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ORCIDThomas L. Griffiths 0001
Tom Griffiths 0001
Person information
- affiliation: Princeton University, Department of Psychology, NJ, USA
- affiliation: University of California, Berkeley, Department of Psychology, USA
Other persons with the same name
- Thomas L. Griffiths
- Tom Griffiths 0002 — University of Edinburgh, UK
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2020 – today
- 2023
[j40]Aditi Jha, Joshua C. Peterson, Thomas L. Griffiths:
Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks. Cogn. Sci. 47(1) (2023)- [j39]Natalia Vélez, Brian R. Christian, Mathew D. Hardy, Bill D. Thompson, Thomas L. Griffiths:
How do Humans Overcome Individual Computational Limitations by Working Together? Cogn. Sci. 47(1) (2023) - [j38]Michael Y. Li, Fred Callaway, William D. Thompson, Ryan P. Adams, Thomas L. Griffiths:
Learning to Learn Functions. Cogn. Sci. 47(4) (2023) - [i87]Ilia Sucholutsky, Thomas L. Griffiths:
Alignment with human representations supports robust few-shot learning. CoRR abs/2301.11990 (2023) - [i86]Raja Marjieh, Ilia Sucholutsky, Pol van Rijn, Nori Jacoby, Thomas L. Griffiths:
What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models. CoRR abs/2302.01308 (2023) - [i85]Minkyu Shin, Jin Kim, Bas van Opheusden, Thomas L. Griffiths:
Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty. CoRR abs/2303.07462 (2023) - [i84]Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang:
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. CoRR abs/2303.11373 (2023) - [i83]Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan:
Tree of Thoughts: Deliberate Problem Solving with Large Language Models. CoRR abs/2305.10601 (2023) - [i82]R. Thomas McCoy, Thomas L. Griffiths:
Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. CoRR abs/2305.14701 (2023) - [i81]Zi Wang, Alexander Ku, Jason Baldridge, Thomas L. Griffiths, Been Kim:
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. CoRR abs/2305.18213 (2023) - 2022
- [j37]Mark K. Ho, Thomas L. Griffiths:
Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control. Annu. Rev. Control. Robotics Auton. Syst. 5: 33-53 (2022) - [j36]Rachit Dubey
, Thomas L. Griffiths, Peter Dayan:
The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons. PLoS Comput. Biol. 18(8) (2022) - [j35]Mathew D. Hardy, Peaks M. Krafft, Bill Thompson, Thomas L. Griffiths:
Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations. Top. Cogn. Sci. 14(3): 550-573 (2022) - [c190]Takateru Yamakoshi, Thomas L. Griffiths, Robert D. Hawkins:
Probing BERT's priors with serial reproduction chains. ACL (Findings) 2022: 3977-3992 - [c189]Ishita Dasgupta, Erin Grant, Tom Griffiths:
Distinguishing rule and exemplar-based generalization in learning systems. ICML 2022: 4816-4830 - [c188]Michael Chang, Tom Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. NeurIPS 2022 - [c187]Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins, Jonathan D. Cohen, Nathaniel D. Daw, Karthik Narasimhan, Tom Griffiths:
Using natural language and program abstractions to instill human inductive biases in machines. NeurIPS 2022 - [c186]Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Tom Griffiths, Dylan Hadfield-Menell:
How to talk so AI will learn: Instructions, descriptions, and autonomy. NeurIPS 2022 - [i80]Maya Malaviya, Ilia Sucholutsky, Kerem Oktar, Thomas L. Griffiths:
Can Humans Do Less-Than-One-Shot Learning? CoRR abs/2202.04670 (2022) - [i79]Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers, Nori Jacoby, Thomas L. Griffiths:
Predicting Human Similarity Judgments Using Large Language Models. CoRR abs/2202.04728 (2022) - [i78]Takateru Yamakoshi, Robert D. Hawkins, Thomas L. Griffiths:
Probing BERT's priors with serial reproduction chains. CoRR abs/2202.12226 (2022) - [i77]Sreejan Kumar, Ishita Dasgupta, Raja Marjieh, Nathaniel D. Daw, Jonathan D. Cohen, Thomas L. Griffiths:
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. CoRR abs/2204.01437 (2022) - [i76]Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell:
Linguistic communication as (inverse) reward design. CoRR abs/2204.05091 (2022) - [i75]Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins, Nathaniel D. Daw, Jonathan D. Cohen, Karthik Narasimhan, Thomas L. Griffiths:
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines. CoRR abs/2205.11558 (2022) - [i74]Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby:
Words are all you need? Capturing human sensory similarity with textual descriptors. CoRR abs/2206.04105 (2022) - [i73]Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell:
How to talk so your robot will learn: Instructions, descriptions, and pragmatics. CoRR abs/2206.07870 (2022) - [i72]Michael Chang, Thomas L. Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. CoRR abs/2207.00787 (2022) - [i71]Sunayana Rane, Mira L. Nencheva, Zeyu Wang, Casey Lew-Williams, Olga Russakovsky, Thomas L. Griffiths:
Predicting Word Learning in Children from the Performance of Computer Vision Systems. CoRR abs/2207.09847 (2022) - [i70]Michael Y. Li, Erin Grant, Thomas L. Griffiths:
Gaussian process surrogate models for neural networks. CoRR abs/2208.06028 (2022) - [i69]Mathew D. Hardy, Bill D. Thompson, P. M. Krafft, Thomas L. Griffiths:
Bias amplification in experimental social networks is reduced by resampling. CoRR abs/2208.07261 (2022) - [i68]Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths:
Analyzing Diffusion as Serial Reproduction. CoRR abs/2209.14821 (2022) - [i67]Ilia Sucholutsky, Raja Marjieh, Nori Jacoby, Thomas L. Griffiths:
On the Informativeness of Supervision Signals. CoRR abs/2211.01407 (2022) - [i66]Carlos G. Correa, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw, Thomas L. Griffiths:
Humans decompose tasks by trading off utility and computational cost. CoRR abs/2211.03890 (2022) - 2021
- [j34]Stephan C. Meylan, Thomas L. Griffiths:
The Challenges of Large-Scale, Web-Based Language Datasets: Word Length and Predictability Revisited. Cogn. Sci. 45(6) (2021) - [j33]Frederick Callaway, Antonio Rangel, Thomas L. Griffiths:
Fixation patterns in simple choice reflect optimal information sampling. PLoS Comput. Biol. 17(3) (2021) - [c185]Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths:
Learning Rewards From Linguistic Feedback. AAAI 2021: 6002-6010 - [c184]Sreejan Kumar, Ishita Dasgupta, Jonathan D. Cohen, Nathaniel D. Daw, Thomas L. Griffiths:
Meta-Learning of Structured Task Distributions in Humans and Machines. ICLR 2021 - [c183]Michael Chang, Sidhant Kaushik, Sergey Levine, Tom Griffiths:
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. ICML 2021: 1452-1462 - [c182]Thomas A. Langlois, H. Charles Zhao, Erin Grant, Ishita Dasgupta, Thomas L. Griffiths, Nori Jacoby:
Passive attention in artificial neural networks predicts human visual selectivity. NeurIPS 2021: 27094-27106 - [i65]Stephan C. Meylan, Sathvik Nair, Thomas L. Griffiths:
Evaluating Models of Robust Word Recognition with Serial Reproduction. CoRR abs/2101.09788 (2021) - [i64]Robert X. D. Hawkins, Michael Franke, Michael C. Frank, Kenny Smith, Thomas L. Griffiths, Noah D. Goodman:
From partners to populations: A hierarchical Bayesian account of coordination and convention. CoRR abs/2104.05857 (2021) - [i63]Sonia K. Murthy, Robert X. D. Hawkins, Thomas L. Griffiths:
Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task. CoRR abs/2105.06546 (2021) - [i62]Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths:
Control of mental representations in human planning. CoRR abs/2105.06948 (2021) - [i61]Shikhar Tuli, Ishita Dasgupta, Erin Grant, Thomas L. Griffiths:
Are Convolutional Neural Networks or Transformers more like human vision? CoRR abs/2105.07197 (2021) - [i60]Theodore R. Sumers, Robert X. D. Hawkins, Mark K. Ho, Thomas L. Griffiths:
Extending rational models of communication from beliefs to actions. CoRR abs/2105.11950 (2021) - [i59]Michael Chang, Sidhant Kaushik, Sergey Levine, Thomas L. Griffiths:
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. CoRR abs/2106.14993 (2021) - [i58]Thomas A. Langlois, H. Charles Zhao, Erin Grant, Ishita Dasgupta, Thomas L. Griffiths, Nori Jacoby:
Passive attention in artificial neural networks predicts human visual selectivity. CoRR abs/2107.07013 (2021) - [i57]Mark K. Ho, Thomas L. Griffiths:
Cognitive science as a source of forward and inverse models of human decisions for robotics and control. CoRR abs/2109.00127 (2021) - [i56]Ishita Dasgupta, Erin Grant, Thomas L. Griffiths:
Distinguishing rule- and exemplar-based generalization in learning systems. CoRR abs/2110.04328 (2021) - [i55]Samuel A. Barnett, Robert D. Hawkins, Thomas L. Griffiths:
A pragmatic account of the weak evidence effect. CoRR abs/2112.03799 (2021) - 2020
- [j32]Vael Gates, Thomas L. Griffiths, Anca D. Dragan:
How to Be Helpful to Multiple People at Once. Cogn. Sci. 44(6) (2020) - [j31]Anna N. Rafferty, Rachel Jansen, Thomas L. Griffiths:
Assessing Mathematics Misunderstandings via Bayesian Inverse Planning. Cogn. Sci. 44(10) (2020) - [j30]Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner, Alexander Y. Ku:
Multitasking Capacity: Hardness Results and Improved Constructions. SIAM J. Discret. Math. 34(1): 885-903 (2020) - [c181]Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths:
People Do Not Just Plan, They Plan to Plan. AAAI 2020: 1300-1307 - [c180]Ruairidh M. Battleday, Tom Griffiths:
Analogy as Nonparametric Bayesian Inference over Relational Systems. CogSci 2020 - [c179]Frederick Callaway, Mathew D. Hardy, Tom Griffiths:
Optimal nudging. CogSci 2020 - [c178]Carlos G. Correa, Mark K. Ho, Frederick Callaway, Tom Griffiths:
Resource-rational Task Decomposition to Minimize Planning Costs. CogSci 2020 - [c177]Mathew D. Hardy, Bill Thompson, Peter M. Krafft, Tom Griffiths:
Population-level amplification of perceptual bias. CogSci 2020 - [c176]Robert X. D. Hawkins, Noah D. Goodman, Adele E. Goldberg, Tom Griffiths:
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. CogSci 2020 - [c175]Samee Ibraheem, Vael Gates, John DeNero, Tom Griffiths:
Investigating the Behavior of Malicious Actors Through the Game of Mafia. CogSci 2020 - [c174]Rachel Jansen, Anna N. Rafferty, Tom Griffiths:
A rational model of sequential self-assessment. CogSci 2020 - [c173]Aditi Jha, Joshua C. Peterson, Tom Griffiths:
Extracting low-dimensional psychological representations from convolutional neural networks. CogSci 2020 - [c172]Max Kleiman-Weiner, Felix Sosa, Bill Thompson, Sebastiaan van Opheusden, Tom Griffiths, Samuel Gershman, Fiery Cushman:
Downloading Culture.zip: Social learning by program induction. CogSci 2020 - [c171]Richard Thomas McCoy, Erin Grant, Paul Smolensky, Tom Griffiths, Tal Linzen:
Universal linguistic inductive biases via meta-learning. CogSci 2020 - [c170]Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Tom Griffiths:
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. CogSci 2020 - [c169]Jordan W. Suchow, Tom Griffiths, Joshua K. Hartshorne:
Workshop on Scaling Cognitive Science. CogSci 2020 - [c168]Theodore R. Sumers, Mark K. Ho, Tom Griffiths:
Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation. CogSci 2020 - [c167]Qiong Zhang, Kenneth A. Norman, Tom Griffiths:
The method of loci is an optimal policy for memory search. CogSci 2020 - [c166]Robert X. D. Hawkins, Takateru Yamakoshi, Thomas L. Griffiths, Adele E. Goldberg:
Investigating representations of verb bias in neural language models. EMNLP (1) 2020: 4653-4663 - [c165]Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine:
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. ICML 2020: 1437-1447 - [i54]Robert X. D. Hawkins, Noah D. Goodman, Adele E. Goldberg, Thomas L. Griffiths:
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. CoRR abs/2002.01510 (2020) - [i53]Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths:
The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR abs/2002.05769 (2020) - [i52]Aditi Jha, Joshua Caleb Peterson, Thomas L. Griffiths:
Extracting low-dimensional psychological representations from convolutional neural networks. CoRR abs/2005.14363 (2020) - [i51]Ruairidh M. Battleday, Thomas L. Griffiths:
Analogy as Nonparametric Bayesian Inference over Relational Systems. CoRR abs/2006.04156 (2020) - [i50]R. Thomas McCoy, Erin Grant, Paul Smolensky, Thomas L. Griffiths, Tal Linzen:
Universal linguistic inductive biases via meta-learning. CoRR abs/2006.16324 (2020) - [i49]Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine:
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. CoRR abs/2007.02382 (2020) - [i48]Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths:
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. CoRR abs/2007.08723 (2020) - [i47]Carlos G. Correa, Mark K. Ho, Fred Callaway, Thomas L. Griffiths:
Resource-rational Task Decomposition to Minimize Planning Costs. CoRR abs/2007.13862 (2020) - [i46]Thomas L. Griffiths:
Understanding Human Intelligence through Human Limitations. CoRR abs/2009.14050 (2020) - [i45]Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths:
Learning Rewards from Linguistic Feedback. CoRR abs/2009.14715 (2020) - [i44]Sreejan Kumar, Ishita Dasgupta, Jonathan D. Cohen, Nathaniel D. Daw, Thomas L. Griffiths:
Meta-Learning of Compositional Task Distributions in Humans and Machines. CoRR abs/2010.02317 (2020) - [i43]Robert X. D. Hawkins, Takateru Yamakoshi, Thomas L. Griffiths, Adele E. Goldberg:
Investigating representations of verb bias in neural language models. CoRR abs/2010.02375 (2020) - [i42]Rachit Dubey, Erin Grant, Michael Luo, Karthik Narasimhan, Thomas L. Griffiths:
Context-Conditioning as Cognitive Control: Guiding Meta-learning with Task Information. CoRR abs/2011.13782 (2020) - [i41]Aida Nematzadeh, Zahra Shekarchi, Thomas L. Griffiths, Suzanne Stevenson:
Competition in Cross-situational Word Learning: A Computational Study. CoRR abs/2012.03370 (2020) - [i40]Theodore R. Sumers, Mark K. Ho, Thomas L. Griffiths:
Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation. CoRR abs/2012.09035 (2020)
2010 – 2019
- 2019
- [j29]Joseph L. Austerweil, Sophia Sanborn, Thomas L. Griffiths:
Learning How to Generalize. Cogn. Sci. 43(8) (2019) - [c164]Emmanuel M. Pothos, Jerome R. Busemeyer, Timothy J. Pleskac, James M. Yearsley, Josh Tenenbaum, Noah D. Goodman, Michael Henry Tessler, Tom Griffiths, Falk Lieder, Ralph Hertwig, Thorsten Pachur, Christina Leuker, Richard M. Shiffrin:
Extending Rationality. CogSci 2019: 39-40 - [c163]Rachit Dubey, Tom Griffiths, Tania Lombrozo:
If it's important, then I am curious: A value intervention to induce curiosity. CogSci 2019: 282-288 - [c162]Bill Thompson, Tom Griffiths:
Inductive Biases Constrain Cumulative Cultural Evolution. CogSci 2019: 1111-1117 - [c161]Mayank Agrawal, Joshua C. Peterson, Tom Griffiths:
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. CogSci 2019: 1318-1323 - [c160]Erin Grant, Joshua C. Peterson, Tom Griffiths:
Learning deep taxonomic priors for concept learning from few positive examples. CogSci 2019: 1865-1870 - [c159]Mark K. Ho, Joanna Korman, Tom Griffiths:
The Computational Structure of Unintentional Meaning. CogSci 2019: 1915-1921 - [c158]Thomas Langlois, Nori Jacoby, Jordan W. Suchow, Tom Griffiths:
Orthogonal multi-view three-dimensional object representations in memory revealed by serial reproduction. CogSci 2019: 2078-2083 - [c157]Carlos G. Correa, Frederick Callaway, Mark K. Ho, Tom Griffiths:
Compositional subgoal representations. CogSci 2019: 3255 - [c156]Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Alyosha A. Efros, Tom Griffiths:
Human-level but not human-like: Deep Reinforcement Learning in the dark. CogSci 2019: 3265 - [c155]Mathew D. Hardy, Tom Griffiths:
Demonstrating the Impact of Prior Knowledge in Risky Choice. CogSci 2019: 3278 - [c154]Vishal Lall, Jordan W. Suchow, Gustavo Malkomes, Tom Griffiths:
Automated cognitive modeling with Bayesian active model selection. CogSci 2019: 3503 - [c153]Anna N. Rafferty, Rachel Jansen, Tom Griffiths:
Modeling students' fraction arithmetic strategies using inverse planning. CogSci 2019: 3554 - [c152]Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky:
Human Uncertainty Makes Classification More Robust. ICCV 2019: 9616-9625 - [c151]Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths:
Automatically Composing Representation Transformations as a Means for Generalization. ICLR (Poster) 2019 - [c150]David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths:
Cognitive model priors for predicting human decisions. ICML 2019: 5133-5141 - [c149]Micah Carroll, Rohin Shah, Mark K. Ho, Tom Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca D. Dragan:
On the Utility of Learning about Humans for Human-AI Coordination. NeurIPS 2019: 5175-5186 - [c148]Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller:
Reconciling meta-learning and continual learning with online mixtures of tasks. NeurIPS 2019: 9119-9130 - [i39]Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths:
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. CoRR abs/1902.06744 (2019) - [i38]Ori Plonsky, Reut Apel, Eyal Ert, Moshe Tennenholtz, David Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell, Evan C. Carter, James F. Cavanagh, Ido Erev:
Predicting human decisions with behavioral theories and machine learning. CoRR abs/1904.06866 (2019) - [i37]Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths:
Capturing human categorization of natural images at scale by combining deep networks and cognitive models. CoRR abs/1904.12690 (2019) - [i36]David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell:
Cognitive Model Priors for Predicting Human Decisions. CoRR abs/1905.09397 (2019) - [i35]Mark K. Ho, Joanna Korman, Thomas L. Griffiths:
The Computational Structure of Unintentional Meaning. CoRR abs/1906.01983 (2019) - [i34]Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky:
Human uncertainty makes classification more robust. CoRR abs/1908.07086 (2019) - [i33]Micah Carroll, Rohin Shah, Mark K. Ho, Thomas L. Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca D. Dragan:
On the Utility of Learning about Humans for Human-AI Coordination. CoRR abs/1910.05789 (2019) - [i32]Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths:
Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making. CoRR abs/1910.07581 (2019) - 2018
- [j28]Andrew Whalen, Thomas L. Griffiths, Daphna Buchsbaum:
Sensitivity to Shared Information in Social Learning. Cogn. Sci. 42(1): 168-187 (2018) - [j27]Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths:
Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations. Cogn. Sci. 42(8): 2648-2669 (2018) - [j26]Falk Lieder, Amitai Shenhav, Sebastian Musslick, Thomas L. Griffiths:
Rational metareasoning and the plasticity of cognitive control. PLoS Comput. Biol. 14(4) (2018) - [c147]David Bourgin, Joshua T. Abbott, Tom Griffiths:
Recommendation as Generalization: Evaluating Cognitive Models In the Wild. CogSci 2018 - [c146]Frederick Callaway, Falk Lieder, Priyam Das, Sayan Gul, Paul M. Krueger, Tom Griffiths:
A resource-rational analysis of human planning. CogSci 2018 - [c145]Rachel Jansen, Ruthe Foushee, Tom Griffiths:
A new similarity measure to reveal individual differences and growth in implicit number conceptions. CogSci 2018 - [c144]Rachel Jansen, Anna N. Rafferty, Tom Griffiths:
Modeling the Dunning-Kruger Effect: A Rational Account of Inaccurate Self-Assessment. CogSci 2018 - [c143]Peter M. Krafft, Tom Griffiths:
Levels of Analysis in Computational Social Science. CogSci 2018 - [c142]Paul M. Krueger, Tom Griffiths:
Shaping Model-Free Habits with Model-Based Goals. CogSci 2018 - [c141]Alexandra Paxton, Thomas J. H. Morgan, Jordan W. Suchow, Tom Griffiths:
Interpersonal Coordination of Perception and Memory in Real-Time Online Social Interaction. CogSci 2018 - [c140]Joshua C. Peterson, Jordan W. Suchow, Krisha Aghi, Alexander Y. Ku, Tom Griffiths:
Capturing human category representations by sampling in deep feature spaces. CogSci 2018 - [c139]Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Tom Griffiths:
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. CogSci 2018 - [c138]Sophia Sanborn, David Bourgin, Michael Chang, Tom Griffiths:
Representational efficiency outweighs action efficiency in human program induction. CogSci 2018 - [c137]Jordan W. Suchow, Joshua C. Peterson, Tom Griffiths:
Learning a face space for experiments on human identity. CogSci 2018 - [c136]Kaylee Burns, Aida Nematzadeh, Erin Grant, Alison Gopnik, Thomas L. Griffiths:
Exploiting Attention to Reveal Shortcomings in Memory Models. BlackboxNLP@EMNLP 2018: 378-380