Matt M. Botvinick

Affiliations:
  • University College London, Gatsby Computational Neuroscience Unit, UK
  • Princeton University, Neuroscience Institute, NJ, USA (former)
  • University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, USA (former)
  • Carnegie Mellon University, (PhD 2001)


According to our database1, Matt M. Botvinick authored at least 94 papers between 2001 and 2024.

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Bibliography

2024
Understanding dual process cognition via the minimum description length principle.
PLoS Comput. Biol., 2024

Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem.
CoRR, 2024

2023
Adaptive patch foraging in deep reinforcement learning agents.
Trans. Mach. Learn. Res., 2023

How should the advent of large language models affect the practice of science?
CoRR, 2023

Meta-Learned Models of Cognition.
CoRR, 2023

DiscoGen: Learning to Discover Gene Regulatory Networks.
CoRR, 2023

Cognitive Model Discovery via Disentangled RNNs.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Meta-in-context learning in large language models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Minimum Description Length Control.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning to Induce Causal Structure.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Compositional Sequence Generation in the Entorhinal-Hippocampal System.
Entropy, December, 2022

Meta-learning, social cognition and consciousness in brains and machines.
Neural Networks, 2022

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution.
CoRR, 2022

Learning to Induce Causal Structure.
CoRR, 2022

The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents.
CoRR, 2022

Hierarchical Perceiver.
CoRR, 2022

HCMD-zero: Learning Value Aligned Mechanisms from Data.
CoRR, 2022

Human-centered mechanism design with Democratic AI.
CoRR, 2022

Fine-tuning language models to find agreement among humans with diverse preferences.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

General-purpose, long-context autoregressive modeling with Perceiver AR.
Proceedings of the International Conference on Machine Learning, 2022

Perceiver IO: A General Architecture for Structured Inputs & Outputs.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study.
CoRR, 2021

Deep reinforcement learning models the emergent dynamics of human cooperation.
CoRR, 2021

Synthetic Returns for Long-Term Credit Assignment.
CoRR, 2021

Alchemy: A structured task distribution for meta-reinforcement learning.
CoRR, 2021

Collaborating with Humans without Human Data.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Attention over Learned Object Embeddings Enables Complex Visual Reasoning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Rapid Task-Solving in Novel Environments.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
A distributional code for value in dopamine-based reinforcement learning.
Nat., 2020

Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures.
CoRR, 2020

Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences.
CoRR, 2020

Deep Reinforcement Learning and its Neuroscientific Implications.
CoRR, 2020

Stabilizing Transformers for Reinforcement Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control.
Proceedings of the 8th International Conference on Learning Representations, 2020

Environmental drivers of systematicity and generalization in a situated agent.
Proceedings of the 8th International Conference on Learning Representations, 2020

The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget.
Proceedings of the 8th International Conference on Learning Representations, 2020

MEMO: A Deep Network for Flexible Combination of Episodic Memories.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Subgoal- and Goal-related Reward Prediction Errors in Medial Prefrontal Cortex.
J. Cogn. Neurosci., 2019

Emergent Systematic Generalization in a Situated Agent.
CoRR, 2019

Meta-learning of Sequential Strategies.
CoRR, 2019

Learned human-agent decision-making, communication and joint action in a virtual reality environment.
CoRR, 2019

Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette.
CoRR, 2019

MONet: Unsupervised Scene Decomposition and Representation.
CoRR, 2019

Causal Reasoning from Meta-reinforcement Learning.
CoRR, 2019

Multi-Object Representation Learning with Iterative Variational Inference.
Proceedings of the 36th International Conference on Machine Learning, 2019

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning.
Proceedings of the 36th International Conference on Machine Learning, 2019

Deep reinforcement learning with relational inductive biases.
Proceedings of the 7th International Conference on Learning Representations, 2019

Relational Forward Models for Multi-Agent Learning.
Proceedings of the 7th International Conference on Learning Representations, 2019

InfoBot: Transfer and Exploration via the Information Bottleneck.
Proceedings of the 7th International Conference on Learning Representations, 2019

A resource-rational model of physical abstraction for efficient mental simulation.
Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

2018
Relational Forward Models for Multi-Agent Learning.
CoRR, 2018

Relational Deep Reinforcement Learning.
CoRR, 2018

Relational inductive biases, deep learning, and graph networks.
CoRR, 2018

Probing Physics Knowledge Using Tools from Developmental Psychology.
CoRR, 2018

Unsupervised Predictive Memory in a Goal-Directed Agent.
CoRR, 2018

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents.
CoRR, 2018

Learning to Share and Hide Intentions using Information Regularization.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Been There, Done That: Meta-Learning with Episodic Recall.
Proceedings of the 35th International Conference on Machine Learning, 2018

Machine Theory of Mind.
Proceedings of the 35th International Conference on Machine Learning, 2018

On the importance of single directions for generalization.
Proceedings of the 6th International Conference on Learning Representations, 2018

SCAN: Learning Hierarchical Compositional Visual Concepts.
Proceedings of the 6th International Conference on Learning Representations, 2018

Episodic Control through Meta-Reinforcement Learning.
Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

2017
Predictive representations can link model-based reinforcement learning to model-free mechanisms.
PLoS Comput. Biol., 2017

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017.
CoRR, 2017

SCAN: Learning Abstract Hierarchical Compositional Visual Concepts.
CoRR, 2017

Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study.
Proceedings of the 34th International Conference on Machine Learning, 2017

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning.
Proceedings of the 34th International Conference on Machine Learning, 2017

Learning to Learn without Gradient Descent by Gradient Descent.
Proceedings of the 34th International Conference on Machine Learning, 2017

beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework.
Proceedings of the 5th International Conference on Learning Representations, 2017

Structure Learning in Motor Control: A Deep Reinforcement Learning Model.
Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

Learning to reinforcement learn.
Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

Tutorial: Recent Advances in Deep Learning.
Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

2016
One-shot Learning with Memory-Augmented Neural Networks.
CoRR, 2016

Meta-Learning with Memory-Augmented Neural Networks.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Leveraging Preposition Ambiguity to Assess Compositional Distributional Models of Semantics.
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, 2015

2014
Optimal Behavioral Hierarchy.
PLoS Comput. Biol., 2014

The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers.
Cogn. Sci., 2014

Design Principles of the Hippocampal Cognitive Map.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013
Neural Representation of Reward Probability: Evidence from the Illusion of Control.
J. Cogn. Neurosci., 2013

Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments.
Artif. Intell., 2013

Simitar: Simplified Searching of Statistically Significant Similarity Structure.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2013

Structured cognitive representations and complex inference in neural systems.
Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans.
Proceedings of the Computational and Robotic Models of the Hierarchical Organization of Behavior, 2013

2012
Commentary: Why I Am Not a Dynamicist.
Top. Cogn. Sci., 2012

A systematic approach to extracting semantic information from functional MRI data.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011
Errors of interpretation and modeling: A reply to Grinband et al.
NeuroImage, 2011

Information mapping with pattern classifiers: A comparative study.
NeuroImage, 2011

Classification of functional magnetic resonance imaging data using informative pattern features.
Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

2009
Machine learning classifiers and fMRI: A tutorial overview.
NeuroImage, 2009

2008
Goal-directed decision making in prefrontal cortex: a computational framework.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2005
Viewing facial expressions of pain engages cortical areas involved in the direct experience of pain.
NeuroImage, 2005

2001
Anterior Cingulate Cortex, Conflict Monitoring, and Levels of Processing.
NeuroImage, 2001


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