Griffiths, Thomas L.

Rational use of cognitive resources in human planning

Callaway, F., van Opheusden, B., Gul, S., Das, P., Krueger, P. M., Griffiths, T. L., & Lieder, F. (2022) · Nature Human Behaviour

Decision-Making and Behavioral EconomicsAI-based Problem Solving and Planning

Cited by 121

Are Convolutional Neural Networks or Transformers more like human vision?

Tuli, S., Dasgupta, I., Grant, E., & Griffiths, T. L. (2021) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 119

A foundation model to predict and capture human cognition

Binz, M., Akata, E., Bethge, M., Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C., Eckstein, M. K., Éltető, N., et al. (2025) · Nature

Cognitive Science and Education ResearchDecision-Making and Behavioral Economics

Cited by 119

Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty

Shin, M., Kim, J., van Opheusden, B., & Griffiths, T. L. (2023) · Proceedings of the National Academy of Sciences

Artificial Intelligence in GamesArtificial Intelligence Applications

Cited by 106

Superhuman artificial intelligence can improve human decision-making by increasing novelty

Shin, M., Kim, J., van Opheusden, B., & Griffiths, T. L. (2023) · Proceedings of the National Academy of Sciences

Artificial Intelligence in GamesArtificial Intelligence Applications

Cited by 106

A rational model of the Dunning–Kruger effect supports insensitivity to evidence in low performers

Jansen, R. A., Rafferty, A. N., & Griffiths, T. L. (2021) · Nature Human Behaviour

Decision-Making and Behavioral EconomicsMemory Processes and Influences

Cited by 100

Embers of autoregression show how large language models are shaped by the problem they are trained to solve

McCoy, R. T., Yao, S., Friedman, D., Hardy, M. D., & Griffiths, T. L. (2024) · Proceedings of the National Academy of Sciences

Topic ModelingNatural Language Processing Techniques

Cited by 96

Understanding Human Intelligence through Human Limitations

Griffiths, T. L. (2020) · Trends in Cognitive Sciences

Psychiatry, Mental Health, NeuroscienceCognitive Abilities and Testing

Cited by 88

Deep models of superficial face judgments

Peterson, J. C., Uddenberg, S., Griffiths, T. L., Todorov, A., & Suchow, J. W. (2022) · Proceedings of the National Academy of Sciences

Face Recognition and PerceptionFace recognition and analysis

Cited by 82

Large language models predict human sensory judgments across six modalities

Marjieh, R., Sucholutsky, I., van Rijn, P., Jacoby, N., & Griffiths, T. L. (2024) · Scientific Reports

Categorization, perception, and languageMultimodal Machine Learning Applications

Cited by 73

Cognitive Architectures for Language Agents

Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2023) · arXiv

AI-based Problem Solving and PlanningPsychiatry, Mental Health, Neuroscience

Cited by 72

Human biases limit cumulative innovation

Thompson, B., & Griffiths, T. L. (2021) · Proceedings of the Royal Society B: Biological Sciences

Language and cultural evolutionDecision-Making and Behavioral Economics

Cited by 40

Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve

McCoy, R. T., Yao, S., Friedman, D., Hardy, M., & Griffiths, T. L. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 37

Getting aligned on representational alignment

Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Cueva, C. J., Grant, E., Groen, I., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningExplainable Artificial Intelligence (XAI)

Cited by 36

Cognitive prostheses for goal achievement

Lieder, F., Chen, O. X., Krueger, P. M., & Griffiths, T. L. (2019) · Nature Human Behaviour

Decision-Making and Behavioral EconomicsPerfectionism, Procrastination, Anxiety Studies

Cited by 32

Measuring Implicit Bias in Explicitly Unbiased Large Language Models

Bai, X., Wang, A., Sucholutsky, I., & Griffiths, T. L. (2024) · arXiv

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 17

Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning

Prabhakar, A., Griffiths, T. L., & McCoy, R. T. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024

Topic ModelingNatural Language Processing Techniques

Cited by 14

Universal linguistic inductive biases via meta-learning

McCoy, R. T., Grant, E., Smolensky, P., Griffiths, T. L., & Linzen, T. (2020) · arXiv

Language Development and DisordersDomain Adaptation and Few-Shot Learning

Cited by 13

Rational Simplification and Rigidity in Human Planning

Ho, M. K., Cohen, J. D., & Griffiths, T. L. (2023) · Psychological Science

Decision-Making and Behavioral EconomicsAI-based Problem Solving and Planning

Cited by 11

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning

Kumar, S., Dasgupta, I., Daw, N. D., Cohen, J. D., & Griffiths, T. L. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 10

Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

Kumar, S., Correa, C. G., Dasgupta, I., Marjieh, R., Hu, M. Y., Hawkins, R. D., Daw, N. D., Cohen, J. D., Narasimhan, K., & Griffiths, T. L. (2022) · arXiv

Natural Language Processing TechniquesMachine Learning and Algorithms

Cited by 9

Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse

Liu, R., Geng, J., Wu, A. J., Sucholutsky, I., Lombrozo, T., & Griffiths, T. L. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 6

RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation

Liang, K., Hu, H., Liu, R., Griffiths, T. L., & Fisac, J. F. (2025) · arXiv

Reinforcement Learning in RoboticsEthics and Social Impacts of AI

Cited by 3

How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?

Liu, R., Sumers, T. R., Dasgupta, I., & Griffiths, T. L. (2024) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 1