Learning Biases in Person-Number Linerization. Maldonado, M.; Saldana, C.; and Culbertson, J. In The 50th Annual Meeting of the North East Linguistic Society, August, 2020.
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The idea that universal representations of hierarchical structure constrain patterns of linear order is a central to many linguistic theories. In this paper we use Artificial Language Learning techniques to experimentally probe this claim. Specifically, we investigate how a hypothesized hierarchy of $ǎrphi$-features impacts the linearization of person and number affixes by (English-speaking) learners in the lab.
@inproceedings{MaldonadoEtAl2020,
  title = {Learning Biases in Person-Number Linerization},
  booktitle = {The 50th {{Annual Meeting}} of the {{North East Linguistic Society}}},
  author = {Maldonado, Mora and Saldana, Carmen and Culbertson, Jennifer},
  year = {2020},
  month = aug,
  doi = {10.31234/osf.io/5s2r8},
  abstract = {The idea that universal representations of hierarchical structure constrain patterns of linear order is a central to many linguistic theories. In this paper we use Artificial Language Learning techniques to experimentally probe this claim. Specifically, we investigate how a hypothesized hierarchy of {$\varphi$}-features impacts the linearization of person and number affixes by (English-speaking) learners in the lab.},
  file = {/Users/mmaldona/Zotero/storage/KDQB78CC/Maldonado et al. - 2020 - Learning biases in person-number linerization.pdf},
  keywords = {person, artificial language learning, number, universals}
}
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