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Neural similarity at temporal lobe and cerebellum predicts out-of-sample preference and recall for video stimuli

Author

Listed:
  • Hang-Yee Chan

    (EM - EMLyon Business School)

  • Ale Smidts
  • Vincent C. Schoots
  • Roeland C. Dietvorst
  • Maarten A. S. Boksem

Abstract

The extent to which brains respond similarly to a specific stimulus, across a small group of individuals, has been previously found to predict out-of-sample aggregate preference for that stimulus. However, the location in the brain where neural similarity predicts out-of-sample preference remains unclear. In this article, we attempt to identify the neural substrates in three functional magnetic resonance imaging (fMRI) studies. Two fMRI studies (N = 40 and 20), using previously broadcasted TV commercials, show that spatiotemporal neural similarity at temporal lobe and cerebellum predict out-of-sample preference and recall. A follow-up fMRI study (N = 28) with previously unseen movie-trailers replicated the predictive effect of neural similarity. Moreover, neural similarity provided unique information on out-of-sample preference above and beyond in-sample preference. Overall, the findings suggest that neural similarity at temporal lobe and cerebellum – traditionally associated with sensory integration and emotional processing – may reflect the level of engagement with video stimuli.

Suggested Citation

  • Hang-Yee Chan & Ale Smidts & Vincent C. Schoots & Roeland C. Dietvorst & Maarten A. S. Boksem, 2019. "Neural similarity at temporal lobe and cerebellum predicts out-of-sample preference and recall for video stimuli," Post-Print hal-03188209, HAL.
  • Handle: RePEc:hal:journl:hal-03188209
    Note: View the original document on HAL open archive server: https://hal.science/hal-03188209
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    Cited by:

    1. Juan Sánchez-Fernández & Luis-Alberto Casado-Aranda & Ana-Belén Bastidas-Manzano, 2021. "Consumer Neuroscience Techniques in Advertising Research: A Bibliometric Citation Analysis," Sustainability, MDPI, vol. 13(3), pages 1-20, February.
    2. 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.
    3. Hang-Yee Chan & Ale Smidts & Vincent C. Schoots & Alan G. Sanfey & Maarten A. S. Boksem, 2020. "Decoding dynamic affective responses to naturalistic videos with shared neural patterns," Post-Print hal-03188208, HAL.

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