Learning biases in person-number linearization
Keyword(s):
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 φ-features impacts the linearization of person and number affixes by (English-speaking) learners in the lab.
2018 ◽
2021 ◽
2018 ◽
Vol 9
(1)
◽
pp. 2
Keyword(s):
2018 ◽
2013 ◽
Vol 133
(5)
◽
pp. 3337-3337