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Are Human Brains Supercomputers?

Scientists have discovered that the brain uses Bayesian inference combining prior knowledge with new evidence, to interpret visual stimuli.

Imagine a world where scientists unlock the secrets of how the human brain interprets visual information. This groundbreaking feat is no longer science fiction, as researchers have achieved precisely that! They’ve developed a sophisticated mathematical model mirroring the brain’s inner workings, revealing its remarkable ability to perform complex calculations.

Think of the brain as a high-powered computer, constantly analyzing visual data through a process called “Bayesian inference.” This intricate method involves integrating past experiences with new information to create a clear and accurate picture of the world around us. By unraveling this mechanism, researchers hope to unlock advancements in both artificial intelligence and the field of clinical neurology, potentially leading to improved healthcare solutions.

Think of it like this: you see a furry creature with four legs. Based on past experiences (prior knowledge), you might “guess” it’s a dog. This inherent ability, far superior to even the most advanced machines, allows us to navigate the world with remarkable ease.

But how does the brain perform these calculations? Dr. Reuben Rideaux, a leading researcher in this field, explains, “We finally have an answer! The brain’s visual system is designed to perform Bayesian inference on incoming sensory data, like a computer running complex algorithms.”

This discovery holds immense potential. Not only does it confirm existing theories, but it opens doors to groundbreaking applications:

  • Revolutionizing AI: By mimicking this “detective-like” approach, we can create smarter machines that learn and adapt like humans.
  • Unveiling New Treatments: Understanding how the brain processes information could lead to innovative therapies for neurological conditions.

This research, led by Dr. William Harrison, involved recording brain activity while participants viewed specific images. By analyzing these signals, they developed mathematical models that shed light on the brain’s inner workings.

The implications extend far beyond vision, influencing various fields like psychology and neuroscience. By understanding how the brain perceives the world, we can unlock a new era of advancements in healthcare, technology, and beyond.


Perception is often modelled as a process of active inference, whereby prior expectations are combined with noisy sensory measurements to estimate the structure of the world. This mathematical framework has proven critical to understanding perception, cognition, motor control, and social interaction. While theoretical work has shown how priors can be computed from environmental statistics, their neural instantiation could be realised through multiple competing encoding schemes. Using a data-driven approach, here we extract the brain’s representation of visual orientation and compare this with simulations from different sensory coding schemes. We found that the tuning of the human visual system is highly conditional on stimulus-specific variations in a way that is not predicted by previous proposals. We further show that the adopted encoding scheme effectively embeds an environmental prior for natural image statistics within the sensory measurement, providing the functional architecture necessary for optimal inference in the earliest stages of cortical processing.

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