Abstract: To understand and navigate our world, both individuals and scientific communities create simplifying representations, such as concepts and theories. How do we construct useful representations from our experiences, and how do we use these representations to guide our learning? In this talk, I discuss empirical research on the mechanisms of human concept learning, highlighting the ease with which we adopt new, even arbitrary, conceptualizations. I illustrate how these acquired concepts instantly shape how we perceive and explore the world. For example, I show that our perception of objects becomes biased by our conceptual needs and our knowledge about these objects. I demonstrate that similar mechanisms are at play when scientific conceptualizations, such as the DSM in psychology and the periodic table in chemistry, guide scientific exploration. Then, I discuss the double-edged nature of theory-guided discovery—although conceptualizations can efficiently steer us towards new experiences that further refine our knowledge, they can also lead our exploration astray. I present a computational model of scientific discovery in which agents conduct experiments, build theories, and share results to advance collective understanding of the world. The model reveals that when new experiments are guided by existing theoretical frameworks, scientific communities risk missing important aspects of the world not yet captured by their theories. I conclude by reviewing my current and future research that integrates cognitive psychology, machine learning, and philosophy of science to enhance our understanding of how theories and observations can inform each other to support—rather than hinder—human learning and scientific progress.