capture subjective liking and emotional response but may be biased by personality-driven reporting styles. Taste intensity ratings may be affected by personality-driven sensitivity. Facial expression analysis provides objective behavioral data on emotional reactions, potentially less influenced by reporting bias but still modulated by individual expressivity. Autonomic nervous system responses, such as heart rate variability and skin conductance, offer physiological evidence of emotional and sensory processing and might correlate with neuroticism (higher arousal to stimuli) and extraversion (differential baseline arousal levels), enhancing predictive accuracy about preference and liking beyond self-report.
While this study provides valuable insights, it is important to note that direct empirical studies testing these specific interactions between personality, sensory measures, and taste preferences are currently not detailed in the search results. Future research in this area could further our understanding of how personality traits influence our taste preferences and help refine prediction models for taste preferences.
- To enhance the accuracy of consumer research in the health-and-wellness sector, particularly in predicting preferences towards basic taste solutions, incorporating personality traits, such as neuroticism and extraversion, into prediction models could be beneficial, as these traits significantly impact emotional responses and overall liking.
- In addition to self-reported emotion measures which capture subjective liking and emotional response, other methods like eye tracking (facial expression analysis) could offer objective behavioral data on emotional reactions towards taste samples, potentially reducing bias and improving the predictive accuracy of taste preference models.
