Thomas L. Griffiths

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I am the Director of the Computational Cognitive Science Lab and the Institute of Cognitive and Brain Sciences at the University of California, Berkeley

From partners to populations: A hierarchical Bayesian account of coordination and convention

Languages are powerful solutions to coordination problems: they provide ...
04/12/2021 ∙ by Robert D. Hawkins, et al. ∙ 10 ∙ share

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Evaluating Models of Robust Word Recognition with Serial Reproduction

Spoken communication occurs in a "noisy channel" characterized by high l...
01/24/2021 ∙ by Stephan C. Meylan, et al. ∙ 1 ∙ share

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Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation

Successful teaching entails a complex interaction between a teacher and ...
12/16/2020 ∙ by Theodore R. Sumers, et al. ∙ 0 ∙ share

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Competition in Cross-situational Word Learning: A Computational Study

Children learn word meanings by tapping into the commonalities across di...
12/06/2020 ∙ by Aida Nematzadeh, et al. ∙ 0 ∙ share

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Investigating representations of verb bias in neural language models

Languages typically provide more than one grammatical construction to ex...
10/05/2020 ∙ by Robert D. Hawkins, et al. ∙ 0 ∙ share

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Meta-Learning of Compositional Task Distributions in Humans and Machines

Modern machine learning systems struggle with sample efficiency and are ...
10/05/2020 ∙ by Sreejan Kumar, et al. ∙ 0 ∙ share

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Learning Rewards from Linguistic Feedback

We explore unconstrained natural language feedback as a learning signal ...
09/30/2020 ∙ by Theodore R. Sumers, et al. ∙ 0 ∙ share

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Understanding Human Intelligence through Human Limitations

Recent progress in artificial intelligence provides the opportunity to a...
09/29/2020 ∙ by Thomas L. Griffiths, et al. ∙ 0 ∙ share

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Resource-rational Task Decomposition to Minimize Planning Costs

People often plan hierarchically. That is, rather than planning over a m...
07/27/2020 ∙ by Carlos G. Correa, et al. ∙ 0 ∙ share

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End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior

Traditional models of category learning in psychology focus on represent...
07/17/2020 ∙ by Pulkit Singh, et al. ∙ 20 ∙ share

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Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions

This paper seeks to establish a framework for directing a society of sim...
07/05/2020 ∙ by Michael Chang, et al. ∙ 0 ∙ share

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Universal linguistic inductive biases via meta-learning

How do learners acquire languages from the limited data available to the...
06/29/2020 ∙ by R. Thomas McCoy, et al. ∙ 0 ∙ share

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Analogy as Nonparametric Bayesian Inference over Relational Systems

Much of human learning and inference can be framed within the computatio...
06/07/2020 ∙ by Ruairidh M. Battleday, et al. ∙ 0 ∙ share

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Extracting low-dimensional psychological representations from convolutional neural networks

Deep neural networks are increasingly being used in cognitive modeling a...
05/29/2020 ∙ by Aditi Jha, et al. ∙ 0 ∙ share

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The Efficiency of Human Cognition Reflects Planned Information Processing

Planning is useful. It lets people take actions that have desirable long...
02/13/2020 ∙ by Mark K Ho, et al. ∙ 7 ∙ share

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Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks

A key property of linguistic conventions is that they hold over an entir...
02/04/2020 ∙ by Robert D. Hawkins, et al. ∙ 0 ∙ share

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Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making

Do large datasets provide value to psychologists? Without a systematic m...
10/16/2019 ∙ by Mayank Agrawal, et al. ∙ 23 ∙ share

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

While we would like agents that can coordinate with humans, current algo...
10/13/2019 ∙ by Micah Carroll, et al. ∙ 8 ∙ share

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Human uncertainty makes classification more robust

The classification performance of deep neural networks has begun to asym...
08/19/2019 ∙ by Joshua C. Peterson, et al. ∙ 0 ∙ share

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The Computational Structure of Unintentional Meaning

Speech-acts can have literal meaning as well as pragmatic meaning, but t...
06/03/2019 ∙ by Mark K Ho, et al. ∙ 0 ∙ share

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Cognitive Model Priors for Predicting Human Decisions

Human decision-making underlies all economic behavior. For the past four...
05/22/2019 ∙ by David D. Bourgin, et al. ∙ 10 ∙ share

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Capturing human categorization of natural images at scale by combining deep networks and cognitive models

Human categorization is one of the most important and successful targets...
04/26/2019 ∙ by Ruairidh M. Battleday, et al. ∙ 16 ∙ share

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Predicting human decisions with behavioral theories and machine learning

Behavioral decision theories aim to explain human behavior. Can they hel...
04/15/2019 ∙ by Ori Plonsky, et al. ∙ 0 ∙ share

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Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning

Large-scale behavioral datasets enable researchers to use complex machin...
02/18/2019 ∙ by Mayank Agrawal, et al. ∙ 0 ∙ share

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Online gradient-based mixtures for transfer modulation in meta-learning

Learning-to-learn or meta-learning leverages data-driven inductive bias ...
12/14/2018 ∙ by Ghassen Jerfel, et al. ∙ 0 ∙ share

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Multitasking Capacity: Hardness Results and Improved Constructions

We consider the problem of determining the maximal α∈ (0,1] such that ev...
09/08/2018 ∙ by Noga Alon, et al. ∙ 0 ∙ share

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Evaluating Theory of Mind in Question Answering

We propose a new dataset for evaluating question answering models with r...
08/28/2018 ∙ by Aida Nematzadeh, et al. ∙ 0 ∙ share

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Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network

Effective representation of a text is critical for various natural langu...
08/08/2018 ∙ by Pengfei Liu, et al. ∙ 0 ∙ share

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Representational efficiency outweighs action efficiency in human program induction

The importance of hierarchically structured representations for tractabl...
07/18/2018 ∙ by Sophia Sanborn, et al. ∙ 0 ∙ share

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Automatically Composing Representation Transformations as a Means for Generalization

How can we build a learner that can capture the essence of what makes a ...
07/12/2018 ∙ by Michael B. Chang, et al. ∙ 10 ∙ share

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Learning a face space for experiments on human identity

Generative models of human identity and appearance have broad applicabil...
05/19/2018 ∙ by Jordan W. Suchow, et al. ∙ 0 ∙ share

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Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels

Modern convolutional neural networks (CNNs) are able to achieve human-le...
05/19/2018 ∙ by Joshua C. Peterson, et al. ∙ 0 ∙ share

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Capturing human category representations by sampling in deep feature spaces

Understanding how people represent categories is a core problem in cogni...
05/19/2018 ∙ by Joshua C. Peterson, et al. ∙ 0 ∙ share

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Investigating Human Priors for Playing Video Games

What makes humans so good at solving seemingly complex video games? Unli...
02/28/2018 ∙ by Rachit Dubey, et al. ∙ 0 ∙ share

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Generating Plans that Predict Themselves

Collaboration requires coordination, and we coordinate by anticipating o...
02/14/2018 ∙ by Jaime F. Fisac, et al. ∙ 0 ∙ share

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Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration

The study of human-robot interaction is fundamental to the design and us...
02/06/2018 ∙ by Chang Liu, et al. ∙ 0 ∙ share

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Learning to select computations

Efficient use of limited computational resources is essential to intelli...
11/18/2017 ∙ by Falk Lieder, et al. ∙ 0 ∙ share

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Modeling Human Categorization of Natural Images Using Deep Feature Representations

Over the last few decades, psychologists have developed sophisticated fo...
11/13/2017 ∙ by Ruairidh M. Battleday, et al. ∙ 0 ∙ share

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Pragmatic-Pedagogic Value Alignment

For an autonomous system to provide value (e.g., to customers, designers...
07/20/2017 ∙ by Jaime F. Fisac, et al. ∙ 0 ∙ share

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Leveraging deep neural networks to capture psychological representations

Artificial neural networks have seen a recent surge in popularity for th...
06/08/2017 ∙ by Joshua C. Peterson, et al. ∙ 0 ∙ share

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Evaluating vector-space models of analogy

Vector-space representations provide geometric tools for reasoning about...
05/12/2017 ∙ by Dawn Chen, et al. ∙ 0 ∙ share

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A rational analysis of curiosity

We present a rational analysis of curiosity, proposing that people's cur...
05/11/2017 ∙ by Rachit Dubey, et al. ∙ 0 ∙ share

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Evidence for the size principle in semantic and perceptual domains

Shepard's Universal Law of Generalization offered a compelling case for ...
05/09/2017 ∙ by Joshua C. Peterson, et al. ∙ 0 ∙ share

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Word forms - not just their lengths- are optimized for efficient communication

The inverse relationship between the length of a word and the frequency ...
03/06/2017 ∙ by Stephan C. Meylan, et al. ∙ 0 ∙ share

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Adapting Deep Network Features to Capture Psychological Representations

Deep neural networks have become increasingly successful at solving clas...
08/06/2016 ∙ by Joshua C. Peterson, et al. ∙ 0 ∙ share

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The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies

We present the nested Chinese restaurant process (nCRP), a stochastic pr...
10/03/2007 ∙ by David M. Blei, et al. ∙ 0 ∙ share

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Thomas L. Griffiths

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