Scientists using highly sophisticated imaging tools have measured the real-time brain processes that help us recognise a friend's face with ease - even if we have not seen them in a decade.
Researchers at Carnegie Mellon University in the US are closer than ever before to understanding the neural basis of facial identification.
They used highly sophisticated brain imaging tools and computational methods to measure the real-time brain processes that convert the appearance of a face into the recognition of an individual.
The research team is hopeful that the findings might be used in the near future to locate the exact point at which the visual perception system breaks down in different disorders and injuries, ranging from developmental dyslexia to prosopagnosia, or face blindness.
"Our results provide a step toward understanding the stages of information processing that begin when an image of a face first enters a person's eye and unfold over the next few hundred milliseconds, until the person is able to recognise the identity of the face," said Mark D Vida, a postdoctoral research fellow at the Centre for the Neural Basis of Cognition (CNBC) at Carnegie.
To determine how the brain rapidly distinguishes faces, the researchers scanned the brains of four people using magnetoencephalography (MEG).
MEG allowed them to measure ongoing brain activity throughout the brain on a millisecond-by-millisecond basis while the participants viewed images of 91 different individuals with two facial expressions each: happy and neutral.
The participants indicated when they recognised that the same individual's face was repeated, regardless of expression.
The MEG scans allowed the researchers to map out, for each of many points in time, which parts of the brain encode appearance-based information and which encode identity-based information.
The team also compared the neural data to behavioural judgements of the face images from humans, whose judgements were based mainly on identity-based information.
Then, they validated the results by comparing the neural data to the information present in different parts of a computational simulation of an artificial neural network that was trained to recognise individuals from the same face images.
"Combining the detailed timing information from MEG imaging with computational models of how the visual system works has the potential to provide insight into the real-time brain processes underlying many other abilities beyond face recognition," said David C Plaut, professor at CNBC.
The study was published in the journal Proceedings of the National Academy of Sciences (PNAS).