{"id":175512,"date":"2016-08-12T12:53:57","date_gmt":"2016-08-12T16:53:57","guid":{"rendered":"http:\/\/www.rochester.edu\/newscenter\/?p=175512"},"modified":"2016-08-18T11:50:57","modified_gmt":"2016-08-18T15:50:57","slug":"this-is-your-brain-on-sentences","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/newscenter\/this-is-your-brain-on-sentences\/","title":{"rendered":"This is your brain on sentences"},"content":{"rendered":"

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.<\/p>\n

The study used functional magnetic resonance imaging (fMRI) to measure human brain activation. \u201cUsing fMRI data, we wanted to know if given a whole sentence, can we filter out what the brain\u2019s representation of a word is\u2014that 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,\u201d said Andrew Anderson, a research fellow who led the study as a member of the lab of Rajeev Raizada<\/a><\/strong>, assistant professor of brain and cognitive sciences<\/a><\/strong> at Rochester.<\/p>\n

\u201cWe found that we can predict brain activity patterns\u2014not perfectly [on average 70% correct], but significantly better than chance,\u201d said Anderson, The study is published in the journal Cerebral Cortex<\/em><\/a>.<\/em><\/strong><\/p>\n

Anderson and his colleagues say the study makes key advances toward understanding how information is represented throughout the brain. \u201cFirst, we introduced a method for predicting the neural patterns of words within sentences\u2014which is a more complex problem than has been addressed by previous studies,\u00a0<\/span>which have almost all focused on single words,\u201d Anderson said. \u201cAnd second, we devised a novel approach to map semantic characteristics of words that we then correlated to neural activity patterns.\u201d<\/p>\n

Finding a word in a sentence<\/strong><\/p>\n

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: \u201cThe green car crossed the bridge,\u201d \u201cThe magazine was in the car,\u201d and \u201cThe accident damaged the yellow car.\u201d fMRI data was collected from 14 participants as they silently read 240 unique sentences.<\/p>\n

\u201cWe estimate the representation of a word \u2018car,\u2019 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,\u201d explained Anderson.<\/p>\n

\"fMRI
These brain maps show how accurately it was possible to predict neural activation patterns for new, previously unseen sentences, in different regions of the brain. The brighter the area, the higher the accuracy. The most accurate area, which can be seen as the bright yellow strip, is a region in the left side of the brain known as the Superior Temporal Sulcus. This region achieved statistically significant sentence predictions in 11 out of the 14 people whose brains were scanned. Although that was the most accurate region, several other regions, broadly distributed across the brain, also produced significantly accurate sentence predictions. (University of Rochester graphic \/ Andrew Anderson and Xixi Wang)<\/figcaption><\/figure>\n

 <\/p>\n

\"fMRI
Brain activation patterns for different sensory and emotional aspects of the word \u201cplay.\u201d The numbers to the left of each brain pattern show how strongly the word is associate with each feature. For example, \u201cplay\u201d is positively associated with \u201cBiomotion\u201d, because playing often involves people moving their bodies. But it is negatively associated with \u201cUnpleasant\u201d, because play is rarely an unpleasant activity.” (University of Rochester graphic \/ Andrew Anderson)<\/figcaption><\/figure>\n

What does the meaning of a word look like?<\/strong><\/p>\n

\u00a0<\/strong>\u201cCoffee has a color, smell, you can drink it\u2014coffee makes you feel good\u2014it has sensory, emotional, and social aspects,\u201d said senior author Raizada. \u201cSo we built upon a model created by Jeffrey Binder at the Medical College of Wisconsin, a coauthor on the paper, and surveyed people to tell us about the sensory, emotional, social and other aspects for a set of words. Together, we then took that approach in a new direction, by going beyond individual words to entire sentences.\u201d<\/p>\n

\u00a0<\/strong>The new semantic model employs 65 attributes\u2014such as \u201ccolor,\u201d \u201cpleasant,\u201d \u201cloud,\u201d and \u201ctime.\u201d Participants in the survey rated, on a scale of 0-6, the degree to which a given root concept was associated with a particular experience. For example, \u201cTo what degree do you think of \u2018coffee\u2019 as having a characteristic or defining temperature?\u201d In total, 242 unique words were rated with each of the 65 attributes.<\/p>\n

\u201cThe strength of association of each word and its attributes allowed us to estimate how its meanings would be represented across the brain using fMRI,\u201d said Raizada.<\/p>\n

The model captures a wider breadth of experience than previous semantic models, said Anderson, \u201cwhich made it easier to interpret the relationship between the predictive model and brain activity patterns.\u201d<\/p>\n

\u00a0<\/strong>The team was then able to recombine activity patterns for individual words, in order to predict brain patterns for entire sentences built up out of new combinations of those words. For example, the computer model could predict the brain pattern for a sentence such as, \u201cThe family played at the beach,\u201d even though it had never seen that specific sentence before. Instead, it had only seen other sentences containing those words in different contexts, such as \u201cThe beach<\/em> was empty\u201d and \u201cThe young girl played<\/em> soccer.\u201d<\/p>\n

The researchers said the study opens a new set of questions toward understanding how meaning is represented in the brain. \u201cNot now, not next year, but this kind of research may eventually help individuals who have problems with producing language, including those who suffer from traumatic brain injuries or stroke,\u201d said Anderson.<\/p>\n

The Intelligence Advanced Research Projects Activity and the National Science Foundation supported the research.<\/p>\n","protected":false},"excerpt":{"rendered":"

What does the meaning of a word look like? Researchers 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.<\/p>\n","protected":false},"author":562,"featured_media":175712,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[116],"tags":[18672,4626,18572,16072],"class_list":["post-175512","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sci-tech","tag-department-of-brain-and-cognitive-sciences","tag-featured-post","tag-research-finding","tag-school-of-arts-and-sciences"],"acf":[],"yoast_head":"\nThis is your brain on sentences<\/title>\n<meta name=\"description\" content=\"What does the meaning of a word look like? 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