Investigating the neural mechanisms underlying how social networks influence our decisions

Social scientists have been trying to figure out how social networks can affect people’s beliefs and actions for about a decade. Even though there have been a lot of studies done on this, very little is known about how the human brain makes decisions when people share their views in networked environments.

Understanding what happens in the brain when people make decisions based on their interactions with others in their social network could also help to better comprehend common social group phenomena. It might be helpful, for instance, to uncover the neural mechanisms that underlie misinformation, the dissemination of information across social groups, and polarized viewpoints.

A recent study by Peking University researchers sought to better comprehend how people learn new things by observing the actions of others. A neurocomputational mechanism that the brain uses to filter information sources has been identified, and their findings, which were published in Nature Neuroscience, may provide an explanation for phenomena like biased learning and misinformation.

“We anticipated that the human brain processes information differently when it is sent via social networks than when it is not,”

 Lusha Zhu, one of the researchers who carried out the study, 

Lusha Zhu, one of the study’s authors, told Medical Xpress, “We suspected that the human brain processes information differently when the information is transmitted through social networks compared to when it is not.”

“This is due to the fact that messages that travel along social connections frequently become entangled, contradictory, or unnecessary, making it extremely challenging for the brain to reconcile various sources of information derived from networks. In our most recent study, the following inquiries were posed: How does the brain combine information from peers who are connected? Why do individuals behave differently when embedded in the same network? And how does a person’s performance in a networked environment differ depending on their structural location on the network?”

Zhu and her colleagues asked groups of human participants to participate in a game that required them to observe and learn from the actions of others. The participants were basically assigned at random to various “locations” in a temporary artificial society made up of a network of people who were connected to each other. They could only talk to people who were their direct “neighbors” in this network configuration.
Using functional magnetic resonance imaging (fMRI) technology, the participants’ brain activity was recorded while they completed this group activity. A well-known imaging method, functional magnetic resonance imaging (FMRI), enables researchers to create 3D and detailed images of brain activity during specific tasks.

Zhu explained, “The subject’s objective was to make a series of decisions by observing their neighbors’ decisions, similar to choosing a movie to watch on the weekend by looking at what their friends have watched.” While subjects were observing each neighbor’s decision, we recorded brain activity.”

Zhu and her colleagues used sophisticated computational models to analyze their data after they had recorded it. These models permitted them to reveal associations between the cerebrum movement they noticed and the choices made by their review members, thereby acquiring understanding of the mind processes engaged in dynamic interactions affected by interpersonal organizations.

“We find that the cerebrum settles on choices in manners that are both like and not the same as those in the non-arranged climate,” Zhu said. “Our brain, like that of a non-networked environment, has a belief about, say, the quality of competing movies and updates that belief based on how surprising the choices of neighbors are. The more seriously astonishing the neighbors’ decisions are, the more probable it is for the cerebrum to refresh its conviction.”

In the prefrontal part of the participants’ brains, Zhu and her colleagues observed a very common learning signal that reflected the process by which they updated their decisions after observing those of their neighbors. They also found patterns of brain activity that were distinct from those that are observed when humans make decisions in non-networked settings, which is noteworthy.

All the more explicitly, the analysts found that while pursuing choices in arranged conditions, the degree to which members refreshed their convictions changed as per the organizational areas of their neighbors. In contrast to belief updating in non-network environments, this extent varied depending on how connected participants were to neighbors in their network.

According to Zhu’s explanation, “essentially, our brain tends to assign higher weight to the observations by neighbors with better connections and down-weight or even ignore the observations by neighbors with less connections, even when these neighbors have valuable information.” “At the time the brain is processing a decision observed from the neighbor, we found that activity in the dorsal anterior cingulate cortex, which is typically implicated in adjusting our behavior to adapt to the external environment, tracks how well connected the neighbor is.”

The new work by this group of specialists features mind systems that could underlie one-sided learning in interpersonal organizations, which can thus fuel the proliferation of deception. It might spur additional research into the neural mechanisms they discovered in the future.

“In our next work, we aim to understand how social animals gradually learn the structure of social networks and how the brain stores and retrieves such information for flexible decision-making based on specific goals and contexts,” Zhu added.

More information: Yaomin Jiang et al, Neurocomputational mechanism of real-time distributed learning on social networks, Nature Neuroscience (2023). DOI: 10.1038/s41593-023-01258-y.

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