Researchers at the University of Rochester have, for the first time, decoded and predicted the brain activity patterns of word meanings within sentences, and successfully predicted what the brain patterns would be for new sentences.
The study used functional magnetic resonance imaging (fMRI) to measure human brain activation. “Using fMRI data, we wanted to know if given a whole sentence, can we filter out what the brain’s representation of a word is—that is to say, can we break the sentence apart into its word components, then take the components and predict what they would look like in a new sentence,” said Andrew Anderson, a research fellow who led the study as a member of the lab of Rajeev Raizada, assistant professor of brain and cognitive sciences at Rochester.
“We found that we can predict brain activity patterns—not perfectly [on average 70% correct], but significantly better than chance,” said Anderson, The study is published in the journal Cerebral Cortex.
Anderson and his colleagues say the study makes key advances toward understanding how information is represented throughout the brain. “First, we introduced a method for predicting the neural patterns of words within sentences—which is a more complex problem than has been addressed by previous studies, which have almost all focused on single words,” Anderson said. “And second, we devised a novel approach to map semantic characteristics of words that we then correlated to neural activity patterns.”
Finding a word in a sentence
To predict the patterns of particular words within sentences, the researchers used a broad set of sentences, with many words shared between them. For example: “The green car crossed the bridge,” “The magazine was in the car,” and “The accident damaged the yellow car.” fMRI data was collected from 14 participants as they silently read 240 unique sentences.
“We estimate the representation of a word ‘car,’ in this case, by taking the neural activity pattern associated with all of the sentences which that word occurred in and we decomposed sentence level brain activity patterns to build an estimate of the representation of the word,” explained Anderson.
Source: University of Rochester