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.
The modeling of early infant-parent relationships is central to a rigorous quantitative understanding of social development. Objective measurements of communicative behavior and related genetic markers in infant and mother will be used to model the development of self- regulation and interactive influence as they develop longitudinally. Genetically informed modeling of the infant-mother interactive system will produce a rigorous understanding of parameters that describe the diversity of early developmental pathways and potential psychopathological deviations from those pathways.
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|Chow, Sy-Miin; Hoijtink, Herbert (2017) Bayesian estimation and modeling: Editorial to the second special issue on Bayesian data analysis. Psychol Methods 22:609-615|
|Tang, Niansheng; Chow, Sy-Miin; Ibrahim, Joseph G et al. (2017) Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness. Psychometrika 82:875-903|
|Lu, Zhao-Hua; Chow, Sy-Miin; Loken, Eric (2017) A comparison of Bayesian and frequentist model selection methods for factor analysis models. Psychol Methods 22:361-381|
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