课题基金 / 基金详情

Motor learning; second language acquisition; sign language

Motor learning; second language acquisition; sign language
运动学习;
批准号:
2884835
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

项目摘要

项目成果

相似基金

相关文献

中文摘要
翻译
本研究的重点是运动学习在第二语言习得中的作用,提出了以下问题:(1)学习第二语言的运动模式与学习其他运动模式(例如,在舞蹈)?或者,(2)是受语言(第一语言L1或L2)独特影响的L2运动模式,例如,与动作相关的意义是否发挥了作用?第二语言的理解和产出过程是否相同?在研究口语二语习得时,来自口语一语和习得的运动模式的潜在影响是混淆的。因此,我们无法确定L2学习者是否有,例如,由于L1语言模式,或由于从L1学习的运动程序,在生产过程中口音缓慢,费力的讲话或在理解过程中理解力差。手语使用与口语不同的运动系统:手/手臂与舌头/嘴唇。这意味着L1说话者在学习手势时不会受到L1肌肉记忆的影响,但L1签名者可以。通过比较母语为英语的人和母语为英国手语的人在不同条件下学习(理解和产生)一种新的手语(香港手语,HKSL)的情况,我们可以梳理出动作学习对语言学习的相对贡献。
英文摘要
This study focuses on the role of motor learning in second language (L2) acquisition asking: (1) to what degree is learning the motor patterns for an L2 the same as learning other motor patterns (e.g., in dance)? Alternatively, (2) are L2 motor patterns uniquely influenced by language (either the first language, L1, or the L2), e.g., does the meaning linked to movements play a role? And, (3) are these processes the same for L2 comprehension and production? When researching spoken L2 attainment, potential influences from the spoken L1 and from learned motor patterns are confounded. We therefore cannot determine if L2 learners have e.g., accented slow, effortful speech during production or poor understanding during comprehension due to L1 language patterns, or due to motor programs learned from the L1. Sign languages make use of different motor systems from spoken languages: hands/arms versus tongue/lips. This means L1 speakers cannot be influenced from L1 muscle memory when learning signs, but L1 signers can. By comparing how L1 English speakers and L1 British Sign Language signers learn (comprehend and produce) a novel sign language (Hong Kong Sign Language, HKSL) under different conditions we can tease apart the relative contribution of motor learning from language learning.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Understanding structural evolution of galaxies with machine learning
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    Nicola Rosario Napolitano
  • 依托单位:
煤矿安全人机混合群智感知任务的约束动态多目标Q-learning进化分配
  • 批准号:
    --
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30万元
  • 批准年份:
    2022
  • 负责人:
    吉建娇
  • 依托单位:
基于领弹失效考量的智能弹药编队短时在线Q-learning协同控制机理
  • 批准号:
    62003314
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2020
  • 负责人:
    沈剑
  • 依托单位: