Learning syntactic parameters without triggers by assigning credit and blame
Brandon Prickett, Kaden Holladay, Shay Hucklebridge, Max Nelson, Rajesh Bhatt, Gaja Jarosz, Kyle Johnson, Aleksei Nazarov, and Joe Pater. 2019. Learning syntactic parameters without triggers by assigning credit and blame. Proceedings of the 55th Annual Meeting of the Chicago Linguistic…
Sonority Sequencing in Polish: the Combined Roles of Prior Bias & Experience
Jarosz, Gaja and Amanda Rysling. Oct 2016. Sonority Sequencing in Polish: the Combined Roles of Prior Bias & Experience. Annual Meetings on Phonology 2016, University of Southern California.
Refining UG: Connecting Phonological Theory and Learning (NELS 47 Invited Talk)
Jarosz, Gaja. Oct 2016. Refining UG: Connecting Phonological Theory and Learning. Invited talk, NELS 47. University of Massachusetts Amherst, MA.
Defying the Stimulus: Acquisition of Complex Onsets in Polish
Jarosz, Gaja. 2017. Defying the Stimulus: Acquisition of Complex Onsets in Polish. In Phonology 34(2). 269-298.
Sonority Sequencing in Polish: Defying the Stimulus? (FASL Invited Talk)
Jarosz, Gaja. May 2016. Sonority Sequencing in Polish: Defying the Stimulus? Invited talk, Formal Approaches to Slavic Linguistics. Ithaca, NY.
Input Frequency and the Acquisition of Syllable Structure in Polish
Jarosz, Gaja, Shira Calamaro & Jason Zentz. 2017. Input Frequency and the Acquisition of Syllable Structure in Polish. In Language Acquisition 24(4). 261-399. https://doi.org/10.1080/10489223.2016.1179743
The Richness of Distributional Cues to Word Boundaries in Speech to Young Children
Jarosz, Gaja & J. Alex Johnson. 2013. The Richness of Distributional Cues to Word Boundaries in Speech to Young Children. In Language Learning and Development 9(2), 175-210.
Implicational Markedness and Frequency in Constraint-Based Computational Models of Phonological Learning
Jarosz, Gaja. 2010. Implicational Markedness and Frequency in Constraint-Based Computational Models of Phonological Learning. In Journal of Child Language 37(3), Special Issue on Computational models of child language learning, 565-606. Cambridge: Cambridge University Press. DOI: http://dx.doi.org/10.1017/S0305000910000103