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CAREER: Automaton Theories of Human Sentence Comprehension

CAREER: Automaton Theories of Human Sentence Comprehension
职业:人类句子理解的自动机理论
批准号:
0741666
负责人:
John Hale
金额:
$49.84万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-09-15 至 2015-08-31

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中文摘要
翻译
人类用来理解一个句子的心理过程,比如这个句子,可以通过自动机来模拟。这些自动机是特定类型的计算机程序,当运行时,它们模仿认知科学家认为发生在人们阅读或听到单个单词到他们知道整个句子意思之间的步骤。在这个项目中,研究小组正在构建自动机,其中三个因素通常是单独研究的,而现在却结合在一起。第一个因素是语言语法,它表征了人们在了解一种语言时所了解的内容。第二个是控制策略,它决定了理解者及时部署这些知识的特定顺序。第三个因素是单词和短语的记忆理论。研究人员希望这些三重赋予的自动机能够在各种句子类型中与人类的表现相匹配。此外,由于自动机可以在数学上进行改变,以考虑其他语法、记忆和控制策略,因此它们可以用来获得不易从真人行为实验中获得的见解。例如,针对老年人的行为实验可能会显示记忆容量减少对句子理解过程的影响。然而,使用自动机使研究人员不仅可以选择性地改变记忆,还可以改变控制和语法,以人类实验无法做到的方式揭示每一种的作用。类似地,例如,用数学方法将自动机的语法呈现得更像西班牙语(而不是像英语),也可以更深入地了解这三个因素是如何相互作用的,更广泛地说,人们是如何相互理解的。
英文摘要
The mental process humans use to comprehend a sentence, like this one,can be simulated by automata. These automata are specific kinds of computer programs that, when run, mimic the steps cognitive scientists think occur between the point at which people read or hear individual words and when they know what whole sentences mean.In this project, the research team is constructing automata in which three factors, usually studied singly, are combined. The first factor is the linguistic grammar, which characterizes what people know when they know a language. The second is the control strategy, which determines the particular order in which comprehenders deploy this knowledge in time. The third factor is the theory of memory for words and phrases. The researchers expect these triply-endowed automata to match human performance in a variety of sentence types. In addition, since automata can be mathematically altered to take into account alternative grammars, memories and control strategies, they can be used to gain insights that would not come easily from behavioral experiments with real people. For example, behavioral experiments with elderly adults might show the effects of reduced memory capacity on the sentence comprehension process. However, using automata allows the researchers to selectively alter not only memory but also control and grammar, revealing the role of each in ways that human experiments could not. Similarly, mathematically rendering the automaton's grammar more Spanish-like (as opposed to English-like), for example, could also yield a deeper understanding of how the three factors interact and, more broadly, how people understand one another.
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US-French Collaboration: Collaborative Research: Neuro-Computational Models of Natural Language
US-French Collaboration: Collaborative Research: Neuro-Computational Models of Natural Language
  • 批准号:
    1607441
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $56.98万
  • 财政年份:
    2016
  • 负责人:
    John Hale
  • 依托单位:
MRI: Development of Heterogeneous Cluster for Cyber-Physical System Hybrid Analytics
  • 批准号:
    1531270
  • 项目类别:
    Standard Grant
  • 资助金额:
    $18.07万
  • 财政年份:
    2015
  • 负责人:
    John Hale
  • 依托单位:
TWC: Small: Scalable Hybrid Attack Graph Modeling and Analysis
  • 批准号:
    1524940
  • 项目类别:
    Standard Grant
  • 资助金额:
    $48.84万
  • 财政年份:
    2015
  • 负责人:
    John Hale
  • 依托单位:
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