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On Empirical Methodology, Constraints, and Hierarchy in Artificial Grammar Learning
Author(s) -
Levelt Willem J. M.
Publication year - 2020
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12441
Subject(s) - computer science , artificial intelligence , hierarchy , grammar , perspective (graphical) , taxonomy (biology) , natural language processing , point (geometry) , empirical research , linguistics , machine learning , epistemology , mathematics , philosophy , botany , geometry , economics , market economy , biology
This paper considers the AGL literature from a psycholinguistic perspective. It first presents a taxonomy of the experimental familiarization test procedures used, which is followed by a consideration of shortcomings and potential improvements of the empirical methodology. It then turns to reconsidering the issue of grammar learning from the point of view of acquiring constraints, instead of the traditional AGL approach in terms of acquiring sets of rewrite rules. This is, in particular, a natural way of handling long‐distance dependences. The final section addresses an underdeveloped issue in the AGL literature, namely how to detect latent hierarchical structure in AGL response patterns.