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The prediction of market-level food choices by the neural valuation signal

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Listed:
  • Andrew Kislov
  • Anna Shestakova
  • Vadim Ushakov
  • Mario Martinez-Saito
  • Valeria Beliaeva
  • Olga Savelo
  • Aleksey Vasilchuk
  • Vasily Klucharev

Abstract

Neuroimaging studies have demonstrated the ability to use the brain activity of a group of individuals to forecast the behavior of an independent group. In the current study, we attempted to forecast aggregate choices in a popular restaurant chain. During our functional magnetic resonance imaging (fMRI) study, 22 participants were exposed to 78 photos of dishes from a new menu of a popular restaurant chain. In addition to self-reported preferences, fMRI data was extracted from an a priori domain-general and task-specific region of interest—the ventral striatum. We investigated the relationship between the neural activity and real one-year sales provided by the restaurant chain. Activity in the ventral striatum, which was defined using the task-specific region of interest, significantly correlated (r = 0.28, p = 0.01) with one-year sales. A regression analysis, which included ventral striatum activity together with the objective characteristics of the products (price and weight), behavioral, and survey data, showed R2 values of 0.33. Overall, our results confirm prior studies, which have suggested, that brain activity in the reward system of a relatively small number of individuals can forecast the aggregate choice of a larger independent group of people.

Suggested Citation

  • Andrew Kislov & Anna Shestakova & Vadim Ushakov & Mario Martinez-Saito & Valeria Beliaeva & Olga Savelo & Aleksey Vasilchuk & Vasily Klucharev, 2023. "The prediction of market-level food choices by the neural valuation signal," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-18, June.
    • Handle: RePEc:plo:pone00:0286648
    DOI: 10.1371/journal.pone.0286648
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    References listed on IDEAS

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    1. Motoki, Kosuke & Suzuki, Shinsuke & Kawashima, Ryuta & Sugiura, Motoaki, 2020. "A Combination of Self-Reported Data and Social-Related Neural Measures Forecasts Viral Marketing Success on Social Media," Journal of Interactive Marketing, Elsevier, vol. 52(C), pages 99-117.
    2. Jacek P. Dmochowski & Matthew A. Bezdek & Brian P. Abelson & John S. Johnson & Eric H. Schumacher & Lucas C. Parra, 2014. "Audience preferences are predicted by temporal reliability of neural processing," Nature Communications, Nature, vol. 5(1), pages 1-9, December.
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