Researchers at the Carnegie Mellon University have come up with a method to find the areas in the brain where a person's thought and awareness of known objects originates.
This comes as a result of the efforts made by a team of computer scientists and cognitive neuroscientists at the university, led by neuroscientist Marcel Just and Computer Science Professor Tom M. Mitchell, who combined methods of machine learning and brain imaging to identify the patterns of brain activity associated with the objects.
During the course of study, a dozen participants enveloped in an MRI scanner were shown line drawings of 10 different objects ó five tools and five dwellings ó one at a time. The subjects were then asked to think about their properties.
The process enabled researchers to accurately determine which of the 10 drawings a participant was viewing, based on their characteristic whole-brain neural activation patterns.
For making the task even more challenging for themselves, the researchers excluded information in the brain's visual cortex, where raw visual information is available, and focused more on the "thinking" parts of the brain.
The researchers found that the activation pattern evoked by an object was not located in just one place in the brain. They, for example, revealed that thinking about a hammer activated many locations in the subjects' brains.
When the participants thought about how they would swing a hammer, the motor area in their brains got activated. Whereas, thinking about what a hammer was used for, and about the shape of a hammer, activated other areas.
Just and Mitchell claim that their research is the first to report the ability to identify the thought process associated with a single object.
According to them, while earlier work showed that it was possible to distinguish broad categories of objects such as "tools" and "buildings", the new research shows that it is possible to distinguish between items with very similar meanings, like two different tools.
The machine-learning method involves training a computer algorithm, a set of mathematical rules, to extract the patterns from a participant's brain activation. Data collected in one part of the study is then tested on information gathered in another part of the same study.
Just and Mitchell say that they have also determined whether different brains exhibit the same or different activity patterns to encode individual objects. In order to do so, they tried identifying objects represented in one participant's brain after training their algorithms using data collected from other participants.
The researchers found that the algorithm was indeed able to identify a participant's thoughts based on the patterns extracted from the other participants.
"This part of the study establishes, as never before, that there is a commonality in how different people's brains represent the same object. There has always been a philosophical conundrum as to whether one person's perception of the colour blue is the same as another person's. Now we see that there is a great deal of commonality across different people's brain activity corresponding to familiar tools and dwellings," said Mitchell.
Just, who directs the Center for Cognitive Brain Imaging at Carnegie Mellon, said that one application that filled the team with excitement was comparing the activation patterns of people with neurological disorders, such as autism. He also revealed that he was developing a brain-based theory of autism.
"We are looking forward to determining how people with autism neurally represent social concepts such as friend and happy. People with autism perceive others in a distinctive way that has been difficult to characterize. This machine learning approach offers a way to discover that characterization," he said.
Just and Mitchell's work has been reported online in the science journal PLoS One.