Griffiths, Thomas L.

Finding scientific topics

Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences

Topic ModelingComputational and Text Analysis Methods

Cited by 6,049

How to grow a mind: Statistics, structure, and abstraction

Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science

Bayesian Modeling and Causal InferenceCategorization, perception, and language

Cited by 1,815

Natural speech reveals the semantic maps that tile human cerebral cortex

Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016) · Nature

Neurobiology of Language and BilingualismFunctional Brain Connectivity Studies

Cited by 1,504

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023) · arXiv

AI-based Problem Solving and PlanningTopic Modeling

Cited by 1,158

Topics in semantic representation

Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007) · Psychological Review

Topic ModelingNatural Language Processing Techniques

Cited by 1,122

Toward a rational and mechanistic account of mental effort

Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017) · Annual Review of Neuroscience

Neural and Behavioral Psychology StudiesBehavioral Health and Interventions

Cited by 1,077

Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources

Lieder, F., & Griffiths, T. L. (2020) · Behavioral and Brain Sciences

Decision-Making and Behavioral EconomicsCognitive Science and Education Research

Cited by 892

Theory-based Bayesian models of inductive learning and reasoning

Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006) · Trends in Cognitive Sciences

Bayesian Modeling and Causal InferenceCategorization, perception, and language

Cited by 880

Probabilistic models of cognition: exploring representations and inductive biases

Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010) · Trends in Cognitive Sciences

Bayesian Modeling and Causal InferenceCognitive Science and Education Research

Cited by 568

A rational analysis of rule-based concept learning

Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008) · Cognitive Science

Categorization, perception, and languageBayesian Modeling and Causal Inference

Cited by 469

Innateness and culture in the evolution of language

Kirby, S., Dowman, M., & Griffiths, T. L. (2007) · Proceedings of the National Academy of Sciences

Language and cultural evolutionSyntax, Semantics, Linguistic Variation

Cited by 442

Integrating explanation and prediction in computational social science

Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., et al. (2021) · Nature

Philosophy of Biology and Scientific ExplanationMulti-Agent Systems and Negotiation

Cited by 371

Rational approximations to rational models: alternative algorithms for category learning

Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010) · Psychological Review

Categorization, perception, and languageBayesian Modeling and Causal Inference

Cited by 369

When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships

Lucas, C. G., Bridgers, S., Griffiths, T. L., & Gopnik, A. (2014) · Cognition

Child and Animal Learning DevelopmentBayesian Modeling and Causal Inference

Cited by 240

The anchoring bias reflects rational use of cognitive resources

Lieder, F., Griffiths, T. L., M. Huys, Q. J., & Goodman, N. D. (2018) · Psychonomic Bulletin & Review

Decision-Making and Behavioral EconomicsCognitive Science and Education Research

Cited by 230

Iterated learning: Intergenerational knowledge transmission reveals inductive biases

Kalish, M. L., Griffiths, T. L., & Lewandowsky, S. (2007) · Psychonomic Bulletin & Review

Language and cultural evolutionChild and Animal Learning Development

Cited by 220

Human Uncertainty Makes Classification More Robust

Peterson, J., Battleday, R., Griffiths, T., & Russakovsky, O. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 196

How the Bayesians got their beliefs (and what those beliefs actually are): Comment on Bowers and Davis (2012).

Griffiths, T. L., Chater, N., Norris, D., & Pouget, A. (2012) · Psychological Bulletin

Statistical Methods and Bayesian InferenceBayesian Modeling and Causal Inference

Cited by 192

Reconciling intuitive physics and Newtonian mechanics for colliding objects

Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013) · Psychological Review

Visual perception and processing mechanismsCategorization, perception, and language

Cited by 189

On the Utility of Learning about Humans for Human-AI Coordination

Carroll, M., Shah, R., Ho, M. K., Griffiths, T. L., Seshia, S. A., Abbeel, P., & Dragan, A. (2019) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 168

Formalizing Neurath’s ship: approximate algorithms for online causal learning.

Bramley, N. R., Dayan, P., Griffiths, T. L., & Lagnado, D. A. (2017) · Psychological Review

Bayesian Modeling and Causal InferencePhilosophy of Biology and Scientific Explanation

Cited by 136

Machine culture

Brinkmann, L., Baumann, F., Bonnefon, J.-F., Derex, M., Müller, T. F., Nussberger, A.-M., Czaplicka, A., Acerbi, A., Griffiths, T. L., Henrich, J., et al. (2023) · Nature Human Behaviour

Language and cultural evolutionCybernetics and Technology in Society

Cited by 132

Theoretical and empirical evidence for the impact of inductive biases on cultural evolution

Griffiths, T. L., Kalish, M. L., & Lewandowsky, S. (2008) · Philosophical Transactions of the Royal Society B: Biological Sciences

Language and cultural evolutionEvolutionary Psychology and Human Behavior

Cited by 131

Rational variability in children's causal inferences: the sampling hypothesis

Denison, S., Bonawitz, E., Gopnik, A., & Griffiths, T. L. (2013) · Cognition

Child and Animal Learning DevelopmentBayesian Modeling and Causal Inference

Cited by 126