Scientists now have a mathematical model that closely reflects the way the human brain interprets visual data.
Researchers have determined that the human brain is naturally hardwired to perform sophisticated calculations, similar to high-powered computers, in order to understand the world through a process known as Bayesian inference.
In a recent study published in nature communicationsresearchers University of Sydneythe University of Queensland, and the University of Cambridge have developed a comprehensive mathematical model that encompasses all the components needed to perform Bayesian inference.
Bayesian inference is a statistical method that combines prior knowledge with new evidence to make intelligent inferences. For example, if he knows what a dog looks like and sees a furry, four-legged animal, he might use his prior knowledge to guess that it is a dog.
This unique capability allows humans to interpret the environment with extraordinary accuracy and speed, unlike machines, which can beat a simple CAPTCHA security measure when asked to identify a fire hydrant in an image panel.
“Despite the conceptual appeal and explanatory power of the Bayesian approach, how the brain calculates probabilities remains largely a mystery,” said Dr Reuben Rideau, from the University of Sydney’s School of Psychology, lead researcher on the study. ” he said.
“Our new research sheds light on this mystery. The fundamental structures and connections within our brain’s visual system are set up to allow us to perform Bayesian inference on the sensory data we receive. I found out that there is.
“What makes this discovery important is confirmation that our brains have a unique design that enables this advanced processing and allows us to more effectively interpret our environment.”
The findings not only support existing theories about the brain’s use of Bayesian analogy inference, but also support new research and innovations that can harness the brain’s natural capacity for Bayesian inference for practical applications that benefit society. Open the door.
“Although our research primarily focuses on vision, it has broader implications across the fields of neuroscience and psychology,” Dr. Rideau said.
“Understanding the fundamental mechanisms the brain uses to process and interpret sensory data can pave the way for advances in fields ranging from artificial intelligence to mimicking such brain functions. By doing so, we can start a revolution.” machine learning, which has potential applications in clinical neurology and may provide new strategies for therapeutic intervention in the future. ”
A research team led by Dr. William Harrison made this discovery by recording the brain activity of volunteers as they passively viewed a display designed to elicit specific neural signals related to visual processing. They then devised a mathematical model to compare competing hypotheses about how the human brain perceives vision.
References: “Neuromodulation instantiates prior expectations in the human visual system,” by William J. Harrison, Paul M. Bayes, and Ruben Rideau, September 1, 2023. nature communications.
DOI: 10.1038/s41467-023-41027-w