I am a lecturer in Cognitive Psychology. I am interested in higher level cognition, particularly how people represent the world and think about its alternatives, plus how they use these abilities to plan, imagine, explain, blame and solve problems. I generally use interactive online experiments and games combined with computational modelling to investigate these issues.
Contact: firstname.lastname@example.org Accepting students: No
I am interested in how distributed systems (like brains and artificial neural networks) come to represent and reason about relational concepts (like ‘above’, ‘next-to’, or ‘likes’). Specifically, I am interested in how children and adults learn to think about, represent, and use relations for solving problems. My students, my collaborators, and I explore these issues in domains like analogy making, mathematical reasoning, and learning. We employ empirical methods with children and adults, computational models, and techniques from neuroscience to understand how we think and learn.
My current research focuses on three interrelated areas: (1) moral intuition and how it interacts with social and political orientation, (2) the psychology of (the desire for) power and its impact on judgement and decision making, and (3) the influence of both of these on reasoning ability/performance.
My research investigates the intuitive processes that underlie scientific misconceptions, and aims to develop interventions to correct these misconceptions. I address these issues using a range of methods including behavioral studies with adults and children, data mining and machine learning techniques, surveys of experts, and Bayesian statistical modeling.
I study how people learn and make decisions, especially when they lack knowledge about important elements of their environment. My research focuses broadly on how people create and improve representations of their environments under such conditions. I’m interested in questions such as: How do people interpret and use environmental feedback, and how do they know what kind of feedback to pay attention to in the first place? What is the role of people’s background knowledge (e.g., of communicational and other cultural norms, of common environmental regularities, etc.) in building representations? How and why do people become and remain motivated to create and improve representations (or, when they don’t, why not)?
My research questions tackle how mental representation and abstraction work. What is gained and what is lost when we conceptualise or formalise? How do we choose the best level of detail for an explanation? I have a strong interest in methodological issues in psychology and how that relates to the wider philosophy of science. When is it helpful to lever opposing viewpoints, and when is it important to adopt a definite stance?
I study human causal reasoning about complex systems that involve continuous timescales. My research is motivated by questions like: how do people make causal inferences so efficiently and adaptively with limited cognitive recourses? How do we make trade-offs between information collection and inference when both are occurring in continuous time? What information do we outweigh from the large continuous data flow? How do we know not only what to intervene on but also when to intervene if we can actively learn a causal system? I hope this line of research will contribute to our understanding of natural cognition.
I am interested in multi-agent systems and probabilistic representations of beliefs (e.g., in graphical models). For example, I am interested in how optimal network configurations can be found to optimize network performance. Currently, I am investigating information cascades and the emergence of echo chambers through agent-based simulations. At the same time, I aim to develop a better understanding of how people deal with statistical dependencies in social learning. These findings might help to further improve current models of belief propagation.
I am interested in analogy, relational reasoning, mental representation and creativity.
How do people use previous experience to navigate through novel situations? What’s the role of causal reasoning in these processes? How do we solve the intractibility problem of Bayesian inference in modeling causal generalization? I study these questions using interactive online experiments and a hybrid approach combining symbolic and sub-symbolic techniques.
Former Lab Members
PhD (University of Edinburgh) Now: Postdoc at University of Bristol
Lab manager (Arizona State) Now: PhD student at University of Pittsburgh
Masters student (Arizona State) Now: PhD student at University of Pittsburgh
Research Assistant (Arizona State) Now: PhD student at UCLA
Masters student (Arizona State) Now: PhD student at Michigan State University
Masters student (Arizona State) Now: Metascience Research Coordinator
PhD (University of Hawaii) Now: Apple
PhD (Arizona State) Now: Postdoc at University of California, Davis