Tania Lombrozo is a Professor of Psychology at Princeton University, as well as an Associate of the Department of Philosophy and the University Center for Human Values. She previously served as a Professor of Psychology at the University of California, Berkeley. She received her Ph.D. in Psychology from Harvard University in 2006 after receiving a B.S. in Symbolic Systems and a B.A. in Philosophy from Stanford University. Dr. Lombrozo’s research aims to address foundational questions about cognition using the empirical tools of cognitive psychology and the conceptual tools of analytic philosophy. Her work focuses on explanation and understanding, conceptual representation, categorization, social cognition, causal reasoning, and folk epistemology. She is the recipient of numerous early-career awards including the Stanton Prize from the Society for Philosophy and Psychology, the Spence Award from the Association for Psychological Science, a CAREER award from the National Science Foundation, and a James S. McDonnell Foundation Scholar Award in Understanding Human Cognition. She blogs about psychology, philosophy, and cognitive science at Psychology Today and for NPR’s 13.7: Cosmos & Culture.

CV   www.tanialombrozo.com

Mel Andrews is a postdoctoral fellow jointly affiliated with the Princeton AI Lab’s Natural and Artificial Minds initiative and the Center for Information Technology Policy (CITP). They are generously supported by a Sloan Foundation Metascience and AI grant. Dr. Andrews’ research concerns the promise (and pitfalls) of incorporating AI into scientific pipelines and the scientific nature of AI deployed in socially-sensitive arenas. Current projects evaluate prospective uses of machine learning in peer review, grant review, and metascientific applications, looking to offer guidance to institutions for science oversight in their development of AI use policies. Andrews earned a PhD in philosophy of science from the University of Cincinnati in 2025. Their work is cross-disciplinary, drawing on the scholarly traditions of history and philosophy of science, science and technology studies, and formal methods, alongside firsthand knowledge of practices in both laboratory and computer science. 

Joseph is a postdoctoral research associate affiliated with the UCHV in the Cognitive Science of Values. He received his Ph.D. from Rutgers University in cognitive psychology. His work emphasizes the severe difficulties of forming accurate beliefs in our extremely complicated world as well as how the mind adaptively directs its limited resources to navigate this complex environment. Joseph's research draws on a broad intersection of fields relevant to belief including judgment and decision-making, reasoning, and the psychology of attitudes and persuasion, as well as the philosophy of science, mental representation, and theory of computation. He is primarily interested in understanding the cognitive mechanisms underlying belief, including how beliefs are updated (or not) in response to evidence. Within belief and more broadly, Joseph is passionate about achieving cumulative theories in cognitive science.

Anna Tsvetkov is an AI Postdoctoral Research Fellow at Princeton. She received her PhD in Philosophy and ScM in Computer Science (AI/ML) from Brown. Her research focuses on human-centered AI. The goal of her work is to use philosophy to guide experiments on AI that deepen our understanding of the mind and, in turn, to use insights about the mind to build explainable and ethical AI.

Josh is a graduate student who works at the intersection of analytic philosophy and experimental psychology. In 2026, he began working in Princeton's Concepts & Cognition Lab. Prior to Princeton, he earned his M.S. in Philosophy at the University of Utah. Before that, he earned a M.A. in Philosophy at Colorado State University, and then taught philosophy at Colorado State University and the University of Northern Colorado. He has also worked at the USDA-ARS, where he helped develop a Python package called pyfao56. He is originally from a small farming community in Northeastern Colorado, and he is a proud first-generation college student.

Bella is a graduate student studying how people form mental models of competence, both their own and other agents', and when those models lead them astray. Previously, Bella studied young children's cognitive development, including toddlers' intrinsic motivation to explore their own competence. Her current projects examine when and why people defer decisions to AI across domains (e.g., moral, legal, etc.), and how surface features of AI outputs, such as perceived fluency, implicitly bias people's trust and deference in LLM-generated explanations. She can be reached at [email protected].

Sarah is a graduate student studying explanation. Her research prior to grad school has focused on understanding folk teleology and teleological explanation (or reasoning about purposes and explanations referring to those purposes). More broadly, Sarah is interested in what makes an intuitively satisfying explanation and what role those intuitions should play in broader theories. She can be reached at [email protected].

Casey is a graduate student studying how causal reasoning – how people attribute causes to outcomes, events, or beliefs – and moral reasoning – how people decide who, what, and when to morally blame – are intertwined. Specifically, how do people form explanations for why things happen, and what kinds of explanations are viewed as good or preferable? In what ways does this process of forming explanations shape the moral judgments that we make, and vice versa? Casey studies these topics in both adults and children from perspectives of psychology and philosophy.

Sebastian is a graduate student working at the intersection of philosophy of science and psychology. His research examines how people intuitively compare theories that fit the evidence equally but differ in their theoretical virtues (e.g., simplicity or lack of ad-hocness), and whether relying on these virtues confers explanatory or practical advantages. He is also interested in questions at the intersection of philosophy of mind, cognitive science, and religion, such as how people intuitively conceptualize consciousness, and the evolutionary origins of religious belief. Prior to graduate school, he worked at the National Institutes of Health, where he conducted research on visual perception and predictive processing using MEG. 

Kathryn is our lab manager and is interested in using insights from human behavior to better understand, use and build AI models. Additionally, they hope to use insights from computational models to better understand human behavior across a wide variety of domains. They can be reached at [email protected]. 

Haruka is an undergraduate student pursuing an A.B. in Psychology with certificates in Cognitive Science and Finance. In the lab, she is excited to explore the variety of inconsistencies in belief, meta-cognitive reasoning, and attitudes towards interpersonal disagreement. She can be reached at [email protected]. 

Justin is an undergraduate student pursuing an A.B. in Psychology with a minor in Statistics and Machine Learning. In the lab, he is interested in exploring how modern technologies such as the internet and LLMs affect people's understanding of their explanatory knowledge. He can be reached at [email protected].