Our research examines human learning, problem solving, and motivation with an aim to understand, predict, and promote knowledge transfer. Transfer is the ability to use prior knowledge and experience to solve novel problems. We have focused on four interrelated lines of research on transfer: 1) investigating the cognitive and metacognitive processes underlying transfer success and failure, 2) exploring the relationship between motivation, cognition, metacognition, and transfer, 3) examining the social (e.g., collaboration and competition) and ecological processes that support or inhibit transfer, and 4) investigating the effects of mindfulness meditation on cognition, learning, and transfer. An overarching goal is to develop instructional theories to promote knowledge transfer for both children and adults across a range of formal and informal learning environments. We are particularly interested in creating and testing forms of instruction that integrate psychological theories with technology innovations (e.g., intelligent tutoring systems) to achieve this goal.


Below are descriptions of the three main areas of work done in the lab.

Cognitive Processes and Mechanisms of Transfer

Transfer Scenario image A central aim of cognitive science is to develop a general theory of transfer to explain how people use and apply their prior knowledge to solve new problems. Previous work has identified multiple mechanisms of transfer including (but not limited to) analogy, knowledge compilation, and constraint violation. The central hypothesis investigated in the current work is that the particular profile of transfer processes triggered for a given situation depends on both (a) the knowledge present and how it is represented, and (b) the processing demands of the transfer task. We hypothesize that there is a trade-off between the mechanisms in terms of their scope of application (i.e., near vs. far transfer) and the amount of cognitive processing required to transfer the knowledge. Current laboratory studies examine when these mechanisms are triggered and their interaction in complex learning environments. Initial results show that people can adaptively shift between transfer mechanisms depending on their prior knowledge and the characteristics of the transfer task. The goal of this work is to develop a general theory of transfer based on a multiple mechanisms approach.

Conceptual Learning

Linear Distance Arc Length image A central focus of our work is to investigate and understand the mechanisms of conceptual learning. We are interested in determining the cognitive processes that lead to the development of deep conceptual knowledge – knowledge that is flexible and transfers to new contexts and situations. Much work focuses on the role of explanation and analogical comparison in conceptual learning in probability and physics. We are also interested in how the form of the learning materials (e.g., principles or worked examples) affects learning and transfer. One issue is how the level of abstraction impacts the representation of the acquired knowledge. Other topics include the role of meta-cognition and interest in conceptual learning.

A major theme of the lab (that runs across both the transfer and conceptual learning strand) is to understand the dynamic interaction of multiple cognitive processes in learning and transfer.

Examining the Collaborative Processes and Factors that Support or Inhibit Transfer

In this work we examine the effects of expertise on collaborative problem solving. Specific questions include: What cognitive and social processes lead to successful collaborative performance? What impact does domain expertise have on one’s ability to effectively cross-cue and share information in a collaborative context? To examine these questions we are conducting laboratory experiments where novice and expert pilots are asked to come up with possible solutions for simple and complex flight problem scenarios either working individually or in collaboration with another person (with the same level of expertise). Initial results show a larger collaborative benefit for experts than for novices on the complex problems. We are currently analyzing verbal protocols to identify the types of collaborative interactions associated with this advantage.


Beckman Foundation Center for Research and Development in Cognition Science Instruction Department of Education image IES image Pittsburgh Science of Learning Center LearnLab image NSF image