Scientists have discovered that the human brain inherently uses Bayesian inference, a statistical method that combines previous knowledge with new evidence, to interpret visual stimuli. This research shows that understanding these mechanisms could advance fields such as artificial intelligence and clinical neuroscience.
Scientists now have a mathematical model that closely reflects the way the human brain interprets visual data.
The researchers emphasize that the human brain is naturally equipped to perform advanced calculations, similar to high-powered computers, to understand the world through a process known as Bayesian inference.
In a recent study published in Nature Communicationsresearchers from the University of Sydney, the University of Queensland and the University of Cambridge have developed a comprehensive mathematical model that includes all the components required to perform Bayesian inference.
Dr. Robin Rideau. Credit: Robin Rideau
Bayesian inference is a statistical method that combines previous knowledge with new evidence to make educated guesses. For example, if you know what a dog looks like and see a furry, four-legged animal, you can use your previous knowledge to guess that it is a dog.
This built-in capability allows humans to interpret the environment with incredible accuracy and speed, unlike machines that can be tricked with simple CAPTCHA security measures when asked to identify fire hydrants in an image panel.
“While the Bayesian approach has conceptual and explanatory appeal, how the brain calculates probability remains largely mysterious,” said lead researcher Dr Robin Riddo, from the University of Sydney’s School of Psychology.
“Our new study sheds light on this conundrum. We have discovered that the infrastructure and connections in our brain’s visual system are organized in a way that allows it to perform Bayesian inference on the sensory data it receives.
“What makes this discovery important is the confirmation that our brains have an innate design that allows for this sophisticated form of processing, allowing us to interpret our surroundings more effectively.”
The results of this study not only confirm existing theories about the brain’s use of Bayesian inference, but open the door to new research and innovation, where the brain’s natural ability for Bayesian inference can be harnessed for practical applications that benefit society.
“Our research, although focused primarily on visual perception, has broader implications across the spectrum of neuroscience and psychology,” said Dr. Rideau.
“By understanding the basic mechanisms the brain uses to process and interpret sensory data, we can pave the way for advances in fields ranging from artificial intelligence, where mimicking brain function could revolutionize… machine learning to clinical neuroscience, and will likely provide new strategies for future therapeutic interventions.
The research team, led by Dr. William Harrison, made this discovery by recording the brain activity of volunteers while they passively watched a show, designed to elicit specific neural signals related to visual processing. They then created a mathematical model to compare a series of competing hypotheses about how the human brain perceives vision.
Reference: “Neural tuning creates prior expectations in the human visual system” by William J. Harrison, Paul M. Bays, and Reuben Rideaux, September 1, 2023, Nature Communications.
doi: 10.1038/s41467-023-41027-s
2023-09-16 11:49:37
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