- By measuring the activation in each brain system, the new technology can tell us what types of thoughts are being contemplated
- The model was able to predict the features of the left-out sentence, with 87 percent accuracy, despite never being exposed to its activation before
New York, July 2, 2017: Using machine learning algorithm, US researchers have developed a novel brain imaging technology that can “read minds” and identify complex thoughts with 87 percent accuracy.
The findings indicate that the mind’s building blocks for constructing complex thoughts are formed by the brain’s various sub-systems and are not word-based.
By measuring the activation in each brain system, the new technology can tell us what types of thoughts are being contemplated.
“We have finally developed a way to see thoughts of such complexity in the fMRI signal. The discovery of this correspondence between thoughts and brain activation patterns tells us what the thoughts are built of,” said Marcel Just, Professor at Carnegie Mellon University, Pennsylvania.
For the study, published in the journal Human Brain Mapping, the team included seven participants and used a computational model to assess how the brain activation patterns for 239 sentences corresponded to the neurally plausible semantic features that characterised each sentence.
Then the programme was able to decode the features of the 240th left-out sentence. They went through leaving out each of the 240 sentences in turn, in what is called cross-validation.
The model was able to predict the features of the left-out sentence, with 87 percent accuracy, despite never being exposed to its activation before.
“Our method overcomes the unfortunate property of fMRI to smear together the signals emanating from brain events that occur close together in time, like the reading of two successive words in a sentence,” Just explained.
“This advance makes it possible for the first time to decode thoughts containing several concepts. That’s what most human thoughts are composed of,” the professor added. (IANS)
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