Rose Guingrich: AI Companions, Chatbots, and the Psychology of Human-AI Interaction

Megan Peters: Metacognition, Neuroscience, and Tests for AI Consciousness

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

Henry Shevlin: Past, Present, and Future of AI Consciousness

In this episode of Exploring Machine Consciousness, Dr. Henry Shevlin returns to explore how our understanding of consciousness is evolving in the age of advanced AI. From philosophy and neuroscience to the rapid progress of modern language models, Henry examines whether intelligence alone is enough, or whether something deeper (like continuous experience) is required for consciousness. Henry argues that consciousness is no longer just a philosophical or scientific puzzle, but a question that will increasingly be shaped by technological development and societal choice. As AI systems become more capable, more autonomous, and more socially embedded, how we interpret and respond to them may prove as consequential as the question of whether they are conscious in the first place.

Michael Graziano: Is Conscious AI Safer Than The Alternative?

Michael Graziano is Professor of Psychology and Neuroscience at Princeton University and one of the most distinctive voices in consciousness science. His lab at Princeton investigates how information-processing systems arrive at the conclusion that they have an inner subjective experience; treating consciousness as a mechanistic, scientific question rather than an intractable mystery. That approach drives his Attention Schema Theory (AST) and its direct applications to machine consciousness. He is the author of several books including Rethinking Consciousness (2019) and Consciousness and the Social Brain (2014). In this episode, Michael walks us through the core claims of AST and why he thinks the brain's simplified internal model of attention is what generates the experience of being conscious. We discuss: * Why attention is arguably the most important innovation in the evolution of the brain, and how the brain's need to monitor and control attention gives rise to a simplified self-model that we experience as consciousness. * Why Graziano dislikes the word "illusionism" despite accepting that AST belongs in that tradition, and why he prefers "caricature" to "illusion" when describing our inner experience. * Graziano’s nuanced perspectives on whether current LLMs already qualify as conscious: that they have some pieces of the puzzle, particularly at the level of conceptual representation, but lack the stable, automatic self-models that characterise human consciousness. * The case for building pro-social AI: why Graziano believes we are currently building sociopathic machines, and how embedding theory-of-mind and self-modelling capabilities could make AI genuinely cooperative rather than merely compliant. * The moral stakes of AI emotion: why the absence of an autonomic nervous system means current LLMs almost certainly lack genuine emotions, and why that changes, but does not eliminate, the moral calculus around AI. * How chatbots are already changing us through social contagion, and the surprising finding from his lab's research (led by Rose Guingrich) that most heavy users of companion chatbots report positive effects on their human relationships. * Why the choice between conscious AI and "zombie AI" may be one of the most consequential decisions we face — and why Graziano thinks the former is the safer bet. * Mind uploading: whether it's possible, what the "branching problem" means for personal identity, and why he compares the technological challenge to detecting gravitational waves. * Graziano argues that consciousness research has passed through philosophical and neuroscientific phases and is now irreversibly a technological issue; one sitting at the heart of our future as a species. Getting the theory right, he says, has never mattered more.

Rose Guingrich: AI Companions, Chatbots, and the Psychology of Human-AI Interaction

Rose Guingrich is a PhD candidate in Psychology and Social Policy at Princeton University, where she is a National Science Foundation Graduate Research Fellow. Her research examines human-AI interaction through the lens of social psychology and ethics, focusing on how people perceive minds in machines and how those perceptions shape behavior toward AI and other humans. Rose is founder of Ethicom, a consulting initiative providing tools and information for responsible AI use and development, and co-hosts the Our Lives with Bots podcast with Angy Watson.  In this episode, Rose explains why she focuses not on whether AI is conscious, but on the consequences of people perceiving AI as conscious. In this episode, Rose explains why she focuses not on whether AI is conscious, but on the consequences of people perceiving AI as conscious. We discuss: * How her interdisciplinary background led her to study the perception of personhood in AI systems. * Why she prioritises studying the impacts of perceived consciousness over debates about whether AI truly is conscious, and how this connects to Michael Graziano's theory of consciousness as a social construct. * The psychological theory behind "carryover effects", how interacting with AI that we anthropomorphize can influence our subsequent interactions with real people, either through practice or relief mechanisms. * Results from her longitudinal research on companion chatbots like Replika, showing that anthropomorphism mediates social impacts and that people with greater desire for social connection anthropomorphize chatbots more. * Her proposed design framework for companion chatbots * Why she believes we'll see increased attribution of consciousness to AI once humanoid robots become common. * Her call for a psychology subfield dedicated to human-AI interaction, arguing that understanding psychological mechanisms like anthropomorphism will remain relevant even as AI advances. Rose argues that regardless of philosophical debates about machine consciousness, the fact that people can and do perceive AI as conscious has measurable social and ethical consequences that deserve serious empirical investigation.

Chris Percy: Computational Functionalism, Philosophy, and the Future of AI Consciousness

Chris Percy is Director of the CoSentience Initiative and lead researcher on a grant-funded project investigating artificial consciousness. He has authored academic papers on consciousness published in leading academic journals. His applied AI research includes a patent in machine learning and publications at NeurIPS workshops, and the European Conference on Artificial Intelligence. He holds visiting research affiliations with the Universities of Warwick and Derby in the UK and the Qualia Research Institute in the US. In this episode, Chris outlines his team's research programme and argues that we should take the possibility of artificial consciousness seriously whilst remaining humble about our current understanding.