London, June 3 : Mind reading has long been a thing of folklore and science fiction. But, according to a new research, a team of scientists is closer to knowing how specific thoughts activate our brains.
The research results demonstrate the power of computational modeling to improve our understanding of how the brain processes information and thoughts.
The research was conducted by a computer scientist, Tom Mitchell, and a cognitive neuroscientist, Marcel Just, both of Carnegie Mellon University.
Their previous research, supported by the National Science Foundation (NSF) and the W.M. Keck Foundation, had shown that functional magnetic resonance imaging (fMRI) can detect and locate brain activity when a person thinks about a specific word.
Using this data, the researchers developed a computational model that enabled a computer to correctly determine what word a research subject was thinking about by analyzing brain scan data.
In their most recent work, Just and Mitchell used fMRI data to develop a more sophisticated computational model that can predict the brain activation patterns associated with concrete nouns, or things that we experience through our senses, even if the computer did not already have the fMRI data for that specific noun.
The researchers first built a model that took the fMRI activation patterns for 60 concrete nouns broken down into 12 categories including animals, body parts, buildings, clothing, insects, vehicles and vegetables.
The model also analyzed a text corpus, or a set of texts that contained more than a trillion words, noting how each noun was used in relation to a set of 25 verbs associated with sensory or motor functions. Combining the brain scan information with the analysis of the text corpus, the computer then predicted the brain activity pattern of thousands of other concrete nouns.
In cases where the actual activation patterns were known, the researchers found that the accuracy of the computer model's predictions was significantly better than chance.
The computer can effectively predict what each participant's brain activation patterns would look like when each thought about these words, even without having seen the patterns associated with those words in advance.
"We believe we have identified a number of the basic building blocks that the brain uses to represent meaning. Coupled with computational methods that capture the meaning of a word by how it is used in text files, these building blocks can be assembled to predict neural activation patterns for any concrete noun. And we have found that these predictions are quite accurate for words where fMRI data is available to test them," said Mitchell.
Just said the computational model provides insight into the nature of human thought.
In addition to representations in these sensory-motor areas of the brain, the Carnegie Mellon researchers found significant activation in other areas, including frontal areas associated with planning functions and long-term memory.
The study is published in the journal Science.