Paper Abstract and Keywords |
Presentation |
2022-01-21 11:20
A Study on Elucidating Hierarchical Neural Processing of Visual and Semantic Information using Deep Learning Haruka Kawasaki (Ochadai), Satoshi Nishida (NICT), Ichiro Kobayashi (Ochadai) NC2021-32 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
As an objective to explore the neural hierarchical processing underlying the transition from visual to semantic information, this study conducted encoding modeling using convolutional neural network (CNN) and language NN, which handle visual and verbal information, respectively. The encoding model predicted brain activity, evoked by movies, from the features of the movies extracted from each NN layer. By analyzing encoding models’ prediction in terms of the modality and hierarchy of the NNs, we investigated the cortical localization and representational content of visual and semantic neural information from the aspect of hierarchical processing. We found that the encoding models for the CNN higher layers and those for the language NN explained neural information in similar cortical regions but captured highly different representational content. Our finding suggests that although visual and semantic information shows more similar cortical localization as the visual information gets more complicated, the representational content of these information has a substantial gap, even in the same cortical region, which cannot be sufficiently bridged by the modeling with CNNs and language NNs alone. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Brain activity / Hierachial processing / CNN / BERT / fMRI / Representational similarity analysis / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 338, NC2021-32, pp. 7-12, Jan. 2022. |
Paper # |
NC2021-32 |
Date of Issue |
2022-01-14 (NC) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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NC2021-32 |
Conference Information |
Committee |
NLP MICT MBE NC |
Conference Date |
2022-01-21 - 2022-01-23 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
NC |
Conference Code |
2022-01-NLP-MICT-MBE-NC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study on Elucidating Hierarchical Neural Processing of Visual and Semantic Information using Deep Learning |
Sub Title (in English) |
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Brain activity |
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Hierachial processing |
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CNN |
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BERT |
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fMRI |
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Representational similarity analysis |
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1st Author's Name |
Haruka Kawasaki |
1st Author's Affiliation |
Ochanomizu University (Ochadai) |
2nd Author's Name |
Satoshi Nishida |
2nd Author's Affiliation |
National Institute of Information and Communications Technology (NICT) |
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Ichiro Kobayashi |
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Ochanomizu University (Ochadai) |
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Speaker |
Author-1 |
Date Time |
2022-01-21 11:20:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2021-32 |
Volume (vol) |
vol.121 |
Number (no) |
no.338 |
Page |
pp.7-12 |
#Pages |
6 |
Date of Issue |
2022-01-14 (NC) |
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