Anterior Cingulate Cortex, Conflict Monitoring, and Levels of Processing. - Vincent van Veen; Jonathan D. Cohen 003; Matthew M. Botvinick; V. Andrew Stenger; Cameron S. Carter (NeuroImage, 2001)

Viewing facial expressions of pain engages cortical areas involved in the direct experience of pain. - Matthew M. Botvinick; Amishi P. Jha; Lauren M. Bylsma; Sara A. Fabian; Patricia E. Solomon; Kenneth M. Prkachin (NeuroImage, 2005)

Goal-directed decision making in prefrontal cortex: a computational framework. - Matthew M. Botvinick; James An (NIPS, 2008)

Machine learning classifiers and fMRI: A tutorial overview. - Francisco Pereira 001; Tom M. Mitchell; Matthew M. Botvinick (NeuroImage, 2009)

Classification of functional magnetic resonance imaging data using informative pattern features. - Francisco Pereira 001; Matthew M. Botvinick (KDD, 2011)

Information mapping with pattern classifiers: A comparative study. - Francisco Pereira 001; Matthew M. Botvinick (NeuroImage, 2011)

Errors of interpretation and modeling: A reply to Grinband et al. - Nick Yeung; Jonathan D. Cohen 003; Matthew M. Botvinick (NeuroImage, 2011)

A systematic approach to extracting semantic information from functional MRI data. - Francisco Pereira 001; Matthew M. Botvinick (NIPS, 2012)

Commentary: Why I Am Not a Dynamicist. - Matthew M. Botvinick (Top. Cogn. Sci., 2012)

Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans. - Carlos Diuk; Anna C. Schapiro; Natalia Córdova; José Ribas-Fernandes; Yael Niv; Matthew M. Botvinick (Computational and Robotic Models of the Hierarchical Organization of Behavior, 2013)

Structured cognitive representations and complex inference in neural systems. - Samuel Gershman; Joshua B. Tenenbaum; Alexandre Pouget; Matthew M. Botvinick; Peter Dayan (CogSci, 2013)

Simitar: Simplified Searching of Statistically Significant Similarity Structure. - Francisco Pereira 001; Matthew M. Botvinick (PRNI, 2013)

Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments. - Francisco Pereira 001; Matthew M. Botvinick; Greg Detre (Artif. Intell., 2013)

Neural Representation of Reward Probability: Evidence from the Illusion of Control. - Wouter Kool 002; Sarah J. Getz; Matthew M. Botvinick (J. Cogn. Neurosci., 2013)

Design Principles of the Hippocampal Cognitive Map. - Kimberly L. Stachenfeld; Matthew M. Botvinick; Samuel Gershman (NIPS, 2014)

The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers. - Matthew M. Botvinick; Jonathan D. Cohen 003 (Cogn. Sci., 2014)

Optimal Behavioral Hierarchy. - Alec Solway; Carlos Diuk; Natalia Córdova; Debbie Yee; Andrew G. Barto; Yael Niv; Matthew M. Botvinick (PLoS Comput. Biol., 2014)

Leveraging Preposition Ambiguity to Assess Compositional Distributional Models of Semantics. - Samuel Ritter; Cotie Long; Denis Paperno; Marco Baroni; Matthew M. Botvinick; Adele E. Goldberg (*SEM@NAACL-HLT, 2015)

Meta-Learning with Memory-Augmented Neural Networks. - Adam Santoro; Sergey Bartunov; Matthew M. Botvinick; Daan Wierstra; Timothy P. Lillicrap (ICML, 2016)

One-shot Learning with Memory-Augmented Neural Networks. - Adam Santoro; Sergey Bartunov; Matthew M. Botvinick; Daan Wierstra; Timothy P. Lillicrap (CoRR, 2016)

Tutorial: Recent Advances in Deep Learning. - Matthew M. Botvinick; Peter W. Battaglia (CogSci, 2017)

Learning to reinforcement learn. - Jane Wang 001; Zeb Kurth-Nelson; Hubert Soyer; Joel Z. Leibo; Dhruva Tirumala; Rémi Munos; Charles Blundell; Dharshan Kumaran; Matt M. Botvinick (CogSci, 2017)

Structure Learning in Motor Control: A Deep Reinforcement Learning Model. - Ari Weinstein; Matthew M. Botvinick (CogSci, 2017)

beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. - Irina Higgins; Loïc Matthey; Arka Pal; Christopher P. Burgess; Xavier Glorot; Matthew M. Botvinick; Shakir Mohamed; Alexander Lerchner (ICLR (Poster), 2017)

Learning to Learn without Gradient Descent by Gradient Descent. - Yutian Chen 001; Matthew W. Hoffman; Sergio Gomez Colmenarejo; Misha Denil; Timothy P. Lillicrap; Matthew M. Botvinick; Nando de Freitas (ICML, 2017)

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. - Irina Higgins; Arka Pal; Andrei A. Rusu; Loïc Matthey; Christopher P. Burgess; Alexander Pritzel; Matthew M. Botvinick; Charles Blundell; Alexander Lerchner (ICML, 2017)

Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. - Samuel Ritter; David G. T. Barrett; Adam Santoro; Matt M. Botvinick (ICML, 2017)

SCAN: Learning Abstract Hierarchical Compositional Visual Concepts. - Irina Higgins; Nicolas Sonnerat; Loïc Matthey; Arka Pal; Christopher P. Burgess; Matthew M. Botvinick; Demis Hassabis; Alexander Lerchner (CoRR, 2017)

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. - Matthew M. Botvinick; David G. T. Barrett; Peter W. Battaglia; Nando de Freitas; Dharshan Kumaran; Joel Z. Leibo; Tim Lillicrap; Joseph Modayil; S. Mohamed; Neil C. Rabinowitz; Danilo Jimenez Rezende; Adam Santoro; et al. (CoRR, 2017)

Predictive representations can link model-based reinforcement learning to model-free mechanisms. - Evan M. Russek; Ida Momennejad; Matthew M. Botvinick; Samuel J. Gershman; Nathaniel D. Daw (PLoS Comput. Biol., 2017)

Episodic Control through Meta-Reinforcement Learning. - Samuel Ritter; Jane X. Wang; Zeb Kurth-Nelson; Matthew M. Botvinick (CogSci, 2018)

SCAN: Learning Hierarchical Compositional Visual Concepts. - Irina Higgins; Nicolas Sonnerat; Loic Matthey; Arka Pal; Christopher P. Burgess; Matko Bosnjak; Murray Shanahan; Matthew M. Botvinick; Demis Hassabis; Alexander Lerchner (ICLR (Poster), 2018)

On the importance of single directions for generalization. - Ari S. Morcos; David G. T. Barrett; Neil C. Rabinowitz; Matthew M. Botvinick (ICLR (Poster), 2018)

Machine Theory of Mind. - Neil C. Rabinowitz; Frank Perbet; H. Francis Song; Chiyuan Zhang; S. M. Ali Eslami; Matthew M. Botvinick (ICML, 2018)

Been There, Done That: Meta-Learning with Episodic Recall. - Samuel Ritter; Jane X. Wang; Zeb Kurth-Nelson; Siddhant M. Jayakumar; Charles Blundell; Razvan Pascanu; Matthew M. Botvinick (ICML, 2018)

Learning to Share and Hide Intentions using Information Regularization. - Daniel Strouse; Max Kleiman-Weiner; Josh Tenenbaum 001; Matthew M. Botvinick; David J. Schwab (NeurIPS, 2018)

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents. - Joel Z. Leibo; Cyprien de Masson d'Autume; Daniel Zoran; David Amos; Charles Beattie; Keith Anderson; Antonio García Castañeda; Manuel Sanchez; Simon Green; Audrunas Gruslys; Shane Legg; Demis Hassabis; et al. (CoRR, 2018)

Unsupervised Predictive Memory in a Goal-Directed Agent. - Greg Wayne; Chia-Chun Hung; David Amos; Mehdi Mirza; Arun Ahuja; Agnieszka Grabska-Barwinska; Jack W. Rae; Piotr Mirowski; Joel Z. Leibo; Adam Santoro; Mevlana Gemici; Malcolm Reynolds; et al. (CoRR, 2018)

Probing Physics Knowledge Using Tools from Developmental Psychology. - Luis Piloto; Ari Weinstein; Dhruva TB; Arun Ahuja; Mehdi Mirza; Greg Wayne; David Amos; Chia-Chun Hung; Matthew M. Botvinick (CoRR, 2018)

Relational inductive biases, deep learning, and graph networks. - Peter W. Battaglia; Jessica B. Hamrick; Victor Bapst; Alvaro Sanchez-Gonzalez; Vinícius Flores Zambaldi; Mateusz Malinowski; Andrea Tacchetti; David Raposo; Adam Santoro; Ryan Faulkner 001; Çaglar Gülçehre; H. Francis Song; et al. (CoRR, 2018)

Relational Deep Reinforcement Learning. - Vinícius Flores Zambaldi; David Raposo; Adam Santoro; Victor Bapst; Yujia Li 001; Igor Babuschkin; Karl Tuyls; David P. Reichert; Timothy P. Lillicrap; Edward Lockhart; Murray Shanahan; Victoria Langston; et al. (CoRR, 2018)

Relational Forward Models for Multi-Agent Learning. - Andrea Tacchetti; H. Francis Song; Pedro A. M. Mediano; Vinícius Flores Zambaldi; Neil C. Rabinowitz; Thore Graepel; Matthew M. Botvinick; Peter W. Battaglia (CoRR, 2018)

A resource-rational model of physical abstraction for efficient mental simulation. - Tina Zhu; Jessica B. Hamrick; Kevin R. McKee; Raphael Koster; Jan Balaguer; Peter W. Battaglia; Matthew M. Botvinick (CogSci, 2019)

InfoBot: Transfer and Exploration via the Information Bottleneck. - Anirudh Goyal; Riashat Islam; Daniel Strouse; Zafarali Ahmed; Hugo Larochelle; Matthew M. Botvinick; Yoshua Bengio; Sergey Levine (ICLR (Poster), 2019)

Relational Forward Models for Multi-Agent Learning. - Andrea Tacchetti; H. Francis Song; Pedro A. M. Mediano; Vinícius Flores Zambaldi; János Kramár; Neil C. Rabinowitz; Thore Graepel; Matthew M. Botvinick; Peter W. Battaglia (ICLR (Poster), 2019)

Deep reinforcement learning with relational inductive biases. - Vinícius Flores Zambaldi; David Raposo; Adam Santoro; Victor Bapst; Yujia Li 001; Igor Babuschkin; Karl Tuyls; David P. Reichert; Timothy P. Lillicrap; Edward Lockhart; Murray Shanahan; Victoria Langston; et al. (ICLR (Poster), 2019)

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. - Jakob N. Foerster; H. Francis Song; Edward Hughes 001; Neil Burch; Iain Dunning; Shimon Whiteson; Matthew M. Botvinick; Michael Bowling (ICML, 2019)

Multi-Object Representation Learning with Iterative Variational Inference. - Klaus Greff; Raphaël Lopez Kaufman; Rishabh Kabra; Nick Watters; Chris Burgess 001; Daniel Zoran; Loic Matthey; Matthew M. Botvinick; Alexander Lerchner (ICML, 2019)

Causal Reasoning from Meta-reinforcement Learning. - Ishita Dasgupta 001; Jane X. Wang; Silvia Chiappa; Jovana Mitrovic; Pedro A. Ortega; David Raposo; Edward Hughes 001; Peter W. Battaglia; Matthew M. Botvinick; Zeb Kurth-Nelson (CoRR, 2019)

MONet: Unsupervised Scene Decomposition and Representation. - Christopher P. Burgess; Loïc Matthey; Nicholas Watters; Rishabh Kabra; Irina Higgins; Matthew M. Botvinick; Alexander Lerchner (CoRR, 2019)

Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette. - Adam Santoro; Felix Hill; David G. T. Barrett; David Raposo; Matthew M. Botvinick; Timothy P. Lillicrap (CoRR, 2019)

Learned human-agent decision-making, communication and joint action in a virtual reality environment. - Patrick M. Pilarski; Andrew Butcher; Michael Johanson; Matthew M. Botvinick; Andrew Bolt; Adam S. R. Parker (CoRR, 2019)

Meta-learning of Sequential Strategies. - Pedro A. Ortega; Jane X. Wang; Mark Rowland 001; Tim Genewein; Zeb Kurth-Nelson; Razvan Pascanu; Nicolas Heess; Joel Veness; Alexander Pritzel; Pablo Sprechmann; Siddhant M. Jayakumar; Tom McGrath; et al. (CoRR, 2019)

Emergent Systematic Generalization in a Situated Agent. - Felix Hill; Andrew K. Lampinen; Rosalia Schneider; Stephen Clark; Matthew M. Botvinick; James L. McClelland; Adam Santoro (CoRR, 2019)

Subgoal- and Goal-related Reward Prediction Errors in Medial Prefrontal Cortex. - José Ribas-Fernandes; Danesh Shahnazian; Clay B. Holroyd; Matthew M. Botvinick (J. Cogn. Neurosci., 2019)

MEMO: A Deep Network for Flexible Combination of Episodic Memories. - Andrea Banino; Adrià Puigdomènech Badia; Raphael Köster; Martin J. Chadwick; Vinícius Flores Zambaldi; Demis Hassabis; Caswell Barry; Matthew M. Botvinick; Dharshan Kumaran; Charles Blundell (ICLR, 2020)

The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. - Anirudh Goyal; Yoshua Bengio; Matthew M. Botvinick; Sergey Levine (ICLR, 2020)

Environmental drivers of systematicity and generalization in a situated agent. - Felix Hill; Andrew K. Lampinen; Rosalia Schneider; Stephen Clark; Matthew M. Botvinick; James L. McClelland; Adam Santoro (ICLR, 2020)

V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. - H. Francis Song; Abbas Abdolmaleki; Jost Tobias Springenberg; Aidan Clark; Hubert Soyer; Jack W. Rae; Seb Noury; Arun Ahuja; Siqi Liu 002; Dhruva Tirumala; Nicolas Heess; Dan Belov; et al. (ICLR, 2020)

Stabilizing Transformers for Reinforcement Learning. - Emilio Parisotto; H. Francis Song; Jack W. Rae; Razvan Pascanu; Çaglar Gülçehre; Siddhant M. Jayakumar; Max Jaderberg; Raphaël Lopez Kaufman; Aidan Clark; Seb Noury; Matthew M. Botvinick; Nicolas Heess; et al. (ICML, 2020)

Deep Reinforcement Learning and its Neuroscientific Implications. - Matthew M. Botvinick; Jane X. Wang; Will Dabney; Kevin J. Miller; Zeb Kurth-Nelson (CoRR, 2020)

Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences. - Raphael Köster; Kevin R. McKee; Richard Everett 001; Laura Weidinger; William S. Isaac; Edward Hughes 001; Edgar A. Duéñez-Guzmán; Thore Graepel; Matthew M. Botvinick; Joel Z. Leibo (CoRR, 2020)

Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures. - David Ding; Felix Hill; Adam Santoro; Matt M. Botvinick (CoRR, 2020)

A distributional code for value in dopamine-based reinforcement learning. - Will Dabney; Zeb Kurth-Nelson; Naoshige Uchida; Clara Kwon Starkweather; Demis Hassabis; Rémi Munos; Matthew M. Botvinick (Nat., 2020)

Rapid Task-Solving in Novel Environments. - Samuel Ritter; Ryan Faulkner 001; Laurent Sartran; Adam Santoro; Matthew M. Botvinick; David Raposo (ICLR, 2021)

Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents. - Jane Wang 001; Michael King; Nicolas Porcel; Zeb Kurth-Nelson; Tina Zhu; Charles Deck; Peter Choy; Mary Cassin; Malcolm Reynolds; H. Francis Song; Gavin Buttimore; David P. Reichert; et al. (NeurIPS Datasets and Benchmarks, 2021)

Attention over Learned Object Embeddings Enables Complex Visual Reasoning. - David Ding; Felix Hill; Adam Santoro; Malcolm Reynolds; Matt M. Botvinick (NeurIPS, 2021)

SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. - Rishabh Kabra; Daniel Zoran; Goker Erdogan; Loic Matthey; Antonia Creswell; Matt M. Botvinick; Alexander Lerchner; Christopher P. Burgess (NeurIPS, 2021)

Collaborating with Humans without Human Data. - DJ Strouse; Kevin R. McKee; Matt M. Botvinick; Edward Hughes 001; Richard Everett 001 (NeurIPS, 2021)

Alchemy: A structured task distribution for meta-reinforcement learning. - Jane X. Wang; Michael King; Nicolas Porcel; Zeb Kurth-Nelson; Tina Zhu; Charlie Deck; Peter Choy; Mary Cassin; Malcolm Reynolds; H. Francis Song; Gavin Buttimore; David P. Reichert; et al. (CoRR, 2021)

Synthetic Returns for Long-Term Credit Assignment. - David Raposo; Samuel Ritter; Adam Santoro; Greg Wayne; Theophane Weber; Matt M. Botvinick; Hado van Hasselt; H. Francis Song (CoRR, 2021)

Deep reinforcement learning models the emergent dynamics of human cooperation. - Kevin R. McKee; Edward Hughes 001; Tina O. Zhu; Martin J. Chadwick; Raphael Koster; Antonio García Castañeda; Charlie Beattie; Thore Graepel; Matthew M. Botvinick; Joel Z. Leibo (CoRR, 2021)

Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study. - Dylan J. A. Brenneis; Adam S. R. Parker; Michael Bradley Johanson; Andrew Butcher; Elnaz Davoodi; Leslie Acker; Matthew M. Botvinick; Joseph Modayil; Adam White 001; Patrick M. Pilarski (CoRR, 2021)

Perceiver IO: A General Architecture for Structured Inputs & Outputs. - Andrew Jaegle; Sebastian Borgeaud; Jean-Baptiste Alayrac; Carl Doersch; Catalin Ionescu; David Ding; Skanda Koppula; Daniel Zoran; Andrew Brock; Evan Shelhamer; Olivier J. Hénaff; Matthew M. Botvinick; et al. (ICLR, 2022)

General-purpose, long-context autoregressive modeling with Perceiver AR. - Curtis Hawthorne; Andrew Jaegle; Catalina Cangea; Sebastian Borgeaud; Charlie Nash; Mateusz Malinowski; Sander Dieleman; Oriol Vinyals; Matthew M. Botvinick; Ian Simon; Hannah Sheahan; Neil Zeghidour; et al. (ICML, 2022)

Fine-tuning language models to find agreement among humans with diverse preferences. - Michiel A. Bakker; Martin J. Chadwick; Hannah Sheahan; Michael Henry Tessler; Lucy Campbell-Gillingham; Jan Balaguer; Nat McAleese; Amelia Glaese; John Aslanides; Matt M. Botvinick; Christopher Summerfield (NeurIPS, 2022)

Human-centered mechanism design with Democratic AI. - Raphael Koster; Jan Balaguer; Andrea Tacchetti; Ari Weinstein; Tina Zhu; Oliver P. Hauser; Duncan Williams; Lucy Campbell-Gillingham; Phoebe Thacker; Matthew M. Botvinick; Christopher Summerfield (CoRR, 2022)

HCMD-zero: Learning Value Aligned Mechanisms from Data. - Jan Balaguer; Raphael Koster; Ari Weinstein; Lucy Campbell-Gillingham; Christopher Summerfield; Matthew M. Botvinick; Andrea Tacchetti (CoRR, 2022)

Hierarchical Perceiver. - João Carreira 001; Skanda Koppula; Daniel Zoran; Adrià Recasens; Catalin Ionescu; Olivier J. Hénaff; Evan Shelhamer; Relja Arandjelovic; Matthew M. Botvinick; Oriol Vinyals; Karen Simonyan; Andrew Zisserman; et al. (CoRR, 2022)

The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents. - Patrick M. Pilarski; Andrew Butcher; Elnaz Davoodi; Michael Bradley Johanson; Dylan J. A. Brenneis; Adam S. R. Parker; Leslie Acker; Matthew M. Botvinick; Joseph Modayil; Adam White 001 (CoRR, 2022)

Learning to Induce Causal Structure. - Nan Rosemary Ke; Silvia Chiappa; Jane Wang 001; Jörg Bornschein; Theophane Weber; Anirudh Goyal; Matthew M. Botvinick; Michael Mozer; Danilo Jimenez Rezende (CoRR, 2022)

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. - Anthony Zador; Blake A. Richards; Bence Ölveczky; Sean Escola; Yoshua Bengio; Kwabena Boahen 001; Matthew M. Botvinick; Dmitri B. Chklovskii; Anne Churchland; Claudia Clopath; James DiCarlo; Surya Ganguli; et al. (CoRR, 2022)

Meta-learning, social cognition and consciousness in brains and machines. - Angela Langdon; Matthew M. Botvinick; Hiroyuki Nakahara; Keiji Tanaka; Masayuki Matsumoto; Ryota Kanai (Neural Networks, 2022)

Compositional Sequence Generation in the Entorhinal-Hippocampal System. - Daniel C. McNamee; Kimberly L. Stachenfeld; Matthew M. Botvinick; Samuel J. Gershman (Entropy, 2022)

Learning to Induce Causal Structure. - Nan Rosemary Ke; Silvia Chiappa; Jane X. Wang; Jörg Bornschein; Anirudh Goyal; Mélanie Rey; Theophane Weber; Matthew M. Botvinick; Michael Curtis Mozer; Danilo Jimenez Rezende (ICLR, 2023)

Minimum Description Length Control. - Ted Moskovitz; Ta-Chu Kao; Maneesh Sahani; Matt M. Botvinick (ICLR, 2023)

Meta-in-context learning in large language models. - Julian Coda-Forno; Marcel Binz; Zeynep Akata; Matt M. Botvinick; Jane X. Wang; Eric Schulz (NeurIPS, 2023)

Cognitive Model Discovery via Disentangled RNNs. - Kevin J. Miller; Maria K. Eckstein; Matt M. Botvinick; Zeb Kurth-Nelson (NeurIPS, 2023)

DiscoGen: Learning to Discover Gene Regulatory Networks. - Nan Rosemary Ke; Sara-Jane Dunn; Jörg Bornschein; Silvia Chiappa; Mélanie Rey; Jean-Baptiste Lespiau; Albin Cassirer; Jane X. Wang; Theophane Weber; David G. T. Barrett; Matthew M. Botvinick; Anirudh Goyal; et al. (CoRR, 2023)

Meta-Learned Models of Cognition. - Marcel Binz; Ishita Dasgupta 001; Akshay K. Jagadish; Matthew M. Botvinick; Jane X. Wang; Eric Schulz (CoRR, 2023)

How should the advent of large language models affect the practice of science? - Marcel Binz; Stephan Alaniz; Adina Roskies; Balazs Aczel; Carl T. Bergstrom; Colin Allen; Daniel Schad; Dirk U. Wulff; Jevin D. West; Qiong Zhang; Richard M. Shiffrin; Samuel J. Gershman; et al. (CoRR, 2023)

Adaptive patch foraging in deep reinforcement learning agents. - Nathan J. Wispinski; Andrew Butcher; Kory Wallace Mathewson; Craig S. Chapman; Matthew M. Botvinick; Patrick M. Pilarski (Trans. Mach. Learn. Res., 2023)

Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem. - Raphael Koster; Miruna Pîslar; Andrea Tacchetti; Jan Balaguer; Leqi Liu; Romuald Elie; Oliver P. Hauser; Karl Tuyls; Matt M. Botvinick; Christopher Summerfield (CoRR, 2024)

Understanding dual process cognition via the minimum description length principle. - Ted Moskovitz; Kevin J. Miller; Maneesh Sahani; Matthew M. Botvinick (PLoS Comput. Biol., 2024)

Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game. - Edward Hughes 001; Tina O. Zhu; Martin J. Chadwick; Raphael Koster; Antonio García Castañeda; Charles Beattie; Thore Graepel; Matthew M. Botvinick; Joel Z. Leibo (CoRR, 2025)