Background
Dr Lauren Ackerman is a Research Associate based in the School of Education, Communication and Language Sciences at Newcastle University. They are additionally affiliated with the School of English Literature, Language and Linguistics, the School of Modern Languages, School X, and the Centre for Behaviour and Evolution.
Academic background
- PhD, Northwestern University: Influences on Parsing Ambiguity
- MA, Northwestern University: How the prosody of center-embedded sentences relates to their structure
- BA, Boston University: The effect of linguistic environment on vowels sung at high pitches
Google scholar: Click here.
SCOPUS: Click here.
Research
My research examines how the real-time representation of syntactic structure is influenced by non-syntactic sources of information. The two sources my current work focuses on are: (1) observed, deduced, or assumed information about the gender of human referents in the sentence, in particular exploring variation associated with the comprehender’s (reader’s or listener’s) exposure to and experience with people who are of a nonbinary gender; and (2) the disambiguating power of boundaries, and in particular, prosodic boundaries that are typically associated with syntactic clause boundaries.
To investigate the way these sources of information influence real-time processing, I use online (real-time) and offline (untimed) paradigms, including eye tracking while reading, self-paced reading, acceptability judgments, and other types of survey methods. I am also interested in the way different methodologies can complement and supplement each other, and how their utility varies based on experiment design.
Teaching
Office hours: By appointment (https://calendly.com/lauren-ackerman)
Office location: KGVI 2.18A
Dr Lauren Ackerman is a Researcher Education and Development Fellow with the RED team in School X, contributing to postgraduate training for students across the HaSS Faculty. They specialise in teaching quantitative analysis, programming in R and RStudio, and practical application of statistics for people without quantitative backgrounds.
