Artificial intelligence can sometimes predict how the brain responds when people recognize objects. But that resemblance may hide an important weakness: the internal workings of today’s vision models do not necessarily match the processes used by a primate brain.
Over the past decade, artificial neural networks (ANNs), computer models designed for visual tasks, have become some of the leading tools for explaining how the brain processes sight. York University researchers wanted to determine whether these systems truly operate like biological vision.
“Artificial intelligence systems are often described as ‘brain-like’ because they can predict activity in parts of the brain that help us recognize objects,” says York University Assistant Professor Kohitij Kar, senior author of a new study. “Until now, scientists mostly tested this in one direction. They asked whether AI models can predict brain activity.”
The researchers reversed that familiar test. If AI genuinely reflects the brain, they reasoned, then recorded brain activity should also predict the model’s internal responses. To examine this possibility, they developed a reverse predictivity test.
“Ultimately, we need computational models to truly understand the underlying neural mechanisms of how we recognize objects. How do we see objects move? While it’s a very easy task that we do every day, computationally, though, it’s a very challenging problem,” says 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.
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