Follow your own path: Exploring the impact of self-directed information sampling on learning
Any complete account of human learning must explain not only what is learned from the information we experience, but also the capacity for our choices to expose that information. However, laboratory studies of learning and memory typically emphasize "passive" learning by limiting participants' control over the information they experience at each point in time. In this talk, I will discuss recent work in my lab exploring how people gather information in "self-directed" learning environments. The primary aim of this research is to characterize the information sampling strategy that participants use to reduce their uncertainty, and to examine how self-directed learning influences acquisition. To foreshadow, we find that participants learn faster when they can select and sequence learning episodes themselves. However, this advantage depends on the abstract structure of to-be-learned concepts and the space of hypotheses that the learner considers. A series of computational models are developed which try to predict which items people will sample/query as learning unfolds and which help explain why self-directed information sampling might improve learning. Our findings present an interesting challenge for existing theories of learning (both Bayesian and connectionist accounts), and may hold implications for instructional design.