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The basis of transfer in artificial grammar learning
R L Gomez1, L Gerken, R W Schvaneveldt
1University of Arizona, Tucson, USA. gomez@jhu.edu
Memory & Cognition
|May 3, 2000
Summary
Artificial grammar learning relies on repetition, not just sequential dependencies. Learners transfer knowledge only when repeating elements are present, not when abstract grammar rules are learned.
Area of Science:
- Cognitive Psychology
- Artificial Grammar Learning
- Human Learning and Memory
Background:
- Artificial grammar learning (AGL) investigates how humans learn complex rule systems.
- Existing theories propose transfer relies on abstracting sequential dependencies or on repetition patterns.
Purpose of the Study:
- To determine whether transfer in AGL relies on abstract sequential dependencies or on repeating elements.
- To test competing theories of transfer in artificial grammar learning.
Main Methods:
- Two experiments were conducted using artificial grammars.
- Participants were trained on grammars with or without repeating elements.
- Transfer to new vocabulary was assessed based on learned grammar knowledge.
Main Results:
- Learners acquired significant knowledge of sequential dependencies in the training vocabulary.
- This knowledge of sequential dependencies did not facilitate transfer to a new vocabulary.
- Transfer occurred exclusively when the training grammar included repeating elements.
Conclusions:
- Repetition structure, not abstract sequential dependencies, forms the basis of transfer in artificial grammar learning.
- Findings challenge theories where transfer is solely based on abstracting grammatical rules.
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