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Audience preferences are predicted by temporal reliability of neural processing

Author

Listed:
  • Jacek P. Dmochowski

    (City College of New York
    Present address: Department of Psychology, Stanford University, 450 Serra Mall, Stanford, California 94305, USA)

  • Matthew A. Bezdek

    (School of Psychology, Georgia Institute of Technology)

  • Brian P. Abelson

    (Harmony Institute)

  • John S. Johnson

    (Harmony Institute)

  • Eric H. Schumacher

    (School of Psychology, Georgia Institute of Technology)

  • Lucas C. Parra

    (City College of New York)

Abstract

Naturalistic stimuli evoke highly reliable brain activity across viewers. Here we record neural activity from a group of naive individuals while viewing popular, previously-broadcast television content for which the broad audience response is characterized by social media activity and audience ratings. We find that the level of inter-subject correlation in the evoked encephalographic responses predicts the expressions of interest and preference among thousands. Surprisingly, ratings of the larger audience are predicted with greater accuracy than those of the individuals from whom the neural data is obtained. An additional functional magnetic resonance imaging study employing a separate sample of subjects shows that the level of neural reliability evoked by these stimuli covaries with the amount of blood-oxygenation-level-dependent (BOLD) activation in higher-order visual and auditory regions. Our findings suggest that stimuli which we judge favourably may be those to which our brains respond in a stereotypical manner shared by our peers.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5567
    DOI: 10.1038/ncomms5567
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    Cited by:

    1. Rumen Pozharliev & Matteo Angelis & Dario Rossi, 2022. "The effect of augmented reality versus traditional advertising: a comparison between neurophysiological and self-reported measures," Marketing Letters, Springer, vol. 33(1), pages 113-128, March.
    2. Ryota Nomura & Shunichi Maruno, 2019. "Rapid serial blinks: An index of temporally increased cognitive load," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-11, December.
    3. Rumpf, Christopher & Boronczyk, Felix & Breuer, Christoph, 2020. "Predicting consumer gaze hits: A simulation model of visual attention to dynamic marketing stimuli," Journal of Business Research, Elsevier, vol. 111(C), pages 208-217.
    4. Kate Burleson-Lesser & Flaviano Morone & Paul DeGuzman & Lucas C Parra & Hernán A Makse, 2017. "Collective Behaviour in Video Viewing: A Thermodynamic Analysis of Gaze Position," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-19, January.
    5. Shane, Scott & Drover, Will & Clingingsmith, David & Cerf, Moran, 2020. "Founder passion, neural engagement and informal investor interest in startup pitches: An fMRI study," Journal of Business Venturing, Elsevier, vol. 35(4).
    6. Hakim, Adam & Klorfeld, Shira & Sela, Tal & Friedman, Doron & Shabat-Simon, Maytal & Levy, Dino J., 2021. "Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 770-791.

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