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 these mechanisms could lead to advances in fields such as artificial intelligence and clinical neurology.
Scientists now have a mathematical model that closely mirrors the way the human brain interprets visual data.
Researchers have confirmed that human brains are naturally wired to perform advanced calculations, much like high-powered computers, to interpret the world through a process known as Bayesian inference.
In a recent study published in Nature communicationResearchers from University of SydneyThe University of Queensland and the University of Cambridge have developed a comprehensive mathematical model that includes all the components required for Bayesian inference.
Dr. Reuben Redox. Credit: Ruben Redox
Bayesian inference is a statistical method that combines prior knowledge with new evidence to make intelligent predictions. For example, if you know what a dog looks like and you see a furry animal with four legs, you can use your prior knowledge to guess that it is a dog.
This inherent ability enables people to interpret environments with extraordinary accuracy and speed, best done by simple captcha security measures when asked to identify fire hydrants in a panel of images.
The senior investigator of the study Dr. Reuben Redoux, of the University of Sydney’s School of Psychology, said: “Despite the conceptual appeal and explanatory power of the Bayesian approach, how the brain computes probabilities remains largely mysterious.”
“Our new study sheds light on this mystery. We found that the basic structures and connections in our brain’s visual system are set up in such a way that it can make Bayesian inferences on the sensory data it receives.
“What makes this discovery important is that there is an underlying structure in our brains that allows this advanced form of processing, allowing us to interpret our surroundings more effectively.”
The study’s findings not only confirm existing theories about the brain’s use of Bayesian-like inference, but also open the door to new research and innovation, where the brain’s natural capacity for Bayesian inference can be harnessed for practical applications that benefit society.
“Our research, while largely focused on visual perception, holds broad implications across the spectrum of neuroscience and psychology,” said Dr Ridoux.
“By understanding the fundamental mechanisms the brain uses to process and interpret sensory data, we can pave the way for advances in the field from artificial intelligence, where mimicking such brain functions could be revolutionary. Machine learningfor clinical neurology, potentially offering new strategies for future therapeutic interventions.
Dr. The research team, led by William Harrison, made the discovery by recording their brain activity while passively viewing a display engineered to detect specific neural signals associated with visual processing. They then created mathematical models to compare a spectrum of competing hypotheses about how the human brain perceives vision.
Reference: William J. Harrison, Paul M. Baez and Reuben Redoux, “Neural Tuning in the Human Visual System Instantiates Prior Expectations,” September 1, 2023. Nature communication.
DOI: 10.1038/s41467-023-41027-w