Pattern deduction in linguistically attested and unattested grammars

Authors

  • Sadhwi Srinivas William & Mary
  • Kaitlyn Harrigan William & Mary
  • Aidan Burnham William & Mary
  • Nicholas Voivoda William & Mary
  • Berit Wilkins William & Mary

DOI:

https://doi.org/10.3765/plsa.v10i1.5955

Keywords:

hierarchical bias in language learning, pattern deduction, artificial language learning, learnability

Abstract

A popular hypothesis in linguistics posits that language learners are biologically predisposed to learn structures attested in human language – for example, a hierarchically nested phrase structure, while eschewing hypotheses for linguistically unattested structures – for example, one consisting of non-consecutive, linearly alternating “constituents”. The current study explores the robustness of such a predisposition within a controlled artificial language learning task as well as a non-linguistic, general puzzle-solving task. We find evidence that suggests learners more easily acquire linguistically attested hierarchically structured patterns compared to unattested non-hierarchical ones within the non-linguistic task, but not within the language task. We discuss the puzzling nature of this finding, and some work in progress to further unravel the source of this result.

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Published

2025-06-04

How to Cite

Srinivas, Sadhwi, Kaitlyn Harrigan, Aidan Burnham, Nicholas Voivoda, and Berit Wilkins. 2025. “Pattern Deduction in Linguistically Attested and Unattested Grammars”. Proceedings of the Linguistic Society of America 10 (1): 5955. https://doi.org/10.3765/plsa.v10i1.5955.