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How Does Our Brain Know Neural Plasticity A Face And What’s Not?

The quantifier

Patterns in the world, like this rock formation in Ebihens, France, can sometimes fortuitously look like human faces. In a new study, Meng et al. Have used this phenomenon of pareidolia to investigate how the neural processing of faces differs in the left and right halves of the brain.
Image: Erwan Mirabeau

Objects that seem like faces are everywhere. Whether or not it is New Hampshire’s erstwhile granite “Old Man of the Mountain,” or Jesus ‘ face on a tortilla, our brains are adept at locating photographs that look like faces. Nevertheless the ordinary human brain is just about never fooled into believing such objects basically are human faces.

“You can tell that it has some ‘faceness ‘ to it, but on the other hand, you're not misled into believing that it's a real face,” asserts Pawan Sinha, professor of brain and cognitive sciences at MIT.

A new report from Sinha and his associates reveals the brain activity that underlies our capability to make that excellence. On the left side of the brain, the fusiform gyrus — an area long associated with face recognition — meticulously works out how “facelike” an image is. The right fusiform gyrus then seems to use that information to make a fast, categorical call of whether the object is, indeed, a face.

This distribution of labor is one of the first known examples of the right and left sides of the brain taking on different roles in high level visual-processing jobs, Sinha announces, although hemispheric differences have been seen in other brain functions, most significantly language and spatial perception.

Lead writer of the paper, released Jan. 4 in the Proceedings of the Royal Society B, is Ming Meng, a former postdoc in Sinha’s lab and now a helper professor at Dartmouth Varsity. Other authors are Tharian Cherian ’09 and Gaurav Singal, who lately earned an MD from the Harvard-MIT Division of Health Sciences and Technology and is now a resident at Massachusetts General Hospital.

Face versus nonface

Many earlier research has demonstrated that neurons in the fusiform gyrus, found on the brain’s bottom, reply preferentially to faces. Sinha and his students set out to analyze how that brain area decides what is and is not a face, especially in situations where an object seriously resembles a face.

To help them do that, the analysts created a continuum of photographs starting from the ones that look nothing like faces to genuine faces. They found pictures that very closely resemble faces by inspecting footage that machine vision systems had falsely tagged as faces. Human observers then rated how facelike each one of the images were by doing a sequence of one-to-one comparisons; the result of those comparisons authorized the researchers to rank the pictures by how much they seemed like a face.

The study team then used functional magnetic resonance imaging (fMRI) to scan the brains of research subjects as they categorized the pictures. Surprisingly, the scientists found different activity patterns on each side of the brain: On the right side, activation patterns in the fusiform gyrus remained quite consistent for all genuine face pictures, but changed a lot for all nonface photographs, regardless of how much they looked like a face. This implies the right side of the brain is involved in making the specific declaration of whether an image is a face or not.

Meanwhile, in the equivalent area on the left side of the brain, activity patterns changed steadily as pictures became more facelike, and there wasn't any clear divide between faces and nonfaces. From this, the researchers concluded the left side of the brain is ranking pictures on a range of how facelike they are , though not alloting them to one category or another.

“From the computational point of view, one conjecture one can make is that the left does the primary heavy lifting,” Sinha announces. “It attempts to figure out how facelike is a pattern, without making the ultimate choice as to whether I am going to call it a face.”

Key to the research was imaging-analysis technology that permitted the scientists to have a look at patterns of activity across the fusiform gyrus.

“This is a relatively recent innovation — looking at the pattern of activation in opposition to overall activation,” claims Thomas Busey, associate professor of psychological and brain sciences at Indiana University, who wasn't concerned in this research. “Anytime you have got a measure that replicates and correlates with human behavior, that looks to be a pretty pressing story.”

Timing is instructive

The analysts found that activation in the left side of the fusiform gyrus predated that of the right side by two seconds, supporting the theory that the left side does its job first and then passes. Info on to the right side.

Sinha announces that given the sluggishness of fMRI signals (which depend on blood-flow changes), the timing does not yet constitute definitive proof, “but it is a fascinating possibility because it starts to tease apart this monolithic concept of face processing. It’s now beginning to get at what the constituents are of that overall face-processing system.”

The researchers hope to get more solid proof of temporal relations between the two hemispheres with studies using electroencephalography (EEG) or magnetoencephalography (MEG), 2 technologies that offer a much more exact view of the timing of brain activity. They also hope to find out how and when the left and right sides of the fusiform gyrus develop these independent functions by studying blind kids who have their sight restored at a young age. Many such kids have been treated by Project Prakash, an effort initiated by Sinha to find and treat blind youngsters in India.

John has over 40 years of experience in business promoting sales engineering general management online real-Estate Planning, for the past 20 years John has been a active Meditation Student. He has worked for and with worldwide corporations such as IBM, Electronic Data Systems and Mahindra British Telecomm. He has a BS from Brown in Computer Science an MA through IBM in Industrial Electronics, he also has a PhD in International Trade and Management from the London School of Business and Trade.

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