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

• Published in: Scientific reports Vol. 10; no. 1; pp. 22172 - 15
• Main Authors: Alamia, Andrea, Gauducheau, Victor, Paisios, Dimitri, VanRullen, Rufin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.12.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/2649/1398; 631/378/2649/1594; 631/477/2811; Adult; Artificial Intelligence; Behavior; Cognition; Cognitive science; Computer Science; Explicit knowledge; Female; Grammar; Human performance; Humanities and Social Sciences; Humans; Information processing; Language; Learning; Male; Models, Theoretical; multidisciplinary; Neural networks; Neural Networks, Computer; Neuroscience; Reaction Time; Reproducibility of Results; Science; Science (multidisciplinary); Young Adult

In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art models. One advantage of this technological boost...