LET-REMA-LCEX28
Computational Psycholinguistics
Course infoSchedule
Course moduleLET-REMA-LCEX28
Credits (ECTS)6
Category-
Language of instructionEnglish
Offered byRadboud University; Faculty of Arts; Graduate School;
Lecturer(s)
Coordinator
dr. S.L. Frank
Other course modules lecturer
Lecturer
dr. S.L. Frank
Other course modules lecturer
Contactperson for the course
dr. S.L. Frank
Other course modules lecturer
Examiner
dr. S.L. Frank
Other course modules lecturer
Academic year2022
Period
PER 3-PER 4  (30/01/2023 to 03/09/2023)
Starting block
PER 3
Course mode
full-time
RemarksOpen for master students Cognitive Neuroscience, Data Science, Artificial Intelligence, Computer Science, Linguistics
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
In this course you will learn:
- what the basic concepts, results and debates are in the field of computational psycholinguistics
- to identify the core properties, strengths and weaknesses of different modelling approaches
- to critically assess computational models and the claims arising from them
- how to develop and/or evaluate a computational psycholinguistic model
 
Content
Computational psycholinguists develop computational models that simulate aspects of the human language system, for example: learning word meaning from large amounts of language input, assigning syntactic structures to sentences, or generating sentences given the intended meaning. A successful model can explain findings from human experiments and thereby increases our understanding of the human cognitive system. In this course, we will discuss the properties of different model types, look into the details of some of the most important models proposed in the literature, and gain hands-on experience with the development and/or evaluation of a computational psycholinguistic model.
 
Level

Presumed foreknowledge
Basic knowledge of Python programming is required for the practical assignments. If you have experience with another programming language, you should be able to quickly learn enough Python by self study.
Test information

Specifics

Required materials
Articles
One or two scientific articles per meeting. Details will be announced at the first meeting.

Instructional modes
Lecture

Tests
Take home exam
Test weight50
Test typeProject
OpportunitiesBlock PER 4, Block PER 4

Minimum grade
5,5

Assignment
Test weight50
Test typeProject
OpportunitiesBlock PER 3, Block PER 4

Minimum grade
5,5