Psycholinguistics I/II - 2021-2022

LING 640/641


This course is a year-long foundation course sequence in psycholinguistics, aimed at graduate students from any language science field. The course assumes no specific background in psycholinguistics, including experimentation or statistics. The first semester course also requires only limited background in formal linguistics. But all students should have a serious commitment to some area of language science, and relevant expertise that they can contribute to the class group.

Psycholinguistics is a broad field. In principle, it includes all areas of the mentalistic study of language, including the various fields of so-called formal/theoretical linguistics, plus language acquisition and the neuroscience of language. And while we’re at it, why not throw in language disorders and second language acquisition for good measure! Due to this breadth, psycholinguistics can sometimes appear like a scientific archipelago – many interesting but disconnected islands. We will make no attempt to tour all of these islands in this course. Instead, we will focus on trying to understand the overall space, how the pieces fit together, and recurring themes and problems. The course will focus on:

  • Understanding the landscape of psycholinguistics
  • Psycholinguistic thinking: finding good questions, evaluating evidence, resolving conflicts
  • Doing psycholinguistics: tools needed to carry out psycholinguistic research

In the Fall semester (LING 640) we will devote a lot of time to ‘model’ problems, such as speech categorization and word recognition, because these relatively simple cases allow us to probe deeply into psycholinguistic issues with limited linguistic overhead.

In the Spring semester (LING 641) we will devote more attention to the relation between the syntax and semantics of sentences and language learning and language processing.

‘Normal’ will be anything but

We thought that things would be back to “normal” by now. No such luck. It is wonderful that we can be together again. But this semester may in some ways be the most complicated yet. 

 We will need to adapt and be flexible. There is a great deal of uncertainty now. And we are likely to see more change over the course of this semester.

 We also need to be mindful of the different situations that we are all emerging from, and the different perspectives that this brings.

Location, location, location

By default we meet in a traditional classroom indoors. That has some advantages, but right now it also has some disadvantages. Such as, we can’t see each other so well.

We may want to experiment, in order to create the kind of environment that supports the kind of interaction that we want. Soon we will have glorious fall weather, so we could maybe meet outdoors sometimes. Or we could meet on Zoom sometimes if that helps. This will be a time of experimentation.

We do not know whether the mask mandate will extend through the entire semester. UMD will likely remove the mandate as soon as Prince George’s County does so.  


COVID-19 will be around us for a long while yet. How it will impact us locally at UMD is much less clear. We will be learning a lot about this in the Fall 2021 semester. Experiments with tens of thousands of people who are almost entirely vaccinated have not yet happened, but they are now happening at many US campuses.

We hope that you will not be sidelined by COVID-19 during this academic year. But it could happen. UMD has extensive guidelines on how that impacts your class participation.

Remember that mental health is an important element of good health, especially for graduate students. Be aware, and seek help if needed. 


A research university thrives on connectivity. We have lost a great deal of that over the past year and a half. This is a time for rebuilding. We have learned a great deal over the course of the pandemic so far about this.

We have seen fewer people. We have had fewer spontaneous encounters. We have had fewer shared experiences. So we have needed to take extra steps to be connected.

One tool that helped us last year was a class Slack channel, created within the “Maryland Psycholinguistics” workspace. It proved to be useful for sharing questions, documents, and class updates. It could be good to do this again.

Individual meetings

 Individual and small group conversations are especially valuable right now. Seek them out!

I am very happy to have individual discussions. Just drop me a line.

I welcome opportunities for in person meetings. I had many outdoor meetings on campus in 2020-2021 and plan to continue in 2021-2022. The UMD campus is beautiful year round, and it is good for graduate students and faculty to be visibly active on campus. (Did you know that the entire campus is an arboretum? — Check out the University of Maryland Arboretum Explorer app.)




Schedule – Fall

Mondays & Wednesdays, 12:00 – 1:30. Mostly 1108B Marie Mount Hall … but this may change to facilitate interaction.

August 30: Introduction. The psycholinguistic landscape

September 1: Some core concepts

September 8: Development of Speech Perception

September 13: Development of Speech Perception

September 15:  Becoming a native listener

September 20: Distributional learning

September 22: Learning contrasts

September 27:

September 29: 

October 4: Neuroscience of speech perception and production

October 6: Word recognition

October 11: Active processing

October 13: Recognizing words in context

October 18:

October 20: Neuroscience of word recognition

October 25:

October 27: Word production

November 1:

November 3:

November 8:

November 10:

November 15:

November 17:

November 22:


November 29: 

December 1:

December 6:

December 8:

December 13:




This is graduate school. Your grade should not be your top concern here. You should be aiming to get a top grade, but your focus should be on using the course to develop the skills that will serve you well in your research. There will be no exams for this course. The focus of the course is on reading, discussing, writing and doing throughout the semester, and hence your entire grade will be based upon this.

Grades will be aligned with the values that guide this course: (i) active engagement with the core questions, (ii) thinking and writing clearly, (iii) taking risks and exploring new ideas, (iv) communicating and collaborating with others. 

If you want to get the maximum benefit from this class (i.e. learn lots and have a grade to show for it at the end), you will do the following …

1. Come to class prepared, and participate (40% of grade).

Being prepared means having done some reading and thinking before coming to class. Writing down your initial thoughts or questions about the article(s) is likely to help. Although many readings are listed for this course, you are not expected to read them all from beginning to end. An important skill to develop is the ability to efficiently extract ideas and information from writing. Particpating in class discussions is valuable because it makes you an active learner and greatly increases the likelihood that you will understand and retain the material. You should also feel free to contact me outside of class with questions that you have about the material.

2. Think carefully and write clearly in assignments (60% of grade).

The assignments will come in a variety of formats. In lab assignments you will get hands-on experience with various research techniques in psycholinguistics, plus experience in reporting the results of those experiments. In writing assignments you will think and write about issues raised in class and in the assigned readings. The writing assignment will often be due before the material is discussed in class: this will help you to be better prepared for class and to form your own opinions in advance of class discussion. In your writing it is important to write clearly and provide support for claims that you make.

We will plan to have many shorter writing assignments, typically involving responses to questions about individual readings, for which you will have relatively limited time. These are not intended to be major writing assignments. But they will all be read, and they will contribute to your class grade, following the guiding values of the class.

If you are worried about how you are doing in the course, do not hesitate to contact me. Email is generally the most reliable way of reaching me.

Grade scale

 A 80-100%  B- 60-65%
 A- 75-80%  C+ 55-60%
 B+ 70-75%  C 50-55%
 B 65-70%  C- 45-50%

Note that even in the A range there is plenty of room for you to show extra initiative and insight. The threshold for A is deliberately set low, so that you have an opportunity to get additional credit for more creative work.


Written work should be submitted individually, unless the assignment guidelines state otherwise or you have made prior arrangements with the instructor, but you are strongly encouraged to work together on labs and homeworks in addition to group projects. Academic honesty includes giving appropriate credit to collaborators.  Although collaboration is encouraged, collaboration should not be confused with writing up the results of a classmate’s work – this is unacceptable. If you work as a part of a group, you should indicate this at the top of your assignment when you submit it.  



The assignments for the course consist of a mix of shorter and longer written assignments, together with practical lab assignments.

The lab assignments are an important component of the course, and they are designed to give you first-hand experience with experimental and computational techniques used in psycholinguistic research. Typically you will have around 2 weeks for each lab assignment. 

Discussion note #1 [9/1/21]: Whistled languages are a striking example of the adaptation of human speech to different environments. Generally they are not distinct languages, but versions of spoken languages that are conveyed in whistled form. A new review of whistled languages from around the world by Julien Meyer reveals striking similarities in how languages adapt to the whistled medium. This is also summarized in a broad audience piece (with demos!) by Bob Holmes in Knowable Magazine. Please answer the following questions: (i) Why does whistling force languages to limit the information that is conveyed to the listener? Describe a couple of regularities in how languages choose to do this. (ii) Are there psycholinguistic implications of how languages adapt to the whistled medium? In particular, do the adaptations seem more suited to helping speakers or helping listeners (or neither)?

Discussion note #2 [9/8/21]: Marr’s (1982) discussion of visual perception highlights the “goal” of the computation. Does linguistic computation have a “goal”? Does Marr’s view on levels of analysis align with Chomsky’s contrasting of competence and performance? [See especially Marr, pp. 20-28, Chomsky pp. 3-15.]

Note that these classic distinctions from Chomsky and Marr are by no means the last word on these issues. Some argue that the distinctions are unnecessary, others argue for additional distinctions. In thinking about these issues, you may find it useful to apply the contrasts highlighted in class: (i) levels of analysis, (ii) tasks, (iii) mechanisms.

Disussion note #3 [9/15/21]: A very short paper by Stager & Werker (1997) reports four experiments on infants’ sensitivity to labels assigned to pictures. In one key experiment, 8-month olds appear to “outperform” 14-month olds. This seems counterintuitive. What is going on, and why is this an elegant demonstration? (The paper appeared in Nature, it has been cited around 1,000 times, and it has generated a lot of interesting subsequent work.)

Discussion note #4 [9/29/21]: Feldman et al. (2021, Open Mind) propose “perceptual space learning” as an alternative to “phonetic category learning” by infants. What’s the difference? In class we have discussed at length whether learning words is (or is not) important for learning the sounds of a native language. We also discussed (on Monday 9/27) the idea that learning words could help learners to recognize systematic differences between pairs of sounds, e.g., learning that a language phonologically contrasts short and long vowels. Does the new hypothesis by Feldman et al. predict a role for word learning? And can it help to explain the puzzle in figuring out vowel length categories? [Note, a second new paper by the same group, Schatz et al. (PNAS, 2021) provides a deeper dive into the computational simulations. This is a good example of state-of-the-art integration of cognitive science with machine learning. But you can answer the discussion note without this paper, and it is not recommended to start with that one.]

Discussion note #5 [10/6/21]: Näätänen et al. (1997) and Kazanina et al. (2006) are two short papers that both use the “mismatch” paradigm in EEG/MEG to try to identify correlates of native language speech perception abilities. Both test speakers of two languages and focus on a specific phonological difference between those two languages. But they apply a different logic from each other in how they try to link the EEG/MEG responses to the language-specific sound representations. What is different between the two approaches? What kinds of representations of sounds do they succeed in tapping into? (Abstract categories? Acoustics?) [Note: I was involved in one of the papers, but you should feel free to challenge the logic of that paper. I am not wedded to it.]

Discussion note #6 [10/13/21]; Read the short Science paper by Van Turennout et al. (1998) that makes an attention-grabbing claim about going “from syntax to phonology” in 40 milliseconds. The study is certainly clever, but the title is a bit of a simplification. Question: describe as accurately as possible what the 40 milliseconds corresponds to. Can we say what mental computations/processes occur in that time interval? Does that seem (im)plausibly fast? (Rough estimate, as a neural signal passes through different steps in the brain, it takes around 10ms per step, e.g., there are 6 steps between the cochlea and auditory cortex, and it takes 50-60ms for signals to travel from one to the other.)

Note that you do not need to understand the fine details of the “Temporal Response Function” (TRF) analysis method in order to follow the argument, though a high level grasp of what it aims to do could be useful. It is, however, very useful to understand the notions of “phoneme surprisal” and “cohort entropy”, as these play a key role. Recall the example we discussed in class of a gradually narrowing cohort of word candidates as the word SPINACH is gradually heard. PHONEME SURPRISAL corresponds to how much the cohort of candidates shrinks when a specific new phoneme is added, e.g., how much the cohort shrinks from SPI to SPIN. COHORT ENTROPY corresponds to the size of the cohort at a given point in the word, e.g., the number of candidates that remain in the cohort after SPIN has been heard.

Discussion Note 9 [11/17/21]Federmeier & Kutas (1999) is a notable paper in a couple of ways. Among its two main findings, one is widely known and influential, one much less so. For each of the two, I’m interested to hear your comments on what the evidence tells us about how words are processed in context, and whether you find the evidence surprising (and why it is(n’t) surprising, of course). (i) The reduced N400 amplitude for words that are anomalous in context, but related to an expected word. (ii) The hemispheric contrast.

Discussion Note 10 [12/7/21]: For our final discussion note of the semester, please read a new paper by (current HESP postdoc) Tal Ness (Ness & Meltzer-Asscher 2021). This study is part of an emerging cottage industry that looks at speech timing in cloze tasks to understand how words are accessed. Ness & Meltzer-Asscher make the interesting claim that semantic similarity between word candidates has the opposite effect in a cloze task than it has in single-word tasks … but that those different effects have the same cause. QUESTION: Try to explain in accessible terms why semantic similarity has these different effects in different tasks. Do you find this argument persuasive?
The paper is a short one, but it is relatively dense. One of its interesting features is its use of explicit models of how words are activated in a cloze task, prior to utterance, and how this relates to prior models of lexical activation in single word tasks.
An important prior study, which we briefly touched upon in class on Monday, is Staub et al. 2015, which used speech timing in a cloze task to argue for a “Race Model” of the cloze task. It’s worth a look. There are two main findings in the paper. The first (Figs 1 & 5) is that high cloze items are produced more quickly. This is not especially surprising. The more notable finding (Figs 3 & 7) is that when two words have the same cloze probability, it is the one with higher cloze competitors that is produced faster. It’s what I referred to as the “Usain Bolt effect”. It’s counterintuitive at first. But it’s super useful for understanding what is really going on in a cloze task. 










These are links to the slides used in the course. But note that they include some things that were not discussed in class, and in many cases the slides do not do justice to our extensive discussions in class.






This list will be updated over the course of the year.


Introduction (Fall)


Whistled languages – something completely different … maybe


Meyer, J. (2021). Environmental and linguistic typology of whistled languages. Annual Review of Linguistics, 7, 493-510.


Holmes, B. (2021). Speaking in whistles. Knowable Magazine, publ. 8/16/21.


Higher level background


Chomsky, N. (1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press. [chapter 1]


Marr, D. (1982). Vision. Cambridge, MA: MIT Press. [excerpt]


Jackendoff, R. (2002). Foundations of language. Oxford University Press. [chapter 1, chapter 2, chapter 3, chapter 4]


Lewis, S. & Phillips, C. (2015). Aligning grammatical theories and language processing modelsJournal of Psycholinguistic Research, 44, 27-46.


Momma, S. & Phillips, C. (2018). The relationship between parsing and generation. Annual Review of Linguistics, 4, 233-254


Speech Perception, Learning Sound Categories


Stager, C. & Werker, J. (1997). Infants listen for more phonetic detail in speech perception than word learning tasksNature, 388, 381-382. [This is one of the primary readings for the section of the course on phonetic/phonological representations. A very short, but very important study. Why are younger infants better than older infants, even on native-language contrasts?]


Feldman, N, Goldwater, S., Dupoux, E., & Schatz, T. (2021). Do infants really learn phonetic categories? Open Mind (in press). 


Schatz, T., Feldman, N., Goldwater, S., Cao, X., & Dupoux, E. (2021). Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic inputProceedings of the National Academies of Science, 118.


Werker, J. (1994). Cross-language speech perception: Developmental change does not involve loss. In: Goodman & Nusbaum (eds.), The Development of Speech Perception. Cambridge, MA: MIT Press, pp:93-120. [Useful for Lab 1. This paper reviews in more details the reasons why Werker adopts a structure-adding view of phonetic development.]


Werker, J. (1995). Exploring developmental changes in cross-language speech perception. In L. Gleitman & M. Liberman (eds) Language: An Invitation to Cognitive Science, Vol 1 (2nd edn.), 87-106. [This paper is the best starting point for this section of the course. It presents an overview of Werker’s views on phonetic development up to 1995, including a straightforward study of her important cross-language experiments from the early 1980s.] 


Werker, J. F., Pons, F., Dietrich, C., Kajikawa, S., Fais, L., & Amano, S. (2007). Infant-directed speech supports phonetic category learning in English and JapaneseCognition, 103, 147-162. [Analysis of what infants actually hear. It is presented as an argument for unsupervised distributional learning, but I suspect that it shows the opposite.]



Cognitive Neuroscience of Speech Perception

Näätänen et al. 1997. Language-specific phoneme representations revealed by electric and magnetic brain responsesNature, 385, 432-434.

Kazanina, N., Phillips, C., & Idsardi, W. 2006. The influence of meaning on the perception of speech soundsProceedings of the National Academy of Sciences, 103, 11381-11386.

van Turennout, M., Hagoort, P., & Brown, C. 1998. Brain activity during speaking: from syntax to phonology in 40 millisecondsScience, 280, 572-574.

Word Recognition

An accessible introduction to some foundational concepts and findings: 

Altmann, G. 1997. Words and how we (eventually) find them. Chapter 6 of The Ascent of Babel. Oxford University Press. [A good introductory chapter.]

Some recommended readings for class discussion.

Chen, L. & Boland, J. 2008. Dominance and context effects on activation of alternative homophone meaningsMemory and Cognition, 36, 1306-1323.

Magnuson, J., Mirman, D., & Myers, E. 2013. Spoken word recognition. In D. Reisberg (ed.), The Oxford Handbook of Cognitive Psychology, p. 412-441. Oxford University Press.

Gaston, P., Lau, E., & Phillips, C. 2020. How does(n’t) syntactic context guide auditory word recognition. Submitted.

Lau, E., Phillips, C., & Poeppel, D. 2008. A cortical network for semantics: (de)constructing the N400. Nature Reviews Neuroscience, 9, 920-933.

Federmeier, K. & Kutas, M. 1999. Right words and left words: electrophysiological evidence for hemispheric differences in meaning processingCognitive Brain Research 8, 373-392.

Ness, A. & Meltzer-Asscher, A. 2021. Love thy neighbor: Facilitation and inhibition in the competition between parallel predictionsCognition 207, 104509.

Staub, A., Grant, M., Astheimer, L., & Cohen, A. 2015. The influence of cloze probability and item constraint on cloze task response timeJournal of Memory and Language 82, 1-17.


Some seminal papers discussed in class.

Marslen-Wilson, W. 1975. Sentence perception as an interactive parallel processScience, 189, 226-228 

Marslen-Wilson, W. 1987. Functional parallelism in spoken word recognitionCognition, 25, 71-102.

Boland, J. and Cutler, A. 1996. Interaction with autonomy: Multiple output models and the inadequacy of the Great DivideCognition, 58-309-320.

Dahan, D., Magnuson, J., & Tanenhaus, M. 2001. Time course of frequency effects in spoken word recognition: Evidence from eye-movementsCognitive Psychology, 42, 317-367.

Kutas, M. & Federmeier, K. 2000. Electrophysiology reveals semantic memory use in language comprehension. Trends in Cognitive Sciences, 4, 463-470