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Your Brain on the Movies: A Computational Approach for Predicting Box-office Performance from Viewer’s Brain Responses to Movie Trailers

Overview of attention for article published in Frontiers in Neuroinformatics, December 2017
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  • Good Attention Score compared to outputs of the same age (71st percentile)

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Title
Your Brain on the Movies: A Computational Approach for Predicting Box-office Performance from Viewer’s Brain Responses to Movie Trailers
Published in
Frontiers in Neuroinformatics, December 2017
DOI 10.3389/fninf.2017.00072
Pubmed ID
Authors

Christoforos Christoforou, Timothy C. Papadopoulos, Fofi Constantinidou, Maria Theodorou

Abstract

The ability to anticipate the population-wide response of a target audience to a new movie or TV series, before its release, is critical to the film industry. Equally important is the ability to understand the underlying factors that drive or characterize viewer's decision to watch a movie. Traditional approaches (which involve pilot test-screenings, questionnaires, and focus groups) have reached a plateau in their ability to predict the population-wide responses to new movies. In this study, we develop a novel computational approach for extracting neurophysiological electroencephalography (EEG) and eye-gaze based metrics to predict the population-wide behavior of movie goers. We further, explore the connection of the derived metrics to the underlying cognitive processes that might drive moviegoers' decision to watch a movie. Towards that, we recorded neural activity-through the use of EEG-and eye-gaze activity from a group of naive individuals while watching movie trailers of pre-selected movies for which the population-wide preference is captured by the movie's market performance (i.e., box-office ticket sales in the US). Our findings show that the neural based metrics, derived using the proposed methodology, carry predictive information about the broader audience decisions to watch a movie, above and beyond traditional methods. In particular, neural metrics are shown to predict up to 72% of the variance of the films' performance at their premiere and up to 67% of the variance at following weekends; which corresponds to a 23-fold increase in prediction accuracy compared to current neurophysiological or traditional methods. We discuss our findings in the context of existing literature and hypothesize on the possible connection of the derived neurophysiological metrics to cognitive states of focused attention, the encoding of long-term memory, and the synchronization of different components of the brain's rewards network. Beyond the practical implication in predicting and understanding the behavior of moviegoers, the proposed approach can facilitate the use of video stimuli in neuroscience research; such as the study of individual differences in attention-deficit disorders, and the study of desensitization to media violence.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 147 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 15%
Student > Ph. D. Student 19 13%
Student > Bachelor 14 10%
Student > Doctoral Student 13 9%
Student > Master 13 9%
Other 22 15%
Unknown 44 30%
Readers by discipline Count As %
Business, Management and Accounting 18 12%
Computer Science 14 10%
Neuroscience 14 10%
Psychology 13 9%
Arts and Humanities 9 6%
Other 30 20%
Unknown 49 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 June 2021.
All research outputs
#6,805,521
of 24,843,842 outputs
Outputs from Frontiers in Neuroinformatics
#317
of 812 outputs
Outputs of similar age
#127,269
of 451,705 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#11
of 14 outputs
Altmetric has tracked 24,843,842 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 812 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 451,705 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.