"How do we make sense of so much data around us, of so many different types, so quickly and robustly?" said Santosh Vempala, distinguished professor of computer science.

Vempala and colleagues presented test subjects with original, abstract images and then asked whether they could correctly identify that same image when randomly shown just a small portion of it. Next, researchers tested a computational algorithm to allow machines to complete the same tests.

Machines performed as well as humans, which provides a new understanding of how humans learn. "We found evidence that, in fact, the human and the machine's neural network behave very similarly," Arriaga noted.

It is believed to be the first study of 'random projection', the core component of the researchers' theory, with human subjects. "We were surprised by how close the performance was between extremely simple neural networks and humans," Vempala said.

The results were published in the journal Neural Computation (MIT press).