The Influence of Morphosyntactic Network Complexity on Typical and Atypical Language Learning
The Influence of Morphosyntactic Network Complexity on Typical and Atypical Language Learning
批准号:
10218819
负责人:
Elisabeth Karuza
金额:
$24.08万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-08-01 至 2024-01-31
关键词:
2 year oldAddressAdultArchitectureAreaBlueberriesChildChild LanguageChildhoodComplexDependenceDevelopmentDiagnosisEatingEmploymentEnvironmentExhibitsExposure toFamilyFrequenciesHumanImpairmentIndividualInstinctInterventionKnowledgeLanguageLanguage DevelopmentLanguage Development DisordersLanguage DisordersLeadLearningLinguisticsLinkMeasuresMethodsMorphologyNatural Language ProcessingOutputParticipantPathway AnalysisPatternPopulationPrevalenceProcessProductionPropertyRaspberriesRecordsResearchRoleSamplingScienceSemanticsSeriesSignal TransductionSpeechStrawberriesStructureSystemTestingTherapeutic InterventionToddlerWorkagedclassical conditioningdesignearly childhoodeducational atmosphereexperimental studyflexibilityindexinginnovationkindergartenlanguage impairmentnatural languagenetwork architecturephrasesstatisticssyntaxtherapeutic developmenttoolyoung adult
中文摘要
项目摘要
拟议的项目详细说明了一条创新的研究路线,它利用了网络领域的先进工具
科学阐明成人典型和非典型的语言学习机制。自然界的网络分析
语言揭示了许多从关系中涌现出来的全球结构模式(用于构建
网络边缘)单词之间(用作网络节点)。由自然产生的网络提供信息
架构真实世界的语言,我们将构建显示紧急情况的微型人工语言
现有语言系统的特性,并衡量成年人学习这些语言的程度
典型发育组(TD)和发展性语言障碍组(DLD)(目标1)。鉴于DLD
通常与加工和产生复杂形态合成的缺陷有关,我们将集中在
单词之间的边表示它们在句子中同现或在句子中重叠的网络
形态家族(例如,食者、进食)。在目标2中,我们将研究成年人是否存在语言学习障碍
使用DLD可以被重塑为对局部级别信息的超聚焦(即,“古怪”结构或单个单词
频率),以牺牲语言学习环境的更广泛架构为代价。加强联系
在语言学习障碍和表达语言之间,目标3呼吁对引出的语言进行网络分析
语音样本揭示个体在学习过程中是否对复杂性不那么敏感
他们的语言输出很复杂。被诊断为接受性和表现性语言障碍的个人
在童年,绝大多数人在成年后仍在与语言障碍作斗争。尽管迫不及待
需要扩大面向成人的语言干预措施,全面描述#年缺陷的特征
患有DLD的人,除了他们童年以后的语言学习机制外,还没有得到充分的研究
区域。
英文摘要
Project Summary
The proposed project details an innovative line of research that draws on advanced tools from the field of network
science to elucidate typical and atypical language learning mechanisms in adults. Network analysis of natural
language has revealed a number of global structural patterns emerging from relationships (used to construct
network edges) between words (used as network nodes). Informed by the naturally-occurring network
architecture of real-world languages, we will construct miniature artificial languages that display emergent
properties of existing language systems and measure the extent to which these languages are learned by adults
with typical development (TD) and those with developmental language disorder (DLD) (Aim 1). Given that DLD
is often associated with deficits in processing and producing complex morphosyntax, we will concentrate on
networks in which edges between words represent either their co-occurrence in a sentence or overlap in
morphological family (e.g., eater, eating). In Aim 2, we will examine whether language learning deficits in adults
with DLD might be recast as a hyperfocus on local-level information (i.e., “oddball” structures or individual word
frequency) at the expense of the broader architecture of the language learning environment. To strengthen links
between deficits in language learning and expressive language, Aim 3 calls for the network analysis of elicited
speech samples to reveal whether individuals less sensitive to complexity in learning might also display reduced
complexity in their language output. Of individuals diagnosed with receptive and expressive language disorder
in childhood, the vast majority continue to struggle with language impairment in adulthood. Despite a pressing
need for expansion of adult-oriented language interventions, characterization of the full scope of deficits in
individuals with DLD, in addition to their language learning mechanisms beyond childhood, is an understudied
area.
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