LING 490

Fall 2019 All Classes

All Classes

Special Topics in Linguistics

SHOW FINAL EXAM SCHEDULE

Credit: 3 OR 4 hours.

Course provides an opportunity to focus on various subfields of the linguistic sciences, depending on the interests of the faculty and student.

3 undergraduate hours. 4 graduate hours. May be repeated as topic varies to a maximum of 9 undergraduate hours or 12 graduate hours. Students may register for up to two sections in the same term. Prerequisite: LING 100, LING 400, or consent of instructor.

My Favorites

My Schedule

Section Status updates every 10 minutes.

LING 490 class schedule data for fall 2019
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
63731	Lecture-Discussion	G4	12:30PM -1:50PM	TR	Bevier Hall	Green, J	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Info: Topic: Neurolinguistics and ERP methods. Description: This course is an overview in topics in neurolinguistics, focusing on the use of event-related potentials (ERPs) as a tool to study human language comprehension. The main goals of this course are for students to become familiar with the types of questions different neurolinguistic methods can answer and to become proficient in reading, understanding, and critically analyzing ERP research. The course will cover basic principles of human electrophysiology and the organization of language in the brain. We will then learn what different components of ERPs can tell us by looking at experiments from primary literature. As a final project, students will propose a novel experiment using ERPs designed to address a question of their choice related to language. Note: Instructor approval required.
70668	Lecture-Discussion	GMB	9:00AM -9:50AM	MWF	Foreign Languages Building	Barlaz, M	Part of Term: 1 Date Range: 08/26/19-12/11/19 Section Info: This course is an overview of statistical analysis for linguistics. No prior background in statistics is assumed. The goal of the course is for students to understand statistical concepts and analyses, in order to do analyses that are reasonable and appropriate. Focus will be placed on a critical analysis of current methods, as well as on scientific reproducibility and transparency. The course will begin with an overview of descriptive statistics, and will continue to hypothesis testing and linear regression. The course will be taught using the R software environment.
63730	Lecture-Discussion	U3	12:30PM -1:50PM	TR	Bevier Hall	Green, J	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 3 hours Section Info: Topic: Neorolinguistics and ERP methods. Description: This course is an overview in topics in neurolinguistics, focusing on the use of event-related potentials (ERPs) as a tool to study human language comprehension. The main goals of this course are for students to become familiar with the types of questions different neurolinguistic methods can answer and to become proficient in reading, understanding, and critically analyzing ERP research. The course will cover basic principles of human electrophysiology and the organization of language in the brain. We will then learn what different components of ERPs can tell us by looking at experiments from primary literature. As a final project, students will propose a novel experiment using ERPs designed to address a question of their choice related to language. Note: Instructor approval required.
70664	Lecture-Discussion	UMB	9:00AM -9:50AM	MWF	Foreign Languages Building	Barlaz, M	Part of Term: 1 Date Range: 08/26/19-12/11/19 Section Info: This course is an overview of statistical analysis for linguistics. No prior background in statistics is assumed. The goal of the course is for students to understand statistical concepts and analyses, in order to do analyses that are reasonable and appropriate. Focus will be placed on a critical analysis of current methods, as well as on scientific reproducibility and transparency. The course will begin with an overview of descriptive statistics, and will continue to hypothesis testing and linear regression. The course will be taught using the R software environment.