Behavioral and Neural Measures of Spoken Word Recognition in Late Language Emergence

摘要 语言出现迟缓(LLE)或说话迟缓(LTS)的幼儿是18-35个月大的儿童，他们的语言能力有限 口语词汇，但平均非语言能力，没有明显的感觉障碍或其他发展 延迟，如自闭症谱系障碍(Colisson，2016；Paul&Jennings，1992)。约15%的 蹒跚学步的孩子符合LLE标准(Singleton，2018；Paul，1992)，并且有更高的风险坚持终身语言 和识字缺陷，导致不良的社会、学术和职业成果(Paul，1993；Rescorla，2009； Singleton，2018)。超过16%的LTS患者可能会被诊断为口语和/或书面语 9岁时的语言障碍(Paul，Murray，Clancy，&Andrews，1997)，而大多数外语学习者的语言能力都不是最好的 在成年早期发挥作用(Rescorla，2002；Singleton，2018)。拟议的研究旨在(1) 确定慢性影响的早期行为和神经标记物，以便早期最佳分配稀缺资源 干预资源，以及(2)检查行为表型的变异和分布，这将提供 为更有针对性和更有针对性的LTS干预形式奠定基础，包括一系列临床和亚临床 语言结果。 该项目将通过评估罚款来补充先前关于语言产生的LLE的工作- LTS和两个对照组(典型的发育期年龄--译注)口语再认(SWR)的粒状时程 匹配和语言匹配的同龄人)使用行为(眼球跟踪)和神经(脑电) 测量。所有参与者将完成全面的临床特征，包括评估 语言和非语言功能。在实验1中，参与者将接受一个简单的选择任务的培训 使用八个在语音上重叠的熟悉单词(例如，在开头，兔泡泡，在偏移处，猫- Mitten)和语义(例如，Bunny-kitten)。眼睛跟踪将被用来估计群体和个人 词汇激活和竞争随着时间的推移在选择任务中的差异。在实验2中，神经反应 将被记录在被动听力范式中，使用脑电，当参与者观看一部无声电影时，熟悉的 实验1中的单词重复多次。我们将训练一个机器学习算法(支持向量 机器，或支持向量机)，以解码每个蹒跚学步的孩子的特定单词的完整脑电反应。团体和个人 支持向量机相对成功率的差异可能反映了神经反应的一致性和保真度。ERP (事件相关电位)分析也将被用来检验群体和个体在平均值上的差异 对所说的话的反应。眼球跟踪、脑电和/或事件相关电位测量的群体和个体差异 将对LTS的语言处理能力产生新的见解，并将为未来的工作提供基础 在找出结果不佳的风险最大的低收入者方面。

ABSTRACT Toddlers with late language emergence (LLE), or late talkers (LTs), are 18-35 month-olds with a limited spoken vocabulary, but average non-linguistic abilities and no overt sensory impairments or other developmental delays such as autism spectrum disorder (Collisson, 2016; Paul & Jennings, 1992). Approximately 15% of toddlers meet LLE criteria (Singleton, 2018; Paul, 1992) and are at elevated risk for persistent, lifelong language and literacy deficits that result in poor social, academic and vocational outcomes (Paul, 1993; Rescorla, 2009; Singleton, 2018). Upwards of 16% of LTs will prospectively be diagnosed with a spoken and/or written language disorder at age 9 (Paul, Murray, Clancy, & Andrews, 1997) while the majority of LTs retain suboptimal language functioning through early adulthood (Rescorla, 2002; Singleton, 2018). The proposed research seeks to (1) identify early behavioral and neural markers of chronic impact in order to optimally allocate scarce early intervention resources, and (2) examine the variation and distribution of behavioral phenotypes which will provide the foundation for more focused and targeted forms of interventions for LTs with a range of clinical and subclinical language outcomes. The project will complement prior work on LLE focused on language production by evaluating the fine- grained timecourse of spoken word recognition (SWR) in LTs and two control groups (typically developing age- matched and language-matched peers) using behavioral (eye tracking) and neural (electroencephalography) measurements. All participants will complete comprehensive clinical characterization including assessments of language and non-linguistic functioning. In Experiment 1, participants will be trained on a simple selection task using eight familiar words that overlap phonologically (e.g., at onset, BUNNY-BUBBLES, at offset, KITTEN- MITTEN) and semantically (e.g., BUNNY-KITTEN). Eye tracking will be used to estimate group and individual differences in lexical activation and competition over time in the selection task. In Experiment 2, neural responses will be recorded in a passive listening paradigm, using EEG, as participants view a silent movie while the familiar words from Experiment 1 are repeated many times. We will train a machine-learning algorithm (support vector machine, or SVM) to decode the full EEG response to specific words for each toddler. Group and individual differences in the relative success of the SVM may reflect the consistency and fidelity of neural responses. ERP (event-related potential) analysis will also be used to examine group and individual differences in mean responses to the spoken words. Group and individual differences in eye tracking, EEG and/or ERP measures will yield new insights into the language processing abilities of LTs and will provide a basis for future work aimed at identifying those LTs at greatest risk for poor outcomes.