Grammatical encoding in Language production

• This is a seminar on how people generate utterances.

• When: 12:15 - 13:45, Mondays
  Where: C7 3 / Seminar room 1.12

• The first class in on 14th April.

• Prerequisites: None, but attend the first two classes for the background knowledge.

• You MUST join the seminar Teams channel to be in this seminar. If you haven't join it here.

• All course-related communications should be done via Teams, but in case of any problems in Teams, I am also available at nakamura@lst.uni-saarland.de.

When we speak, we have some ideas that we want to convey. We encode them into some linguistic form and output them as speech (or strings of letters) so that the comprehender can decode them and obtain the original ideas. How exactly does this happen? It must at least involve constructing the message, forming the syntactic structures and identifying lexical representations among many other processes. This course will explore those processes underlying language production. We will focus on semantic, syntactic, and lexical processing involved in language production. (On the other hand, we will not spend a lot of time on phonological processing and articulation.)

We will be asking following questions in this seminar:

• What kind of processing steps are involved in language production?

• How do we decide on what to say?

• How much do we plan ahead for language production?

• Each student is expected to attend, prepare, for and actively participate in every class. Please communicate in advance with me if you have to miss a class. If you cannot do so due to an emergency, please communicate it  as soon as you can.

• Turn in assignments on time and fully completed.

• Check Teams regularly and receive notifications for posts. I assume that you see a post or a message within 24 hours during weekdays.

• Ask for help!

• You can call me Masato. (If you want to address me by the last name for some reason, I would prefer "Dr. Nakamura" over "Mr. Nakamura".)

• Feel free to contact me to set up a meeting.

• All online communications from students or from instructors should be via Teams. You can also contact me at nakamura@lst.uni-saarland.de only if there is any issue using Teams.

• I usually respond to a Teams message by the next (working) day. If I don't, I might have missed your message so feel free to follow up on it.

I recognize the importance of a diverse student body, and we are committed to fostering inclusive and equitable classroom environments. I invite you, if you wish, to tell us how you want to be referred to in this class, both in terms of your name and your pronouns (he/him, she/her, they/them, etc.). Keep in mind that the pronouns someone uses are not necessarily indicative of their gender identity. Additionally, it is your choice whether to disclose how you identify in terms of your gender, race, class, sexuality, religion, and dis/ability, among all aspects of your identity (e.g., should it come up in classroom conversation about our experiences and perspectives) and should be self-identified, not presumed or imposed.  I will do my best to address and refer to all students accordingly, and I ask you to do the same for all of your fellows.

It is your responsibility to participate in every course and to submit assignments in time. However, it does not apply if you have a decent excuse and if you communicate it in a timely manner. 

• You must report the issue that prevents participation or timely submission as soon as you can after it happened. For example, if you were sick, ideally you should inform me about it right after you realized that you are sick, or when you feel better enough to send me a message about it at the latest. You cannot tell me a week after you got better that you had been sick and turn in your assignment late. It is always OK to tell me that you MIGHT not be able to submit an assignment in time due to illness and then end up making it before the deadline.

• If the problem is very sensitive and you prefer not to tell me about the details, that is OK. However, you have to at least tell me that a problem happened.

• Unexpected and unavoidable problems (e.g., sickness) would be considered as good excuses. On the other hand, issues such as assignments in other classes or planned travels would not count. You are responsible for managing your own schedules.

Overall structure

4 credits

50%: Active participation

50%: Reading responses

7 credits

50%: Paper

25%: Active participation

25%: Reading responses

Discussions are the most important component of the learning experience in this course. Therefore, everybody must take this very seriously. I highly value coming to the classroom and participating in discussions. You are expected to actively participate in the discussions, such as asking questions, answering other students' questions, sharing your own ideas, etc. Since you should be well prepared for discussions, if you silently sit in the classroom throughout the class, you are deemed to not have done your job.

In order to encourage active participation of each student, I expect everybody to be respectful to what other students say in the class.

Each student submit a reading response for the paper. In the reading response, you will be asked to (1) summarize the content of the paper and (2) provide your own thoughts on the paper. The aim of this assignment is to make sure each of you is ready for the in-class discussions. Therefore reading responses will not be graded if you do not participate in the discussion.

Submit this via Teams by the end of the day before the class. (e.g., if the class is on 10th November, the deadline is 23:59 of 9th November)

Your own thoughts

The aim is to develop ideas/questions that can stimulate discussions.

You can evaluate the paper:

• Is there any problem in the methods? (e.g., participant, task, measures, etc) If so, how does it affect the implication of the study?

• Is there any other way to interpret the data?

Or extend the paper:

• What is not clear after the study?

• Can you think of a follow up study?

• Is there any other way to test the same study? Is there any different implications?

• How is it related to another study?

Here are some tips to start with:

• Think in terms of predictions about the results. If you don't agree with one feature of the methods, how would you improve it? Then, what do you expect the data to look like? Would that be different from the results presented in the paper?

• Think about the consistency with other studies you know. Is there any interesting (in)consistency that the author does not mention? Here think in terms of predictions again. What would the authors of the other studies expect to happen in the experiment? Does that prediction match the actual result or not?

• Think about generalizability. Would you find the same results if you do the study in another language, population, paradigm or in other cases (e.g., parts of speech, specific constructions, idioms, etc)? It is interesting when you think the results would be different: why is that and what can we learn from the difference? 

Other notes:

• It can be framed as a question. But in that case, you have to provide your own answer or at least some thoughts about it. For example, "Is the task valid?" is a bad one but "Is the task valid? It has the advantage of X and the disadvantage of Y, but which is more significant?" is much better. (It is even better if you provide your own answer to the question.)

• When you are proposing a follow-up study, don't just propose a possible extension but also write about what you would newly learn from it and/or why it is interesting to do. "What if we do the same experiment in German?" is not really an interesting question, but "If we do it in German, which has a clear gender makers unlike the language in the original study, the results might be different in such-and-such way and it has different implications of ...." is a very interesting idea.

• When you point out any issues in the methods, don't just make it a complaint, but analyze the consequences of the issue. For example, don't just say that "The task of the experiment is unnatural." but (1) point out how exactly it is different from the "natural language processing", (2) discuss how the difference might have skewed the data or would change the interpretation of the data, and if possible, (3) propose a better alternative.

Summary diagram

We will do a summary discussion in the final class or the one before. Instead of reading responses, I will ask you to create a diagram of a model of language processing to prepare for the discussion.

If you register for 7 credits, submit a final paper where you propose a research related to the course. 

The detailed instructions for the current semester are still in preparation, but it will not be too different from the past semesters (See this page).

This is a tentative schedule and is subject to change.

14 Apr. Introduction to the course

21 Apr. No class - Easter Monday

28 Apr. Introduction to language production

5 May Content retrieval: Vigliocco et al. (1999)

12 May Content retrieval: Schriefers et al. (1990)

19 May Content retrieval: Strijkers et al. (2010)

26 May Structure building: Hartsuiker et al. (1999)

2 Jun. Content vs Structure: Lane & Ferreira (2010)

9 Jun. No class - Whit monday

16 Jun. Content vs Structure: Gleitman et al. (2007)

23 Jun. Content vs Structure: Ferriera (1996)

30 Jun. Incrementality: Brown-Schmidt & Konopka (2010); Momma et al. (2016)

7 Jul. Choices in language production: Cowles & Ferreira (2012)

14 Jul. Summary discussion