Valentin Marcel, Ph.D.

When leaving the aquatic constrained environment of the womb, newborns are thrown into the world with essentially new laws and regularities that govern their interactions with the environment. Here, we study how spontaneous self-contacts can provide material for learning implicit models of the body and its action possibilities in the environment. Specifically, we investigate the space of only somatosensory (tactile and proprioceptive) activations during self-touch configurations in a simple model agent. Using biologically motivated overlapping receptive fields in these modalities, a variational autoencoder (VAE) in a denoising framework is trained on these inputs. The denoising properties of the VAE can be exploited to fill in the missing information. In particular, if tactile stimulation is provided on a single body part, the model provides a configuration that is closer to a previously experienced self-contact configuration. Iterative passes through the VAE reconstructions create a control loop that brings about reaching for stimuli on the body. Furthermore, due to the generative properties of the model, previously unsampled proprioceptive-tactile configurations can also be achieved. In the future, we will seek a closer comparison with empirical data on the kinematics of spontaneous self-touch in infants and the results of reaching for stimuli on the body.

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.