Many practical considerations affect the kinds of scientific questions typically pursued by developmental labs. For instance, large sample sizes, work with special populations, and longitudinal designs are often avoided because of the resources involved in recruitment and testing.  These practical constraints limit our ability to establish small or graded effects and to learn about specific disorders, individual differences, and the effects of interventions.  I will present a new online interface, “Lookit,” for infant and child testing that aims to mitigate these constraints by enabling large-scale participation in developmental studies, analogous to the kind of participation enabled by Amazon Mechanical Turk in adult cognitive science.  While families complete a short, browser-based developmental study, webcam video of the child’s responses is streamed back to our lab for later analysis.

This platform allows us to collect data from a more diverse group of participants than developmental labs do by default, and to collect reliable looking-time and preferential-looking data from infants and children. I will present the results of three exploratory replications to demonstrate the strengths and limitations of this system in collecting verbal, preferential looking, and looking time measures. Finally, as a concrete example of a new type of question we can address by online work, I will discuss plans to collect many samples from individual infants and children on Lookit in order to get a more detailed view of what constitutes ‘partial knowledge’ of physical support.