The Experts below are selected from a list of 78165 Experts worldwide ranked by ideXlab platform
Evelina Fedorenko - One of the best experts on this subject based on the ideXlab platform.
-
toward a universal decoder of Linguistic Meaning from brain activation
Nature Communications, 2018Co-Authors: Francisco Pereira, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Evelina FedorenkoAbstract:Prior work decoding Linguistic Meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new Meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of Meaning relationships between sentences. Previous work decoding Linguistic Meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode Meanings of semantically diverse new sentences with topics not encountered during training.
-
toward a universal decoder of Linguistic Meaning from brain activation
Nature Communications, 2018Co-Authors: Francisco Pereira, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Evelina Fedorenko, Bin Lou, Matthew BotvinickAbstract:Prior work decoding Linguistic Meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new Meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of Meaning relationships between sentences.
Nancy Kanwisher - One of the best experts on this subject based on the ideXlab platform.
-
toward a universal decoder of Linguistic Meaning from brain activation
Nature Communications, 2018Co-Authors: Francisco Pereira, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Evelina FedorenkoAbstract:Prior work decoding Linguistic Meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new Meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of Meaning relationships between sentences. Previous work decoding Linguistic Meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode Meanings of semantically diverse new sentences with topics not encountered during training.
-
toward a universal decoder of Linguistic Meaning from brain activation
Nature Communications, 2018Co-Authors: Francisco Pereira, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Evelina Fedorenko, Bin Lou, Matthew BotvinickAbstract:Prior work decoding Linguistic Meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new Meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of Meaning relationships between sentences.
Francisco Pereira - One of the best experts on this subject based on the ideXlab platform.
-
toward a universal decoder of Linguistic Meaning from brain activation
Nature Communications, 2018Co-Authors: Francisco Pereira, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Evelina FedorenkoAbstract:Prior work decoding Linguistic Meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new Meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of Meaning relationships between sentences. Previous work decoding Linguistic Meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode Meanings of semantically diverse new sentences with topics not encountered during training.
-
toward a universal decoder of Linguistic Meaning from brain activation
Nature Communications, 2018Co-Authors: Francisco Pereira, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Evelina Fedorenko, Bin Lou, Matthew BotvinickAbstract:Prior work decoding Linguistic Meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new Meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of Meaning relationships between sentences.
Brianna Pritchett - One of the best experts on this subject based on the ideXlab platform.
-
toward a universal decoder of Linguistic Meaning from brain activation
Nature Communications, 2018Co-Authors: Francisco Pereira, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Evelina FedorenkoAbstract:Prior work decoding Linguistic Meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new Meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of Meaning relationships between sentences. Previous work decoding Linguistic Meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode Meanings of semantically diverse new sentences with topics not encountered during training.
-
toward a universal decoder of Linguistic Meaning from brain activation
Nature Communications, 2018Co-Authors: Francisco Pereira, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Evelina Fedorenko, Bin Lou, Matthew BotvinickAbstract:Prior work decoding Linguistic Meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new Meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of Meaning relationships between sentences.
Samuel J. Gershman - One of the best experts on this subject based on the ideXlab platform.
-
toward a universal decoder of Linguistic Meaning from brain activation
Nature Communications, 2018Co-Authors: Francisco Pereira, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Evelina FedorenkoAbstract:Prior work decoding Linguistic Meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new Meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of Meaning relationships between sentences. Previous work decoding Linguistic Meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode Meanings of semantically diverse new sentences with topics not encountered during training.
-
toward a universal decoder of Linguistic Meaning from brain activation
Nature Communications, 2018Co-Authors: Francisco Pereira, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Evelina Fedorenko, Bin Lou, Matthew BotvinickAbstract:Prior work decoding Linguistic Meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new Meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of Meaning relationships between sentences.
