研究課題/領域番号 |
19K12733
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研究種目 |
基盤研究(C)
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配分区分 | 基金 |
応募区分 | 一般 |
審査区分 |
小区分90030:認知科学関連
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研究機関 | 神戸市外国語大学 |
研究代表者 |
CHANG Franklin 神戸市外国語大学, 外国語学部, 教授 (60827343)
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研究期間 (年度) |
2019-04-01 – 2024-03-31
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研究課題ステータス |
交付 (2022年度)
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配分額 *注記 |
4,420千円 (直接経費: 3,400千円、間接経費: 1,020千円)
2023年度: 780千円 (直接経費: 600千円、間接経費: 180千円)
2022年度: 390千円 (直接経費: 300千円、間接経費: 90千円)
2021年度: 780千円 (直接経費: 600千円、間接経費: 180千円)
2020年度: 780千円 (直接経費: 600千円、間接経費: 180千円)
2019年度: 1,690千円 (直接経費: 1,300千円、間接経費: 390千円)
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キーワード | Vision / Language / Learning / Event understanding / Computational model / Deep Learning / Priming / Verbs / Syntax / Eyetracking / language / thematic roles / object tracking / connectionist model |
研究開始時の研究の概要 |
The first project will be the development of a computational model which can explain behavioral data from both adults and children within multiple object tracking tasks. The next step will be to extend this model to address motion understanding. The next project will link this computational model of action understanding to language. To test this computational model, we will do a series of eye-tracking studies which test various assumptions of the model.
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研究実績の概要 |
I have written a paper on a deep learning model of how visual input is used to select verbs. The model is trained on data from human experiments on adults and the model was tested by comparing its verb use to children and adults. This paper has been accepted at Cognitive Science.
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現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
I am continuing to do research on structural priming to test the assumptions about prediction error-based learning in the deep learning model. Recently, I have started to examine the lexical boost as well.
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今後の研究の推進方策 |
Now that the previous paper is almost done, I will start some new deep learning modeling work.
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