Evoked emotions predict food choice.

Area of Science:

• Consumer Behavior
• Sensory Science
• Food Psychology

Background:

• Liking scores correlate with food choice but are weak predictors in real-world settings.
• Emotion-profiling methods are increasingly used to differentiate equally liked products.
• The predictive accuracy of emotion scores for actual food choice and consumption remains unclear.

Purpose of the Study:

• To evaluate the predictive power of non-verbal food-evoked emotion scores for food choice.
• To compare the predictive accuracy of liking scores versus emotion scores.
• To determine the combined predictive strength of liking and emotion scores.

Main Methods:

• Principal Component Analysis (PCA) to decompose emotion scores into valence and arousal.
• Multinomial Logit Models (MLM) to estimate food choice using liking, valence, and arousal.
• Utilized data from 123 participants rating 7 unlabeled breakfast drinks, with subsequent real-choice consumption.

Main Results:

• Individualized food choice was predicted for over 50% of participants, increasing to nearly 80% for the top 2 choices.
• Evoked emotions significantly improved food choice prediction compared to liking alone.
• Non-verbal emotion scores (PrEmo) were more accurate predictors than verbal scores (EsSense Profile).

Conclusions:

• Non-verbal food-evoked emotions are strong predictors of food choice.
• A combination of evoked emotions and liking provides the highest predictive accuracy for food choice.
• Emotion-profiling methods, particularly non-verbal ones, offer valuable insights beyond simple liking measures.