Dissociating Sources of Knowledge in Artificial Grammar Learning

Abstract

Previous studies have suggested that individuals use both implicit and explicit, as well as rule and exemplar-based knowledge, to make grammaticality judgments in artificial grammar learning (AGL) tasks. Experiment 1 explored the importance of explicit mechanisms in the learning of exemplar and rule-based information by using a dual-task during AGL training. We utilized a balanced chunk strength grammar, assuring an equal proportion of explicit exemplar-based cues (i.e. chunks) between grammatical and non-grammatical test items. Experiment 2 explored the importance of perceptual cues by changing letters between AGL training and test, while still incorporating the dual-task design and balanced chunk strength grammar used in Experiment 1. Results indicated that participants with a working memory load learned the grammar in Experiment 1 just as well as the single-task no-load group, presumably by relying solely on implicit learning mechanisms. However, changing the letters from training to test resulted in no significant learning for dual-task participants in Experiment 2, suggesting that exemplar-based perceptual cues may the major contributor to implicit knowledge. Overall, the results suggest that implicit and explicit mechanisms for learning rule-based and exemplar-based information may both contribute to AGL via four independent, parallel routes, providing a new framework for understanding the complex dynamic of learning in AGL tasks.


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