TECH
Visual illusion reveals what today’s AI vision is missing – York University study
Our eyes do not always tell us exactly where things are – and that may be a feature of how biological vision works, rather than simply a flaw. A new study by York University researchers uses a common illusion to ask if artificial intelligence is meant to see more like us, should it make some of the same systematic perceptual “mistakes”?
For example, after staring at something moving steadily in one direction, a stationary object viewed immediately afterward can appear slightly displaced in the opposite direction. This well-known visual illusion, called a motion aftereffect, gives scientists an unusual window into the computations underlying perception: the image itself has not moved, but our experience of where it is has changed.
“Today’s AI vision systems are impressive, but they still do not always see the world the way we do. This study captures the promise of NeuroAI and what it can do when neuroscience and artificial intelligence are brought together. By using smart experiments to reveal the computations biological vision uses and AI still lacks, we can use those insights to build better, more brain-like artificial systems,” says senior author York Assistant Professor Kohitij Kar, the Canada Research Chair in Visual Neuroscience and a member of York’s Centre for Vision Research and Centre for Integrative and Applied Neuroscience.
Current AI vision systems can often determine where an object is accurately, but they generally do not reproduce the way recent visual experience can reshape that answer. In humans, staring at motion can make a subsequently viewed stationary object appear displaced even though its pixels have not moved. The researchers found a corresponding change in the primate visual cortex – but not in the AI models they tested.
To gain better insight into the issue, the researchers examined whether artificial neural network models capture the same history-dependent changes in spatial representations seen in biological vision, or whether their position representations primarily reflect the physical properties of the image. The researchers, including the paper’s first author and York graduate student Elizaveta Yakubovskaya, used precise measurements to find out where AI differs from biological vision and how that gap could be bridged.
“Combining recordings from primate visual cortex with human perception experiments, we used motion adaptation to induce a visual illusion and make a stationary object appear slightly shifted in position, then asked whether the brain and AI showed the same effect. Human observers reported the illusion, and neural representations of position in the primate inferior temporal (IT) cortex shifted in the same direction, even though the image itself had not changed,” says Yakubovskaya.
The researchers leveraged motion adaptation to show where perceived and pixel-based positions diverge, allowing them to test the behavioral relevance of IT codes. The findings not only further our understanding of IT’s role in spatial information encoding but provide a new benchmark to evaluate dynamic vision models.
“There is a growing question in AI about whether increasingly capable systems will become more like us or increasingly different from us,” says Kar of the Faculty of Science and a member of the York-led Connected Minds. “If we want AI that works with humans and understands the world in more human-compatible ways, we cannot focus only on whether it gets the right answer. We also need to understand the computations that produce human perception and behavior. Neuroscience gives us a way to discover those computations and, potentially, build them into AI,” adds Kar.
Visual illusions reveal that today’s AI vision is missing history-dependent, dynamic perceptual computations that allow biological brains to interpret a changing world rather than just recording static pixels.
A recent study from York University published in Current Biology highlights a fundamental gap between artificial intelligence and primate vision:
The core findings:
• The Illusion Divide: When humans and macaque monkeys look at a stationary object after experiencing motion adaptation (a motion aftereffect), they perceive the object as physically shifted in position, even though the image hasn’t changed.
• Neural Mirroring: Recordings from the primate inferior temporal (IT) cortex show that biological brain cells actually shift their spatial position codes to match this subjective, illusory perception.
• AI Failure: Current state-of-the-art artificial neural networks and AI vision models do not replicate this shift. They remain rigid and strictly tethered to static pixel coordinates.
What AI is missing:
• Passive vs. Active Processing: Human eyes and primate brains do not operate like digital cameras that capture raw, objective pixels frame by frame.
• Temporal Integration: Biological vision actively blends immediate visual history and recent temporal experience into its spatial calculations.
• Perceptual Alignment: Illusions are not just computational mistakes; they are the functional byproduct of an adaptive brain that predicts and prioritizes what is useful for survival based on context. AI lacks this fluid, context-aware framework
A new study from York University reveals that today’s AI vision lacks history-dependent, dynamic perceptual computations that allow biological brains to shift object position based on recent visual experience.
