Lei Yuan, Ph.D.

I am a cognitive and developmental psychologist, studying human intelligence and its development. I am currently a Postdoctoral Fellow at Indiana University, working with Linda Smith and Chen Yu. I also work with Kelly Mix from University of Maryland. I received my PhD from Northwestern University in 2016, working with David Uttal, Steve Franconeri, and Dedre Gentner.

My research on learning and development is guided by four core principles. First, learning is adaptive behavior to environmental input and the product of a complex system, including many areas (e.g., perception, action, language, social interaction) operating on different temporal scales (e.g., brain activation, visual attention, and behavior). Second, learning has a cascading effect—early experience affects later behavior and later learning. Thus, I study both “low-level” processes—e.g., eye gaze, saccadic movement, sustained attention, multisensory-coordination—and “high-level” behaviors—e.g., play, learning, social interaction—as well as their interaction across developmental times. To some extent, both of the above principles of human learning also apply to animal learning (e.g. there are abundant similarities in how the memory system works across different species). But, one important and arguably unique aspect of human learning is our ability to go beyond the immediately perceived and extract deep relational structures. We draw maps to represent the spatial relations among places in the world rather than simply taking a photograph; we use symbolic numbers, diagrams, and graphs to capture the relational patterns for events in the world. Finally, it is intuitive to think about these relational structures as something that we are taught at schools, and they do set the barriers to mastering some of the most difficult subjects in formal education (e.g., mathematics, physics, computer science). But, my research concerns about the developmental pathways and mechanisms through which early learning environment can create hidden competencies and hidden deficits that cast long-term consequences for later school learning. My overall goal is to understand learning and cognition in the wild—where children succeed or fail—and to use experimental methods and computational models—to better understand and contribute to in-the-world learning.