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

Passive attention in artificial neural networks predicts human visual selectivity

Developments in machine learning interpretability techniques over the pa...
07/14/2021 ∙ by Thomas A. Langlois, et al. ∙ 11 ∙ share

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Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment

Many transfer problems require re-using previously optimal decisions for...
06/28/2021 ∙ by Michael Chang, et al. ∙ 0 ∙ share

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Extending rational models of communication from beliefs to actions

Speakers communicate to influence their partner's beliefs and shape thei...
05/25/2021 ∙ by Theodore R. Sumers, et al. ∙ 4 ∙ share

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Are Convolutional Neural Networks or Transformers more like human vision?

Modern machine learning models for computer vision exceed humans in accu...
05/15/2021 ∙ by Shikhar Tuli, et al. ∙ 0 ∙ share

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Control of mental representations in human planning

One of the most striking features of human cognition is the capacity to ...
05/14/2021 ∙ by Mark K Ho, et al. ∙ 5 ∙ share

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Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task

There is substantial variability in the expectations that communication ...
05/13/2021 ∙ by Sonia K. Murthy, et al. ∙ 0 ∙ share

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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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Thomas L. Griffiths

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