Applying machine learning to infant interaction: the development is in the details.

The face-to-face interactions of infants and their parents are a model system in which critical communicative abilities emerge. We apply machine learning methods to explore the predictability of infant and mother behavior during interaction with an eye to understanding the preconditions of infant intentionality. Overall, developmental changes were most evident when the probability of specific behaviors was examined in specific interactive contexts. Mother's smiled predictably in response to infant smiles, for example, and infant smile initiations become more predictable over developmental time. Analysis of face-to-face interaction--a tractable model system--promise to pave the way for the construction of virtual and physical agents who are able to interact and develop.