Connor Mayer

Department of Linguistics
3125 Campbell Hall, UCLA
Los Angeles, CA

Recent updates:

  • I presented a poster titled "Gradient opacity in Uyghur backness harmony" at the 2020 Annual Meeting on Phonology, which was (virtually) hosted by UCSC and took place from September 18-20, 2020. You can find the poster here.
  • Our paper "Quantal biomechanical effects in speech postures of the lips" has been accepted to the Journal of Neurophysiology! You can find a pre-print here.
  • My paper on distributional learning of phonological classes has been published in the latest Phonology! You can find my pre-print version here and the published version here.

I'm a fifth year PhD candidate in the Department of Linguistics at UCLA. I am a computational linguist, with a focus on the areas of phonetics and theoretical phonology. My research combines computation with a wide range of converging methodologies to explore questions of interest to the field. The questions that guide much of my research are (a) the extent to which linguistic structure is learned vs. innate; (b) how innate biases shape the learning process; and (c) how we can better understand the source and nature of these biases. I view computation as a tool that can be used to complement empirical and theoretical methodologies, testing claims and providing new predictions. I have used such methodology to probe the role of distributional information in phonological learning, to investigate the ways in which the biomechanics and motor control of the vocal tract shape our speech systems, and to explore the interaction between acoustic cue weighting and vocabulary acquisition, among other topics.

I also work extensively on the Uyghur language (Turkic: China). My dissertation provides a detailed empirical description of Uyghur backness harmony and investigates its learnability. Uyghur speakers asked to generalize backness harmony to wug forms do so in a way that is not consistent with lexical statistics. I show using tools from formal language theory that the surface pattern of Uyghur backness harmony exceeds the upper bound of complexity typically proposed for segmental phonology, and that the grammar learned by speakers is computationally simpler. I therefore suggest that a bias towards a certain level of computational complexity influences how phonological grammars are learned. This work attempts to bridge the gap between formal language phonology and constraint-based models. I have also performed descriptive research on this underdocumented language, including its intonation.