Source: Scientific American, Jun 2017
The ease of recognizing faces masks its underlying cognitive complexity. Faces have eyes, noses and mouths in the same relative place, yet we can accurately identify them from different angles, in dim lighting and even while moving. Brain-imaging studies have revealed we evolved several tiny regions the size of blueberries in the temporal lobe—the area under the temple—that specialize in responding to faces. Neuroscientists call these regions “face patches”. But neither data from brain scanners—functional magnetic resonance imaging—nor clinical studies of patients with implanted electrodes have explained exactly how the cells in these face patches work.
Now, using a combination of brain imaging and single-neuron recording in macaques, biologist Doris Tsao and her colleagues at Caltech have finally cracked the neural code for face recognition. The researchers found the firing rate of each face cell corresponds to separate facial features along an axis. Like a set of dials, the cells are fine-tuned to bits of information, which they can then channel together in different combinations to create an image of every possible face. “This was mind-blowing,” Tsao says. “The values of each dial are so predictable that we can re-create the face that a monkey sees, by simply tracking the electrical activity of its face cells.”
Tsao’s recent study suggests scientists may have been mistaken. “She has shown that neurons in face patches don’t encode particular people at all, they just encode certain features,” he says. “That completely changes our understanding of how we recognize faces.”
All that mattered for each neuron was a single-feature axis. Even when viewing different faces, a neuron that was sensitive to hairline width, for example, would respond to variations in that feature. But if the faces had the same hairline and different-size noses, the hairline neuron would stay silent, Chang says. The findings explained a long-disputed issue in the previously held theory of why individual neurons seemed to recognize completely different people.
Moreover, the neurons in different face patches processed complementary information. Cells in one face patch—the anterior medial patch—processed information about the appearance of faces such as distances between facial features like the eyes or hairline. Cells in other patches—the middle lateral and middle fundus areas—handled information about shapes such as the contours of the eyes or lips. Like workers in a factory, the various face patches did distinct jobs, cooperating, communicating and building on one another to provide a complete picture of facial identity.
… only needed readings from a small set of neurons for the algorithm to accurately re-create the faces monkeys were viewing, Tsao says. Recordings from just 205 cells—106 cells in one patch and 99 cells in another—were enough. “It really speaks to how compact and efficient this feature-based neural code is,” she says. It may also explain why primates are so good at facial recognition, and how we can potentially identify billions of different people without needing an equally large number of face cells.