Sometimes what we see doesn’t make sense right away. A shadow may briefly resemble a face, or one object may momentarily be mistaken for another. New research suggests that the brain can resolve these visual conflicts by allowing different regions to compare their interpretations until a more consistent image emerges.
In a study published in Nature NeuroscienceCold Spring Harbor Laboratory member Mitra Javadzadeh, Cynthia R. Stebbins, and collaborators from the University of Cambridge and University College London examined how two neighboring regions of the visual cortex interact. They found that when activity in the two regions matched, the shared pattern persisted. However, when the regions produced conflicting patterns, the disagreement vanished in a split second.
The findings offer a possible explanation for how the brain combines information from specialized regions into a single, coherent perception.
How specialized brain regions work together
Different parts of the brain are responsible for processing different streams of sensory information, but our experience of the world often seems unified rather than fragmented. Understanding how these specialized systems coordinate with each other is a major challenge in neuroscience.
“We are trying to understand how it is possible to have such a high level of specialization between these different blocks and at the same time always get a consistent holistic result,” says Javadzadeh.
The researchers focused on two well-known visual processing regions in the brain’s neocortex: the primary visual cortex (V1) and the lateromedial visual area (LM). Visual processing does not simply move in one direction from one region to the next. Instead, these areas continually exchange information with each other.
Testing what happens when visual areas don’t match
To explore that communication, Javadzadeh and his colleagues trained mice to notice the difference between two visual patterns tilted in opposite directions. The animals received a reward for recognizing only one of the orientations.
During the task, the researchers temporarily silenced V1 or LM and observed how the remaining region behaved without its usual partner. The team then used those observations to create an artificial neural network model of the V1-LM circuit. With that model, they were able to test how the system could respond when certain neurons were altered.
The results revealed a surprising pattern. Conflicting activity between the two brain regions disappeared quickly, while activity shared by both areas lasted longer.
“We found that, over time, these types of connections between areas implement a mechanism we call consensus building,” Javadzadeh explains.
A possible brain consensus mechanism
The study examined just two regions involved in vision, but the researchers are now investigating whether the same process may operate more broadly throughout the neocortex.
“For example, when what you see contradicts what you hear, do you still use the same type of mechanisms to reconcile these two?” she wonders.
If dynamic consensus building proves widespread, it could help scientists better understand how the brain combines competing signals into a stable interpretation of the world. It could also shed light on what happens when different regions of the brain fail to reach the same conclusion.
The idea may even have implications beyond neuroscience. Similar principles could help researchers think about how AI systems might handle conflicting information flows and decide which signals to trust.
“While we understand the individual components of the brain, what is the glue that holds them together?” Javadzadeh asks. “Knowing that can ultimately help us understand how the brain works as a whole.”