Face-selective neurons in the macaque visual cortex dynamically change their tuning properties, according to a study published last month in Nature. The findings suggest that artificial neural networks—which are stably tuned—may not fully model visual systems in primates. Neurons in the inferotemporal cortex initially respond to both faces and inanimate objects, the study found. But then those cells switch to respond to specific facial features, including inter-eye distance and hair color. That switch likely corresponds to a shift from the brain recognizing the presence of a face to gauging what that face looks like, according to the study investigators. “[This shift] is a phenomenon that has never been characterized before,” says Laura Gwilliams, assistant professor of psychology at Stanford University, who was not involved in the work. “We need to rethink how neurons code information.” Conflicting evidence has fueled a debate in face perception research: On the one hand, the visual cortex contains clusters of highly selective neurons—known as face patches—indicating that the region uses specialized mechanisms for face processing. And stimulating face-selective brain areas in primates, including humans, distorts the perception of faces but not of non-face objects. Yet, other studies suggest that the neurons employ a general code to respond to a range of visual features. The new paper “offers a potential solution to solve this controversy,” says Shahab Bakhtiari, assistant professor of psychology at the Université de Montréal, who did not take part in the study. “The two theories were looking at the same thing but at different [timepoints].” Neurons in the inferotemporal cortex initially use a general code, then switch to a face-specific code in a matter of milliseconds, the new study found. By contrast, convolutional neural networks—image-processing algorithms that attempt to model visual pathways—use a nonspecific approach for facial recognition. “I think it’s very interesting