Advancing the methodological skills and conceptual prowess of students of linguistics and psycholinguistics since 2014.
The Centre for Comparative Psycholinguistics (CCP, University of Alberta Department of Linguistics) organizes a week long Spring Training Workshop in current issues and methods in psycholinguistics. This 7th STEP will take place in Edmonton, Alberta, May 25-30, 2020. The Spring School is directed at postdoctoral fellows, graduate and advanced undergraduate students, and anyone else interested in learning how to turn their research ideas into concrete steps towards experimental designs, data collection and analysis using advanced experimental and statistical methods.
Location: University of Alberta, Edmonton, Alberta, Canada
Dates: May 25-30, 2020
Fee: CAD390 + GST — The workshop fee includes attendance, workshop materials, tea/coffee, light snacks and lunches (6 days).
Monday Sign-in: This will happen from 8-8:30am on Monday the 25th, in the hallway outside of Arts 104. This is located on the south side of the basement of the Arts and Convocation Hall building. Room assignments will be available at this time.
For inquiries: firstname.lastname@example.org
**With regrets, given the current circumstances and the unpredictability of the months to come, we have decided to cancel this year’s edition of STEP. However, we are already planning the 2021 edition, so keep an eye on this page for updates about venues and dates.
Yu-Ying Chuang & Harald Baayen (Eberhard Karls University Tübingen) – Modeling lexical processing with Linear Discriminative Learning – An introduction to the discriminative lexicon with the WpmWithL dl package for R
Elena Nicoladis (University of Alberta) – Nonverbal communication – Gestures and speech
Liam Blything (University of Alberta) – Creating a visual world paradigm step-by-step using Experiment Builder
Isabell Hubert Lyall (University of Alberta) – Introduction to R for experimental data processing
Aki-Juhani Kyröläinen & Vincent Porretta (McMaster University) – Introduction to linear mixed-effects regression using R