The ability to interpret demonstrations from the perspective of the teacher plays a critical role in human learning. Robotic systems that aim to learn effectively from human teachers must similarly be able to engage in perspective taking. We present an integrated architecture wherein the robot's cognitive functionality is organized around the ability to understand the environment from the perspective of a social partner as well as its own. The performance of this architecture on a set of learning tasks is evaluated against human data derived from a novel study examining the importance of perspective taking in human learning. Perspective taking, both in humans and in our architecture, focuses the agent's attention on the subset of the problem space that is important to the teacher. This constrained attention allows the agent to overcome ambiguity and incompleteness that can often be present in human demonstrations and thus learn what the teacher intends to teach.