Active and Passive Statistical Learning: Exploring the Role of Feedback in Artificial Grammar Learning and Language

Language is immersed in a rich and active environment. One general dimension of that environment, feedback, may contribute greatly to learning language structure. Artificialgrammar learning offers an experimental means of exploring different kinds of potential feedback. In this paper, two experiments sought to investigate the role of feedback in an artificial-grammar learning task designed to resemble some aspects of language acquisition. An artificial language composed of auditory nonsense syllables and an accompanying visual semantics were created. Participants faced the task of mapping a sample sentence to a visual semantic scene. Results indicated that feedback is highly useful, allows participants to reach a high level of competence in the language, and also helps the acquisition of detailed aspects of the artificial grammar. Implications for language acquisition are discussed, and future directions considered.