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.
