| 2025 | CogSci | Complexity in Complexity: Understanding Visual Complexity Through Structure, Color, and Surprise. | Karahan Saritas, Peter Dayan, Tingke Shen, Surabhi S. Nath |
| 2025 | CogSci | Striking the Right Chord Between Reuse and Improvisation: Melody Learning as Resource-Rational Program Induction. | Hanqi Zhou, David G. Nagy, Peter Dayan, Charley M. Wu |
| 2025 | ICLR | Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences. | Shuchen Wu, Mirko Thalmann, Peter Dayan, Zeynep Akata, Eric Schulz |
| 2024 | CogSci | Optimal and sub-optimal temporal decisions can explain procrastination in a real-world task. | Sahiti Chebolu, Peter Dayan |
| 2024 | CogSci | State-Independent and State-Dependent Learning in a Motivational Go/NoGo task. | Azadeh Nazemorroaya, Dan Bang, Peter Dayan |
| 2024 | CogSci | Simplicity in Complexity: Explaining Visual Complexity using Deep Segmentation Models. | Tingke Shen, Surabhi S. Nath, Aenne Brielmann, Peter Dayan |
| 2023 | CogSci | Habits of Mind: Reusing Action Sequences for Efficient Planning. | Nomi lteto, Peter Dayan |
| 2023 | CogSci | Compositionality under time pressure. | Valerio Rubino, Mani Hamidi, Peter Dayan, Charley M. Wu |
| 2022 | ICML | Neural Network Poisson Models for Behavioural and Neural Spike Train Data. | Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli |
| 2021 | CogSci | Exploring learning trajectories with dynamic infinite hidden Markov models. | Sebastian A. Bruijns, International Brain Laboratory, Peter Dayan |
| 2021 | CogSci | Tracking the Unknown: Modeling Long-Term Implicit Skill Acquisition as Non-Parametric Bayesian Sequence Learning. | Nomi lteto, Dezso Nmeth, Karolina Janacsek, Peter Dayan |
| 2021 | CogSci | Confidence in control: Metacognitive computations for information search. | Lion Schulz, Stephen M. Fleming, Peter Dayan |
| 2021 | ICLR | Correcting experience replay for multi-agent communication. | Sanjeevan Ahilan, Peter Dayan |
| 2020 | UAI | Static and Dynamic Values of Computation in MCTS. | Eren Sezener, Peter Dayan |
| 2018 | ICML | Fast Parametric Learning with Activation Memorization. | Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap |
| 2017 | CogSci | A model of structure learning, inference, and generation for scene understanding. | David Raposo, Peter Dayan, Demis Hassabis, Peter W. Battaglia |
| 2015 | CogSci | Staying afloat on Neurath's boat - Heuristics for sequential causal learning. | Neil Bramley, Peter Dayan, David A. Lagnado |
| 2013 | CogSci | Structured cognitive representations and complex inference in neural systems. | Samuel Gershman, Joshua B. Tenenbaum, Alexandre Pouget, Matthew M. Botvinick, Peter Dayan |
| 2012 | CogSci | Computational, Cognitive, and Neural Models of Decision-making Biases. | Jonathan Malmaud, Joshua B. Tenenbaum, Peter Dayan, Laurence T. Maloney, Edward Vul, Nick Chater |
| 2012 | CogSci | Dynamic decision making: neuronal, computational, and cognitive underpinnings. | Magda Osman, Maarten Speekenbrink, Peter Dayan, Masataka Watanabe, Nigel Harvey |
| 2005 | IDEAL | Differential Priors for Elastic Nets. | Miguel . Carreira-Perpin, Peter Dayan, Geoffrey J. Goodhill |
| 1997 | IJCAI | Combining Probabilistic Population Codes. | Richard S. Zemel, Peter Dayan |
| 1995 | COLT | Predictive Hebbian Learning. | Terrence J. Sejnowski, Peter Dayan, P. Read Montague |