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Published on: July 13, 2019
Two modes of transfer in artificial grammar learning.
1Department of Psychology, University of York, United Kingdom. R.Tunney@ucl.ac.uk
Summary
This study shows how grammatical knowledge transfers across different vocabularies. Sequential dependencies are key for transfer, but abstract analogy offers a more robust alternative when language patterns change.
Area of Science:
- Cognitive psychology
- Artificial grammar learning
- Linguistics
Background:
- Implicit grammatical knowledge can transfer to new linguistic contexts.
- Two main theories explain this transfer: sequential dependencies and abstract analogy.
Purpose of the Study:
- To investigate the distinct contributions of sequential dependencies and abstract analogy to grammatical knowledge transfer.
- To determine how changes in language distributional properties affect these transfer mechanisms.
Main Methods:
- Three experiments were conducted to test the proposed theories.
- Experiment 1 isolated the role of sequential dependencies.
- Experiments 2 and 3 manipulated language properties to differentiate between sequential and analogical transfer.
Main Results:
- Sequential dependencies were the sole basis for transfer in Experiment 1.
- Transfer via sequential dependencies was impaired by changes in language distributional properties (Experiment 2).
- Abstract analogy, specifically of repetition structure, proved relatively immune to such changes (Experiment 3).
Conclusions:
- Both sequential dependencies and abstract analogy contribute to grammatical knowledge transfer.
- Abstract analogy offers a more stable mechanism for transfer, particularly under changing linguistic conditions.
- Findings inform theories of artificial grammar learning and the mechanisms of grammatical knowledge transfer.
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