Audience preferences are predicted by temporal reliability of neural processing
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DOI: 10.1038/ncomms5567
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Cited by:
- 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.
- 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.
- 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.
- Kate Burleson-Lesser & Flaviano Morone & Paul DeGuzman & Lucas C Parra & HernĂ¡n 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.
- 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).
- 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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