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Arxiv|February 27, 2024
CURRICULUM EFFECTS AND COMPOSITIONALITY EMERGE WITH IN-CONTEXT LEARNING IN NEURAL NETWORKSJacob Russin, Ellie Pavlick, Michael J FrankProceedings of the National Academy of Sciences of the United States of America|August 28, 2025
Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learningJacob Russin, Ellie Pavlick, Michael J FrankThe Behavioral and Brain Sciences|September 23, 2024
Is human compositionality meta-learned?Jacob Russin, Sam Whitman McGrath, Ellie Pavlick, et al.The Behavioral and Brain Sciences|September 28, 2023
Properties of LoTs: The footprints or the bear itself?Sam Whitman McGrath, Jacob Russin, Ellie Pavlick, et al.Current Directions in Psychological Science|February 14, 2025
How Can Deep Neural Networks Inform Theory in Psychological Science?Sam Whitman McGrath, Jacob Russin, Ellie Pavlick, et al.Journal of Cognitive Neuroscience|July 29, 2026
Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory TasksAneri Soni, Aaron Traylor, Jack Merullo, et al.Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|June 4, 2023
Symbols and grounding in large language modelsEllie PavlickNeuron|October 23, 2025
From prediction to understanding: Will AI foundation models transform brain science?Thomas Serre, Ellie PavlickPlos One|February 22, 2020
SNAP judgments into the digital age: Reporting on food stamps varies significantly with time, publication type, and political leaningBenjamin W Chrisinger, Eliza W Kinsey, Ellie Pavlick, et al.Handbook of Clinical Neurology|October 9, 2019
Computational models of motivated frontal functionRandall C O'Reilly, Jacob Russin, Seth A HerdPageof 23