Lexical Prediction in Human Sentence Processing

Basic course information


In everyday life, we hear and read hundreds and thousands of utterances, and understand what they mean (including this website!). Since it is such a usual part of our life, we rarely realize what a complicated task it is, and how quickly and effortless we complete it. This might be all the more surprising given that computers with huge computational resources and with state-of-the-art models have yet to reach what humans can normally do. Why we humans are such experts of language processing, even we are equipped with only limited computational abilities?

Psycholinguists have argued that predictive processing is one key to solve this puzzle. That is, people use what they have already heard/read to facilitate processing of expected continuations. For example, when you hear "I would like to have some coffee with ...", you guess cream or sugar among other candidates are likely to follow, and you start processing them even before you hear them.

This course will explore the predictive processing in human sentence processing. We will focus on the process of identification of words, and explore how prediction is involved in it.
We will be asking following questions:

Course policies

Student responsibility

Communication with Instructor

Names/Pronouns and Self-Identifications

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.


Overall structure

4 credits

75%: Presentation

15%: Discussion questions

10%: Active participation in discussion

7 credits

45%: Paper

40%: Presentation

10%: Discussion questions

5%: Active participation in discussion

See the evaluation guidelines for more details.
See the guidelines for the term paper here.

Reading list

NOTE: These are tentative lists and would be updated in the coming couple of weeks. You are more than welcome to suggest a paper or a topic!

The PDF files of the papers can be found in the reading folder in the Discussion channel.

What can influence prediction?

Verbs and arguments

Altmann & Kamide (1999): https://www.sciencedirect.com/science/article/pii/S0010027799000591

Kamide et al. (2003): http://www.sciencedirect.com/science/article/pii/S0749596X03000238


Nieuwland & Van Berkum (2006): https://doi.org/10.1162/jocn.2006.18.7.1098

Xiang & Kuperberg (2015): https://doi.org/10.1080/23273798.2014.995679

What may NOT influence prediction?


Fischler et al. (1983): https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1469-8986.1983.tb00920.x

Nieuwland & Kuperberg (2008): https://doi.org/10.1111/j.1467-9280.2008.02226.x

Local coherence (vs Global, event coherence)

Nieuwland and Van Berkum (2005): https://www.sciencedirect.com/science/article/pii/S0926641005001102

Metusalem et al. (2012): http://www.sciencedirect.com/science/article/pii/S0749596X12000034

Rabs et al. (2022): https://www.tandfonline.com/doi/full/10.1080/23273798.2021.2022171

Argument roles

Chow et al. (2016): https://www.tandfonline.com/doi/full/10.1080/23273798.2015.1066832

Liao et al. (2022): https://www.sciencedirect.com/science/article/pii/S0749596X22000377

What about words can be predicted?


Federmeier & Kutas (1999): https://www.sciencedirect.com/science/article/pii/S0749596X99926608

Rabovsky et al. (2018): http://www.nature.com/articles/s41562-018-0406-4


(DeLong et al. (2005): https://www.nature.com/articles/nn1504)

How can we measure prediction?

Kutas & Hillyard (1984): https://www.nature.com/articles/307161a0

Staub et al. (2015): http://www.sciencedirect.com/science/article/pii/S0749596X15000236