Exploring Variation Between Artificial Grammar Learning Experiments: Outlining a Meta-Analysis Approach.

ABSTRACT: Artificial grammar learning (AGL) has become an important tool used to understand aspects of human language learning and whether the abilities underlying learning may be unique to humans or found in other species. Successful learning is typically assumed when human or animal participants are able to distinguish stimuli generated by the grammar from those that are not at a level better than chance. However, the question remains as to what subjects actually learn in these experiments. Previous studies of AGL have frequently introduced multiple potential contributors to performance in the training and testing stimuli, but meta-analysis techniques now enable us to consider these multiple information sources for their contribution to learning-enabling intended and unintended structures to be as

SUBMITTER: Trotter AS 

PROVIDER: S-EPMC7496870 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature