The human brain may start making decisions before sensory information even reaches the frontal cortex, according to new research from the University of Illinois Urbana Champaign. Scientists found that primary sensory regions are influenced by higher brain areas through rapid feedback loops, challenging the long held view that decisions emerge only after a step by step flow of information through a strict hierarchy.
Early brain regions show decision making activity
Electrical and computer engineering professor Yurii Vlasov led the study, published in Proceedings of the National Academy of Science. The research points to an unexpected role for early sensory brain regions in decision making. For decades, many artificial intelligence systems, including convolutional neural networks, were built on the idea that the brain processes information in a one way sequence. Sensory information was thought to travel upward through increasingly complex brain regions until it reached the frontal cortex, where decisions were made. Vlasov and other researchers have questioned whether that picture is complete. Instead, they are exploring a model based on natural intelligence, refined through evolution over hundreds of millions of years. In this framework, decision making depends on interconnected feedback loops that allow information to move in both directions between brain regions.
A model for more efficient artificial intelligence
Biological intelligence performs remarkably complex tasks while using far less energy than today's AI systems. Understanding this architecture could help guide the development of future artificial intelligence. Vlasov said researchers want to learn from a billion years of evolution, asking how biological intelligence is organized architecturally and whether engineers can emulate that to make AI more effective, less power hungry, and more intelligent. He noted that current AI is lacking in the level of decision making. The National Academy of Engineering identified reverse engineering the brain in 2008 as one of the 14 grand challenges for engineering in the 21st century. The new findings suggest that building AI systems that think more like biological brains could lead to major gains in capability while using far less power.
What this means for the future of AI design
The study offers fresh ideas for designing future AI systems that are more capable and far more energy efficient. By showing that the brain begins making decisions earlier than expected, the research challenges the traditional model that has guided AI development for decades. Engineers may now look to incorporate rapid feedback loops and bidirectional information flow into artificial systems, rather than relying on a one way sequence of processing. The work was conducted at The Grainger College of Engineering and published in Proceedings of the National Academy of Science.
