Simultaneously Uncovering the Patterns of Brain Regions Involved in Different Story Reading Subprocesses
Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses,...
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| Published in | PloS one Vol. 9; no. 11; p. e112575 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
26.11.2014
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0112575 |
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| Abstract | Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders. |
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| AbstractList | Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders. Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders.Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders. |
| Audience | Academic |
| Author | Wehbe, Leila Ramdas, Aaditya Talukdar, Partha Murphy, Brian Fyshe, Alona Mitchell, Tom |
| AuthorAffiliation | 1 Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States University of Leicester, United Kingdom 4 Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, Karnataka, India 3 School of Electronics, Electrical Engineering and Computer Science, Queen's University, Belfast, United Kingdom 2 Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States |
| AuthorAffiliation_xml | – name: 4 Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, Karnataka, India – name: 2 Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States – name: 3 School of Electronics, Electrical Engineering and Computer Science, Queen's University, Belfast, United Kingdom – name: University of Leicester, United Kingdom – name: 1 Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States |
| Author_xml | – sequence: 1 givenname: Leila surname: Wehbe fullname: Wehbe, Leila – sequence: 2 givenname: Brian surname: Murphy fullname: Murphy, Brian – sequence: 3 givenname: Partha surname: Talukdar fullname: Talukdar, Partha – sequence: 4 givenname: Alona surname: Fyshe fullname: Fyshe, Alona – sequence: 5 givenname: Aaditya surname: Ramdas fullname: Ramdas, Aaditya – sequence: 6 givenname: Tom surname: Mitchell fullname: Mitchell, Tom |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25426840$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2014 Public Library of Science 2014 Wehbe et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2014 Wehbe et al 2014 Wehbe et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Conceived and designed the experiments: LW AF TM. Performed the experiments: LW. Analyzed the data: LW AR. Wrote the paper: LW TM. Contributed annotations for the text: BM PT LW TM. Contributed experimental code: LW AF. Participated in discussions about analysis methods: LW BM PT AF AR TM. |
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| Title | Simultaneously Uncovering the Patterns of Brain Regions Involved in Different Story Reading Subprocesses |
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