Scientists now have a mathematical model that closely mirrors the way the human brain interprets visual data.
The researchers emphasized that human brains are naturally equipped to perform advanced calculations, similar to any high-powered computer, to understand the world through a process known as Bayesian inference.
In a recent study published in Nature Communicationsresearchers from University of SydneyThe University of Queensland and the University of Cambridge have developed a comprehensive mathematical model that includes all the components necessary to perform Bayesian inference.
Bayesian inference is a statistical method that combines prior knowledge with new evidence to make an intelligent guess. 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 outsmarted by simple CAPTCHA security measures when asked to identify fire hydrants in a panel of images.
“Despite the conceptual appeal and explanatory power of the Bayesian approach, how the brain calculates probabilities is largely mysterious,” said study lead researcher Dr Robin Riddo, from the University of Sydney’s School of Psychology.
“Our new study sheds light on this puzzle. We have discovered that the infrastructure and connections within our brain’s visual system are set up 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 inherent design that allows for this advanced form of processing, enabling us to interpret our surroundings more effectively.”
The study results not only confirm existing theories about the brain’s use of Bayesian inference, but open the doors to new research and innovations, where the brain’s natural ability for Bayesian inference 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 mimicking brain functions could revolutionize… Machine learningto 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 brain activity in volunteers while they passively watched shows, which were designed to elicit specific neural signals related to visual processing. They then created mathematical models to compare a range 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.
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