MIT scientists have reported new insights into how the human brain recognises objects, especially faces, in work that could lead to improved computer vision systems, diagnostics for certain neurological conditions, and more.
In the April issue of Science, Pawan Sinha, an assistant professor in the Department of Brain and Cognitive Sciences (BCS) and colleagues said that a specific brain region known to be activated by clear images of faces is also strongly activated by very blurred images, just so long as surrounding contextual cues (such as a body) are present. Sinha explained: ‘In other words, the neural circuitry in the human brain can use context to compensate for extreme levels of image degradations.'
The team found that only clear faces and blurred faces with proper contextual cues elicited strong fusiform face area (FFA) – the area of the brain that recognises faces – responses. The team used functional magnetic resonance imaging to map neuronal responses of the FFA to a variety of images including clear faces, blurred faces attached to bodies, blurred faces alone, bodies alone, and a blurred face placed in the wrong context.
Computer recognition systems work reasonably well when images are clear, but they break down catastrophically when images are degraded. BCS technical assistant Ethan Meyers stated: ‘A human's ability is so far beyond what the computer can do. The new work could aid the development of better systems by changing our concept of the kind of image information useful for determining what an object is.’
There could also be clinical applications. For example, Sinha said: ‘Contextually evoked neural activity, or the lack of it, could potentially be used as an early diagnostic marker for specific neurological conditions like autism, which are believed to be correlated with impairments in information integration. We hope to address such questions as part of the Brain Development and Disorders Project, a collaboration between MIT and Children's Hospital [in Boston].'
