Structure Learning in Human Causal Induction. - Joshua B. Tenenbaum; Thomas L. Griffiths 001 (NIPS, 2000)

Using Vocabulary Knowledge in Bayesian Multinomial Estimation. - Thomas L. Griffiths 001; Joshua B. Tenenbaum (NIPS, 2001)

Dynamical Causal Learning. - David Danks; Thomas L. Griffiths 001; Joshua B. Tenenbaum (NIPS, 2002)

Prediction and Semantic Association. - Thomas L. Griffiths 001; Mark Steyvers (NIPS, 2002)

Theory-Based Causal Inference. - Joshua B. Tenenbaum; Thomas L. Griffiths 001 (NIPS, 2002)

Hierarchical Topic Models and the Nested Chinese Restaurant Process. - David M. Blei; Thomas L. Griffiths 001; Michael I. Jordan; Joshua B. Tenenbaum (NIPS, 2003)

From Algorithmic to Subjective Randomness. - Thomas L. Griffiths 001; Joshua B. Tenenbaum (NIPS, 2003)

Semi-Supervised Learning with Trees. - Charles Kemp; Thomas L. Griffiths 001; Sean Stromsten; Joshua B. Tenenbaum (NIPS, 2003)

Probabilistic author-topic models for information discovery. - Mark Steyvers; Padhraic Smyth; Michal Rosen-Zvi; Thomas Griffiths 001 (KDD, 2004)

Integrating Topics and Syntax. - Thomas L. Griffiths 001; Mark Steyvers; David M. Blei; Joshua B. Tenenbaum (NIPS, 2004)

The Author-Topic Model for Authors and Documents. - Michal Rosen-Zvi; Thomas L. Griffiths 001; Mark Steyvers; Padhraic Smyth (UAI, 2004)

Interpolating between types and tokens by estimating power-law generators. - Sharon Goldwater; Thomas L. Griffiths 001; Mark Johnson 001 (NIPS, 2005)

Infinite latent feature models and the Indian buffet process. - Thomas L. Griffiths 001; Zoubin Ghahramani (NIPS, 2005)

Learning Systems of Concepts with an Infinite Relational Model. - Charles Kemp; Joshua B. Tenenbaum; Thomas L. Griffiths 001; Takeshi Yamada; Naonori Ueda (AAAI, 2006)

Contextual Dependencies in Unsupervised Word Segmentation. - Sharon Goldwater; Thomas L. Griffiths 001; Mark Johnson 001 (ACL, 2006)

Unsupervised Topic Modelling for Multi-Party Spoken Discourse. - Matthew Purver; Konrad P. Körding; Thomas L. Griffiths 001; Joshua B. Tenenbaum (ACL, 2006)

Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models. - Mark Johnson 001; Thomas L. Griffiths 001; Sharon Goldwater (NIPS, 2006)

A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments. - Daniel J. Navarro; Thomas L. Griffiths 001 (NIPS, 2006)

Particle Filtering for Nonparametric Bayesian Matrix Factorization. - Frank D. Wood; Thomas L. Griffiths 001 (NIPS, 2006)

Structured Priors for Structure Learning. - Vikash K. Mansinghka; Charles Kemp; Thomas L. Griffiths 001; Joshua B. Tenenbaum (UAI, 2006)

A Non-Parametric Bayesian Method for Inferring Hidden Causes. - Frank D. Wood; Thomas L. Griffiths 001; Zoubin Ghahramani (UAI, 2006)

A fully Bayesian approach to unsupervised part-of-speech tagging. - Sharon Goldwater; Thomas L. Griffiths 001 (ACL, 2007)

A Probabilistic Approach to Diachronic Phonology. - Alexandre Bouchard-Côté; Percy Liang; Thomas Griffiths 001; Dan Klein 001 (EMNLP-CoNLL, 2007)

Bayesian Inference for PCFGs via Markov Chain Monte Carlo. - Mark Johnson 001; Thomas L. Griffiths 001; Sharon Goldwater (HLT-NAACL, 2007)

A Probabilistic Approach to Language Change. - Alexandre Bouchard-Côté; Percy Liang; Thomas L. Griffiths 001; Dan Klein 001 (NIPS, 2007)

Markov Chain Monte Carlo with People. - Adam Sanborn; Thomas L. Griffiths 001 (NIPS, 2007)

Language Evolution by Iterated Learning With Bayesian Agents. - Thomas L. Griffiths 001; Michael L. Kalish (Cogn. Sci., 2007)

Parametric Embedding for Class Visualization. - Tomoharu Iwata; Kazumi Saito; Naonori Ueda; Sean Stromsten; Thomas L. Griffiths 001; Joshua B. Tenenbaum (Neural Comput., 2007)

Analyzing human feature learning as nonparametric Bayesian inference. - Joseph L. Austerweil; Thomas L. Griffiths 001 (NIPS, 2008)

Modeling human function learning with Gaussian processes. - Thomas L. Griffiths 001; Christopher G. Lucas; Joseph Jay Williams; Michael L. Kalish (NIPS, 2008)

Modeling the effects of memory on human online sentence processing with particle filters. - Roger Levy; Florencia Reali; Thomas L. Griffiths 001 (NIPS, 2008)

A rational model of preference learning and choice prediction by children. - Christopher G. Lucas; Thomas L. Griffiths 001; Fei Xu; Christine Fawcett (NIPS, 2008)

How memory biases affect information transmission: A rational analysis of serial reproduction. - Jing Xu; Thomas L. Griffiths 001 (NIPS, 2008)

The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. - Kurt T. Miller; Thomas L. Griffiths 001; Michael I. Jordan (UAI, 2008)

A Rational Analysis of Rule-Based Concept Learning. - Noah D. Goodman; Joshua B. Tenenbaum; Jacob Feldman; Thomas L. Griffiths 001 (Cogn. Sci., 2008)

Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases. - Thomas L. Griffiths 001; Brian R. Christian; Michael L. Kalish (Cogn. Sci., 2008)

Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach. - Daniel J. Navarro; Thomas L. Griffiths 001 (Neural Comput., 2008)

A Probabilistic Model of Meetings That Combines Words and Discourse Features. - Mike Dowman; Virginia Savova; Thomas L. Griffiths 001; Konrad P. Körding; Joshua B. Tenenbaum; Matthew Purver (IEEE Trans. Speech Audio Process., 2008)

Connecting human and machine learning via probabilistic models of cognition. - Thomas L. Griffiths 001 (INTERSPEECH, 2009)

Improved Reconstruction of Protolanguage Word Forms. - Alexandre Bouchard-Côté; Thomas L. Griffiths 001; Dan Klein 001 (HLT-NAACL, 2009)

Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning. - Anne S. Hsu; Thomas L. Griffiths 001 (NIPS, 2009)

Nonparametric Latent Feature Models for Link Prediction. - Kurt T. Miller; Thomas L. Griffiths 001; Michael I. Jordan (NIPS, 2009)

Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling. - Lei Shi 009; Thomas L. Griffiths 001 (NIPS, 2009)

The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning. - Stephan Lewandowsky; Thomas L. Griffiths 001; Michael L. Kalish (Cogn. Sci., 2009)

Online Inference of Topics with Latent Dirichlet Allocation. - Kevin Robert Canini; Lei Shi 009; Thomas L. Griffiths 001 (AISTATS, 2009)

Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process. - Kevin Robert Canini; Mikhail M. Shashkov; Thomas L. Griffiths 001 (ICML, 2010)

Learning invariant features using the Transformed Indian Buffet Process. - Joseph L. Austerweil; Thomas L. Griffiths 001 (NIPS, 2010)

Learning the Form of Causal Relationships Using Hierarchical Bayesian Models. - Christopher G. Lucas; Thomas L. Griffiths 001 (Cogn. Sci., 2010)

The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. - David M. Blei; Thomas L. Griffiths 001; Michael I. Jordan (J. ACM, 2010)

Learning author-topic models from text corpora. - Michal Rosen-Zvi; Chaitanya Chemudugunta; Thomas L. Griffiths 001; Padhraic Smyth; Mark Steyvers (ACM Trans. Inf. Syst., 2010)

A Nonparametric Bayesian Model of Multi-Level Category Learning. - Kevin Robert Canini; Thomas L. Griffiths 001 (AAAI, 2011)

Exploring the Relationship Between Learnability and Linguistic Universals. - Anna N. Rafferty; Thomas L. Griffiths 001; Marc Ettlinger (CMCL@ACL, 2011)

Faster Teaching by POMDP Planning. - Anna N. Rafferty; Emma Brunskill; Thomas L. Griffiths 001; Patrick Shafto (AIED, 2011)

Exploring the influence of particle filter parameters on order effects in causal learning. - Joshua T. Abbott; Thomas L. Griffiths 001 (CogSci, 2011)

Grow your own representations: Computational constructivism. - Joseph L. Austerweil; Thomas L. Griffiths 001; Todd M. Gureckis; Robert L. Goldstone; Kevin Robert Canini; Matt Jones 002 (CogSci, 2011)

A Simple Sequential Algorithm for Approximating Bayesian Inference. - Elizabeth Bonawitz; Stephanie Denison; Annie Chen; Alison Gopnik; Thomas L. Griffiths 001 (CogSci, 2011)

Segmenting and Recognizing Human Action using Low-level Video Features. - Daphna Buchsbaum; Kevin Robert Canini; Thomas L. Griffiths 001 (CogSci, 2011)

Discovering Inductive Biases in Categorization through Iterated Learning. - Kevin Robert Canini; Thomas L. Griffiths 001; Wolf Vanpaemel; Michael L. Kalish (CogSci, 2011)

Young Toddlers' Understanding of Graded Preferences. - Jane C. Hu; Christopher G. Lucas; Thomas L. Griffiths 001; Fei Xu (CogSci, 2011)

From preferences to choices and back again: evidence for human inconsistency and its implications. - Christopher G. Lucas; Charles Kemp; Thomas L. Griffiths 001 (CogSci, 2011)

A Bayesian model of navigation in squirrels. - Anna Waisman; Christopher G. Lucas; Thomas L. Griffiths 001; Lucia Jacobs (CogSci, 2011)

Estimating human priors on causal strength. - Saiwing Yeung; Thomas L. Griffiths 001 (CogSci, 2011)

Testing a Bayesian Measure of Representativeness Using a Large Image Database. - Joshua T. Abbott; Katherine A. Heller; Zoubin Ghahramani; Thomas L. Griffiths 001 (NIPS, 2011)

An ideal observer model for identifying the reference frame of objects. - Joseph L. Austerweil; Abram L. Friesen; Thomas L. Griffiths 001 (NIPS, 2011)

A rational model of causal inference with continuous causes. - Michael Pacer; Thomas L. Griffiths 001 (NIPS, 2011)

Seeking Confirmation Is Rational for Deterministic Hypotheses. - Joseph L. Austerweil; Thomas L. Griffiths 001 (Cogn. Sci., 2011)

Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. - Thomas L. Griffiths 001; David M. Sobel; Joshua B. Tenenbaum; Alison Gopnik (Cogn. Sci., 2011)

Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models. - Sharon Goldwater; Thomas L. Griffiths 001; Mark Johnson 001 (J. Mach. Learn. Res., 2011)

The Indian Buffet Process: An Introduction and Review. - Thomas L. Griffiths 001; Zoubin Ghahramani (J. Mach. Learn. Res., 2011)

Constructing a hypothesis space from the Web for large-scale Bayesian word learning. - Joshua T. Abbott; Joseph L. Austerweil; Thomas L. Griffiths 001 (CogSci, 2012)

Predicting focal colors with a rational model of representativeness. - Joshua T. Abbott; Terry Regier; Thomas L. Griffiths 001 (CogSci, 2012)

Look-Ahead Monte Carlo with People. - Charles Blundell; Adam Sanborn; Thomas L. Griffiths 001 (CogSci, 2012)

Do I know that you know what you know? Modeling testimony in causal inference. - Daphna Buchsbaum; Sophie Bridgers; Andrew Whalen; Elizabeth Seiver; Thomas L. Griffiths 001; Alison Gopnik (CogSci, 2012)

Thirty years of Marr's Vision: Levels of Analysis in Cognitive Science. - Chris Eliasmith; Thomas L. Griffiths 001; Valerie Gray Hardcastle; Bradley C. Love; William Bechtel; Richard P. Cooper 002; David Peebles (CogSci, 2012)

Comparing the inductive biases of simple neural networks and Bayesian models. - Thomas L. Griffiths 001; Joseph L. Austerweil; Vincent G. Berthiaume (CogSci, 2012)

Identifying representations of categories of discrete items using Markov chain Monte Carlo with People. - Anne S. Hsu; Jay B. Martin; Adam N. Sanborn; Thomas L. Griffiths 001 (CogSci, 2012)

A Bayesian Model of Rule Induction in Raven's Progressive Matrices. - Daniel R. Little; Stephan Lewandowsky; Thomas L. Griffiths 001 (CogSci, 2012)

Connecting input filtering and selection in language evolution. - Luke Maurits; Thomas L. Griffiths 001 (CogSci, 2012)

Elements of a rational framework for continuous-time causal induction. - Michael Pacer; Thomas L. Griffiths 001 (CogSci, 2012)

Optimally Designing Games for Cognitive Science Research. - Anna N. Rafferty; Matei Zaharia; Thomas L. Griffiths 001 (CogSci, 2012)

Determining people's expectations about the form of causal relationships. - Saiwing Yeung; Christopher G. Lucas; Thomas L. Griffiths 001 (CogSci, 2012)

Inferring learners' knowledge from observed actions. - Anna N. Rafferty; Michelle M. LaMar; Thomas L. Griffiths 001 (EDM, 2012)

Human memory search as a random walk in a semantic network. - Joshua T. Abbott; Joseph L. Austerweil; Thomas L. Griffiths 001 (NIPS, 2012)

"Burn-in, bias, and the rationality of anchoring". - Falk Lieder; Thomas L. Griffiths 001; Noah D. Goodman (NIPS, 2012)

Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories. - Jay B. Martin; Thomas L. Griffiths 001; Adam Sanborn (Cogn. Sci., 2012)

Approximating Bayesian inference with a sparse distributed memory system. - Joshua T. Abbott; Jessica B. Hamrick; Thomas L. Griffiths 001 (CogSci, 2013)

Inferring mass in complex physical scenes via probabilistic simulation. - Jessica B. Hamrick; Peter W. Battaglia; Thomas L. Griffiths 001; Joshua B. Tenenbaum (CogSci, 2013)

When does the majority rule? Preschoolers' trust in majority informants varies by task domain. - Jane C. Hu; Daphna Buchsbaum; Thomas L. Griffiths 001; Fei Xu (CogSci, 2013)

How do you know that? Sensitivity to statistical dependency in social learning. - Andrew Whalen; Daphna Buchsbaum; Thomas L. Griffiths 001 (CogSci, 2013)

Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies. - Yangqing Jia; Joshua T. Abbott; Joseph L. Austerweil; Thomas L. Griffiths 001; Trevor Darrell (NIPS, 2013)

Evaluating computational models of explanation using human judgments. - Michael Pacer; Joseph Jay Williams; Xi Chen; Tania Lombrozo; Thomas L. Griffiths 001 (UAI, 2013)

The Effects of Cultural Transmission Are Modulated by the Amount of Information Transmitted. - Thomas L. Griffiths 001; Stephan Lewandowsky; Michael L. Kalish (Cogn. Sci., 2013)

Automated reconstruction of ancient languages using probabilistic models of sound change. - Alexandre Bouchard-Côté; David Hall 006; Thomas L. Griffiths 001; Dan Klein 001 (Proc. Natl. Acad. Sci. USA, 2013)

Is Holism A Problem For Inductive Inference? A Computational Analysis. - Maxwell A. Bertolero; Thomas L. Griffiths 001 (CogSci, 2014)

Empirical Evidence for Markov Chain Monte Carlo in Memory Search. - David Bourgin; Joshua T. Abbott; Thomas L. Griffiths 001; Kevin A. Smith 001; Ed Vul (CogSci, 2014)

What to simulate? Inferring the right direction for mental rotation. - Jessica B. Hamrick; Thomas L. Griffiths 001 (CogSci, 2014)

The high availability of extreme events serves resource-rational decision-making. - Falk Lieder; Ming Hsu; Thomas L. Griffiths 001 (CogSci, 2014)

Moot Point Process Models. - Bradley C. Love; Jana Jarecki; Jerome R. Busemeyer; Niels A. Taatgen; Thomas L. Griffiths 001; Mirjam Jenny (CogSci, 2014)

The Telephone Game: Exploring Inductive Biases In Naturalistic Language Use. - Stephan C. Meylan; Brett Goldstein; Anna N. Rafferty; Thomas L. Griffiths 001 (CogSci, 2014)

A Bounded Rationality Account of Wishful Thinking. - Rebecca Neumann; Anna N. Rafferty; Thomas L. Griffiths 001 (CogSci, 2014)

Caching Algorithms and Rational Models of Memory. - Avi Press; Michael Pacer; Thomas L. Griffiths 001; Brian R. Christian (CogSci, 2014)

Cultural evolution with sparse testimony: when does the cultural ratchet slip? - Andrew Whalen; Luke Maurits; Michael Pacer; Thomas L. Griffiths 001 (CogSci, 2014)

Diagnosing Algebra Understanding via Bayesian Inverse Planning. - Anna N. Rafferty; Thomas L. Griffiths 001 (EDM, 2014)

Algorithm selection by rational metareasoning as a model of human strategy selection. - Falk Lieder; Dillon Plunkett; Jessica B. Hamrick; Stuart J. Russell; Nicholas Hay; Thomas L. Griffiths 001 (NIPS, 2014)

Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor. - Anna N. Rafferty; Thomas L. Griffiths 001; Dan Klein 001 (Cogn. Sci., 2014)

One and Done? Optimal Decisions From Very Few Samples. - Edward Vul; Noah D. Goodman; Thomas L. Griffiths 001; Joshua B. Tenenbaum (Cogn. Sci., 2014)

Tracing the roots of syntax with Bayesian phylogenetics. - Luke Maurits; Thomas L. Griffiths 001 (Proc. Natl. Acad. Sci. USA, 2014)

Interpreting Freeform Equation Solving. - Anna N. Rafferty; Thomas L. Griffiths 001 (AIED, 2015)

Think again? The amount of mental simulation tracks uncertainty in the outcome. - Jessica B. Hamrick; Kevin A. Smith 001; Thomas L. Griffiths 001; Ed Vul (CogSci, 2015)

Can children balance the size of a majority with the quality of their information? - Jane C. Hu; Andrew Whalen; Daphna Buchsbaum; Thomas L. Griffiths 001; Fei Xu (CogSci, 2015)

When to use which heuristic: A rational solution to the strategy selection problem. - Falk Lieder; Thomas L. Griffiths 001 (CogSci, 2015)

Children and adults differ in their strategies for social learning. - Falk Lieder; Zi Lin Sim; Jane C. Hu; Thomas L. Griffiths 001; Fei Xu (CogSci, 2015)

Generative and Discriminative Models in Cognitive Science. - Bradley C. Love; Michael Ramscar; Thomas L. Griffiths 001; Matt Jones 002 (CogSci, 2015)

A Bayesian Framework for Learning Words From Multiword Utterances. - Stephan C. Meylan; Thomas L. Griffiths 001 (CogSci, 2015)

What the Baldwin Effect affects. - Thomas J. H. Morgan; Thomas L. Griffiths 001 (CogSci, 2015)

Upsetting the contingency table: Causal induction over sequences of point events. - Michael Pacer; Thomas L. Griffiths 001 (CogSci, 2015)

Children search for information as efficiently as adults, but seek additional confirmatory evidence. - Azzurra Ruggeri; Tania Lombrozo; Thomas L. Griffiths 001; Fei Xu (CogSci, 2015)

Inferring Learners' Knowledge From Their Actions. - Anna N. Rafferty; Michelle M. LaMar; Thomas L. Griffiths 001 (Cogn. Sci., 2015)

Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic. - Thomas L. Griffiths 001; Falk Lieder; Noah D. Goodman (Top. Cogn. Sci., 2015)

Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration. - Chang Liu 002; Jessica B. Hamrick; Jaime F. Fisac; Anca D. Dragan; J. Karl Hedrick; S. Shankar Sastry; Thomas L. Griffiths 001 (AAMAS, 2016)

The Sapir-Whorf Hypothesis and Probabilistic Inference: Evidence from the Domain of Color. - Emily Cibelli; Yang Xu 023; Joseph L. Austerweil; Thomas L. Griffiths 001; Terry Regier (CogSci, 2016)

Lexical Complexity of Child-Directed and Overheard Speech: Implications for Learning. - Ruthe Foushee; Tom Griffiths 001; Mahesh Srinivasan (CogSci, 2016)

The Emergence of Conventions. - Robert X. D. Hawkins; Noah D. Goodman; Olga Feher; Kenny Smith; Robert L. Goldstone; Tom Griffiths 001 (CogSci, 2016)

Helping people make better decisions using optimal gamification. - Falk Lieder; Tom Griffiths 001 (CogSci, 2016)

Do Simple Probability Judgments Rely on Integer Approximation? - Shaun O'Grady; Tom Griffiths 001; Fei Xu (CogSci, 2016)

Adapting Deep Network Features to Capture Psychological Representations. - Joshua C. Peterson; Joshua T. Abbott; Thomas L. Griffiths 001 (CogSci, 2016)

Deciding to Remember: Memory Maintenance as a Markov Decision Process. - Jordan W. Suchow; Tom Griffiths 001 (CogSci, 2016)

Wallace: Automating Cultural Evolution Experiments Through Crowdsourcing. - Jordan W. Suchow; Thomas J. H. Morgan; Jessica B. Hamrick; Michael D. Pacer; Stephan C. Meylan; Thomas L. Griffiths 001 (CogSci, 2016)

Design from Zeroth Principles. - Jordan W. Suchow; Michael D. Pacer; Thomas L. Griffiths 001 (CogSci, 2016)

Using Inverse Planning for Personalized Feedback. - Anna N. Rafferty; Rachel Jansen; Thomas L. Griffiths 001 (EDM, 2016)

Generating Plans that Predict Themselves. - Jaime F. Fisac; Chang Liu 002; Jessica B. Hamrick; Shankar Sastry 001; J. Karl Hedrick; Thomas L. Griffiths 001; Anca D. Dragan (WAFR, 2016)

Faster Teaching via POMDP Planning. - Anna N. Rafferty; Emma Brunskill; Thomas L. Griffiths 001; Patrick Shafto (Cogn. Sci., 2016)

Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning. - Neil R. Bramley; Peter Dayan; Thomas L. Griffiths 001; David A. Lagnado (CoRR, 2016)

A Graph-Theoretic Approach to Multitasking. - Jonathan D. Cohen 003; Biswadip Dey; Tom Griffiths 001; Sebastian Musslick; Kayhan Özcimder; Daniel Reichman 001; Igor Shinkar; Tal Wagner (CoRR, 2016)

Infant directed speech is consistent with teaching. - Baxter S. Eaves Jr.; Naomi H. Feldman; Thomas L. Griffiths 001; Patrick Shafto (CoRR, 2016)

Natural speech reveals the semantic maps that tile human cerebral cortex. - Alexander G. Huth; Wendy A. de Heer; Thomas L. Griffiths 001; Frédéric E. Theunissen; Jack L. Gallant (Nat., 2016)

Exploring Human Cognition Using Large Image Databases. - Thomas L. Griffiths 001; Joshua T. Abbott; Anne S. Hsu (Top. Cogn. Sci., 2016)

When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems? - Smitha Milli; Falk Lieder; Thomas L. Griffiths 001 (AAAI, 2017)

Modeling human categorization of natural images using deep feature representations. - Ruairidh M. Battleday; Joshua C. Peterson; Tom Griffiths 001 (CogSci, 2017)

The Structure of Goal Systems Predicts Human Performance. - David Bourgin; Falk Lieder; Daniel Reichman 001; Nimrod Talmon; Tom Griffiths 001 (CogSci, 2017)

Discovering simple heuristics from mental simulation. - Frederick Callaway; Jessica B. Hamrick; Tom Griffiths 001 (CogSci, 2017)

Evaluating vector-space models of analogy. - Dawn Chen; Joshua C. Peterson; Tom Griffiths 001 (CogSci, 2017)

A rational analysis of curiosity. - Rachit Dubey; Tom Griffiths 001 (CogSci, 2017)

Empirical tests of large-scale collaborative recall. - Monica A. Gates; Jordan W. Suchow; Thomas L. Griffiths 001 (CogSci, 2017)

How Can Memory-Augmented Neural Networks Pass a False-Belief Task? - Erin Grant; Aida Nematzadeh; Thomas L. Griffiths 001 (CogSci, 2017)

Exploring inductive bias of visual scenes. - Jessica B. Hamrick; David Bourgin; Thomas Langlois; Tom Griffiths 001 (CogSci, 2017)

Algebra is not like trivia: Evaluating self-assessment in an online math tutor. - Rachel Jansen; Anna N. Rafferty; Tom Griffiths 001 (CogSci, 2017)

Enhancing metacognitive reinforcement learning using reward structures and feedback. - Paul M. Krueger; Falk Lieder; Tom Griffiths 001 (CogSci, 2017)

Uncovering visual priors in spatial memory using serial reproduction. - Thomas Langlois; Nori Jacoby; Jordan W. Suchow; Thomas L. Griffiths 001 (CogSci, 2017)

An automatic method for discovering rational heuristics for risky choice. - Falk Lieder; Paul M. Krueger; Tom Griffiths 001 (CogSci, 2017)

Inferring Intentional Agents From Violation of Randomness. - Yuan Meng; Tom Griffiths 001; Fei Xu (CogSci, 2017)

Evaluating Vector-Space Models of Word Representation, or, The Unreasonable Effectiveness of Counting Words Near Other Words. - Aida Nematzadeh; Stephan C. Meylan; Thomas L. Griffiths 001 (CogSci, 2017)

Evidence for the size principle in semantic and perceptual domains. - Joshua C. Peterson; Thomas L. Griffiths 001 (CogSci, 2017)

Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report. - Joshua C. Peterson; Joshua T. Abbott; Thomas L. Griffiths 001 (IJCAI, 2017)

Pragmatic-Pedagogic Value Alignment. - Jaime F. Fisac; Monica A. Gates; Jessica B. Hamrick; Chang Liu 002; Dylan Hadfield-Menell; Malayandi Palaniappan; Dhruv Malik; S. Shankar Sastry; Thomas L. Griffiths 001; Anca D. Dragan (ISRR, 2017)

A graph-theoretic approach to multitasking. - Noga Alon; Daniel Reichman 001; Igor Shinkar; Tal Wagner; Sebastian Musslick; Jonathan D. Cohen 003; Tom Griffiths 001; Biswadip Dey; Kayhan Özcimder (NIPS, 2017)

Word forms - not just their lengths- are optimized for efficient communication. - Stephan C. Meylan; Thomas L. Griffiths 001 (CoRR, 2017)

Leveraging deep neural networks to capture psychological representations. - Joshua C. Peterson; Joshua T. Abbott; Thomas L. Griffiths 001 (CoRR, 2017)

Recommendation as Generalization: Evaluating Cognitive Models In the Wild. - David Bourgin; Joshua T. Abbott; Tom Griffiths 001 (CogSci, 2018)

A resource-rational analysis of human planning. - Frederick Callaway; Falk Lieder; Priyam Das; Sayan Gul; Paul M. Krueger; Tom Griffiths 001 (CogSci, 2018)

A new similarity measure to reveal individual differences and growth in implicit number conceptions. - Rachel Jansen; Ruthe Foushee; Tom Griffiths 001 (CogSci, 2018)

Modeling the Dunning-Kruger Effect: A Rational Account of Inaccurate Self-Assessment. - Rachel Jansen; Anna N. Rafferty; Tom Griffiths 001 (CogSci, 2018)

Levels of Analysis in Computational Social Science. - Peter M. Krafft; Tom Griffiths 001 (CogSci, 2018)

Shaping Model-Free Habits with Model-Based Goals. - Paul M. Krueger; Tom Griffiths 001 (CogSci, 2018)

Interpersonal Coordination of Perception and Memory in Real-Time Online Social Interaction. - Alexandra Paxton; Thomas J. H. Morgan; Jordan W. Suchow; Tom Griffiths 001 (CogSci, 2018)

Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. - Joshua C. Peterson; Paul Soulos; Aida Nematzadeh; Tom Griffiths 001 (CogSci, 2018)

Representational efficiency outweighs action efficiency in human program induction. - Sophia Sanborn; David Bourgin; Michael Chang 003; Tom Griffiths 001 (CogSci, 2018)

Learning a face space for experiments on human identity. - Jordan W. Suchow; Joshua C. Peterson; Tom Griffiths 001 (CogSci, 2018)

Exploiting Attention to Reveal Shortcomings in Memory Models. - Kaylee Burns; Aida Nematzadeh; Erin Grant; Alison Gopnik; Thomas L. Griffiths 001 (BlackboxNLP@EMNLP, 2018)

Evaluating Theory of Mind in Question Answering. - Aida Nematzadeh; Kaylee Burns; Erin Grant; Alison Gopnik; Thomas L. Griffiths 001 (EMNLP, 2018)

Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. - Erin Grant; Chelsea Finn; Sergey Levine; Trevor Darrell; Thomas L. Griffiths 001 (ICLR (Poster), 2018)

Capturing Human Category Representations by Sampling in Deep Feature Spaces. - Joshua C. Peterson; Krisha Aghi; Jordan W. Suchow; Alexander Y. Ku; Tom Griffiths 001 (ICLR (Workshop), 2018)

Investigating Human Priors for Playing Video Games. - Rachit Dubey; Pulkit Agrawal 001; Deepak Pathak; Tom Griffiths 001; Alexei A. Efros (ICML, 2018)

Learning to select computations. - Frederick Callaway; Sayan Gul; Paul M. Krueger; Thomas L. Griffiths 001; Falk Lieder (UAI, 2018)

Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations. - Joshua C. Peterson; Joshua T. Abbott; Thomas L. Griffiths 001 (Cogn. Sci., 2018)

Sensitivity to Shared Information in Social Learning. - Andrew Whalen; Thomas L. Griffiths 001; Daphna Buchsbaum (Cogn. Sci., 2018)

Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network. - Pengfei Liu; Ji Zhang; Cane Wing-Ki Leung; Chao He; Thomas L. Griffiths 001 (CoRR, 2018)

Adaptive Sampling for Convex Regression. - Max Simchowitz; Kevin G. Jamieson; Jordan W. Suchow; Thomas L. Griffiths 001 (CoRR, 2018)

Online gradient-based mixtures for transfer modulation in meta-learning. - Ghassen Jerfel; Erin Grant; Thomas L. Griffiths 001; Katherine A. Heller (CoRR, 2018)

Rational metareasoning and the plasticity of cognitive control. - Falk Lieder; Amitai Shenhav; Sebastian Musslick; Thomas L. Griffiths 001 (PLoS Comput. Biol., 2018)

Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. - Mayank Agrawal; Joshua C. Peterson; Tom Griffiths 001 (CogSci, 2019)

Compositional subgoal representations. - Carlos G. Correa; Frederick Callaway; Mark K. Ho; Tom Griffiths 001 (CogSci, 2019)

Human-level but not human-like: Deep Reinforcement Learning in the dark. - Rachit Dubey; Pulkit Agrawal 001; Deepak Pathak; Alyosha A. Efros; Tom Griffiths 001 (CogSci, 2019)

If it's important, then I am curious: A value intervention to induce curiosity. - Rachit Dubey; Tom Griffiths 001; Tania Lombrozo (CogSci, 2019)

Learning deep taxonomic priors for concept learning from few positive examples. - Erin Grant; Joshua C. Peterson; Tom Griffiths 001 (CogSci, 2019)

Demonstrating the Impact of Prior Knowledge in Risky Choice. - Mathew D. Hardy; Tom Griffiths 001 (CogSci, 2019)

The Computational Structure of Unintentional Meaning. - Mark K. Ho; Joanna Korman; Tom Griffiths 001 (CogSci, 2019)

Automated cognitive modeling with Bayesian active model selection. - Vishal Lall; Jordan W. Suchow; Gustavo Malkomes; Tom Griffiths 001 (CogSci, 2019)

Orthogonal multi-view three-dimensional object representations in memory revealed by serial reproduction. - Thomas Langlois; Nori Jacoby; Jordan W. Suchow; Tom Griffiths 001 (CogSci, 2019)

Extending Rationality. - Emmanuel M. Pothos; Jerome R. Busemeyer; Timothy J. Pleskac; James M. Yearsley; Josh Tenenbaum 001; Noah D. Goodman; Michael Henry Tessler; Tom Griffiths 001; Falk Lieder; Ralph Hertwig; Thorsten Pachur; Christina Leuker; et al. (CogSci, 2019)

Modeling students' fraction arithmetic strategies using inverse planning. - Anna N. Rafferty; Rachel Jansen; Tom Griffiths 001 (CogSci, 2019)

Inductive Biases Constrain Cumulative Cultural Evolution. - Bill Thompson 001; Tom Griffiths 001 (CogSci, 2019)

Human Uncertainty Makes Classification More Robust. - Joshua C. Peterson; Ruairidh M. Battleday; Thomas L. Griffiths 001; Olga Russakovsky (ICCV, 2019)

Automatically Composing Representation Transformations as a Means for Generalization. - Michael Chang 003; Abhishek Gupta 004; Sergey Levine; Thomas L. Griffiths 001 (ICLR (Poster), 2019)

Cognitive model priors for predicting human decisions. - David D. Bourgin; Joshua C. Peterson; Daniel Reichman 001; Stuart J. Russell; Thomas L. Griffiths 001 (ICML, 2019)

On the Utility of Learning about Humans for Human-AI Coordination. - Micah Carroll; Rohin Shah; Mark K. Ho; Tom Griffiths 001; Sanjit A. Seshia; Pieter Abbeel; Anca D. Dragan (NeurIPS, 2019)

Reconciling meta-learning and continual learning with online mixtures of tasks. - Ghassen Jerfel; Erin Grant; Tom Griffiths 001; Katherine A. Heller (NeurIPS, 2019)

Learning How to Generalize. - Joseph L. Austerweil; Sophia Sanborn; Thomas L. Griffiths 001 (Cogn. Sci., 2019)

Predicting human decisions with behavioral theories and machine learning. - Ori Plonsky; Reut Apel; Eyal Ert; Moshe Tennenholtz; David Bourgin; Joshua C. Peterson; Daniel Reichman 001; Thomas L. Griffiths 001; Stuart J. Russell; Evan C. Carter; James F. Cavanagh; Ido Erev (CoRR, 2019)

Capturing human categorization of natural images at scale by combining deep networks and cognitive models. - Ruairidh M. Battleday; Joshua C. Peterson; Thomas L. Griffiths 001 (CoRR, 2019)

Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making. - Mayank Agrawal; Joshua C. Peterson; Thomas L. Griffiths 001 (CoRR, 2019)

nbgrader v0.5.5. - Project Jupyter; Douglas Blank; David Bourgin; Alexander Brown; Matthias Bussonnier; Jonathan Frederic; Brian E. Granger; Thomas L. Griffiths 001; Jessica B. Hamrick; Kyle Kelley; Michael Pacer; Logan Page; et al. (Zenodo, 2019)

People Do Not Just Plan, They Plan to Plan. - Mark K. Ho; David Abel; Jonathan D. Cohen 003; Michael L. Littman; Thomas L. Griffiths 001 (AAAI, 2020)

Analogy as Nonparametric Bayesian Inference over Relational Systems. - Ruairidh M. Battleday; Tom Griffiths 001 (CogSci, 2020)

Optimal nudging. - Frederick Callaway; Mathew D. Hardy; Tom Griffiths 001 (CogSci, 2020)

Resource-rational Task Decomposition to Minimize Planning Costs. - Carlos G. Correa; Mark K. Ho; Frederick Callaway; Tom Griffiths 001 (CogSci, 2020)

Population-level amplification of perceptual bias. - Mathew D. Hardy; Bill Thompson 001; Peter M. Krafft; Tom Griffiths 001 (CogSci, 2020)

Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. - Robert X. D. Hawkins; Noah D. Goodman; Adele E. Goldberg; Tom Griffiths 001 (CogSci, 2020)

Investigating the Behavior of Malicious Actors Through the Game of Mafia. - Samee Ibraheem; Vael Gates; John DeNero; Tom Griffiths 001 (CogSci, 2020)

A rational model of sequential self-assessment. - Rachel Jansen; Anna N. Rafferty; Tom Griffiths 001 (CogSci, 2020)

Downloading Culture.zip: Social learning by program induction. - Max Kleiman-Weiner; Felix Sosa; Bill Thompson 001; Sebastiaan van Opheusden; Tom Griffiths 001; Samuel Gershman; Fiery Cushman (CogSci, 2020)

Universal linguistic inductive biases via meta-learning. - Richard Thomas McCoy; Erin Grant; Paul Smolensky; Tom Griffiths 001; Tal Linzen (CogSci, 2020)

End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. - Pulkit Singh; Joshua C. Peterson; Ruairidh M. Battleday; Tom Griffiths 001 (CogSci, 2020)

Workshop on Scaling Cognitive Science. - Jordan W. Suchow; Tom Griffiths 001; Joshua K. Hartshorne (CogSci, 2020)

Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation. - Theodore R. Sumers; Mark K. Ho; Tom Griffiths 001 (CogSci, 2020)

The method of loci is an optimal policy for memory search. - Qiong Zhang; Kenneth A. Norman; Tom Griffiths 001 (CogSci, 2020)

Investigating representations of verb bias in neural language models. - Robert X. D. Hawkins; Takateru Yamakoshi; Thomas L. Griffiths 001; Adele E. Goldberg (EMNLP (1), 2020)

Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. - Michael Chang 003; Sidhant Kaushik; S. Matthew Weinberg; Tom Griffiths 001; Sergey Levine (ICML, 2020)

How to Be Helpful to Multiple People at Once. - Vael Gates; Thomas L. Griffiths 001; Anca D. Dragan (Cogn. Sci., 2020)

Assessing Mathematics Misunderstandings via Bayesian Inverse Planning. - Anna N. Rafferty; Rachel Jansen; Thomas L. Griffiths 001 (Cogn. Sci., 2020)

The Efficiency of Human Cognition Reflects Planned Information Processing. - Mark K. Ho; David Abel; Jonathan D. Cohen 003; Michael L. Littman; Thomas L. Griffiths 001 (CoRR, 2020)

Understanding Human Intelligence through Human Limitations. - Thomas L. Griffiths 001 (CoRR, 2020)

Meta-Learning of Compositional Task Distributions in Humans and Machines. - Sreejan Kumar; Ishita Dasgupta 001; Jonathan D. Cohen 003; Nathaniel D. Daw; Thomas L. Griffiths 001 (CoRR, 2020)

Context-Conditioning as Cognitive Control: Guiding Meta-learning with Task Information. - Rachit Dubey; Erin Grant; Michael Luo; Karthik Narasimhan; Thomas L. Griffiths 001 (CoRR, 2020)

Competition in Cross-situational Word Learning: A Computational Study. - Aida Nematzadeh; Zahra Shekarchi; Thomas L. Griffiths 001; Suzanne Stevenson (CoRR, 2020)

Multitasking Capacity: Hardness Results and Improved Constructions. - Noga Alon; Jonathan D. Cohen 003; Thomas L. Griffiths 001; Pasin Manurangsi; Daniel Reichman 001; Igor Shinkar; Tal Wagner; Alexander Y. Ku (SIAM J. Discret. Math., 2020)

Learning Rewards From Linguistic Feedback. - Theodore R. Sumers; Mark K. Ho; Robert X. D. Hawkins; Karthik Narasimhan; Thomas L. Griffiths 001 (AAAI, 2021)

The Dynamics of Exemplar and Prototype Representations Depend on Environmental Statistics. - Arjun Devraj; Qiong Zhang; Tom Griffiths 001 (CogSci, 2021)

Modeling rules and similarity in colexification. - Sammy Floyd; Kavindya Dalawella; Adele Goldberg 002; Casey Lew-Williams; Tom Griffiths 001 (CogSci, 2021)

Respect the code: Speakers expect novel conventions to generalize within but not across social group boundaries. - Robert D. Hawkins; Irina Liu; Adele Goldberg 002; Tom Griffiths 001 (CogSci, 2021)

Mutual Exclusivity as Competition in Cross-situational Word Learning. - Zahra Shekarchi; Aida Nematzadeh; Tom Griffiths 001; Suzanne Stevenson (CogSci, 2021)

Extending rational models of communication from beliefs to actions. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Tom Griffiths 001 (CogSci, 2021)

Are Convolutional Neural Networks or Transformers more like human vision? - Shikhar Tuli; Ishita Dasgupta 001; Erin Grant; Tom Griffiths 001 (CogSci, 2021)

A Rational Account of Anchor Effects in Hindsight Bias. - Samarie A. Wilson; Somya Arora; Qiong Zhang; Tom Griffiths 001 (CogSci, 2021)

Meta-Learning of Structured Task Distributions in Humans and Machines. - Sreejan Kumar; Ishita Dasgupta 001; Jonathan D. Cohen 003; Nathaniel D. Daw; Thomas L. Griffiths 001 (ICLR, 2021)

Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. - Michael Chang 003; Sidhant Kaushik; Sergey Levine; Tom Griffiths 001 (ICML, 2021)

Passive attention in artificial neural networks predicts human visual selectivity. - Thomas A. Langlois; H. Charles Zhao; Erin Grant; Ishita Dasgupta 001; Thomas L. Griffiths 001; Nori Jacoby (NeurIPS, 2021)

The Challenges of Large-Scale, Web-Based Language Datasets: Word Length and Predictability Revisited. - Stephan C. Meylan; Thomas L. Griffiths 001 (Cogn. Sci., 2021)

Evaluating Models of Robust Word Recognition with Serial Reproduction. - Stephan C. Meylan; Sathvik Nair; Thomas L. Griffiths 001 (CoRR, 2021)

From partners to populations: A hierarchical Bayesian account of coordination and convention. - Robert X. D. Hawkins; Michael Franke; Michael C. Frank; Kenny Smith; Thomas L. Griffiths 001; Noah D. Goodman (CoRR, 2021)

Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task. - Sonia K. Murthy; Robert X. D. Hawkins; Thomas L. Griffiths 001 (CoRR, 2021)

Control of mental representations in human planning. - Mark K. Ho; David Abel; Carlos G. Correa; Michael L. Littman; Jonathan D. Cohen 003; Thomas L. Griffiths 001 (CoRR, 2021)

A pragmatic account of the weak evidence effect. - Samuel A. Barnett; Robert D. Hawkins; Thomas L. Griffiths 001 (CoRR, 2021)

Fixation patterns in simple choice reflect optimal information sampling. - Frederick Callaway; Antonio Rangel; Thomas L. Griffiths 001 (PLoS Comput. Biol., 2021)

Probing BERT's priors with serial reproduction chains. - Takateru Yamakoshi; Thomas L. Griffiths 001; Robert D. Hawkins (ACL (Findings), 2022)

Can Humans Do Less-Than-One-Shot Learning? - Maya Malaviya; Ilia Sucholutsky; Kerem Oktar; Tom Griffiths 001 (CogSci, 2022)

Predicting Human Similarity Judgments Using Large Language Models. - Raja Marjieh; Ilia Sucholutsky; Theodore R. Sumers; Nori Jacoby; Tom Griffiths 001 (CogSci, 2022)

Distinguishing rule and exemplar-based generalization in learning systems. - Ishita Dasgupta 001; Erin Grant; Tom Griffiths 001 (ICML, 2022)

Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. - Michael Chang 003; Tom Griffiths 001; Sergey Levine (NeurIPS, 2022)

Using natural language and program abstractions to instill human inductive biases in machines. - Sreejan Kumar; Carlos G. Correa; Ishita Dasgupta 001; Raja Marjieh; Michael Y. Hu; Robert D. Hawkins; Jonathan D. Cohen 003; Nathaniel D. Daw; Karthik Narasimhan; Tom Griffiths 001 (NeurIPS, 2022)

How to talk so AI will learn: Instructions, descriptions, and autonomy. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Tom Griffiths 001; Dylan Hadfield-Menell (NeurIPS, 2022)

Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control. - Mark K. Ho; Thomas L. Griffiths 001 (Annu. Rev. Control. Robotics Auton. Syst., 2022)

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. - Sreejan Kumar; Ishita Dasgupta 001; Raja Marjieh; Nathaniel D. Daw; Jonathan D. Cohen 003; Thomas L. Griffiths 001 (CoRR, 2022)

Linguistic communication as (inverse) reward design. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Thomas L. Griffiths 001; Dylan Hadfield-Menell (CoRR, 2022)

Words are all you need? Capturing human sensory similarity with textual descriptors. - Raja Marjieh; Pol van Rijn; Ilia Sucholutsky; Theodore R. Sumers; Harin Lee; Thomas L. Griffiths 001; Nori Jacoby (CoRR, 2022)

How to talk so your robot will learn: Instructions, descriptions, and pragmatics. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Thomas L. Griffiths 001; Dylan Hadfield-Menell (CoRR, 2022)

Bias amplification in experimental social networks is reduced by resampling. - Mathew D. Hardy; Bill D. Thompson; P. M. Krafft; Thomas L. Griffiths 001 (CoRR, 2022)

On the Informativeness of Supervision Signals. - Ilia Sucholutsky; Raja Marjieh; Nori Jacoby; Thomas L. Griffiths 001 (CoRR, 2022)

The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons. - Rachit Dubey; Thomas L. Griffiths 001; Peter Dayan (PLoS Comput. Biol., 2022)

Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations. - Mathew D. Hardy; Peaks M. Krafft; Bill Thompson 001; Thomas L. Griffiths 001 (Top. Cogn. Sci., 2022)

Large language models meet cognitive science: LLMs as tools, models, and participants. - Mathew D. Hardy; Ilia Sucholutsky; Bill Thompson 001; Tom Griffiths 001 (CogSci, 2023)

What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models. - Raja Marjieh; Ilia Sucholutsky; Pol van Rijn; Nori Jacoby; Tom Griffiths 001 (CogSci, 2023)

To each their own theory: Exploring the limits of individual differences in decisions under risk. - Joshua C. Peterson; Marina Mancoridis; Tom Griffiths 001 (CogSci, 2023)

Predicting Word Learning in Children from the Performance of Computer Vision Systems. - Sunayana Rane; Mira L. Nencheva; Zeyu Wang 004; Casey Lew-Williams; Olga Russakovsky; Tom Griffiths 001 (CogSci, 2023)

The joint evolution of sensory systems and decision policy allows cognition. - Cameron Rouse Turner; Thomas J. H. Morgan; Tom Griffiths 001 (CogSci, 2023)

Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms. - Feng Xia; Jian-Qiao Zhu; Tom Griffiths 001 (CogSci, 2023)

Computation-Limited Bayesian Updating. - Jian-Qiao Zhu; Adam Sanborn; Nick Chater; Tom Griffiths 001 (CogSci, 2023)

Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. - Michael Chang 003; Alyssa L. Dayan; Franziska Meier; Thomas L. Griffiths 001; Sergey Levine; Amy Zhang 001 (ICLR, 2023)

Words are all you need? Language as an approximation for human similarity judgments. - Raja Marjieh; Pol van Rijn; Ilia Sucholutsky; Theodore R. Sumers; Harin Lee; Thomas L. Griffiths 001; Nori Jacoby (ICLR, 2023)

Analyzing Diffusion as Serial Reproduction. - Raja Marjieh; Ilia Sucholutsky; Thomas A. Langlois; Nori Jacoby; Thomas L. Griffiths 001 (ICML, 2023)

Im-Promptu: In-Context Composition from Image Prompts. - Bhishma Dedhia; Michael Chang 003; Jake Snell; Tom Griffiths 001; Niraj K. Jha (NeurIPS, 2023)

Alignment with human representations supports robust few-shot learning. - Ilia Sucholutsky; Tom Griffiths 001 (NeurIPS, 2023)

Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. - Zi Wang; Alexander Ku; Jason Baldridge; Tom Griffiths 001; Been Kim (NeurIPS, 2023)

Tree of Thoughts: Deliberate Problem Solving with Large Language Models. - Shunyu Yao; Dian Yu; Jeffrey Zhao; Izhak Shafran; Tom Griffiths 001; Yuan Cao 007; Karthik Narasimhan (NeurIPS, 2023)

Gaussian Process Surrogate Models for Neural Networks. - Michael Y. Li; Erin Grant; Thomas L. Griffiths 001 (UAI, 2023)

On the informativeness of supervision signals. - Ilia Sucholutsky; Ruairidh M. Battleday; Katherine M. Collins; Raja Marjieh; Joshua C. Peterson; Pulkit Singh; Umang Bhatt; Nori Jacoby; Adrian Weller; Thomas L. Griffiths 001 (UAI, 2023)

Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty. - Minkyu Shin; Jin Kim 006; Bas van Opheusden; Thomas L. Griffiths 001 (CoRR, 2023)

Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. - Michael Chang 003; Alyssa L. Dayan; Franziska Meier; Thomas L. Griffiths 001; Sergey Levine; Amy Zhang 001 (CoRR, 2023)

Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. - R. Thomas McCoy; Thomas L. Griffiths 001 (CoRR, 2023)

The Universal Law of Generalization Holds for Naturalistic Stimuli. - Raja Marjieh; Nori Jacoby; Joshua C. Peterson; Thomas L. Griffiths 001 (CoRR, 2023)

Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve. - R. Thomas McCoy; Shunyu Yao; Dan Friedman; Matthew Hardy; Thomas L. Griffiths 001 (CoRR, 2023)

Structurally guided task decomposition in spatial navigation tasks. - Ruiqi He; Carlos G. Correa; Thomas L. Griffiths 001; Mark K. Ho (CoRR, 2023)

Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence. - Kerem Oktar; Ilia Sucholutsky; Tania Lombrozo; Thomas L. Griffiths 001 (CoRR, 2023)

Getting aligned on representational alignment. - Ilia Sucholutsky; Lukas Muttenthaler; Adrian Weller; Andi Peng; Andreea Bobu; Been Kim; Bradley C. Love; Erin Grant; Jascha Achterberg; Joshua B. Tenenbaum; Katherine M. Collins; Katherine L. Hermann; et al. (CoRR, 2023)

Concept Alignment as a Prerequisite for Value Alignment. - Sunayana Rane; Mark K. Ho; Ilia Sucholutsky; Thomas L. Griffiths 001 (CoRR, 2023)

Improving Interpersonal Communication by Simulating Audiences with Language Models. - Ryan Liu 001; Howard Yen; Raja Marjieh; Thomas L. Griffiths 001; Ranjay Krishna (CoRR, 2023)

Bayes in the age of intelligent machines. - Thomas L. Griffiths 001; Jian-Qiao Zhu; Erin Grant; R. Thomas McCoy (CoRR, 2023)

Machine Culture. - Levin Brinkmann; Fabian Baumann; Jean-François Bonnefon; Maxime Derex; Thomas F. Müller; Anne-Marie Nussberger; Agnieszka Czaplicka; Alberto Acerbi; Thomas L. Griffiths 001; Joseph Henrich; Joel Z. Leibo; Richard McElreath; et al. (CoRR, 2023)

Exploring the hierarchical structure of human plans via program generation. - Carlos G. Correa; Sophia Sanborn; Mark K. Ho; Frederick Callaway; Nathaniel D. Daw; Thomas L. Griffiths 001 (CoRR, 2023)

Toward a More Biologically Plausible Neural Network Model of Latent Cause Inference. - Qihong Lu; Tan T. Nguyen; Qiong Zhang; Uri Hasson; Thomas L. Griffiths 001; Jeffrey M. Zacks; Samuel J. Gershman; Kenneth A. Norman (CoRR, 2023)

Deep de Finetti: Recovering Topic Distributions from Large Language Models. - Liyi Zhang; R. Thomas McCoy; Theodore R. Sumers; Jian-Qiao Zhu; Thomas L. Griffiths 001 (CoRR, 2023)

Humans decompose tasks by trading off utility and computational cost. - Carlos G. Correa; Mark K. Ho; Frederick Callaway; Nathaniel D. Daw; Thomas L. Griffiths 001 (PLoS Comput. Biol., 2023)

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. - Sreejan Kumar; Ishita Dasgupta 001; Nathaniel D. Daw; Jonathan D. Cohen 003; Thomas L. Griffiths 001 (PLoS Comput. Biol., 2023)

Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks. - Aditi Jha; Joshua C. Peterson; Thomas L. Griffiths 001 (Cogn. Sci., 2023)

How do Humans Overcome Individual Computational Limitations by Working Together? - Natalia Vélez; Brian R. Christian; Mathew D. Hardy; Bill D. Thompson; Thomas L. Griffiths 001 (Cogn. Sci., 2023)

Learning to Learn Functions. - Michael Y. Li; Fred Callaway; William D. Thompson; Ryan P. Adams; Thomas L. Griffiths 001 (Cogn. Sci., 2023)

The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance. - Daniel Reichman 001; Falk Lieder; David D. Bourgin; Nimrod Talmon; Thomas L. Griffiths 001 (Cogn. Sci., 2023)

Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract). - Ruiqi He; Carlos G. Correa; Tom Griffiths 001; Mark K. Ho (AAAI, 2024)

Pushing the Limits of Learning from Limited Data. - Maya Malaviya; Ilia Sucholutsky; Thomas L. Griffiths 001 (AAAI Spring Symposia, 2024)

Comparing Human Behavior to an Optimal Policy for Innovation. - Bonan Zhao 001; Natalia Vélez; Thomas L. Griffiths 001 (AAAI Spring Symposia, 2024)

Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning. - Akshara Prabhakar; Thomas L. Griffiths 001; R. Thomas McCoy (EMNLP (Findings), 2024)

Preference-Conditioned Language-Guided Abstraction. - Andi Peng; Andreea Bobu; Belinda Z. Li; Theodore R. Sumers; Ilia Sucholutsky; Nishanth Kumar; Thomas L. Griffiths 001; Julie A. Shah (HRI, 2024)

Implicit Maximum a Posteriori Filtering via Adaptive Optimization. - Gianluca M. Bencomo; Jake Snell; Thomas L. Griffiths 001 (ICLR, 2024)

Learning with Language-Guided State Abstractions. - Andi Peng; Ilia Sucholutsky; Belinda Z. Li; Theodore R. Sumers; Thomas L. Griffiths 001; Jacob Andreas; Julie Shah (ICLR, 2024)

How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? - Ryan Liu 001; Theodore R. Sumers; Ishita Dasgupta 001; Thomas L. Griffiths 001 (ICML, 2024)

MacGyver: Are Large Language Models Creative Problem Solvers? - Yufei Tian; Abhilasha Ravichander; Lianhui Qin; Ronan Le Bras 001; Raja Marjieh; Nanyun Peng 001; Yejin Choi 001; Thomas L. Griffiths 001; Faeze Brahman (NAACL-HLT, 2024)

Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem. - Declan Campbell; Sunayana Rane; Tyler Giallanza; Nicolò De Sabbata; Kia Ghods; Amogh Joshi 004; Alexander Ku; Steven Frankland; Tom Griffiths 001; Jonathan D. Cohen 003; Taylor W. Webb (NeurIPS, 2024)

A Metalearned Neural Circuit for Nonparametric Bayesian Inference. - Jake Snell; Gianluca M. Bencomo; Tom Griffiths 001 (NeurIPS, 2024)

Learning Human-like Representations to Enable Learning Human Values. - Andrea Wynn; Ilia Sucholutsky; Tom Griffiths 001 (NeurIPS, 2024)

Concept Alignment. - Sunayana Rane; Polyphony J. Bruna; Ilia Sucholutsky; Christopher T. Kello; Thomas L. Griffiths 001 (CoRR, 2024)

Incoherent Probability Judgments in Large Language Models. - Jian-Qiao Zhu; Thomas L. Griffiths 001 (CoRR, 2024)

Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo. - Jian-Qiao Zhu; Haijiang Yan; Thomas L. Griffiths 001 (CoRR, 2024)

Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction. - Sreejan Kumar; Raja Marjieh; Byron Zhang; Declan Campbell; Michael Y. Hu; Umang Bhatt; Brenden M. Lake; Thomas L. Griffiths 001 (CoRR, 2024)

Measuring Implicit Bias in Explicitly Unbiased Large Language Models. - Xuechunzi Bai; Angelina Wang; Ilia Sucholutsky; Thomas L. Griffiths 001 (CoRR, 2024)

Human-Like Geometric Abstraction in Large Pre-trained Neural Networks. - Declan Campbell; Sreejan Kumar; Tyler Giallanza; Thomas L. Griffiths 001; Jonathan D. Cohen 003 (CoRR, 2024)

A Rational Analysis of the Speech-to-Song Illusion. - Raja Marjieh; Pol van Rijn; Ilia Sucholutsky; Harin Lee; Thomas L. Griffiths 001; Nori Jacoby (CoRR, 2024)

Distilling Symbolic Priors for Concept Learning into Neural Networks. - Ioana Marinescu; R. Thomas McCoy; Thomas L. Griffiths 001 (CoRR, 2024)

Program-Based Strategy Induction for Reinforcement Learning. - Carlos G. Correa; Thomas L. Griffiths 001; Nathaniel D. Daw (CoRR, 2024)

Embodied LLM Agents Learn to Cooperate in Organized Teams. - Xudong Guo; Kaixuan Huang; Jiale Liu; Wenhui Fan; Natalia Vélez; Qingyun Wu; Huazheng Wang; Thomas L. Griffiths 001; Mengdi Wang (CoRR, 2024)

Analyzing the Roles of Language and Vision in Learning from Limited Data. - Allison Chen; Ilia Sucholutsky; Olga Russakovsky; Thomas L. Griffiths 001 (CoRR, 2024)

Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases. - Raja Marjieh; Sreejan Kumar; Declan Campbell; Liyi Zhang; Gianluca M. Bencomo; Jake Snell; Thomas L. Griffiths 001 (CoRR, 2024)

Eliciting the Priors of Large Language Models using Iterated In-Context Learning. - Jian-Qiao Zhu; Thomas L. Griffiths 001 (CoRR, 2024)

Analyzing the Benefits of Prototypes for Semi-Supervised Category Learning. - Liyi Zhang; Logan Nelson; Thomas L. Griffiths 001 (CoRR, 2024)

What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. - Liyi Zhang; Michael Y. Li; Thomas L. Griffiths 001 (CoRR, 2024)

Representational Alignment Supports Effective Machine Teaching. - Ilia Sucholutsky; Katherine M. Collins; Maya Malaviya; Nori Jacoby; Weiyang Liu; Theodore R. Sumers; Michalis Korakakis; Umang Bhatt; Mark K. Ho; Joshua B. Tenenbaum; Bradley C. Love; Zachary A. Pardos; et al. (CoRR, 2024)

Building Machines that Learn and Think with People. - Katherine M. Collins; Ilia Sucholutsky; Umang Bhatt; Kartik Chandra; Lionel Wong; Mina Lee 002; Cedegao E. Zhang; Tan Zhi-Xuan; Mark K. Ho; Vikash Mansinghka 001; Adrian Weller; Joshua B. Tenenbaum; et al. (CoRR, 2024)

Capturing the Complexity of Human Strategic Decision-Making with Machine Learning. - Jian-Qiao Zhu; Joshua C. Peterson; Benjamin Enke; Thomas L. Griffiths 001 (CoRR, 2024)

Automating the Practice of Science - Opportunities, Challenges, and Implications. - Sebastian Musslick; Laura Bartlett; Suyog H. Chandramouli; Marina Dubova; Fernand Gobet; Thomas L. Griffiths 001; Jessica Hullman; Ross D. King; J. Nathan Kutz; Christopher G. Lucas; Suhas Mahesh; Franco Pestilli; et al. (CoRR, 2024)

When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1. - R. Thomas McCoy; Shunyu Yao; Dan Friedman; Mathew D. Hardy; Thomas L. Griffiths 001 (CoRR, 2024)

Rational Metareasoning for Large Language Models. - Nicolò De Sabbata; Theodore R. Sumers; Thomas L. Griffiths 001 (CoRR, 2024)

Centaur: a foundation model of human cognition. - Marcel Binz; Elif Akata; Matthias Bethge; Franziska Brändle; Fred Callaway; Julian Coda-Forno; Peter Dayan; Can Demircan; Maria K. Eckstein; Noémi Élteto; Thomas L. Griffiths 001; Susanne Haridi; et al. (CoRR, 2024)

Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse. - Ryan Liu 001; Jiayi Geng; Addison J. Wu; Ilia Sucholutsky; Tania Lombrozo; Thomas L. Griffiths 001 (CoRR, 2024)

Erratum: Multitasking Capacity: Hardness Results and Improved Constructions. - Noga Alon; Jonathan D. Cohen 003; Thomas L. Griffiths 001; Pasin Manurangsi; Daniel Reichman 001; Igor Shinkar; Tal Wagner (SIAM J. Discret. Math., 2024)

Cognitive Architectures for Language Agents. - Theodore R. Sumers; Shunyu Yao; Karthik Narasimhan; Thomas L. Griffiths 001 (Trans. Mach. Learn. Res., 2024)

Word Forms Reflect Trade-Offs Between Speaker Effort and Robust Listener Recognition. - Stephan C. Meylan; Thomas L. Griffiths 001 (Cogn. Sci., 2024)

Inverting Cognitive Models With Neural Networks to Infer Preferences From Fixations. - Evan M. Russek; Frederick Callaway; Thomas L. Griffiths 001 (Cogn. Sci., 2024)

Towards Foundation Models for 3D Vision: How Close are We? - Yiming Zuo 001; Karhan Kayan; Maggie Wang; Kevin Jeon; Jia Deng 001; Thomas L. Griffiths 001 (3DV, 2025)

Large Language Models Assume People are More Rational than We Really are. - Ryan Liu 001; Jiayi Geng; Joshua C. Peterson; Ilia Sucholutsky; Thomas L. Griffiths 001 (ICLR, 2025)

Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice. - Jian-Qiao Zhu; Haijiang Yan; Thomas L. Griffiths 001 (ICLR, 2025)

Hindsight Merging: Diverse Data Generation with Language Models. - Veniamin Veselovsky; Benedikt Stroebl; Gianluca M. Bencomo; Dilip Arumugam; Lisa Schut; Arvind Narayanan; Thomas L. Griffiths 001 (UAI, 2025)

What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. - Liyi Zhang; Michael Y. Li; R. Thomas McCoy; Theodore R. Sumers; Jian-Qiao Zhu; Thomas L. Griffiths 001 (Trans. Mach. Learn. Res., 2025)

RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation. - Kaiqu Liang; Haimin Hu; Ryan Liu 001; Thomas L. Griffiths 001; Jaime Fernández Fisac (CoRR, 2025)

What is a Number, That a Large Language Model May Know It? - Raja Marjieh; Veniamin Veselovsky; Thomas L. Griffiths 001; Ilia Sucholutsky (CoRR, 2025)

Characterizing the Interaction of Cultural Evolution Mechanisms in Experimental Social Networks. - Raja Marjieh; Manuel Anglada-Tort; Thomas L. Griffiths 001; Nori Jacoby (CoRR, 2025)

Conformal Prediction as Bayesian Quadrature. - Jake C. Snell; Thomas L. Griffiths 001 (CoRR, 2025)

Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks. - Gianluca M. Bencomo; Max Gupta; Ioana Marinescu; R. Thomas McCoy; Thomas L. Griffiths 001 (CoRR, 2025)

On Benchmarking Human-Like Intelligence in Machines. - Lance Ying; Katherine M. Collins; Lionel Wong; Ilia Sucholutsky; Ryan Liu 001; Adrian Weller; Tianmin Shu; Thomas L. Griffiths 001; Joshua B. Tenenbaum (CoRR, 2025)

Using the Tools of Cognitive Science to Understand Large Language Models at Different Levels of Analysis. - Alexander Ku; Declan Campbell; Xuechunzi Bai; Jiayi Geng; Ryan Liu 001; Raja Marjieh; R. Thomas McCoy; Andrew Nam; Ilia Sucholutsky; Veniamin Veselovsky; Liyi Zhang; Jian-Qiao Zhu; et al. (CoRR, 2025)

Predicting Multi-Agent Specialization via Task Parallelizability. - Elizabeth Mieczkowski; Ruaridh Mon-Williams; Neil Bramley; Christopher G. Lucas; Natalia Vélez; Thomas L. Griffiths 001 (CoRR, 2025)

Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation. - Max Gupta; Sunayana Rane; R. Thomas McCoy; Thomas L. Griffiths 001 (CoRR, 2025)

Localized Cultural Knowledge is Conserved and Controllable in Large Language Models. - Veniamin Veselovsky; Berke Argin; Benedikt Stroebl; Chris Wendler; Robert West 001; James Evans; Thomas L. Griffiths 001; Arvind Narayanan (CoRR, 2025)

Identifying and Mitigating the Influence of the Prior Distribution in Large Language Models. - Liyi Zhang; Veniamin Veselovsky; R. Thomas McCoy; Thomas L. Griffiths 001 (CoRR, 2025)

Toward Efficient Exploration by Large Language Model Agents. - Dilip Arumugam; Thomas L. Griffiths 001 (CoRR, 2025)

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints. - Jian-Qiao Zhu; Haijiang Yan; Thomas L. Griffiths 001 (CoRR, 2025)

Predictability Shapes Adaptation: An Evolutionary Perspective on Modes of Learning in Transformers. - Alexander Y. Ku; Thomas L. Griffiths 001; Stephanie C. Y. Chan (CoRR, 2025)

Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions. - Jian-Qiao Zhu; Hanbo Xie; Dilip Arumugam; Robert C. Wilson; Thomas L. Griffiths 001 (CoRR, 2025)

Steering Risk Preferences in Large Language Models by Aligning Behavioral and Neural Representations. - Jian-Qiao Zhu; Haijiang Yan; Thomas L. Griffiths 001 (CoRR, 2025)

Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers. - Andrew Nam; Henry Conklin; Yukang Yang; Thomas L. Griffiths 001; Jonathan D. Cohen 003; Sarah-Jane Leslie (CoRR, 2025)

Understanding Task Representations in Neural Networks via Bayesian Ablation. - Andrew Nam; Declan Campbell; Thomas L. Griffiths 001; Jonathan D. Cohen 003; Sarah-Jane Leslie (CoRR, 2025)

Partner Modelling Emerges in Recurrent Agents (But Only When It Matters). - Ruaridh Mon-Williams; Max Taylor-Davies; Elizabeth Mieczkowski; Natalia Vélez; Neil R. Bramley; Yanwei Wang; Thomas L. Griffiths 001; Christopher G. Lucas (CoRR, 2025)

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems. - Jiayi Geng; Howard Chen 003; Dilip Arumugam; Thomas L. Griffiths 001 (CoRR, 2025)

VideoGameBench: Can Vision-Language Models complete popular video games? - Alex L. Zhang; Thomas L. Griffiths 001; Karthik R. Narasimhan; Ofir Press (CoRR, 2025)

Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models. - Kaiqu Liang; Haimin Hu; Xuandong Zhao; Dawn Song; Thomas L. Griffiths 001; Jaime Fernández Fisac (CoRR, 2025)

Assessing Adaptive World Models in Machines with Novel Games. - Lance Ying; Katherine M. Collins; Prafull Sharma; Cedric Colas; Kaiya Ivy Zhao; Adrian Weller; Zenna Tavares; Phillip Isola; Samuel J. Gershman; Jacob Andreas; Thomas L. Griffiths 001; François Chollet; et al. (CoRR, 2025)

Whither symbols in the era of advanced neural networks? - Thomas L. Griffiths 001; Brenden M. Lake; R. Thomas McCoy; Ellie Pavlick; Taylor W. Webb (CoRR, 2025)

Generation and Evaluation in the Human Invention Process through the Lens of Game Design. - Katherine M. Collins; Graham Todd; Cedegao E. Zhang; Adrian Weller; Julian Togelius; Junyi Chu; Lionel Wong; Thomas L. Griffiths 001; Joshua B. Tenenbaum (CoRR, 2025)

Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models. - Liyi Zhang; Jake Snell; Thomas L. Griffiths 001 (CoRR, 2025)

A foundation model to predict and capture human cognition. - Marcel Binz; Elif Akata; Matthias Bethge; Franziska Brändle; Fred Callaway; Julian Coda-Forno; Peter Dayan; Can Demircan; Maria K. Eckstein; Noémi Élteto; Thomas L. Griffiths 001; Susanne Haridi; et al. (Nat., 2025)