As the second most popular social media platform, roughly one billion hours of videos are watched on the website YouTube each day. With the growth of the internet, and an ever-expanding preoccupation with cyberspace, researchers at Stanford University used neuroimaging to study behavioral video viewing tasks.
The research team found our brains to be more efficient at forecasting viral potential of online videos than previously known. The study was publicized in the peer-reviewed journal Proceedings of the National Academy of Sciences.
“The growth of the internet has spawned new “attention markets,” in which people devote increasing amounts of time to consuming online content, but the neurobehavioral mechanisms that drive engagement in these markets have yet to be elucidated,” the findings say.
By recruiting and analyzing the data of 36 participants, researchers investigated an approach known as neuroforecasting, incorporating functional MRI (fMRI) scans. The technique was used to determine neural responses to videos, which could then be used to initiate predictions of video view frequency and duration.
“Combining neuroimaging with a behavioral task allowed us to test whether activity in affective brain regions might foreshadow peoples’ allocation of time to watching videos—both in individuals undergoing scanning and out of sample in an internet attention market (youtube.com),” according to the findings.
“In individuals, brain activity in regions previously shown to predict allocation of money also predicted choices to allocate time to watching videos. In an internet attention market, sampled activity in a subset of these regions implicated in anticipatory affect at video onset generalized to forecast the frequency of choices to allocate time as well as the duration of time allocated to videos.”
From a variety of videos given to the participants to watch, their behavior was monitored and questions were administered thereafter to rate its chances of virality.
Based on the results, irregular activity in particular reward-sensitive regions of the brain was correlated with longer frequency of video views. The opposite was true of shorter frequency of video views.
“Notably, brain activity during a previous video choice task did not forecast online engagement, suggesting that brain activity in response to stimuli that directly generate aggregate engagement metrics (i.e., video viewing) may support more robust forecasts,” the research team concluded.
“These findings highlight contextual factors that might help sharpen neural forecasts of time allocation related to regions (implicated in anticipatory affect), timing (in response to video onset), and task (matched across levels of analysis).”
“Overall, this research extends a growing literature on neuroforecasting by demonstrating the possibility of forecasting time allocation online.”
