Ing. Filipe Gama

Children are not born tabula rasa. However, interacting with the environment through their body movements in the first months after birth is critical to building the models or representations that are the foundation for everything that follows. We present longitudinal data on spontaneous behavior of three infants observed between about 8 and 25 weeks of age in supine position. We combined manual scoring of video recordings with an automatic extraction of motion data in order to study infants’ behavioral patterns and developmental progression such as: (i) spatial distribution of self-touches on the body, (ii) spatial patterns and regularities of hand movements, (iii) midline crossing, (iv) preferential use of one arm, and (v) dynamic patterns of movements indicative of goal-directedness. From the patterns observed in this pilot data set, we can speculate on the development of first body and peripersonal space representations. Several methods of extracting 3D kinematics from videos have recently been made available by the computer vision community. We applied one of these methods on infant videos and provide guidelines on its possibilities and limitations—a methodological contribution to automating the analysis of infant videos. In the future, we plan to use the patterns we extracted from the recordings as inputs to embodied computational models of learning of body representations in infancy.

The mechanisms of infant development are far from understood. Learning about one's own body is likely a foundation for subsequent development. Here we look specifically at the problem of how spontaneous touches to the body in early infancy may give rise to first body models and bootstrap further development such as reaching competence. Unlike visually elicited reaching, reaching to own body requires connections of the tactile and motor space only, bypassing vision. Still, the problems of high dimensionality and redundancy of the motor system persist. In this work, we present an embodied computational model on a simulated humanoid robot with artificial sensitive skin on large areas of its body. The robot should autonomously develop the capacity to reach for every tactile sensor on its body. To do this efficiently, we employ the computational framework of intrinsic motivations and variants of goal babbling-as opposed to motor babbling-that prove to make the exploration process faster and alleviate the ill-posedness of learning inverse kinematics. Based on our results, we discuss the next steps in relation to infant studies: what information will be necessary to further ground this computational model in behavioral data.