| 2025 | CogSci | Humans learn proactively in ways that language models don't. | Simon Jerome Han, Jay McClelland |
| 2025 | CogSci | Extending a Mathematical Theory of the Emergence of Knowledge from the Experience to Capture Learning Dynamics in Transformers. | Sabrina Jones, Jay McClelland |
| 2024 | CogSci | Symbolic Variables in Distributed Networks that Count. | Satchel Grant, Zhengxuan Wu, Jay McClelland, Noah D. Goodman |
| 2021 | CogSci | Are people still smarter than machines? If so, why? | Jay McClelland |
| 2020 | CogSci | Cognitive consequences of structured education in a connectionist model of analogical reasoning. | David Barrett, Felix Hill, Adam Santoro, Jay McClelland |
| 2020 | CogSci | A computational model of learning to count in a multimodal, interactive environment. | Silvester Sabathiel, Jay McClelland, Trygve Solstad |
| 2020 | CogSci | Human-like learning Framework for frequency-skewed multi-level classification. | Amarjot Singh, Jay McClelland |
| 2019 | CogSci | Symposium in Memory of Jeff Elman: Language Learning, Prediction, and Temporal Dynamics. | Jay McClelland, Ken McRae |
| 2019 | CogSci | Modeling Number Sense Acquisition in A Number Board Game by Coordinating Verbal, Visual, and Grounded Action Components. | Arianna Yuan, Jay McClelland |
| 2018 | CogSci | Can Generic Neural Networks Estimate Numerosity Like Humans? | Sharon Chen, Zhenglong Zhou, Mengting Fang, Jay McClelland |
| 2018 | CogSci | Can a Recurrent Neural Network Learn to Count Things? | Mengting Fang, Zhenglong Zhou, Sharon Chen, Jay McClelland |