Synchronized violin players reveal uniqueness of human networks
There’s rarely time to write about every cool science-y story that comes our way. So this year, we’re once again running a special Twelve Days of Christmas series of posts, highlighting one science story that fell through the cracks in 2020, each day from December 25 through January 5. Today: experiments in synchronization in a network of violin players demonstrated that humans can drown out distractions and miscommunications, the better to stay in sync.
An unusual experiment involving 16 violinists trying to synchronize their playing while wearing noise-canceling headphones yielded some intriguing results, according to an August 2020 paper published in Nature Communications. The study concluded that human networks are fundamentally different from other networks in terms of synchronized behavior because of our decision-making ability. That could lead to better models for complex human behavior, with applications in such diverse areas as economics, epidemiology, politics, traffic management, and the spread of misinformation.
There have been prior studies of synchronization in human behavior, most notably with regard to bridge dynamics. For instance, as we’ve reported previously, people walking on a bridge that starts to shift will instinctively adjust their stride to match the bridge’s swaying motion as it lurches sideways. This will be familiar to anyone who has tried to walk on a fast-moving train and needed to find steady footing as the train wobbled from side to side. But a bridge exacerbates the problem, giving rise to additional small sideways oscillations that amplify the swaying. The result is a positive feedback loop (the technical term is “synchronous lateral excitation”).
Get a large enough crowd matching its stride to the bridge’s motion, and the swaying can become dangerously severe, as happened with the Millennium Bridge when it first opened in June 2000. Approximately 90,000 people crossed the bridge on opening day, with around 2,000 people on it at any given time, and the motion of the crowds gave rise to significant shaking and swaying.
Over time, the pedestrians inadvertently fell into sync with each other and thereby caused the bridge to wobble even more severely. The spontaneous synchrony of the crowd was similar to what happens with the highly synchronized flashing of fireflies or firing of neurons in the brain. Londoners nicknamed it “Wobbly Bridge.” Officials shut it down after just two days, and the bridge remained closed for the next two years until appropriate modifications could be made to stop the swaying.
The phenomenon has also been observed in stock market brokers, according to a 2011 study, which found that the daily instant messaging patterns of traders are closely associated with their level of synchronous trading. Conclusion: “The higher the traders’ synchronous trading is, the less likely they are to lose money at the end of the day,” the authors wrote. Cornell University applied mathematician Steven Strogatz has conducted synchronization experiments with crickets in soundproof boxes.
Moti Fridman—a physicist at Bar-Ilan University in Israel and a co-author on the violin players paper—also has a longstanding interest in synchronization, having published studies on synchronized large laser networks and, on a smaller scale, the unusual coupling that explains why rubbing the rim of one wineglass produces a tone and induces oscillations on other wineglasses. For the violin study, he teamed up with Elad Shniderman, a music graduate student at Stony Brook University in New York, as well as colleagues at Bar-Ilan and the Weizman Institute of Science.
Prior studies have largely involved simple networks where every person (or node) is connected to every other person. In a more complex network, the number of connections between each person can vary, and there may also be delayed messaging between them that can prevent the transition to a synchronized state. As Fridman et al. wrote in their paper, “Research on network links or coupling has focused predominantly on all-to-do coupling, whereas current social networks and human interactions are often based on complex coupling configurations.”
The participating violinists donned noise-canceling headphones and began playing the same musical phrase on repeat, without looking at or listening to the other players. They could only rely on what they heard through the headphones, which were connected to a computer system. The researchers then introduced intermittent delays in signals between coupled violinists, varying the delays and the combinations of violinists. It’s called a “frustrated situation,” and most network models assume that in such a frustrated state, each node will attempt to find a middle ground between all the various inputs.
Instead, Fridman et al. found that the players reacted by adjusting their playing, quickening or slowing their tempo to better synchronize with their fellow violinists. “Human networks behave differently than any other network we’ve ever measured,” Fridman told The Jerusalem Post. “In a state of frustration, they don’t look for a ‘middle,’ but ignore one of the inputs. This is a critical phenomenon that is changing the dynamics of the network. Human networks are able to change their inner structure in order to reach a better solution than what’s possible in existing models.”
It’s similar to a phenomenon known as the “cocktail party effect“: the ability of humans to pick out one conversational thread amid a cacophony of babble in a crowded room. But the effect has not been included in network synchronization studies until now. The next step is to take the experiment online, attempting to synchronize hundreds and thousands of violinists over the Internet.
Building better models of complex human behavior would impact a diverse array of fields, such as better controlling epidemics—something of particular concern these days, given the ongoing coronavirus pandemic—and preventing the spread of false information across social media (“fake news”). “Our results are also related to any network where each node in the network has decision-making ability, such as autonomous cars, or introducing AI into our highly connected world,” Fridman told Inside Science. “Our model can predict with high accuracy the dynamic of such systems, beyond what was possible before.”
Listing image by Chen Damari