Thomas L. Griffiths

Princeton University — United States

Psychology · Social Sciences

98h-index49.6kcitations772works4.82yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Child and Animal Learning Development(131), Bayesian Modeling and Causal Inference(112), Language and cultural evolution(111), Topic Modeling(83), Decision-Making and Behavioral Economics(74)

Publications772 total

Cognitive Foundations for Reasoning and Their Manifestation in LLMs
arXiv (Cornell University)·2025Open Access
Are Large Language Models Sensitive to the Motives Behind Communication?
ArXiv.org·2025· 1 citedOpen Access
Time Spent Thinking in Online Chess Reflects the Value of Computation
Cognitive Science·2025Open Access
Characterizing the Large‐Scale Structure of Multimodal Semantic Networks
Cognitive Science·2025Open Access
Characterizing the Large-Scale Structure of Multimodal Semantic Networks
2025Open Access
Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models
arXiv (Cornell University)·2025Open Access
Children, but not capuchins, rationally integrate social and physical information when deciding which actions to copy
2025· 1 citedOpen Access
Characterizing the Large-Scale Structure of Grounded Semantic Networks
2025Open Access
Whither symbols in the era of advanced neural networks?
arXiv (Cornell University)·2025Open Access
Teaching Recombinable Motifs Through Simple Examples
Cognitive Science·2025Open Access
Predicting human decisions with behavioural theories and machine learning
Nature Human Behaviour·2025· 3 cited
People make suboptimal decisions about existential risks
Cognition·2025Open Access
Assessing Adaptive World Models in Machines with Novel Games
arXiv (Cornell University)·2025· 2 citedOpen Access
Rational vigilance of intentions and incentives guides learning from advice
2025Open Access
People evaluate idle collaborators based on their impact on task efficiency
Cognition·2025· 1 citedOpen Access
Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models
arXiv (Cornell University)·2025Open Access
Looking deeper into the algorithms underlying human planning
Trends in Cognitive Sciences·2025· 2 cited
A foundation model to predict and capture human cognition
Nature·2025· 50 citedOpen Access
Rational causal induction from events in time.
Psychological Review·2025Open Access
Capturing the complexity of human strategic decision-making with machine learning
Nature Human Behaviour·2025· 3 cited
page 1 of 39Next →

Frequent Co-authors

Joshua B. Tenenbaum(15), Noah D. Goodman(6), Sharon Goldwater(6), Mark Steyvers(5), Charles Kemp(5), Falk Lieder(4), Mark Johnson(4), Christopher G. Lucas(3), Michael L. Kalish(3), Michael I. Jordan(3), David M. Blei(3), Alison Gopnik(3), Alexander G. Huth(2), Wendy A. de Heer(2), Frédéric E. Theunissen(2), Jack L. Gallant(2), Amy Perfors(2), Zoubin Ghahramani(2), Simon Kirby(2), Sophie Bridgers(2)