Modeling the Dynamics of Early Communication and Development

描述（由申请人提供）： 意义计算建模对于严格理解儿童第一次社会关系的发展至关重要。该项目将通过模拟早期社会互动动态的纵向变化来应对这一挑战。建模将整合情感和注意力的客观（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对交流信号的物理特性（如面部表情和发声）进行自动建模和分类。在二元互动过程中，自我调节和互动影响的动态模型将利用表达行为的精确测量，这些表达行为由与多巴胺能和多巴胺能功能相关的遗传标记调节。该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，他们对各种时间尺度的动态交互过程进行建模。 Approach.在2、4、6和8个月时，将使用面对面/静止面（FFSF）程序研究母婴相互作用、其干扰及其发展。面部建模、头部和手臂/手建模将用于对多模态交互行为套件进行客观测量，包括面部表情、注视方向、头部运动、搔痒和发声。模型将被训练和评估专家编码和非专家的看法情绪效价结构。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。逆最优控制建模将被用来推断婴儿和母亲的偏好，特定的二元状态给定观察到的行为模式。这些参数的上下文依赖性将被评估相对于引入的扰动的静止的脸（一个短暂的时期的干扰器要求的成人无响应）。将根据婴儿和母亲多巴胺能和多巴胺能功能的遗传指数对婴儿和母亲行为参数的个体差异进行建模。建模算法，测量软件和编码记录将与科学界分享，以促进对行为系统的理解。这些努力将增加对健康认知和社会情感发展途径的理解，并揭示改变的潜力，为早期干预工作提供信息。

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.