学部・大学院区分
Undergraduate / Graduate
人文・博前
時間割コード
Registration Code
2020717
科目区分
Course Category
専門科目
Specialized Courses
カリキュラム年度
Curriculum
2022年度入学以降
教育プログラム・分野・専門等
Major
英語文化学P,G30 LCS
科目名 【日本語】
Course Title
心理言語学Ⅱ(2022入学~)
科目名 【英語】
Course Title
Psycholinguistics II (Enrolled in/after 2022)
コースナンバリングコード
Course Numbering Code
HUMEE5117E
担当教員 【日本語】
Instructor
三輪 晃司 ○
担当教員 【英語】
Instructor
MIWA Koji ○
開講期・開講時間帯
Term / Day / Period
春 水曜日 2時限
Spring Wed 2
隔年開講
Biennial class
単位数
Credits
2
対象学年
Year
他学部生の受講の可否
Propriety of Other department student's attendance
授業形態
Course style
講義
Lecture
教職【入学年度】
Teacher's License
教職【教科】
Teacher's License
学芸員資格(該当の有無)
Curator's Qualifications
講義題目
Title


授業の目的 【日本語】
Goals of the Course(JPN)
本授業ではヒトの頭の中で行われている言語処理を実験を通して学ぶ。
授業の目的 【英語】
Goals of the Course
Students will study language processing in the mind through experiments.
到達目標 【日本語】
Objectives of the Course(JPN)
実験参加者の特性や語彙特性といった多々の変数を含む実験研究を実践できるようになることを目標とする。具体的には、実験計画、PsychoPyを使った実験のプログラミング、データ収集、Rを使用したデータ分析、結果報告に関しての知識と技術を高める。

At the end of the semester, students will be able to conduct experimental research involving many variables such as participant characteristics and lexical properties. The course will enhance students' knowledge and skills in designing experiments, programming experiments using PsychoPy, data collection, data analysis using R, and reporting results.
授業の内容や構成
Course Content / Plan
Weekly classes will be provided following the schedule below. Students will also write a term paper by analyzing psycholinguistic data.

Week 1: Introduction to psycholinguistic experiments
Week 2: Collaboration and authorship
Week 3: Designing an experiment
Week 4: Designing an experiment
Week 5: Programming an experiment
Week 6: Programming an experiment
Week 7: Collecting data, research ethics
Week 8: Collecting data, analyzing data (descriptive statistics, correlation)
Week 9: Collecting data, analyzing data (descriptive statistics, visualization)
Week 10: Analyzing data (ANOVA, multiple regression)
Week 11: Analyzing data (linear mixed-effects modeling)
Week 12: Analyzing data (linear mixed-effects modeling)
Week 13: Analyzing data (generalized additve modeling)
Week 14: Analyzing data (generalized additve modeling)
Week 15: Course review
履修条件・関連する科目
Course Prerequisites and Related Courses
Because the lectures will be conducted in English, it is important that students have a good command of English. It is expected that students have a background in linguistics, psychology, and/or a related discipline in cognitive science. Knowledge of statistics and R is an asset but not the requirement.
成績評価の方法と基準
Course Evaluation Method and Criteria
In-class contribution (20%), experiment (40%), term paper (40%)
If students miss five lectures, they will be considered "absent (= W)." A letter grade will be assigned based on students' absolute performance. To pass, students must earn 60% in total.
教科書・テキスト
Textbook
Relevant materials will be distributed in class.
参考書
Reference Book
Relevant materials will be distributed in class.
課外学習等(授業時間外学習の指示)
Study Load(Self-directed Learning Outside Course Hours)
Every week, students are expected to read an assigned paper to prepare for the lecture. Students are also expected to work with PsychoPy (to design an experiment, see https://www.psychopy.org/) and R (to analyze data, see https://www.r-project.org/).
履修取り下げ制度(利用の有無)学部のみ
Course withdrawal
備考
Others
授業開講形態等
Lecture format, etc.
A-1)対面授業科目(対面のみ)