AI models fall for the same optical illusions as humans, revealing how both brains work

Even AI's mind is blown by this optical illusion

And that could mean the rotating snakes illusion is finally explained. We know from our collection of the best optical illusions that our eyes and brains can play tricks on us. In many cases, scientists still aren't sure why, but the discovery that an AI model can fall for some of the same tricks could help provide an explanation. While some researchers have been training AI models to make optical illusions, others are more concerned about how AI interprets illusions, and whether it can experience them in the same way as humans. And it turns out that the deep neural networks (DNNs) behind many AI algorithms can be susceptible to certain mind benders. Eiji Watanabe is an associate professor of neurophysiology at the National Institute for Basic Biology in Japan. He and his colleagues tested what happens when a DNN is presented with static images that the human brain interprets as being in motion. To do that, they used Akiyoshi Kitaoka's famous rotating snakes illusions. The images show a series of circles, each formed by concentric rings of segments in different colours. The images are still, but viewers tend to think that all the circles are moving other than the specific circle they focus on any one time, which the viewer will usually correctly perceive as static. Watanabe's team used a DNN called PredNet, which was trained to predict future frames in a video based on knowledge acquired from previous ones. It was trained using videos of the kinds of natural landscapes that humans tend to see around them, but not on optical illusions. The model was shown various versions of the rotating snakes illusion as well as an altered version that doesn't trick human brains. The experiment found that the model was fooled by the same images as humans. Watanabe believes the study supports his belief that human brains use something referred to as predictive coding. According to this theory, we don't passively process images of objects in our surroundings. Instead, our visual system first predicts what it expects to see based on past experience. It then processes discrepancies in the new visual input. Based on this theory, the assumption is that there are elements in the rotating snakes images that cause our brain to assume the snakes are moving based on its previous experience. The advantage of this way of processing visual information would presumably be that it allows us to interpret what we see more quickly. The cost is that we sometimes misread a scene as a result. There were some discrepancies between how the AI saw the illusion, though. While humans can 'freeze' any specific circle by starting at it, the AI was unable to do that. The model always sees all of the circles as moving. Watanabe put this down to PredNet's lack of an attention mechanism preventing it from being able to focus on one specific spot. He says that, for now, no deep neural network can experience all optical illusions in the same way that humans do. As tech giants invest billions of dollars trying to create an artificial general intelligence capable of surpassing all human cognitive capabilities, it's perhaps reassuring that - for now - AI has such weaknesses.

Optical Illusions: Why AI models are succumbing to the same tricks we do

Rotating Snakes illusion shows AI vision mirrors human perception We built them to be cold, calculating, and immune to the subjective flaws of the biological mind. Yet, when shown a specific arrangement of static, colored circles known as the "Rotating Snakes," advanced AI models do something unexpectedly human, they get dizzy. For years, neuroscientists and computer engineers believed that optical illusions were "bugs" in the human operating system - evolutionary shortcuts that left us vulnerable to visual trickery. But as detailed in a fascinating new report by BBC Future, recent research suggests these errors are not biological flaws. They are mathematical inevitabilities of any system, biological or artificial, that tries to predict the future. Also read: Elon Musk says Apple-Google AI deal is bad for all of us, here's why The phenomenon was first flagged when researchers at Japan's National Institute for Basic Biology, led by Eiji Watanabe, trained deep neural networks (DNNs) to predict movement in video sequences. The AI was shown thousands of hours of footage - propellers spinning, cars moving, balls rolling - until it learned the physics of motion. Then, they showed it the Rotating Snakes illusion. To a standard camera, this image is a static grid of pixels. There is zero motion energy. But when the researchers analyzed the AI's internal vectors, they found the system was "hallucinating" rotation. The AI didn't just classify the image; it predicted that the wheels were turning clockwise or counter-clockwise, matching human perception almost perfectly. Why would a supercomputer fall for a parlor trick? The answer lies in a theory called Predictive Coding. The human brain is not a passive receiver of information. If it waited to process every photon hitting the retina, you would be reacting to the world on a massive delay. Instead, the brain is a "prediction machine." It constantly guesses what visual input will come next based on memory and context, only updating its model when there is a significant error. Also read: Sam Altman's problem: Apple's Gemini pick for Siri disempowers ChatGPT The illusion is not a failure of vision. It is the signature of a system optimizing for speed. The AI models that succumb to these illusions are built on this same architecture. They are designed to minimize the "prediction error" (E) between the predicted frame and the actual next frame. When the AI sees the high-contrast shading of the "Rotating Snakes," its learned parameters interpret the pattern as a motion cue. It predicts the next frame should be shifted. When the image remains static, the prediction fires again. The result is a continuous loop of anticipated motion, an artificial hallucination born from the drive to be efficient. The fact that AI succumbs to optical illusions is arguably one of the strongest validations of modern AI architecture. It suggests that as neural networks become more capable of navigating the real world, they are converging on the same solution nature found millions of years ago, predictive processing. We often fear AI hallucinations as a sign of unreliability. But in the realm of vision, these hallucinations are proof that the machine is not just recording data, it is trying to understand it. It is learning to see, and in doing so, it is learning to be tricked.