Statistical approaches to linguistic pattern learning
语言模式学习的统计方法
基本信息
- 批准号:7932503
- 负责人:
- 金额:$ 7.08万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2009
- 资助国家:美国
- 起止时间:2009-09-30 至 2011-09-29
- 项目状态:已结题
- 来源:
- 关键词:AccountingAddressAdultAffectAgeAuditoryBenchmarkingBody of uterusCategoriesChildClassificationComplexCuesDevelopmental Delay DisordersDiseaseElementsEventExposure toEye MovementsFeedbackFrequenciesGoalsHearingHourHumanHuman DevelopmentIndiumInfantInstructionJudgmentLanguageLanguage DelaysLanguage DevelopmentLanguage DisordersLeadLearningLegalLinguisticsMachine LearningMapsMethodsNatureNoiseParticipantPatternPerformancePhasePlayPositioning AttributeProcessProductionPropertyReaction TimeRecurrenceRelative (related person)ResearchResourcesRoleSamplingSeriesShapesSpecificityStructureTechniquesTestingTimeTrainingUrsidae FamilyVariantVisualdesignlexicalnatural languagenovelprogramspublic health relevanceremediationresearch studyresponsescale upsoundstatisticsvisual motor
项目摘要
DESCRIPTION (provided by applicant): The purpose of the proposed research is to provide a comprehensive account of the factors that affect how infants, children, and adults learn the categories of their native language from distributional information in linguistic input. The categories of a language consist of sets of words (e.g., noun, verb) that play a functionally equivalent role in grammatical sentences. Distributional information refers to the patterning of elements in a large corpus of sentences and includes how frequently those elements occur, what position they occupy in a sentence, and the context provided by neighboring elements. Our longstanding program of research on statistical learning in word segmentation (how learners determine which sound sequences form words) has documented the power, rapidity, and robustness of infants, children, and adults sensitivity to complex distributional information. Here we extend that program of research to a crucial aspect of learning higher-level structures of language. In our proposed studies, we use a miniature artificial language paradigm that affords us complete control over all the distributional cues in the input, something that is virtually impossible using real languages. Participants listen to a sample of utterances and make judgments about their acceptability. Crucially, during a learning phase, they do not hear all possible utterances that are "legal" in the artificial language; some are withheld for use in a later post-test. The post-test utterances either conform to the distributional patterns present in the learning phase, or they violate those patterns. The key test is whether participants judge novel-but-legal utterances to be acceptable, thereby showing the ability to generalize correctly beyond the input to which they were exposed. Studies of children provide additional support for learning the distributional cues by pairing utterances with videos of simple events. Studies of adults will be used for comparison, and will also present them with learning materials in the visual-motor domain to assess the detailed time-course of learning and the specificity of the results to auditory linguistic materials. Taken together, the results of these studies of infants, children, and adults will document the key structural variables in language learning that enable a distributional mechanism of category formation to operate and will highlight the ways these mechanisms may differ over age and domain. PUBLIC HEALTH RELEVANCE: Language development is one of the hallmarks of the human species, yet it is difficult to study because of the huge variation in early exposure to different amounts of linguistic input. The use of artificial languages that are acquired in the lab over a few hours provides a window on the mechanisms of language development. We will study language learning in the lab to gain a unique perspective on how the categories (noun, verb, etc) are formed from listening to the patterns of words in a small set of sentences. These studies will not only reveal a basic mechanism of language learning, but also establish benchmarks against which language delay can be compared. Moreover, understanding the mechanisms that lead to successful acquisition in normal children can help to identify loci of language disorders and design methods for remediating disorders.
描述(由申请人提供):拟议研究的目的是提供影响婴儿,儿童和成人如何从语言输入的分布信息中学习母语类别的因素的综合说明。一种语言的范畴是由一组在语法句子中发挥同等功能的词(如名词、动词)组成的。分布信息指的是大型句子语料库中元素的模式,包括这些元素出现的频率、它们在句子中的位置以及邻近元素提供的上下文。我们对分词中的统计学习(学习者如何确定哪个音序列构成单词)的长期研究项目已经记录了婴儿、儿童和成人对复杂分布信息的敏感性的力量、速度和稳健性。在这里,我们将这个研究项目扩展到学习高级语言结构的一个关键方面。在我们提出的研究中,我们使用了一种微型的人工语言范式,使我们能够完全控制输入中的所有分布线索,这实际上是使用真实语言不可能做到的。参与者听一段话语样本,并对其可接受性做出判断。至关重要的是,在学习阶段,他们并没有听到人工语言中所有可能的“合法”话语;有些会留到后期测试中使用。测试后的话语要么符合学习阶段的分布模式,要么违背这些模式。关键的测试是参与者是否判断新颖但合法的话语是可以接受的,从而显示出正确概括的能力,而不是他们所接触到的输入。对儿童的研究通过将话语与简单事件的视频配对,为学习分布线索提供了额外的支持。对成人的研究将用于比较,并将向他们展示视觉-运动领域的学习材料,以评估学习的详细时间过程和结果对听觉语言材料的特异性。综上所述,这些对婴儿、儿童和成人的研究结果将记录语言学习中的关键结构变量,这些变量使类别形成的分布机制得以运作,并将突出这些机制在年龄和领域上的差异。公共卫生相关性:语言发展是人类物种的标志之一,但由于早期接触不同数量的语言输入的巨大差异,因此很难进行研究。使用在实验室里几个小时内习得的人工语言为语言发展的机制提供了一个窗口。我们将在实验室中学习语言学习,以获得一个独特的视角,了解类别(名词,动词等)是如何通过听一小组句子中的单词模式形成的。这些研究不仅揭示了语言学习的基本机制,而且为语言延迟的比较建立了基准。此外,了解导致正常儿童成功习得的机制可以帮助识别语言障碍的基因位点和设计治疗障碍的方法。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Richard N. Aslin其他文献
fNIRS Studies of Individuals with Speech and Language Impairment Underreport Sociodemographics: A Systematic Review
- DOI:
10.1007/s11065-023-09618-y - 发表时间:
2023-09-25 - 期刊:
- 影响因子:5.000
- 作者:
Teresa Girolamo;Lindsay Butler;Rebecca Canale;Richard N. Aslin;Inge-Marie Eigsti - 通讯作者:
Inge-Marie Eigsti
Frequency discrimination of pure-tones in human infants
- DOI:
10.1016/s0163-6383(84)80079-x - 发表时间:
1984-04-01 - 期刊:
- 影响因子:
- 作者:
Richard N. Aslin;Joan M. Sinnott - 通讯作者:
Joan M. Sinnott
Horizontal, vertical and oblique eye movements in infants to single- and double-step target displacements
- DOI:
10.1016/s0163-6383(84)80390-2 - 发表时间:
1984-04-01 - 期刊:
- 影响因子:
- 作者:
Sandra L. Shea;Richard N. Aslin - 通讯作者:
Richard N. Aslin
Covert attention modulates the SSVEP in a paradigm suitable for infants and young children
- DOI:
10.3758/s13414-025-03097-4 - 发表时间:
2025-06-05 - 期刊:
- 影响因子:1.700
- 作者:
Natasa Ganea;Richard N. Aslin;David J. Lewkowicz - 通讯作者:
David J. Lewkowicz
Infant neuroscience: how to measure brain activity in the youngest minds
婴儿神经科学:如何测量最幼小的大脑的活动
- DOI:
10.1016/j.tins.2024.02.003 - 发表时间:
2024-05-01 - 期刊:
- 影响因子:15.100
- 作者:
Nicholas B. Turk-Browne;Richard N. Aslin - 通讯作者:
Richard N. Aslin
Richard N. Aslin的其他文献
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{{ truncateString('Richard N. Aslin', 18)}}的其他基金
Statistical approaches to linguistic pattern learning
语言模式学习的统计方法
- 批准号:
8304226 - 财政年份:1999
- 资助金额:
$ 7.08万 - 项目类别:
Statistical approaches to linguistic pattern learning
语言模式学习的统计方法
- 批准号:
10348131 - 财政年份:1999
- 资助金额:
$ 7.08万 - 项目类别:
Statistical approaches to linguistic pattern learning
语言模式学习的统计方法
- 批准号:
8101854 - 财政年份:1999
- 资助金额:
$ 7.08万 - 项目类别:
Statistical approaches to linguistic pattern learning
语言模式学习的统计方法
- 批准号:
8511737 - 财政年份:1999
- 资助金额:
$ 7.08万 - 项目类别:
Statistical approaches to linguistic pattern learning
语言模式学习的统计方法
- 批准号:
7728608 - 财政年份:1999
- 资助金额:
$ 7.08万 - 项目类别:
Statistical approaches to linguistic pattern learning
语言模式学习的统计方法
- 批准号:
7911611 - 财政年份:1999
- 资助金额:
$ 7.08万 - 项目类别:
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