Cooperative information transfer plays a critical role in recent theories of cognitive development and cultural evolution. It also plays a potentially critical role in the development of novel theories in education, and theories and algorithms in machine learning and AI. Adopting a computational approach, I analyze the implications of cooperative information transfer for cognition. I review evidence suggesting that (even young) people's learning is informed by the cooperative intent of others, discuss new modeling indicating that cooperatively selected evidence is provided very early in development, and derive a priori implications of cooperation for the inductive biases that guide learning.
Light refreshments will be served.