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Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning

Computer Science

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.

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