Scientists have discovered that the human brain inherently uses Bayesian inference, a statistical method that combines prior knowledge with new evidence, to interpret visual stimuli. This research suggests that understanding this mechanism could advance fields such as artificial intelligence and clinical neuroscience.
Scientists now have a mathematical model that accurately reflects how the human brain interprets visual data.
Researchers have confirmed that the human mind is naturally programmed to perform advanced calculations, similar to those performed by a high-powered computer, to understand the world through a process known as Bayesian inference.
In a recent study published in Nature Communications, researchers from the University of Sydney, the University of Queensland and the University of Cambridge developed a comprehensive mathematical model that includes all the components needed to perform Bayesian inference.
Dr. Robin Rideau. Credit: Robin Rideau
Bayesian inference is a statistical method that combines prior knowledge with new evidence to make intelligent guesses. For example, if you know what a dog looks like and you see a furry animal with four legs, you might use your prior knowledge to guess that it is a dog.
This inherent ability allows people to interpret the environment with extraordinary accuracy and speed, unlike machines that can be overwhelmed by simple CAPTCHA security measures when asked to identify fire hydrants in a board.
“Despite the conceptual appeal and explanatory power of the Bayesian approach, the way the brain calculates probabilities is largely ambiguous,” said study lead researcher Dr Robin Riddo, from the University of Sydney’s School of Psychology. »
“Our new study sheds light on this mystery. We have discovered that the infrastructure and connections within our brain’s visual system are configured in a way that allows it to make Bayesian inferences about the sensory data it receives.
“What makes this discovery important is the confirmation that our brains have an inherent design that enables this advanced form of processing, allowing us to interpret our environment more effectively.”
The study results not only confirm existing theories about the brain’s use of Bayesian reasoning, but also open the door to new research and innovations, where the brain’s natural ability for Bayesian reasoning can be harnessed for practical applications that benefit society.
“Our research, although primarily focused on visual perception, has broader implications across the spectrum of neuroscience and psychology,” Dr. Rideau said.
“By understanding the basic mechanisms the brain uses to process and interpret sensory data, we can pave the way for advances in areas ranging from artificial intelligence, where simulating brain functions could revolutionize learning to clinical neuroscience, and potentially offer new strategies for future therapeutic interventions.”
The research team, led by Dr. William Harrison, made this discovery by recording volunteers’ brain activity while they passively looked at screens, designed to elicit specific neural signals related to visual processing. They then designed mathematical models to compare a range of competing hypotheses about how the human brain perceives vision.
Reference: “Neural Tuning Creates Preconceived Expectations in the Human Visual System” by William J. Harrison and Paul M. Baez, Ruben Rideau, September 1, 2023, Natural communications.
doi: 10.1038/s41467-023-41027-s
