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Found 6 papers, 2 papers with code
Human Curriculum Effects Emerge with In-Context Learning in Neural Networks
Human learning is sensitive to rule-like structure and the curriculum of examples used for training.
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience.
A Neural Network Model of Continual Learning with Cognitive Control
Here, we build on previous work and show that neural networks equipped with a mechanism for cognitive control do not exhibit catastrophic forgetting when trials are blocked.
Compositional Processing Emerges in Neural Networks Solving Math Problems
A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition.
Complementary Structure-Learning Neural Networks for Relational Reasoning
The neural mechanisms supporting flexible relational inferences, especially in novel situations, are a major focus of current research.
Compositional Generalization by Factorizing Alignment and Translation
Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution.
