The Influence of Morphosyntactic Network Complexity on Typical and Atypical Language Learning

形态句法网络复杂性对典型和非典型语言学习的影响

• 批准号: 10218819
• 负责人: Elisabeth Karuza
• 金额: $ 24.08万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10218819
• 关键词: 2 year old; Address; Adult; Architecture; Area; Blueberries; Child; Child Language; Childhood; Complex; Dependence; Development; Diagnosis; Eating; Employment; Environment; Exhibits; Exposure to; Family; Frequencies; Human; Impairment; Individual; Instinct; Intervention; Knowledge; Language; Language Development; Language Development Disorders; Language Disorders; Lead; Learning; Linguistics; Link; Measures; Methods; Morphology; Natural Language Processing; Output; Participant; Pathway Analysis; Pattern; Population; Prevalence; Process; Production; Property; Raspberries; Records; Research; Role; Sampling; Science; Semantics; Series; Signal Transduction; Speech; Strawberries; Structure; System; Testing; Therapeutic Intervention; Toddler; Work; aged; classical conditioning; design; early childhood; educational atmosphere; experimental study; flexibility; indexing; innovation; kindergarten; language impairment; natural language; network architecture; phrases; statistics; syntax; therapeutic development; tool; young adult

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.

项目概要 拟议的项目详细介绍了利用网络领域先进工具的创新研究路线 阐明成人典型和非典型语言学习机制的科学。自然网络分析 语言揭示了一些从关系中出现的全球结构模式（用于构建 单词（用作网络节点）之间的网络边）。由自然发生的网络通知 现实世界语言的架构，我们将构建微型人工语言来显示新兴的 现有语言系统的属性并衡量成年人学习这些语言的程度 典型发育 (TD) 和发育性语言障碍 (DLD) 的患者（目标 1）。鉴于 DLD 通常与加工和产生复杂形态句法的缺陷有关，我们将集中精力 网络中单词之间的边代表它们在句子中的共现或重叠 形态家族（例如，食者、进食）。在目标 2 中，我们将检查成人是否存在语言学习缺陷 DLD 可能会被重新定义为对本地信息的高度关注（即“奇怪的”结构或单个单词） 频率），但以牺牲更广泛的语言学习环境架构为代价。加强联系 目标 3 要求对语言学习和表达性语言的缺陷进行网络分析 语音样本揭示了对学习复杂性不太敏感的个体是否也可能表现出降低的程度 他们的语言输出的复杂性。被诊断患有接受性和表达性语言障碍的个体 在童年时期，绝大多数人在成年后仍继续与语言障碍作斗争。尽管压力重重 需要扩大以成人为导向的语言干预措施，描述全面的缺陷 患有 DLD 的个体，除了童年期以后的语言学习机制之外，也是一个尚未得到充分研究的问题。 区域。