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Theory of Mind is an essential ability of humans to infer the mental states of others.
The evolution of cooperation, 1984
Axelrod Robert et al · 1984
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Efficient training of artificial neural networks for autonomous navigation
Dean A. Pomerleau · 1991
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Mirror neurons and the simulation theory of mind-reading
Vittorio Gallese and Alvin Goldman · 1998
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Is imitation learning the route to humanoid robots?
Stefan Schaal · 1999
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Theory of mind and autism: A review
Simon Baron-Cohen · 2000
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Chimpanzees know what conspecifics do and do not see
Brian Hare, Josep Call, Bryan Agnetta, and Michael Tomasello · 2000
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Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart Russell · 2000
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Meta-analysis of theory-of-mind development: The truth about false belief
Henry M Wellman, David Cross, and Julanne Watson · 2001
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Do chimpanzees know what conspecifics know?
Brian Hare, Josep Call, and Michael Tomasello · 2001
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Chimpanzee minds: suspiciously human?
Daniel J Povinelli and Jennifer Vonk · 2003
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Chimpanzees understand psychological states–the question is which ones and to what extent
Michael Tomasello, Josep Call, and Brian Hare · 2003
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The influence of language on theory of mind: A training study
Courtney Melinda Hale and Helen Tager-Flusberg · 2003
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Scaling of theory-of-mind tasks
Henry M Wellman and David Liu · 2004
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Do 15-month-old infants understand false beliefs?
Kristine H Onishi and Renée Baillargeon · 2005
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Do infants really understand false belief?
Ted Ruffman and Josef Perner · 2005
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Subcortical face processing
Mark H Johnson · 2005
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Newborns’ preference for face-relevant stimuli: Effects of contrast polarity
Teresa Farroni, Mark H Johnson, Enrica Menon, Luisa Zulian, Dino Faraguna, and Gergely Csibra · 2005
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Steps in theory-of-mind development for children with deafness or autism
Candida C Peterson, Henry M Wellman, and David Liu · 2005
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Language and theory of mind: Meta-analysis of the relation between language ability and false-belief understanding
Karen Milligan, Janet Wilde Astington, and Lisa Ain Dack · 2007
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Emergence of mirror neurons in a model of gaze following
Jochen Triesch, Hector Jasso, and Gedeon O Deák · 2007
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Marvelous minds: The discovery of what children know
Michael Siegal · 2008
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A predisposition for biological motion in the newborn baby
Francesca Simion, Lucia Regolin, and Hermann Bulf · 2008
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Mirrors in the brain: How our minds share actions and emotions
Giacomo Rizzolatti and Corrado Sinigaglia · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Do humans have two systems to track beliefs and belief-like states?
Ian A Apperly and Stephen A Butterfill · 2009
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Mindreaders: the cognitive basis of" theory of mind"
Ian Apperly · 2010
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Origins of human communication
Michael Tomasello · 2010
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Where do mirror neurons come from?
Cecilia Heyes · 2010
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Does the chimpanzee have a theory of mind? 30 years later
Josep Call and Michael Tomasello · 2011
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Implicit and explicit theory of mind: State of the art
Jason Low and Josef Perner · 2012
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From simple innate biases to complex visual concepts
Shimon Ullman, Daniel Harari, and Nimrod Dorfman · 2012
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How to construct a minimal theory of mind
Stephen A Butterfill and Ian A Apperly · 2013
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Theory of mind: How brains think about thoughts
Rebecca Saxe and Liane Young · 2013
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Decoding moral judgments from neural representations of intentions
Jorie Koster-Hale, Rebecca Saxe, James Dungan, and Liane L Young · 2013
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The cultural evolution of mind reading
Cecilia M Heyes and Chris D Frith · 2014
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A natural history of human thinking
Michael Tomasello · 2014
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Making minds: How theory of mind develops
Henry M Wellman · 2014
Cited alongside, same era.
Speaking our minds: Why human communication is different, and how language evolved to make it special
Thom Scott-Phillips · 2014
Cited alongside, same era.
False belief in infancy: A fresh look
Cecilia Heyes · 2014
Cited alongside, same era.
The myth of mirror neurons: The real neuroscience of communication and cognition
Gregory Hickok · 2014
Cited alongside, same era.
Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
Umut Güçlü and Marcel AJ van Gerven · 2015
Cited alongside, same era.
Deep neural networks: a new framework for modeling biological vision and brain information processing
Nikolaus Kriegeskorte · 2015
Techniques for interpretable machine learning
Mengnan Du, Ninghao Liu, and Xia Hu · 2019
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Ablation studies in artificial neural networks
Richard Meyes, Melanie Lu, Constantin Waubert de Puiseau, and Tobias Meisen · 2019
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Human-level performance in 3d multiplayer games with population-based reinforcement learning
Max Jaderberg, Wojciech M Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C Rabinowitz, Ari S Morcos, Avraham Ruderman, et al · 2019
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Deep neural networks as scientific models
Radoslaw M Cichy and Daniel Kaiser · 2019
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What do theory-of-mind tasks actually measure? theory and practice
François Quesque and Yves Rossetti · 2020
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Cited alongside, same era.
A performance-optimized model of neural responses across the ventral visual stream
Darren Seibert, Daniel Yamins, Diego Ardila, Ha Hong, James J DiCarlo, and Justin L Gardner · 2016
Cited alongside, same era.
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Antonio Torralba, and Aude Oliva · 2016
Cited alongside, same era.
Using goal-driven deep learning models to understand sensory cortex
Daniel LK Yamins and James J DiCarlo · 2016
Cited alongside, same era.
Great apes anticipate that other individuals will act according to false beliefs
Christopher Krupenye, Fumihiro Kano, Satoshi Hirata, Josep Call, and Michael Tomasello · 2016
Cited alongside, same era.
Training children’s theory-of-mind: A meta-analysis of controlled studies
Stefan G Hofmann, Stacey N Doan, Manuel Sprung, Anne Wilson, Chad Ebesutani, Leigh A Andrews, Joshua Curtiss, and Paul L Harris · 2016
Cited alongside, same era.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
Cited alongside, same era.
Henry Wellman · 2020
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The alignment problem: Machine learning and human values
Brian Christian · 2020
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Perspective taking in deep reinforcement learning agents
Aqeel Labash, Jaan Aru, Tambet Matiisen, Ardi Tampuu, and Raul Vicente · 2020
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Cognitive machine theory of mind
Thuy Ngoc Nguyen and Cleotilde Gonzalez · 2020
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The hanabi challenge: A new frontier for ai research
Nolan Bard, Jakob N Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, et al · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Emergent reciprocity and team formation from randomized uncertain social preferences
Bowen Baker · 2020
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The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities
Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J Bentley, Samuel Bernard, Guillaume Beslon, David M Bryson, et al · 2020
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Do non-human primates really represent others’ beliefs?
Daniel J Horschler, Evan L MacLean, and Laurie R Santos · 2020
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Enhanced poet: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions
Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, and Kenneth Stanley · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Interpretable machine learning
Christoph Molnar · 2020
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Understanding rl vision
Jacob Hilton, Nick Cammarata, Shan Carter, Gabriel Goh, and Chris Olah · 2020
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The neural architecture of language: Integrative modeling converges on predictive processing
Martin Schrimpf, Idan Asher Blank, Greta Tuckute, Carina Kauf, Eghbal A Hosseini, Nancy Kanwisher, Joshua B Tenenbaum, and Evelina Fedorenko · 2021
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Zachary Kenton, Tom Everitt, Laura Weidinger, Iason Gabriel, Vladimir Mikulik, and Geoffrey Irving · 2021
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The social basis of referential communication: Speakers construct physical reference based on listeners’ expected visual search
Julian Jara-Ettinger and Paula Rubio-Fernandez · 2021
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Theory of mind for deep reinforcement learning in hanabi
Andrew Fuchs, Michael Walton, Theresa Chadwick, and Doug Lange · 2021
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Minihack the planet: A sandbox for open-ended reinforcement learning research
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, and Tim Rocktäschel · 2021
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Open-ended learning leads to generally capable agents
Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, et al · 2021
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Benchmarking the spectrum of agent capabilities
Danijar Hafner · 2021
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity, 2021
William Fedus, Barret Zoph, and Noam Shazeer · 2021
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Recursively summarizing books with human feedback, 2021
Jeff Wu, Long Ouyang, Daniel M. Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, and Paul Christiano · 2021
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Tell me why! – explanations support learning of relational and causal structure, 2021
Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie C. Y. Chan, Allison C. Tam, James L. McClelland, Chen Yan, Adam Santoro, Neil C. Rabinowitz, Jane X. Wang, and Felix Hill · 2021
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Imitating interactive intelligence, 2021
Josh Abramson, Arun Ahuja, Iain Barr, Arthur Brussee, Federico Carnevale, Mary Cassin, Rachita Chhaparia, Stephen Clark, Bogdan Damoc, Andrew Dudzik, Petko Georgiev, Aurelia Guy, Tim Harley, Felix Hill, Alden Hung, Zachary Kenton, Jessica Landon, Timothy Lillicrap, Kory Mathewson, Soňa Mokrá, Alistair Muldal, Adam Santoro, Nikolay Savinov, Vikrant Varma, Greg Wayne, Duncan Williams, Nathaniel Wong, Chen Yan, and Rui Zhu · 2021
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Learning interpretation with explainable knowledge distillation
Raed Alharbi, Minh N Vu, and My T Thai · 2021
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Brains and algorithms partially converge in natural language processing
Charlotte Caucheteux and Jean-Rémi King · 2022
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Shared computational principles for language processing in humans and deep language models
Ariel Goldstein, Zaid Zada, Eliav Buchnik, Mariano Schain, Amy Price, Bobbi Aubrey, Samuel A Nastase, Amir Feder, Dotan Emanuel, Alon Cohen, et al · 2022
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Foundations of theory of mind and its development in early childhood
Hannes Rakoczy · 2022
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What happened to mirror neurons?
Cecilia Heyes and Caroline Catmur · 2022
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Creating multimodal interactive agents with imitation and self-supervised learning, 2022
DeepMind Interactive Agents Team, Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Felix Fischer, Petko Georgiev, Alex Goldin, Mansi Gupta, Tim Harley, Felix Hill, Peter C Humphreys, Alden Hung, Jessica Landon, Timothy Lillicrap, Hamza Merzic, Alistair Muldal, Adam Santoro, Guy Scully, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan, and Rui Zhu · 2022
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Cx-tom: Counterfactual explanations with theory-of-mind for enhancing human trust in image recognition models
Arjun R Akula, Keze Wang, Changsong Liu, Sari Saba-Sadiya, Hongjing Lu, Sinisa Todorovic, Joyce Chai, and Song-Chun Zhu · 2022
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