Shanahan, Murray

The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs.

Carhart-Harris, R. L., Leech, R., Hellyer, P. J., Shanahan, M., Feilding, A., Tagliazucchi, E., Chialvo, D. R., & Nutt, D. (2014) · Frontiers in Human Neuroscience

Psychedelics and Drug StudiesParanormal Experiences and Beliefs

Cited by 1,252

Role play with large language models

Shanahan, M., McDonell, K., & Reynolds, L. (2023) · Nature

Speech and dialogue systemsSocial Robot Interaction and HRI

Cited by 491

Role-Play with Large Language Models

Shanahan, M., McDonell, K., & Reynolds, L. (2023) · arXiv

Speech and dialogue systemsSocial Robot Interaction and HRI

Cited by 490

Talking about Large Language Models

Shanahan, M. (2024) · Communications of the ACM

Social Robot Interaction and HRISpeech and dialogue systems

Cited by 381

Conditional Neural Processes

Garnelo, M., Rosenbaum, D., Maddison, C. J., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D. J., & Eslami, S. M. A. (2018) · arXiv

Gaussian Processes and Bayesian InferenceNeural Networks and Applications

Cited by 287

Reconciling deep learning with symbolic artificial intelligence: representing objects and relations

Garnelo, M., & Shanahan, M. (2019) · Current Opinion in Behavioral Sciences

Logic, Reasoning, and KnowledgeNeural Networks and Applications

Cited by 255

Cognitive flexibility through metastable neural dynamics is disrupted by damage to the structural connectome

Hellyer, P. J., Scott, G., Shanahan, M., Sharp, D. J., & Leech, R. (2015) · Journal of Neuroscience

Functional Brain Connectivity StudiesTraumatic Brain Injury Research

Cited by 187

Applying global workspace theory to the frame problem

Shanahan, M., & Baars, B. (2005) · Cognition

Philosophy and Theoretical ScienceCognitive Science and Education Research

Cited by 149

Towards Deep Symbolic Reinforcement Learning

Garnelo, M., Arulkumaran, K., & Shanahan, M. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 146

The brain's connective core and its role in animal cognition

Shanahan, M. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences

Neural dynamics and brain functionFunctional Brain Connectivity Studies

Cited by 115

Rethink reporting of evaluation results in AI

Burnell, R., Schellaert, W., Burden, J., Ullman, T. D., Martinez-Plumed, F., Tenenbaum, J. B., Rutar, D., Cheke, L. G., Sohl-Dickstein, J., Mitchell, M., et al. (2023) · Science

Ethics and Social Impacts of AIScientific Computing and Data Management

Cited by 104

The Ethics of Advanced AI Assistants

Gabriel, I., Manzini, A., Keeling, G., Hendricks, L. A., Rieser, V., Iqbal, H., Tomašev, N., Ktena, I., Kenton, Z., Rodriguez, M., et al. (2024) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 57

Technological folie à deux: feedback loops between AI chatbots and mental health

Dohnány, S., Kurth-Nelson, Z., Spens, E., Luettgau, L., Reid, A., Gabriel, I., Summerfield, C., Shanahan, M., & Nour, M. M. (2026) · Nature Mental Health

Digital Mental Health InterventionsEthics and Social Impacts of AI

Cited by 51

Faithful Reasoning Using Large Language Models

Creswell, A., & Shanahan, M. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 37

Technological folie à deux: Feedback Loops Between AI Chatbots and Mental Illness

Dohnány, S., Kurth-Nelson, Z., Spens, E., Luettgau, L., Reid, A., Gabriel, I., Summerfield, C., Shanahan, M., & Nour, M. M. (2025) · arXiv

Ethics and Social Impacts of AIDigital Mental Health Interventions

Cited by 26

Simulacra as conscious exotica

Shanahan, M. (2026) · Inquiry

Philosophy and Theoretical ScienceWittgensteinian philosophy and applications

Cited by 25

Abstraction for Deep Reinforcement Learning

Shanahan, M., & Mitchell, M. (2022) · Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 17

A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI

El-Sayed, S., Akbulut, C., McCroskery, A., Keeling, G., Kenton, Z., Jalan, Z., Marchal, N., Manzini, A., Shevlane, T., Vallor, S., et al. (2024) · arXiv

Ethics and Social Impacts of AIMisinformation and Its Impacts

Cited by 10

Schema-learning and rebinding as mechanisms of in-context learning and emergence

Swaminathan, S., Dedieu, A., Raju, R. V., Shanahan, M., Lazaro-Gredilla, M., & George, D. (2023) · arXiv

Neural Networks and ApplicationsCognitive Science and Education Research

Cited by 8

Does It Make Sense to Speak of Introspection in Large Language Models?

Comsa, I. M., & Shanahan, M. (2025) · arXiv

Philosophy and Theoretical ScienceCybernetics and Technology in Society

Cited by 3

On the generalization of language models from in-context learning and finetuning: a controlled study

Lampinen, A. K., Chaudhry, A., Chan, S. C. Y., Wild, C., Wan, D., Ku, A., Bornschein, J., Pascanu, R., Shanahan, M., & McClelland, J. L. (2025) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 0