Summary: The use of social media, specifically to drive “likes” to our content, follows a pattern of “reward learning”, much like when an animal is trained to seek food rewards.
Our use of social media, specifically our efforts to maximize “likes,” follows a pattern of “reward learning,” concludes a new study by an international team of scientists. Its findings, which appear in the journal Nature Communications, reveal parallels with the behavior of animals, such as rats, in seeking food rewards.
“These results establish that social media engagement follows basic, cross-species principles of reward learning,” explains David Amodio, a professor at New York University and the University of Amsterdam and one of the paper’s authors. “These findings may help us understand why social media comes to dominate daily life for many people and provide clues, borrowed from research on reward learning and addiction, to how troubling online engagement may be addressed.”
In 2020, more than four billion people spent several hours per day, on average, on platforms such as Instagram, Facebook, Twitter, and other more specialized forums. This widespread social media engagement has been likened by many to an addiction, in which people are driven to pursue positive online social feedback, such as “likes,” over direct social interaction and even basic needs like eating and drinking.
While social media usage has been studied extensively, what actually drives people to engage, sometimes obsessively, with others on social media is less clear.
To examine these motivations, the Nature Communications study, which also included scientists from Boston University, the University of Zurich, and Sweden’s Karolinska Institute, directly tested, for the first time, whether social media use can be explained by the way our minds process and learn from rewards.
To do so, the authors analyzed more than one million social media posts from over 4,000 users on Instagram and other sites. They found that people space their posts in a way that maximizes how many “likes” they receive on average: they post more frequently in response to a high rate of likes and less frequently when they receive fewer likes.
The researchers then used computational models to reveal that this pattern conforms closely to known mechanisms of reward learning, a long-established psychological concept that posits behavior may be driven and reinforced by rewards.
More specifically, their analysis suggested that social media engagement is driven by similar principles that lead non-human animals, such as rats, to maximize their food rewards in a Skinner Box—a commonly used experimental tool in which animal subjects, placed in a compartment, access food by taking certain actions (e.g., pressing a particular lever).
The researchers then corroborated these results with an online experiment, in which human participants could post funny images with phrases, or “memes,” and receive likes as feedback on an Instagram-like platform. Consistent with the study’s quantitative analysis, the results showed that people posted more often when they received more likes—on average.
“Our findings can help lead to a better understanding of why social media dominates so many people’s daily lives and can also provide leads for ways of tackling excessive online behavior,” says the University of Amsterdam’s Björn Lindström, the paper’s lead author.
The study’s other authors are the University of Amsterdam’s David Schultner, Martin Bellander of Sweden’s Karolinska Institute, Boston University’s Allen Chang, and the University of Zurich’s Philippe Tobler.
Funding: The research was supported, in part, by a grant from the Netherlands Organization for Scientific Research (VICI 016.185.058).
About this psychology research news
Contact: James Devitt – NYU
Image: The image is in the public domain
Original Research: Open access.
“A computational reward learning account of social media engagement” by Björn Lindström, Martin Bellander, David T. Schultner, Allen Chang, Philippe N. Tobler & David M. Amodio. Nature Communications
A computational reward learning account of social media engagement
Social media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant.
Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction.
Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account.
Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.