Behavioral and Neurobiological Underpinnings of Spoken Word Recognition in Late Language Emergence

摘要 大约15%的18-36个月大的幼儿出现语言延迟（LLE; Paul，1992; Singleton，2018）。这些晚说话者（LT）的表达性词汇减少，但平均非语言 能力，在没有明显的感觉或其他发育迟缓的情况下（Collisson，2016; Paul & Jennings，1992）。 超过16%的LT前瞻性符合语言障碍的标准（Rescorla，2009），而其他LT则保留了语言障碍的标准。 次优语言功能（Rescorla，2002; Singleton，2018）。LT是终身语言的风险增加 和识字障碍，对获得学术和职业机会产生负面影响（单身， 2018年;保罗，1993年），甚至亚临床结果也有普遍的负面影响（Rescorla，2002年）。这 该项目解决了LT早期诊断的关键问题，以及申请人长期 目标是建立一个关于LLE的独立研究计划，最终旨在确定变异， 行为表型的分布，为更有针对性的LT干预提供基础。这个项目 通过评估词汇学习的时间进程，补充了之前关于LLE的语言产生的工作 LT和2个对照组（年龄和语言匹配的通常发展中）的口语识别 他们都将完成认知语言能力的标准化评估。在Expt. 1, 参与者将在一个简单的选择任务上进行训练，使用4个新单词和4个熟悉的单词，这些单词在语音上重叠 (e.g.,在开始时，BUNNY-BUTTON，或偏移时，KITTEN-MITTEN）。我们将使用眼动追踪来估计组和 随着时间的推移，词汇激活和竞争的个体差异。在Expt. 2、我们会记录脑电图 （脑电图）在一个被动的听力任务。参与者将观看一段无声视频， 熟悉的单词expt 1重复。ERP（事件相关电位）分析将检查个人和 对新学单词和熟悉单词的反应存在群体差异。我们还将使用机器学习（支持 向量机（SVM）解码EEG响应特定的话为每个参与者，在逻辑上，保真度 响应的一致性将决定SVM分类的成功。眼睛的群体和个体差异 跟踪、ERP和/或EEG解码措施将为LT的接受能力提供新的见解， 为未来旨在识别临床或亚临床语言风险最大的LT的工作提供基础 结果。该项目将在美国进行。和哈斯金斯实验室申请人和赞助人 为申请人制定了培训计划，重点是进一步发展她的（1）EEG和统计技能， (2)典型和非典型语言发展的认知神经科学知识基础，（3） 传播技能;（4）理解负责任地进行研究的原则，目的是 支持她的目标是成为一个独立的研究人员在研究-1的环境。

ABSTRACT Approximately 15% of toddlers 18-36 months of age experience late language emergence (LLE; Paul, 1992; Singleton, 2018). These late talkers (LTs) have a reduced expressive vocabulary, but average non-linguistic abilities, in the absence of overt sensory or other developmental delays (Collisson, 2016; Paul & Jennings, 1992). Upwards of 16% of LTs prospectively meet criteria for language disorder (Rescorla, 2009), while others retain suboptimal language functioning (Rescorla, 2002; Singleton, 2018). LTs are at elevated risk for lifelong language and literacy impairments that negatively impact access to academic and vocational opportunities (Singleton, 2018; Paul, 1993), and even subclinical outcomes have pervasive negative impacts (Rescorla, 2002). This project addresses questions crucial for the early diagnosis of LTs and prerequisite to the applicant’s long-term goal of establishing an independent research program on LLE, aimed ultimately at identifying variation and distribution of behavioral phenotypes to provide a foundation for more targeted interventions for LTs. This project complements prior work on LLE focused on language production by evaluating the time course of word learning and spoken word recognition in LTs and 2 control groups (age- and language-matched typically developing peers), all of whom will complete standardized assessments of cognitive -linguistic abilities. In Expt. 1, participants will train on a simple selection task using 4 novel and 4 familiar words that overlap phonologically (e.g., at onset, BUNNY-BUTTON, or offset, KITTEN-MITTEN). We will use eye tracking to estimate group and individual differences in lexical activation and competition over time. In Expt. 2, we will record EEG (electroencephalography) in a passive listening task. Participants will watch a silent video as newly-learned and familiar words from Expt. 1 are repeated. ERP (event-related potential) analyses will examine individual and group differences in responses to newly-learned vs. familiar words. We will also use machine-learning (support vector machines, SVMs) to decode EEG responses to specific words for each participant, on the logic that fidelity and coherence of responses will determine SVM classification success. Group and individual differences in eye tracking, ERP, and/or EEG-decoding measures will provide new insights into receptive abilities of LTs, and provide a basis for future work aimed at identifying LTs with greatest risk for clinical or subclinical language outcomes. The project will take place at the U. of Connecticut and Haskins Labs. The applicant and sponsors have developed a training plan for the applicant focused on further developing her (1) EEG and statistical skills, (2) knowledge base of the cognitive neuroscience of typical and atypical language development, (3) dissemination skills, and (4) understanding of principles for the responsible conduct of research, with the aim of supporting her goal to be an independent researcher in a Research-1 environment.