Tracking neural coding of perceptual and semantic features of concrete nouns

We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important in...

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Published in:	NeuroImage (Orlando, Fla.) Vol. 62; no. 1; pp. 451 - 463
Main Authors:	Sudre, Gustavo, Pomerleau, Dean, Palatucci, Mark, Wehbe, Leila, Fyshe, Alona, Salmelin, Riitta, Mitchell, Tom
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.08.2012 Elsevier Limited
Subjects:	Accuracy Adult Brain Brain Mapping - methods Comprehension - physiology Digitization Female Humans Knowledge representation Language comprehension Magnetoencephalography Magnetoencephalography - methods Male Nerve Net - physiology Semantics Studies Visual Perception - physiology Young Adult Language comprehension Semantics Magnetoencephalography Knowledge representation
ISSN:	1053-8119, 1095-9572, 1095-9572
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Abstract	We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes. ► Methodological approach to investigate flow of information during noun comprehension. ► Decoded hundreds of semantic and perceptual features from MEG data. ► Semantic features encoded after 250ms and perceptual before 200ms. ► Posterior locations encode perceptual features, anterior regions favor semantic ones. ► Regions associated with semantics in fMRI do not show high decoding results in MEG.
AbstractList	We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes. We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes.We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes. We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes. ► Methodological approach to investigate flow of information during noun comprehension. ► Decoded hundreds of semantic and perceptual features from MEG data. ► Semantic features encoded after 250ms and perceptual before 200ms. ► Posterior locations encode perceptual features, anterior regions favor semantic ones. ► Regions associated with semantics in fMRI do not show high decoding results in MEG.
Author	Wehbe, Leila Sudre, Gustavo Fyshe, Alona Pomerleau, Dean Palatucci, Mark Salmelin, Riitta Mitchell, Tom
AuthorAffiliation	a Center for Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA b Intel Labs, 4720 Forbes Ave., Suite 410, Pittsburgh, PA 15213, USA d Machine Learning Department, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA c Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA e Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University, Espoo, Finland
AuthorAffiliation_xml	– name: b Intel Labs, 4720 Forbes Ave., Suite 410, Pittsburgh, PA 15213, USA – name: d Machine Learning Department, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA – name: e Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University, Espoo, Finland – name: a Center for Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA – name: c Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA
Author_xml	– sequence: 1 givenname: Gustavo surname: Sudre fullname: Sudre, Gustavo email: gsudre@cmu.edu organization: Center for Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA – sequence: 2 givenname: Dean surname: Pomerleau fullname: Pomerleau, Dean organization: Intel Labs, 4720 Forbes Ave., Suite 410, Pittsburgh, PA 15213, USA – sequence: 3 givenname: Mark surname: Palatucci fullname: Palatucci, Mark organization: Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA – sequence: 4 givenname: Leila surname: Wehbe fullname: Wehbe, Leila organization: Center for Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA – sequence: 5 givenname: Alona surname: Fyshe fullname: Fyshe, Alona organization: Center for Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA – sequence: 6 givenname: Riitta surname: Salmelin fullname: Salmelin, Riitta organization: Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University, Espoo, Finland – sequence: 7 givenname: Tom surname: Mitchell fullname: Mitchell, Tom organization: Center for Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA
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Copyright	2012 Elsevier Inc. Copyright © 2012 Elsevier Inc. All rights reserved. Copyright Elsevier Limited Aug 1, 2012 2012 Elsevier Inc. All rights reserved. 2012
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Snippet	We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic...
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SubjectTerms	Accuracy Adult Brain Brain Mapping - methods Comprehension - physiology Digitization Female Humans Knowledge representation Language comprehension Magnetoencephalography Magnetoencephalography - methods Male Nerve Net - physiology Semantics Studies Visual Perception - physiology Young Adult
Title	Tracking neural coding of perceptual and semantic features of concrete nouns
URI	https://www.clinicalkey.com/#!/content/1-s2.0-S1053811912004442 https://dx.doi.org/10.1016/j.neuroimage.2012.04.048 https://www.ncbi.nlm.nih.gov/pubmed/22565201 https://www.proquest.com/docview/1668086488 https://www.proquest.com/docview/1021979697 https://www.proquest.com/docview/1038614201 https://pubmed.ncbi.nlm.nih.gov/PMC4465409
Volume	62
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