Computer ScienceScientific Reports
Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning
A. Alamia, V. Gauducheau, et al.
This fascinating study by Andrea Alamia, Victor Gauducheau, Dimitri Paisios, and Rufin VanRullen explores the competition between feedforward and recurrent neural networks in mimicking human behavior during artificial grammar learning. Discover how recurrent networks outperform their counterparts, especially in simpler grammar tasks, highlighting their potential in modeling explicit learning processes.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Education
Interpretable early warning recommendations in interactive learning environments: a deep-neural network approach based on learning behavior knowledge graph
X. Xia and W. Qi
Psychology
Neural and computational underpinnings of biased confidence in human reinforcement learning
C. Ting, N. Salem-garcia, et al.
Health and Fitness
Exercise intervention in middle-aged and elderly individuals with insomnia improves sleep and restores connectivity in the motor network
R. Chen, S. Wang, et al.
Medicine and Health
Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
J. Kong, H. Lee, et al.
