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

ABSTRACT: 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 is to facilitate comparison between different neural networks and human performance, in order to deepen our understanding of human cognition. Here, we investigate which neural network architecture (feedforward vs. recurrent) matches human behavior in artificial grammar learning, a crucial aspect of language acquisition. Prior experimental studies proved that artificial grammars can be learnt by human subjects after little exposure and often without explicit knowledge of the underlying rules. We

SUBMITTER: Alamia A 

PROVIDER: S-EPMC7747619 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature