Modeling the Dynamics of Early Communication and Development

模拟早期沟通和发展的动态

• 批准号: 8452565
• 负责人: JEFFREY F COHN
• 金额: $ 57.2万
• 依托单位: UNIVERSITY OF MIAMI CORAL GABLES
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8452565
• 关键词: Address; Adult; Affective; Age; Algorithms; Attention; Behavior; Behavioral; Child Development; Classification; Code; Cognitive; Communication; Communities; Complex; Computer Simulation; Computer Vision Systems; Computer software; Computers; Data Set; Dependence; Development; Early Intervention; Elements; Emotional; Emotions; Event; Evolution; Face; Facial Expression; Functional RNA; Genetic; Genetic Markers; Hand; Head; Head Movements; Human; Individual; Individual Differences; Infant; Informal Social Control; Joints; Light; Manuals; Measurement; Measures; Metaphor; Modeling; Mothers; Motion; Neurophysiology - biologic function; Parents; Pathway interactions; Pattern; Perception; Physics; Play; Procedures; Process; Psychology; Research Personnel; Scientist; Series; Signal Transduction; Social Behavior; Social Development; Social Interaction; System; Time; Training; Work; arm; behavior measurement; dyadic interaction; gaze; genetic variant; indexing; infant monitoring; innovation; insight; model development; multilevel analysis; physical property; preference; public health relevance; response; social; social skills; vocalization

DESCRIPTION (provided by applicant): Significance. Computational modeling is central to a rigorous understanding of the development of the child's first social relationships. The project will address this challenge by modeling longitudinal change in the dynamics of early social interactions. Modeling will integrate objective (automated) measurements of emotion and attention and common genetic variants relevant to those constructs. Innovation. Objective measurement of behavior will involve the automated modeling and classification of the physical properties of communicative signals-such as facial expressions and vocalizations. Dynamic models of self-regulation and interactive influence during dyadic interaction will utilize precise measurements of expressive behavior as moderated by genetic markers associated with dopaminergic and serotonergic functioning. The interdisciplinary team includes investigators including from developmental and quantitative psychology, genetics, affective computing, computer vision, and physics who model dynamic interactive processes at a variety of time scales. Approach. Infant-mother interaction, its perturbation, and its development, will be investigated using the Face-to-Face/Still-Face (FFSF) procedure at 2, 4, 6, and 8 months. Facial modeling, head, and arm/hand modeling will be used to conduct objective measurements of a multimodal suite of interactive behaviors including facial expression, gaze direction, head movement, tickling, and vocalization. Models will be trained and evaluated with respect to expert coding and non-experts' perceptions of emotional valence constructs. Dynamic approaches to time-series modeling will focus on the development of self-regulation and interactive influence. Inverse optimal control modeling will be used to infer infant and mother preferences for particular dyadic states given observed patterns of behavior. The context-dependence of these parameters will be assessed with respect to the perturbation introduced by the still-face (a brief period of investigator-requested adult non-responsivity). Individual differences in infant and mother behavioral parameters will be modeled with respect to genetic indices of infant and mother dopaminergic and serotonergic function. Modeling algorithms, measurement software, and coded recordings will be shared with the scientific community to catalyze progress in the understanding of behavioral systems. These efforts will increase understanding of pathways to healthy cognitive and socio-emotional development, and shed light on the potential for change that will inform early intervention efforts.

描述（由申请人提供）： 意义。计算建模对于严格理解儿童最初的社会关系的发展至关重要。该项目将通过对早期社会互动动态的纵向变化进行建模来应对这一挑战。建模将整合情绪和注意力的客观（自动）测量以及与这些结构相关的常见遗传变异。 创新。行为的客观测量将涉及通信信号物理特性（例如面部表情和发声）的自动建模和分类。二元相互作用期间自我调节和相互作用影响的动态模型将利用与多巴胺能和血清素能功能相关的遗传标记调节的表达行为的精确测量。这个跨学科团队包括来自发展和定量心理学、遗传学、情感计算、计算机视觉和物理学的研究人员，他们在各种时间尺度上对动态交互过程进行建模。 方法。将在 2、4、6 和 8 个月时使用面对面/静态 (FFSF) 程序来研究婴儿与母亲的互动、其扰动及其发展。面部建模、头部和手臂/手部建模将用于对多模式交互行为进行客观测量，包括面部表情、注视方向、头部运动、痒痒和发声。将根据专家编码和非专家对情感效价结构的感知来训练和评估模型。时间序列建模的动态方法将侧重于自我调节和交互影响的发展。逆向最优控制模型将用于根据观察到的行为模式推断婴儿和母亲对特定二元状态的偏好。这些参数的上下文依赖性将根据静止面孔（研究者要求的成人无反应的短暂时期）引入的扰动进行评估。婴儿和母亲行为参数的个体差异将根据婴儿和母亲多巴胺能和血清素能功能的遗传指数进行建模。建模算法、测量软件和编码记录将与科学界共享，以促进对行为系统的理解取得进展。这些努力将增进对健康认知和社会情感发展途径的理解，并揭示变革的潜力，为早期干预工作提供信息。