Modeling the Dynamics of Early Communication and Development

描述(由申请人提供)： 意义重大。计算建模对于严格理解儿童第一次社会关系的发展是至关重要的。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。建模将整合对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量。 创新。行为的客观测量将涉及对交流信号的物理特性进行自动建模和分类--例如面部表情和发声。二元相互作用中自我调节和交互影响的动态模型将利用与多巴胺和5-羟色胺能功能相关的遗传标记调节的表达行为的精确测量。跨学科团队包括来自发展和数量心理学、遗传学、情感计算、计算机视觉和物理学的研究人员，他们在不同的时间尺度上对动态交互过程进行建模。 接近。在2、4、6和8个月时，将使用面对面/静止面对面(FFSF)程序来研究婴儿-母亲的相互作用、其干扰和发展。面部建模、头部建模和手臂/手建模将用于对一套多模式交互行为进行客观测量，包括面部表情、凝视方向、头部移动、挠痒和发声。模型将根据专家编码和非专家对情绪配价结构的感知进行培训和评估。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。逆最优控制模型将被用来推断婴儿和母亲对给定观察到的行为模式的特定二元状态的偏好。这些参数的上下文依赖性将根据静默(调查者要求的成人无反应性的短暂时期)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和5-羟色胺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方面的进展。这些努力将增加对健康认知和社会情感发展途径的理解，并揭示将为早期干预努力提供信息的变化的潜力。

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.