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- authors:
- -
- firstname: "Fatma"
- lastname: "Deniz"
- affiliation: "University of California, Berkeley"
- id: "0000-0001-6051-7288"
- -
- firstname: "Anwar O."
- lastname: "Nunez-Elizalde"
- affiliation: "University of California, Berkeley"
- -
- firstname: "Alexander G."
- lastname: "Huth"
- affiliation: "University of California, Berkeley"
- id: "ORCID:0000-0002-7590-3525"
- -
- firstname: "Jack L."
- lastname: "Gallant"
- affiliation: "University of California, Berkeley"
- id: "ORCID:0000-0001-7273-1054"
- title: "The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality"
- description: "This folder contains stimuli, models, and fMRI data of subject reading and listening to english narratives originally collected for Deniz et al. 2019."
- keywords:
- - Neuroscience
- - BOLD
- - cross-model representations
- - fMRI
- - listening
- - reading
- - semantics
- license:
- name: "Creative Commons CC0 1.0 Public Domain Dedication"
- url: "https://creativecommons.org/publicdomain/zero/1.0/"
- funding:
- - "NSF; IIS1208203"
- - "NEI; EY019684"
- - "NEI; EY022454"
- - "IARPA; 86155-Carnegi-1990360-gallant"
- - "CSI; CCF-0939370"
- references:
- -
- id: "doi:10.1523/JNEUROSCI.0675-19.2019"
- reftype: "IsSupplementTo"
- citation: "Deniz F., Nunez-Elizalde A. O., Huth, A. G., & Gallant, J.L. The representation of semantic information across human cerebral cortex during listening versus reading is invariant to stimulus modality. Journal of Neuroscience, 39(39), 7722-7736 (2019)."
- -
- id: "10.1101/2023.01.06.522601"
- reftype: "IsSupplementTo"
- citation: "Chen C., Dupré la Tour T., Gallant J.L., Klein D., Deniz F. The Cortical Representation of Language Timescales is Shared between Reading and Listening. bioRxiv [Preprint]. 2023 Dec 11:2023.01.06.522601. doi: 10.1101/2023.01.06.522601. PMID: 37577530; PMCID: PMC10418083."
- -
- id: "10.18653/v1/2022.findings-emnlp.330"
- reftype: "IsSupplementTo"
- citation: "Lamarre M., Chen C., and Deniz F. Attention weights accurately predict language representations in the brain. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4513–4529, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. (2022)"
- resourcetype: Dataset
- templateversion: 1.2
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