New research challenges traditional views of how the brain makes decisions, suggesting that even its earliest regions play a more active and dynamic role than previously thought.
New research from The Grainger College of Engineering at the University of Illinois Urbana-Champaign suggests that how the brain makes decisions could influence the future design of artificial intelligence. Led by electrical and computer engineering professor Yurii Vlasov and published in Proceedings of the National Academy of Sciences (PNAS), the study shows that early brain regions play a role in decision-making, challenging long-standing ideas about how the brain is organized.
The human brain is often described as the most complex structure in the universe. Its inner workings remain so difficult to understand that reverse-engineering it was named one of the National Academy of Engineering’s 14 grand challenges in 2008. For years, scientists have based artificial intelligence systems such as convolutional neural networks on the assumption that decisions arise through a step-by-step flow of information, starting in early sensory regions and ending in the frontal cortex. However, researchers like Vlasov are now reexamining that assumption.
Beyond Hierarchical Models of Intelligence
Another way to understand the brain focuses on natural intelligence, which has been shaped by evolution rather than designed by humans. In this framework, decision-making does not happen in a simple sequence. Instead, it involves interconnected feedback loops that send signals in both directions across different brain regions.
Natural intelligence also stands out for its efficiency. It can perform complex computations while using far less energy than current AI systems. To better understand how this works, Vlasov and his team studied the brain from a systems-level perspective, looking at how different parts interact rather than examining them in isolation.
“We want to learn from a billion years of evolution,” Vlasov said. “How is that biological intelligence organized architecturally? Can we learn from the architectural side of the brain and emulate that to make AI more effective, less power-hungry, and more intelligent than it currently is? In the level of decision-making, that’s where current AI is lacking.”
Early Brain Regions and Decision Signals
To tackle the complexity of the brain, the researchers focused on its earliest processing stages, which handle sensation and perception. They recorded neural activity in mice as the animals moved through a virtual reality corridor and made decisions based on what they perceived.
The results were unexpected. Signals linked to decision-making appeared in the primary somatosensory cortex (S1), an area traditionally associated with basic sensory processing. This suggests that decision-related activity begins earlier in the brain than previously thought.
Further analysis showed that S1 is influenced by higher-level brain regions through feedback loops. This top-down modulation indicates that decision-making is not driven only by one-way, feed-forward signaling, but instead involves ongoing interactions across multiple levels of the brain.
“The neural code of the brain is still mostly an unknown language,” Vlasov said. “But this systems-level understanding can be viewed as a potential impact on how more efficient artificial neural networks can be built — how the next generation of AI can be thought through. Maybe with these analogies that we learn from real brains, we can improve AI further.”
Implications for Future AI Architectures
The findings do not provide a direct blueprint for building better artificial intelligence, but they offer a new way to think about it. By studying how the brain organizes and processes information, researchers may identify principles that can improve AI systems.
Vlasov and his team plan to continue exploring how brain activity changes over time, with a focus on fast temporal dynamics. They are also developing new tools to measure and analyze neural signals more precisely.
“By looking at the fast temporal dynamics of neural activity, maybe we can understand better how these feedback loops are engaged in making decisions,” Vlasov said. “Maybe that’s the approach that potentially uncovers these currently unknown mechanisms — how these feedback loops are organized dynamically and how they form and shape different levels of processing. Maybe that can be implemented in new architectures for AI.”
Reference: “Neural correlates of perceptual decision-making in the primary somatosensory cortex” by Alex G. Armstrong and Yurii Vlasov, 29 April 2026, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2514107123
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