Summary: In both social circles and neural pathways, sticking with what is familiar feels safe, but it may be creating a \u201cdeath spiral\u201d for new information. A new study uses a new theoretical framework to show how Hebbian learning, the principle that \u201cneurons that fire together, wire together,\u201d actually prevents activity from spreading.<\/p>\n
While positive reinforcement strengthens existing bonds, it also traps ideas in tight loops. Conversely, \u201cnegative\u201d reinforcement or weakening connections is what allows information to break free and explore new areas of a network.<\/p>\n
Key Facts<\/p>\n
The Hebbian Loop: The study integrated the rule that repeated interactions strengthen links. Surprisingly, they found that the stronger a connection becomes, the more it acts as a barrier to outside information, keeping activity trapped in a \u201cfeedback loop.\u201dThe Ant Mill Effect: Lead researcher Istv\u00e1n Kov\u00e1cs compares positive feedback to \u201cant mills,\u201d where ants follow pheromone trails in a circle until they die of exhaustion. In social or neural networks, positive reinforcement can create similar \u201cdeath spirals\u201d where ideas just circle back to the same people or neurons.Efficiency Through Weakness: For an idea, infection, or signal to spread efficiently, the system must avoid old paths. Weakening existing connections (negative reinforcement) forces the activity to find and \u201cexplore\u201d new nodes.Universal Dynamics: The model applies to any system where activity propagates, including social media echo chambers, the spread of viral infections, and the way signals travel through the human brain.<\/p>\n
Source: Northwestern University<\/p>\n
Sticking with the same people might feel safe and comfortable. <\/p>\n
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 \u2014 and toward new ones \u2014 activity can spread more widely.<\/p>\n
To explore how activities spread across networks, physicists developed a new theoretical framework that includes simple \u201clearning\u201d 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.<\/p>\n
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.<\/p>\n
The\u00a0study appeared online today\u00a0(April 27) in\u00a0Communications Physics, a Nature Portfolio journal.<\/p>\n
\u201cLearning and adaptation are intrinsic to biological and social systems, but understanding the effects of learning remains mostly unexplored in even simple models,\u201d said Northwestern\u2019s\u00a0Istv\u00e1n Kov\u00e1cs, who led the study. \u201cWe 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.\u201d<\/p>\n
An expert in complex systems, Kov\u00e1cs is an assistant professor of physics and astronomy at Northwestern\u2019s\u00a0Weinberg College of Arts and Sciences\u00a0and a member of the\u00a0Northwestern Institute on Complex Systems\u00a0and of the\u00a0NSF-Simons National Institute for Theory and Mathematics in Biology. Will Engedal, a recent graduate from Kov\u00e1cs\u2019 research group, is co-first author of the paper.<\/p>\n
\u2018Fire together, wire together\u2019<\/p>\n
In the new study, Kov\u00e1cs 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.<\/p>\n
\u201cHebbian learning is often summarized as \u2018neurons that fire together wire together,\u2019\u201d Kov\u00e1cs said. \u201cIt 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.\u201d<\/p>\n
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.<\/p>\n
Using the new model, Kov\u00e1cs 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.<\/p>\n
Emergent behaviors shifted depending on whether the source, the target or both nodes learned from the interaction, the researchers found.<\/p>\n
Stuck in a \u2018death spiral\u2019<\/p>\n
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.<\/p>\n
\u201cIt\u2019s similar to what happens in the ant mill phenomenon,\u201d Kov\u00e1cs said. \u201cBlind 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 \u2018death spiral\u2019 can happen in our model with positive feedback.\u201d<\/p>\n
Because the model focuses on a fundamental mechanism \u2014 how past interactions shape future ones \u2014 Kov\u00e1cs 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.<\/p>\n
Funding: The study, \u201cActivity propagation with Hebbian learning,\u201d was carried out in collaboration with the HUN-REN Wigner RCP in Hungary and supported by Hungary\u2019s 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.<\/p>\n
Key Questions Answered:Q: If \u201cpositive reinforcement\u201d is bad for spreading ideas, should we stop being agreeable?<\/p>\n
A: Not necessarily in a social sense, but in a network sense, \u201cagreeability\u201d creates echo chambers. If you only talk to people who agree with you, your ideas never leave that circle. To spread an idea, you need the \u201cfriction\u201d of new, less-familiar connections.<\/p>\n
Q: How does this apply to my brain?<\/p>\n
A: It explains how habits and \u201cthought loops\u201d form. When neurons fire together repeatedly, they create a very strong, efficient path. While this is great for memory, it makes it harder for the brain to integrate new, contradictory information because the signal prefers the \u201cwell-worn\u201d path.<\/p>\n
Q: Is this why \u201cviral\u201d content eventually dies out?<\/p>\n
A: Yes. When content gets stuck in a saturated loop of the same people resharing it, it loses its \u201cmomentum\u201d to jump to new clusters. The most successful spreading processes are those that can break out of their initial \u201ccomfortable\u201d networks.<\/p>\n
Editorial Notes:This article was edited by a Neuroscience News editor.Journal paper reviewed in full.Additional context added by our staff.About this neuroscience research news<\/p>\n
Author:\u00a0Amanda Morris<\/a>
Source:\u00a0Northwestern University<\/a>
Contact:\u00a0Amanda Morris \u2013 Northwestern University
Image:\u00a0The image is credited to Neuroscience News<\/p>\n