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Two new studies from researchers at the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT offer evidence that the brain may use a process similar to a machine-learning approach known as “self-supervised learning” to develop an intuitive understanding of the physical world. The studies found that when models known as neural networks were trained using this type of learning, the resulting models generated activity patterns similar to those seen in the brains of animals performing the same tasks. The findings suggest that these models are able to learn representations of the physical world that they can use to make accurate predictions, and that the mammalian brain may be using the same strategy.