Ready to CONNECT: Conversation and Language in Autistic Teens

准备好联系：自闭症青少年的对话和语言

• 批准号: 10807563
• 负责人: Inge-Marie Eigsti
• 金额: $ 54万
• 依托单位: UNIVERSITY OF CONNECTICUT STORRS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10807563
• 关键词: 15 year old; Acoustics; Address; Adherence; Administrator; Adolescent; Age; Back; Biological Markers; Boredom; Categories; Clinical; Collaborations; Communication; Computer Models; Data; Data Set; Development; Diagnostic; Dimensions; Divorce; Education; Empathy; Failure; Frequencies; Funding; Goals; Grain; Head; Individual; Influentials; Intervention; Label; Language; Language Tests; Life; Linguistics; Link; Machine Learning; Measures; Methodology; Modeling; National Institute on Deafness and Other Communication Disorders; Natural Language Processing; Occupational; Outcome; Output; Participant; Persons; Process; Psycholinguistics; Reporting; Research; Response Latencies; Sampling; Semantics; Social Environment; Social Interaction; Speech; Speed; Standardization; Strategic Planning; Structure; Subgroup; Teenagers; Testing; Transcend; Videoconferencing; Visual; Visualization; adolescent with autism spectrum disorder; aged; autism spectrum disorder; autistic; clinical practice; cognitive ability; complex data; data standards; dyadic interaction; experience; girls; improved; individuals with autism spectrum disorder; innovation; interest; lens; lexical; negative affect; network models; novel; personalized intervention; phonology; repaired; response; satisfaction; scaffold; social; social metrics; standardize measure; stem; success; syntax; theories; tool; verbal

Conversations are a critical medium for success in daily life, but predictors and measures of conversational success are poorly understood. The overarching goal of this proposal is to identify networks of naturalistic and standardized psycholinguistic features that lead to successful conversations. Standardized language assessments often do not capture important linguistic processes in real-world conversations, such as pronominal reference, back-channeling, turn-taking, or phonological, lexical, or syntactic alignment. The double empathy theory further posits that autistic conversational difficulties reflect failures of mutual understanding, rather than autistic deficits, indicating that autistic and neurotypical conversation partners differentially use and understand these linguistic processes. This proposal centers individuals with autism spectrum disorder who have age- appropriate scores on standardized language measures, many of whom nonetheless struggle with communication. We will use machine learning to model conversational profiles based on interactional measures of linguistic processes drawn from spontaneous conversation, and standardized language assessments, to evaluate conversational success in neurotype-concordant and neurotype-discordant interactions. Leveraging the ubiquity of videoconferencing, we will collect clinical and psycholinguistic data from dyadic conversations in a large sample of 500 12–15-year-old adolescents. We will also collect in-person conversational data from a group of n = 60. After providing a canonical speech sample, participants will have conversations with neurotype-concordant and -discordant partners in two contexts: (1) a get to know you conversation, and (2) a collaborative conversation, in which partners each hold one of a pair of pictures that differs in five ways and verbally collaborate to find the differences. We objectively define conversational success as the number and speed of correct identifications in Task 2. In addition, partners will rate their interactions post- hoc on subjective social metrics (e.g., likeability, warmth, boredom) and conversational success metrics (e.g., turn-taking, mutual appreciation, interest in further interaction). Conversations and speech samples will be recorded and then scored by naïve third-party raters on the same metrics. Recordings will be analyzed for acoustic, psycholinguistic, and conversational measures (e.g., fundamental frequency, prosodic range, pause duration, linguistic alignment, turn-taking). We will contrast the power of standardized scores and naturalistic psycholinguistic measures to predict both subjectively and objectively defined conversational success (Aim 1) and compare success in neurotype-concordant and neurotype-discordant partnerships (Aim 2). Aim 3 will leverage this rich dataset of acoustic, linguistic, perceptual, and standardized data to model computational predictor networks of conversational success. Results will advance the field by establishing metrics of conversational success in real-world social interactions and using computational models to form meaningful conversational profile clusters that go beyond simple diagnostic dichotomies to inform personalized supports.

对话是日常生活中成功的关键媒介，但对话的预测因素和衡量标准 该提案的总体目标是确定自然主义网络。 以及导致成功对话的标准化心理语言学特征。 评估通常无法捕捉现实世界对话中的重要语言过程，例如代词 参考、反向引导、轮流、或语音、词汇或句法对齐。 该理论进一步认为，自闭症患者的对话困难反映了相互理解的失败，而不是 自闭症缺陷，表明自闭症和神经正常的对话伙伴在使用和理解方面存在差异 这些语言过程的中心是患有自闭症谱系障碍的年龄- 在标准化语言测试中取得了适当的分数，但其中许多人仍然在与 我们将使用机器学习来建模基于交互的对话配置文件。 从自发对话和标准化语言中得出的语言过程的测量 评估，评估神经类型一致和神经类型不一致的会话成功率 利用无处不在的视频会议，我们将从中收集临床和心理语言数据。 我们还将亲自收集 500 名 12-15 岁青少年的二元对话。 来自 n = 60 人的一组对话数据。在提供规范的语音样本后，参与者将获得 在两种情况下与神经类型一致和不一致的伙伴进行对话：（1）了解你 对话，以及 (2) 协作对话，其中伙伴每人持有一对图片中的一张 在五个方面存在差异，并通过口头合作来找出差异。我们客观地定义对话的成功。 作为任务 2 中正确识别的数量和速度。此外，合作伙伴将对他们的互动进行评分。 特别针对主观社交指标（例如，可爱度、温暖度、无聊度）和对话成功指标（例如， 轮流、相互欣赏、对进一步互动的兴趣）。 记录，然后由天真的第三方评估者根据相同的指标进行评分。 声学、心理语言学和会话测量（例如基频、韵律范围、停顿） 我们将对比标准化分数和自然主义分数的力量。 预测主观和客观定义的对话成功的心理语言学措施（目标 1） 并比较神经类型一致和神经类型不一致伙伴关系的成功情况（目标 2）。 利用丰富的声学、语言、感知和标准化数据数据集来建模计算 对话成功的预测网络将通过建立对话成功的指标来推动该领域的发展。 现实世界社交互动中的对话成功并使用计算模型形成有意义的 对话式的个人资料集群超越了简单的诊断二分法，为个性化支持提供信息。