| 研究領域 | 脳の若返りによる生涯可塑性誘導ーｉＰｌａｓｔｉｃｉｔｙー臨界期機構の解明と操作 |
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| 研究課題/領域番号 |
20H05919
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| 研究種目 |
学術変革領域研究(A)
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| 配分区分 | 補助金 |
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| 審査区分 |
学術変革領域研究区分(Ⅲ)
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| 研究機関 | 東京大学 |
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研究代表者 |
辻 晶 東京大学, ニューロインテリジェンス国際研究機構, 連携研究者 (30850490)
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| 研究期間 (年度) |
2021-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2024年度)
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| 配分額 *注記 |
66,040千円 (直接経費: 50,800千円、間接経費: 15,240千円)
2024年度: 10,140千円 (直接経費: 7,800千円、間接経費: 2,340千円)
2023年度: 10,140千円 (直接経費: 7,800千円、間接経費: 2,340千円)
2022年度: 10,140千円 (直接経費: 7,800千円、間接経費: 2,340千円)
2021年度: 9,620千円 (直接経費: 7,400千円、間接経費: 2,220千円)
2020年度: 26,000千円 (直接経費: 20,000千円、間接経費: 6,000千円)
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| キーワード | critical period / language acquisition / speech sound acquisition / Critical periods / Language acquisition / language development / critical periods / speech perception / language input / day-long recordings |
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| 研究開始時の研究の概要 |
This project investigates the contribution of linguistic and social environmental factors on CP timing on multiple levels of language with a focus on perceptual attunement to native language speech sounds, by collecting dense longitudinal and cross-sectional datasets. These data will allow a precise understanding of environmental influences on CP timing in normally developing infants and to identify potential biomarkers of developmental delay, ultimately paving the way towards educational and clinical applications for iPlasticity.
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| 研究実績の概要 |
The present project consists of a longitudinal and cross-sectional dataset. Both were delayed due to the pandemic and were pursued as follows.
As to longitudinal data, we originally planned to include EEG data and start at 1 month of age. We instead focused on behavioral data only (EEG requires close contact) and raised the starting age to 6 months (less vulnerable population). With these changes, we started data collection of 6-18 month-old infants in FY2021, combining at-home audio recordings with in-lab speech perception and word recognition experiments. Throughout FY2022 (carry over from FY2021), we collected a full sample of 30 6-months-old which are progressively followed through. We started developing automatic analyses for the audio recordings (Li et al., 2022).
In FY2021 we bought the EEG machine and started piloting and resolving technical issues with carry-over budget in FY2022. We will not do longitudinal EEG data collection, but will supplement behavioral data with cross-sectional EEG data. As to the planned cross-sectional online dataset, we progressed on technological development to conduct infant speech perception experiments online in FY2021. Online data are still very noisy and lead to imprecise results. We have therefore conducted rigorous tests with adult datasets in order to improve automatic infant gaze direction coding (Hagihara et al., 2022; Tsuji et al., 2022). Finally, while waiting for being able to conduct experiments, we analyzed existing datasets to gain insights on the role of household factors on vocabulary development (Havron et al., 2022).
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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
Due to the ongoing pandemic, any infant experiments were hard to realize even in FY2021.
At one point we were able to at least resume behavioral experiments. We decided to start the planned longitudinal data collection, which is foreseen to take 2 years in total, with purely behavioral measures, thus decoupling it from EEG which is especially tricky in the pandemic context, since adjustments of the cap required close contact between experimenter and infant. We will therefore supplement the longitudinal behavioral data with cross-sectional EEG data, We bought and installed the EEG machine in FY2021, but delayed the actual start of piloting to FY2022, meaning that we carried over the resources for staff to FY2022. While piloting this new technology for our laboratory, we ran into diverse technical problems with noise. Lacking in-house expertise at IRCN, the postdoc recruited on the project visited experts at Boston Children’s Hospital to investigate the sources of noise encountered and continued piloting.
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| 今後の研究の推進方策 |
We are planning to conclude behavioral longitudinal data collection and in parallel develop methods for automatic analysis of large at-home language input data (no algorithms adapted to Japanese exist). This will allow us to longitudinally link infant language input to their speech perception development and word learning, allowing us to quantify the role of the input during sensitive periods.
We will supplement these data with cross-sectional EEG data, which will allow us more fine-grained insights into speech perception during sensitive periods.
We will also continue improving methods for online data collection towards launching the foreseen cross-sectional data collection of speech perception across contrasts, which will provide us insights into the role of the input on speech perception from a different perspective. While the longitudinal dataset give us the impact of natural variation, this study will complement it by experimentally modifying the saliency and frequency of contrasts infants encounter, providing us with potentially stronger, but less ecologically valid evidence.
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