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Modelling brain representations of abstract concepts
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Zeitschriftentitel: | PLOS Computational Biology |
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Personen und Körperschaften: | , , |
In: | PLOS Computational Biology, 18, 2022, 2, S. e1009837 |
Format: | E-Article |
Sprache: | Englisch |
veröffentlicht: |
Public Library of Science (PLoS)
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Schlagwörter: |
author_facet |
Kaiser, Daniel Jacobs, Arthur M. Cichy, Radoslaw M. Kaiser, Daniel Jacobs, Arthur M. Cichy, Radoslaw M. |
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author |
Kaiser, Daniel Jacobs, Arthur M. Cichy, Radoslaw M. |
spellingShingle |
Kaiser, Daniel Jacobs, Arthur M. Cichy, Radoslaw M. PLOS Computational Biology Modelling brain representations of abstract concepts Computational Theory and Mathematics Cellular and Molecular Neuroscience Genetics Molecular Biology Ecology Modeling and Simulation Ecology, Evolution, Behavior and Systematics |
author_sort |
kaiser, daniel |
spelling |
Kaiser, Daniel Jacobs, Arthur M. Cichy, Radoslaw M. 1553-7358 Public Library of Science (PLoS) Computational Theory and Mathematics Cellular and Molecular Neuroscience Genetics Molecular Biology Ecology Modeling and Simulation Ecology, Evolution, Behavior and Systematics http://dx.doi.org/10.1371/journal.pcbi.1009837 <jats:p>Abstract conceptual representations are critical for human cognition. Despite their importance, key properties of these representations remain poorly understood. Here, we used computational models of distributional semantics to predict multivariate fMRI activity patterns during the activation and contextualization of abstract concepts. We devised a task in which participants had to embed abstract nouns into a story that they developed around a given background context. We found that representations in inferior parietal cortex were predicted by concept similarities emerging in models of distributional semantics. By constructing different model families, we reveal the models’ learning trajectories and delineate how abstract and concrete training materials contribute to the formation of brain-like representations. These results inform theories about the format and emergence of abstract conceptual representations in the human brain.</jats:p> Modelling brain representations of abstract concepts PLOS Computational Biology |
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title |
Modelling brain representations of abstract concepts |
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Modelling brain representations of abstract concepts |
title_full |
Modelling brain representations of abstract concepts |
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Modelling brain representations of abstract concepts |
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Modelling brain representations of abstract concepts |
title_short |
Modelling brain representations of abstract concepts |
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modelling brain representations of abstract concepts |
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Computational Theory and Mathematics Cellular and Molecular Neuroscience Genetics Molecular Biology Ecology Modeling and Simulation Ecology, Evolution, Behavior and Systematics |
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http://dx.doi.org/10.1371/journal.pcbi.1009837 |
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2022 |
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<jats:p>Abstract conceptual representations are critical for human cognition. Despite their importance, key properties of these representations remain poorly understood. Here, we used computational models of distributional semantics to predict multivariate fMRI activity patterns during the activation and contextualization of abstract concepts. We devised a task in which participants had to embed abstract nouns into a story that they developed around a given background context. We found that representations in inferior parietal cortex were predicted by concept similarities emerging in models of distributional semantics. By constructing different model families, we reveal the models’ learning trajectories and delineate how abstract and concrete training materials contribute to the formation of brain-like representations. These results inform theories about the format and emergence of abstract conceptual representations in the human brain.</jats:p> |
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author | Kaiser, Daniel, Jacobs, Arthur M., Cichy, Radoslaw M. |
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description | <jats:p>Abstract conceptual representations are critical for human cognition. Despite their importance, key properties of these representations remain poorly understood. Here, we used computational models of distributional semantics to predict multivariate fMRI activity patterns during the activation and contextualization of abstract concepts. We devised a task in which participants had to embed abstract nouns into a story that they developed around a given background context. We found that representations in inferior parietal cortex were predicted by concept similarities emerging in models of distributional semantics. By constructing different model families, we reveal the models’ learning trajectories and delineate how abstract and concrete training materials contribute to the formation of brain-like representations. These results inform theories about the format and emergence of abstract conceptual representations in the human brain.</jats:p> |
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spelling | Kaiser, Daniel Jacobs, Arthur M. Cichy, Radoslaw M. 1553-7358 Public Library of Science (PLoS) Computational Theory and Mathematics Cellular and Molecular Neuroscience Genetics Molecular Biology Ecology Modeling and Simulation Ecology, Evolution, Behavior and Systematics http://dx.doi.org/10.1371/journal.pcbi.1009837 <jats:p>Abstract conceptual representations are critical for human cognition. Despite their importance, key properties of these representations remain poorly understood. Here, we used computational models of distributional semantics to predict multivariate fMRI activity patterns during the activation and contextualization of abstract concepts. We devised a task in which participants had to embed abstract nouns into a story that they developed around a given background context. We found that representations in inferior parietal cortex were predicted by concept similarities emerging in models of distributional semantics. By constructing different model families, we reveal the models’ learning trajectories and delineate how abstract and concrete training materials contribute to the formation of brain-like representations. These results inform theories about the format and emergence of abstract conceptual representations in the human brain.</jats:p> Modelling brain representations of abstract concepts PLOS Computational Biology |
spellingShingle | Kaiser, Daniel, Jacobs, Arthur M., Cichy, Radoslaw M., PLOS Computational Biology, Modelling brain representations of abstract concepts, Computational Theory and Mathematics, Cellular and Molecular Neuroscience, Genetics, Molecular Biology, Ecology, Modeling and Simulation, Ecology, Evolution, Behavior and Systematics |
title | Modelling brain representations of abstract concepts |
title_full | Modelling brain representations of abstract concepts |
title_fullStr | Modelling brain representations of abstract concepts |
title_full_unstemmed | Modelling brain representations of abstract concepts |
title_short | Modelling brain representations of abstract concepts |
title_sort | modelling brain representations of abstract concepts |
title_unstemmed | Modelling brain representations of abstract concepts |
topic | Computational Theory and Mathematics, Cellular and Molecular Neuroscience, Genetics, Molecular Biology, Ecology, Modeling and Simulation, Ecology, Evolution, Behavior and Systematics |
url | http://dx.doi.org/10.1371/journal.pcbi.1009837 |