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About

Tom Stafford is a cognitive scientist whose research sits at the intersection of human decision-making, intrinsic motivation, and the principles underlying open-ended learning. He is perhaps best known for his work on "magnitude-sensitivity," a framework that rethinks how we make decisions by focusing on how we perceive and respond to the magnitude of potential outcomes, rather than just their probabilities. This work, published in 2021, has already garnered 33 citations, signaling its growing influence in the field. Stafford also made foundational contributions to the study of intrinsic motivation—the drive to explore and learn for its own sake—co-authoring a highly cited 2014 editorial (69 citations) that surveyed how this concept operates across animals, humans, and robots. He was a key figure in the IM-CLeVeR project, a major European initiative that aimed to build robots capable of cumulative, intrinsically motivated learning. By bridging insights from psychology, neuroscience, and robotics, Stafford’s work helps explain how both natural and artificial systems can develop flexible, open-ended behaviors without external rewards. For students and researchers, his research offers a compelling lens for understanding the cognitive foundations of curiosity and adaptive decision-making.