Sticking with the same people might feel safe and comfortable. But a new Northwestern University study suggests it can actually trap new ideas and behaviors inside tight echo chambers. By contrast, the research shows that when interactions shift away from familiar contacts - and toward new ones - activity can spread more widely.
To explore how activities spread across networks, physicists developed a new theoretical framework that includes simple "learning" rules. While traditional network models assume relationships do not change, the new model shows what happens when connections change with experience. As interactions strengthen or weaken relationships, they gradually reshape the entire network.
The findings not only apply to ideas moving through social networks but to a wide range of systems where activity spreads, including infections passing among people, signals traveling through the brain and behaviors proliferating through groups of animals. Ultimately, the study suggests that whether something spreads or stalls may hinge on a simple choice: revisit the same connections or explore new ones.
The study appeared online today (April 27) in Communications Physics, a Nature Portfolio journal.
"Learning and adaptation are intrinsic to biological and social systems, but understanding the effects of learning remains mostly unexplored in even simple models," said Northwestern's István Kovács, who led the study. "We wanted to investigate the impact of learning on network dynamics. We found that positive incentives can strengthen existing connections, which, surprisingly, prevents activity from spreading. When connections weaken, however, the system avoids old paths and can lead to more efficient spreading."
An expert in complex systems, Kovács is an assistant professor of physics and astronomy at Northwestern's Weinberg College of Arts and Sciences and a member of the Northwestern Institute on Complex Systems and of the NSF-Simons National Institute for Theory and Mathematics in Biology. Will Engedal, a recent graduate from Kovács' research group, is co-first author of the paper.
'Fire together, wire together'
In the new study, Kovács and his team set out to explore Hebbian learning, a simple principle that describes how connections strengthen through repeated use. First proposed by psychologist Donald Hebb in 1949, the concept helps explain how the brain learns from experience and forms memories.
"Hebbian learning is often summarized as 'neurons that fire together wire together,'" Kovács said. "It means that when two neurons activate at the same time, the connection between them strengthens, making it more likely they will activate together again in the future."
The team incorporated simple Hebbian learning rules into a network model. In traditional models, nodes (representing people, neurons, animals or other objects) connect to each other with links. While activity spreads along those links, the connections do not change. By incorporating learning into the model, connections change based on positive or negative experiences.
Using the new model, Kovács and his team tested two types of learning: positive reinforcement and negative reinforcement. When interacting nodes received positive reinforcement, they were more likely to interact again. Over time, these connections strengthened. When nodes received negative reinforcement, however, they were less likely to interact with each other. These connections weakened over time.
Emergent behaviors shifted depending on whether the source, the target or both nodes learned from the interaction, the researchers found.
Stuck in a 'death spiral'
When positive reinforcement occurred at the source node, activity circled back along the same routes, becoming trapped in tight loops rather than reaching new areas. But when connections weakened, activity spread outward to explore new paths.
"It's similar to what happens in the ant mill phenomenon," Kovács said. "Blind fire ants follow pheromones. But they can accidentally go in a loop. As they follow the loop, the pheromone scent gets stronger, so they continue to follow the same circular trail. The same type of 'death spiral' can happen in our model with positive feedback."
Because the model focuses on a fundamental mechanism - how past interactions shape future ones - Kovács expects the results to hold across many types of spreading processes. Next, his team plans to test whether these learning-driven effects show up in real-world networks and how they interact with more complex, realistic behaviors.
The study, "Activity propagation with Hebbian learning," was carried out in collaboration with the HUN-REN Wigner RCP in Hungary and supported by Hungary's National Research, Development and Innovation Office (award number K146736), the National Science Foundation (award number PHY-2310706), the Hungarian Academy of Sciences and the Baker Program of Undergraduate Research at Northwestern University.
