Simultaneously Uncovering the Patterns of Brain Regions Involved in Different Story Reading Subprocesses

• Published in: PloS one Vol. 9; no. 11; p. e112575
• Main Authors: Wehbe, Leila, Murphy, Brian, Talukdar, Partha, Fyshe, Alona, Ramdas, Aaditya, Mitchell, Tom
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.11.2014; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Artificial intelligence; Attention; Biology and Life Sciences; Brain; Brain - anatomy & histology; Brain - physiology; Brain Mapping; Brain research; Cognition & reasoning; Cognitive ability; Comprehension - physiology; Computational linguistics; Computational neuroscience; Computer and Information Sciences; Computer science; Female; Functional magnetic resonance imaging; Hemodynamics; Humans; Image Processing, Computer-Assisted; Information processing; Language; Language processing; Linguistics; Magnetic Resonance Imaging; Male; Mathematical models; Mental Recall - physiology; Natural language interfaces; Natural language processing; Neural coding; Neurosciences; Physical Sciences; Rapid serial visual presentation; Reading; Representations; Semantics; Sentences; Social Sciences; Studies; Syntax; Trends; Pittsburgh Pennsylvania; United States > US; Pennsylvania; India
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0112575

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.