Megan Peters is Associate Professor in the Department of Cognitive Sciences at the University of California, Irvine, and incoming faculty at University College London, where her lab investigates consciousness, metacognition, uncertainty, and the computational principles underlying subjective experience. She is also a Fellow in the CIFAR Brain, Mind & Consciousness Program, an elected board member of the Association for the Scientific Study of Consciousness, and co-founder and president of Neuromatch, a global educational and research community spanning neuroscience, AI, and computational science. Episode Summary: In this episode, Megan discusses the relationship between metacognition and consciousness, the limits of current AI systems, and the scientific challenges involved in testing for consciousness beyond biological organisms. Drawing from neuroscience, philosophy, and science fiction, she argues that machine consciousness is no longer a purely speculative topic, but an increasingly urgent scientific and societal question. We discuss: How Megan’s early interests in philosophy of mind, cognitive science, and science fiction led her toward studying subjective experience and machine consciousness. Why metacognition; the brain’s ability to monitor and model its own uncertainty, may play a central role in conscious experience, reality monitoring, and adaptive learning.The distinction between effortful, reflective metacognition and the more automatic self-monitoring processes that may exist across humans, animals, and potentially artificial systems.Why current large language models can imitate certain features of metacognitive reasoning while still failing at core forms of reality monitoring, belief stability, and self-consistency. The problem of “privileged access” in AI systems, and whether current models possess any meaningful distinction between representations of themselves and representations of others. Why Megan remains skeptical that present-day LLMs are conscious, particularly given the absence of temporal continuity, coherent selfhood, and persistent internal identity. The difficulty of testing for consciousness in non-human systems, and why most existing consciousness tests are deeply constrained by assumptions rooted in human biology and language. The “iterative natural kind strategy” for consciousness science: a framework for refining tests of consciousness by comparing how different measures co-vary across humans, animals, and potentially artificial systems.Why debates between biological naturalism and computational functionalism may be less binary than they first appear, and how future research may clarify which functions are genuinely necessary for consciousness.The ethical risks posed by both false positives and false negatives in machine consciousness; including social isolation, misplaced moral concern, legal ambiguity, and the possibility of large-scale “mind crime.” How science fiction continues to shape public intuitions about AI consciousness, often conflating intelligence with sentience while overlooking the possibility of highly capable but entirely non-conscious systems. Megan argues that consciousness science is entering a transitional moment: one in which questions that once belonged primarily to philosophy are rapidly becoming technological, empirical, and politically consequential. As increasingly capable AI systems become embedded in everyday life, the challenge is no longer simply defining consciousness, but determining how society should reason under deep uncertainty about minds unlike our own. Credits • Hosts: Henry Shevlin, Calum Chace • Guest: Megan Peters • Podcast: Exploring Machine Consciousness • Produced by: PRISM • Editor: Gerry Okinyi
