• 24 Aug

  • Juhani Jarvikivi

See here for STEP2018

STEP2019 – CCP Spring Training in Experimental Psycholinguistics

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. It will take place in Edmonton, Alberta, May 13-18, 2019. 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 13-18, 2019
Registration Deadline : April 29, 2019. Registrations received after this date will be charged a late fee. Please click here to register.
Fee: $390 + GST — The workshop fee includes attendance, workshop materials, tea/coffee, light snacks and lunches (6 days).  xxx  xxxxxxxxxxxxxxxxxxxxxxxxxxzzzz  z  zzzzzzz Accommodations: If you want to stay on campus, please click here for further details.


Click on schedule to view larger.

STEP 2019 Instructors and Courses

Harald Baayen & Yu-Ying Chuang (Tübingen)Modeling lexical processing with Linear Discriminative Learning: A practical introduction to computational Word and Paradigm Morphology with the WpmWithLdl R package

Petra Hendriks (Groningen) Investigating individual variation in reference production and comprehension

Sidney Segalowitz (Brock)An introduction to cognitive electrophysiology EEG/ERP methods: Past, present and future

Jacolien van Rij (Groningen)   Advanced statistical methods for psycholinguistics: Analyzing time course data

Certificate Courses

Liam Blything (Alberta) Creating a visual world paradigm step-by-step using Experiment Builder

Vincent Porretta & Aki-Juhani Kyröläinen (Windsor & McMaster)Introduction to R for experimental data processing

Aki-Juhani Kyröläinen & Vincent Porretta (McMaster & Windsor)Introduction to linear mixed-effects regression using R