Psycholinguistics I/II - 2022-2023

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 here. Instead, we will focus on trying to understand the overall space, how the pieces fit together, and recurring themes and problems. We 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, both speaking and understanding.

Location, location, location

In person interaction is so valuable. We will do everything possible to maintain that. Online interaction is next best. Hybrid is a nut that has yet to be cracked.

For now, the semester is proceeding as ‘normal’, i.e., roughly as we used to, 3-4 years ago. We hope that it will remain this way, though there may be bumps along the way.  


We hope that you will not be sidelined by COVID-19 during this semester. But it could happen. It probably will happen. If you get infected, UMD has extensive guidelines on how that impacts your class participation. We will try to take it all in our stride.

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 lost a great deal of that over the past 3 years. This is a time for rebuilding. We have learned a great deal over the course of the past couple of years.

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. In 2020-22 I learned to greatly value outdoor meetings in addition to traditional indoor-with-computers meetings. 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 – Spring

Mondays & Wednesdays, 12:00 – 1:30.  1108B Marie Mount Hall.

January 25: Introduction. The psycholinguistic landscape

January 30: Discussion Note 1: Structural priming (Branigan & Pickering 2016)

February 1: Experiments as arbiters (or not)

February 6: Discussion Note 2: Input vs. update (Omaki et al. 2014)

February 8: Misparsing as evidence (Lidz, White, Perkins, …)

February 13: Discussion Note 3: Evidence and variation (Pinker 1989, Goro 2008)

February 15: Invisible variation (Han et al. 2007)

February 20: Discussion Note 4: Islands (Pearl & Sprouse 2013, Kush 2022)

February 22: Transparent vs. opaque learning models

February 27: Discussion Note 5: Grammatical constraints and parsing in children (Conroy et al. 2009)

March 1: Selective fallibility

March 6: Discussion Note 6: Grammatical precision in comprehension (Keshev & Meltzer-Asscher 2017)

March 8: Prediction

March 13: Discussion Note 7: something by Sol Lago

March 15: Guest speaker: Sol Lago (PhD ’14; University of Frankfurt)

March 18 – 26: SPRING BREAK

March 27: (possibly hybrid) Memory access

March 29: (possibly hybrid) Memory access

April 3: Discussion Note 8: Illusions

April 5

April 10: Discussion Note 9: Information theoretic models

April 12

April 17: Discussion Note 10: Production

April 19

April 24: Discussion Note 11: Production

April 26

May 1: Discussion Note 12: TBD

May 3

May 8

May 10: It’s a wrap!

Schedule – Fall

Mondays & Wednesdays, 12:00 – 1:30.  1108B Marie Mount Hall.

August 29: Introduction. The psycholinguistic landscape

August 31: Some core concepts. Discussion of whistled languages.

September 7: Development of Speech Perception

September 12: Development of Speech Perception

September 14:  Becoming a native listener

September 19: Distributional learning

September 21: Learning contrasts

September 26:

September 28:

October 3: Neuroscience of speech perception and production

October 5: Word recognition

October 10: Active processing

October 12: Recognizing words in context

October 17:

October 19: Neuroscience of word recognition

October 24:

October 26: Word production

October 31:

November 2:

November 7:

November 9:

November 14:

November 16:

November 21:


November 28:

November 30:

December 5:

December 7:

December 12:



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 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).

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.
Revising your written work following discussion in and out of class is a very valuable activity. We will make more use of this in the spring semester.

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 anything in this course. 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.

Assignments – Spring

Discussion note #S2 [2/6/22]: Omaki et al. (2014) examines adult and child interpretations of globally ambiguous wh-questions like “Where did Emily say that she hurt herself?”, in both English and Japanese. The motivation is to understand how children process sentences that they encounter.

1.What should we conclude from the cross-language comparison between English and Japanese speaking children?
2.Based on these findings, how serious is the risk that children misunderstand things that are said to them?
3.Some languages, e.g., Russian, are reported to severely limit long-distance wh-dependencies, such that “Where did Emily tell someone that she hurt herself?” can only be understood as a question about the telling event. In light of Omaki et al.’s findings: what would be needed for English and Russian speaking children to correctly figure out whether their language allows long-distance wh-dependencies?
Akira Omaki was a 2010 PhD graduate from UMD who passed away in 2018. Read about his life here.

Discussion Note #S1 [1/30/23] Branigan & Pickering 2017 (“An experimental approach to linguistic representation”; see Readings) argues for the value of syntactic priming as a tool for understanding language structure. Try to address the following three questions: (1) The finding by Bock & Loebell (1990) about priming and by-phrases (see Section 2.1, p. 8) is among the most influential in this literature. Why so? Is this fame justified? (2) What do B&P mean by “The reality of linguistic representation” (p. 3)? (3) What is the role of the evidence from missing (“elided”) elements in Mandarin (p. 10)?

As is standard for articles in this journal, B&P’s target article is followed by a collection of short commentaries, which stake out various positions in support of or in criticism of the authors. You do not need to read all of them, but it can be fun to read a sampling of the opinions.


Assignments – Fall

Discussion Note #12 [12/5/22] For our final Discussion Note of the semester, we’ll look at a topic that sits at a number of intersections: (i) word and sentence processing, (ii) syntactic and semantic processing, (iii) time-based and task-based accounts of differences. It’s also a topic that has been exercising a number of minds locally in the past couple of years. (I would have been shocked if you told me that 10-15 years ago.)

Kim & Osterhout (2005) is an influential ERP study. Its main finding helped to upend received wisdom about the most prominent ERP responses in sentence comprehension. At least two other groups published roughly the same finding at around the same time. I think the K&O study has the most interesting design. K&O take their findings to have architectural implications for the relation between syntactic and semantic processing, specifically challenging the VERY standard assumption that semantic interpretation combines smaller meanings into larger meanings by using syntactic structure as a guide. QUESTION: K&O regard their findings in Experiment 2 (“the dusty tabletops …”) as especially important to their argument. Which aspect of their results is most important to their argument?
Chow et al. (2018) reports experiments carried out in Mandarin Chinese by Wing Yee Chow in 2011-2013 (yeah, the publication process was really arduous). Chow was looking at similar phenomena to Kim & Osterhout, but she was looking at them rather differently. She concluded that she was testing mechanisms of lexical prediction. Lee et al. (2022: it’s a 20-minute video of a talk, starts around the 27:00 mark) looked at very similar phenomena. They even used materials from some of Chow’s studies. But they were focusing on different measures. That was an impact of the pandemic. They reached a different conclusion than Chow did. QUESTION: which aspects of the Chow et al. and Lee et al. findings present the most challenge for the account provided by the other?
READING/WATCHING TIP. The three pieces are closely related enough that they build on each other, and on other things that we have read about ERPs and cloze tasks. So, rather than reading them in a linear and historical fashion, you might do well to start by getting an overview of the different studies, and then from there piecing together how they relate to each other and what the implications are. You might even start by watching the 20-minute video of Rosa Lee’s talk, and working back from there. When reading the articles, it’s often good to start with the abstract and the figures/tables, and work outwards from there.

Discussion Note #11 [11/21/22]. The review by Lau et al. (2008) is widely cited in support of the claim that the N400 ERP component reflects lexical processes. Since the N400 to an incoming word is known to reflect how well that word fits in context (often operationalized in terms of cloze probability), this in turn implies that lexical processes are directly influenced by context. (i) What is the evidence that N400 reflects lexical processes, and do you buy it? (ii) If true, can this be reconciled with the claim from behavioral studies (cross-modal priming, visual world eye-tracking) that lexical access in comprehension proceeds initially in a context-independent fashion?

(Note that you should feel no need to agree with the arguments of Lau et al. (2008). The authors themselves have questions.)

Discussion Note #10 [11/14/22]. Following our discussion of a model of single word production that has been developed over decades, we shift to production of words in context. This is a more complicated phenomenon, but one where interesting recent results are helping to clarify the specifics of the processes involved (and one where there is much current activity locally, aided in part by the pandemic). This is also a topic where thinking in terms of an explicit process model helps to better understand some widely used measures, such as ‘cloze probability’.

Staub et al (2015) offer a study of a deceptively simple “speeded cloze” task, in which speakers simply call out a word to complete a sentence as quickly as possible. The key action in this paper involves the relation between the cloze probability of a response and its timing. It is not so surprising that high cloze responses are produced more quickly. What is more surprising, and hence informative, is the finding that responses of the same cloze probability (e.g., 20% cloze) are produced faster if the alternatives in that context are high cloze (e.g., 40%) than if the alternatives in that context are low cloze (e.g., 10%). In other words, when your competitors are stronger than you, you are produced more quickly. This is what you may hear locally referred to as the “Usain Bolt effect”. Question: what is going on here, and why does it favor a “race” model of word production in context over alternatives? (Related: cloze probability has long been used as a standard measure in psycholinguistics of how well a word fits a context. Why do some now regard speed of production as a better measure than cloze probability?)

Building on the Staub et al. (2015) study, a new paper by (current HESP postdoc) Tal Ness (Ness & Meltzer-Asscher 2021) makes an argument for more specific properties of the production of words in context. 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 Ness & Meltzer-Asscher paper is short, but 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.

Discussion Note #9 [11/7/22]. Building on our discussion of picture-word interference effects on Weds 11/2, this week we will dig further into models of spoken word production. We will focus on one prominent model that has been widely used in computational simulations — Ardi Roelofs’ WEAVER++ model — and examine the empirical evidence for the model’s properties. Roelofs’ model implements a theory of word production that is the result of a monumental research program by Pim Levelt’s group at the Max Planck Institute for Psycholinguistics in Nijmegen, Netherlands. Spanning many years, and with the support of MPI resources, it is probably the most extensive project on speech production ever undertaken.

A chapter by Roelofs and Ferreira (2019) summarizes some key assumptions of the model on pp. 37-40, in particular a series of “major controversies” at the end of p. 38. Look into the evidence surrounding AT LEAST ONE of those controversies. Give a summary of what is at stake, how it impacts the model, and your assessment of the evidence. Since we will not all have read the same evidence, please come prepared to explain the topic/evidence that you chose in class.

Ardi Roelofs has a personal website that is useful, with a summary of WEAVER++ research and links to most of his papers. The ‘classic’ presentation of the model is in a Behavioral and Brain Sciences target article by Levelt, Roelofs, & Meyer (1999). (Meyer became one of the MPI directors following Levelt’s retirement.) Roelofs has published a number of subsequent papers with updates to the model, including illustrations of how it can capture word production difficulties in various different forms of atypical language.

Pim Levelt has a fascinating autobiographical piece in Annual Review of Linguistics, On becoming a physicist of mind (Levelt 2020). It includes the story of the process that led to Roelofs’ model. That story encompasses a lot of important figures in the history of psycholinguistics. Levelt himself was one of the founders of the field.

Discussion Note #8 [10/31/22]. This week we’ll try something a little different that ties together (i) psycholinguistic themes that we’re focusing on in this course, (ii) current national policy debates related to language science, and (iii) UMD campus efforts that are becoming much more prominent, as soon as at Friday’s Language Science Day.

Sold a Story is a new podcast by Emily Hanford, a reporter based in our local area. It’s about educational practices around reading, especially controversies around the role of bottom-up information (word forms, phonics, etc.) and top-down information (context) in becoming a fluent reader. Well-meaning people on both sides of this issue believe that they are being guided by scientific evidence. The first two episodes have already been released, with the third due to appear on Thursday Oct 27th. The reporting frequently appeals to what the science shows, but it’s not so clear what the actual evidence is. Your assignment: listen to the first two episodes (I don’t yet know what is in the third), identify one or more claims about what the science shows, and try to find out what the actual basis for those claims is. Two examples: (i) fluent readers pay attention to all of the letters in a word, (ii) how a person is taught affects which areas of the brain they use to read.

One thing that is different about this assignment is that there is no presumption that we will have all read the same material beforehand. So we can’t work from the same shared starting point as in our normal discussions. So please come armed with evidence, and be ready to give a compact summary of what the evidence consists of.

Please feel very free to discuss and share ideas and sources with other group members. The podcast episodes are well produced, and easy to listen to. But transcripts of the podcast are also available and searchable: Episode 1, Episode 2. Some of the psycholinguists and cognitive neuroscientists invoked in the podcast include Keith Rayner, Mark Seidenberg, and Bruce McCandliss, all highly regarded. (I gather that some of Emily Hanford’s other reporting on reading has featured UMD researchers.)

Also, here is the (equally new!) piece in Science Magazine about education and scientific evidence that I mentioned in class. UPDATE: Oh, interesting: here is an even newer piece from the New York Times about a similar approach to avoiding online misinformation in teens. (Odd coincidence, it’s partly about the work of a new UMD faculty member who recently moved a few houses down the street from me. Small world!) Something that I find striking, interesting, and a bit worrying about both of these is the message, “It’s too hard to evaluate evidence yourself, so instead focus on the credibility of the messenger.”

Discussion Note #7 [10/24/22]. Mostly by chance, two recent UMD graduates examined the effectiveness of simple syntactic contexts in constraining lexical access. Phoebe Gaston et al. (2020) focus on comprehension. Shota Momma et al. (2020) focus on production. Both employ creative experimental designs. The results lead to apparently very different conclusions about the effect of the category constraint. The basic aim of this exercise is to explore possibilities for the contrast.

As a warm-up, to think through the designs, first address the following. (i) Phoebe Gaston originally worried that she was ‘scooped’ by Strand’s 2017 paper on a similar topic. She later decided that she was not. Why do Phoebe’s design changes matter? (ii) In Shota Momma’s design, why does it matter that participants are doing a mixed task?

Finally: (iii) Discuss possible reasons for the contrasting results? Different task details? Difference between speaking and understanding? Different measures? Something else? No real conflict?

The Gaston et al. paper is a long manuscript (submitted + reviewed; reviewers were enthusiastic but had some probing questions). The main experiment is on pp. 34 onwards. The simulations in TRACE on pp. 18-34 are not essential, though you may find them interesting (we learned a lot from them). Phoebe’s discussion of mechanisms involved in guiding eye fixations on pp. 38-42 are very useful.

Discussion note #6 [10/10/22]. 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.)

Two things that it could be helpful to think about in reading the study:(i) what is the step-by-step mental process that a participant in the study has to go through in order to perform the task? (ii) When we refer to times in psycholinguistics, we often use single numbers as a summary of a lot of different numbers (from individual trials by individual participants). The underlying numbers always form a distribution. E.g., if we say that people in a sound discrimination task responded in 620ms, what we generally mean is that there is a distribution of response times for which 620ms reflects a central tendency of that distribution, e.g., average. Sometimes we instead use summary numbers to refer to the edge of a distribution, e.g., the earliest time at which something occurs, rather than the average time. When reading studies like Van Turennout, et al. be mindful of what the time estimates refer to.

Discussion note #5 [10/3/22]. Our next discussion focuses on some ways in which researchers have used electrophysiological measures (EEG/MEG) to try to understand how speech sounds are mentally represented/encoded. Näätänen et al. (1997), Kazanina et al. (2006), and Pelzl et al. (2021) are all examples of studies that rely on comparisons of speakers of different native languages. But they each adopt a different logic to do this. Choose (at least) two of the three studies, and address the following: (i) How do they differ (if at all) in terms of the sound encodings that they are seeking to clarify, and the experimental logic for how they achieve this? (ii) What (if anything) is the “value added” in these studies from the use of electrophysiological (EEG/MEG) measures, rather than behavioral measures?

Two of the articles are short. The newest one is longer, and technically more up-to-date, but the core findings are fairly clear. A couple of clarifications. First, I am not seeking a specific answer on the second question. You might conclude that the more cumbersome method is beneficial or not. What I do want you to do is to think through the rationale of what one might hope to gain from the use of specific methods, as this is the kind of decision that one needs to make again and again in psycholinguistics. Second, although I was a co-author of one of the studies and the logic comes from an earlier study of mine (Phillips et al. 2000), feel free to challenge it. None of the authors are beholden to the claims made there.

Discussion note #4 [9/26/22]. For a number of years, Naomi Feldman and colleagues have been digging into the problem of how infant sensitivity to speech categories might develop, despite lack of minimal pairs and lack of clearly distinct acoustic distributions. The aim of this discussion note is to try to situate this body of work in the context of developmental changes that we have already discussed. In particular, is there a role for early word learning in figuring out speech categories, according to Feldman et al. (Those views might be different in different papers.)

There are a few papers, which are linked to the Readings tab. You do not need to read and respond to all of them. Do read at least one of them, preferably Hitczenko & Feldman (2022), as it is brand new. Preferably look at at least one other. Feldman et al. (2009) adopts a very clear position on the importance of word learning, which may be at odds with later claims. (In that paper, if you can explain why Feldman argues that it’s easier to learn categories from a lexicon without minimal pairs, then you understand the main point.) That one is also the shortest. Of the two 2021 papers, the one in Open Mind is more accessible.

Each of the articles involves some amount of technical material about computational models, which may be more or less accessible to you. You do not need to follow all of that material in order to get the core ideas in the papers. (Also, I do not understand all of it, either. But feel free to discuss with classmates.)

Synthesis note #1 [9/21/22]. How accurate is it to say that infants have phonological category representations like older children and adults?

For purposes of this piece, “infants” can refer to any or all of ages from birth to 18 months. The main aim of this exercise is to think carefully about the relationship between behaviors and experimental evidence on the one hand, and the cognitive representations that are responsible for these behaviors. Your write up does not need to be long, but please explain as clearly as possible.

Discussion note #3 [9/14/22]: 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 over 1,000 times, and it has generated a lot of interesting subsequent work.)

Discussion Note #2 [9/7/22]: This discussion note is about Chomsky (1965, ch 1, pp. 3-15), and Marr, D (1982; pp. 8-28, esp. 20-28). Question: Marr’s 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?

I recommend to keep in mind the distinction that we discussed on Wednesday (8/31) between (i) Levels of analysis, (ii) Tasks, and (iii) Mechanisms.

Discussion note #1 [8/31/22]: 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 recent 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)?

Introduction [8/30/22]: Please send an email to, introducing yourself and relevant background and motivations. I’d love to know the following:

  1. What is your background in psycholinguistics, in (in)formal study or in hands-on experience. What kind of background do you have in experimentation, including knowledge of experiment control platforms, e.g., PC-Ibex, PsychoPy, or statistics/analysis tools, e.g., R, Excel?
  2. What, briefly, is your linguistic background, including languages where you have expertise?
  3. What are the outcomes that you hope to gain from the course (either by December ’22 or by May ’23)?




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.

Set 1: Scene setting

Set 2: Development of speech perception

Set 3: Electrophysiology and speech perception



Readings – Spring

Introduction (Spring)

Bever, T. (2021). How Cognition came into being. Cognition, 213, 104761.

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

Branigan, H. & Pickering, M. (2017). An experimental approach to linguistic representation. Behavioral and Brain Sciences, 40, e282.

Overview: Putting Pieces Together

This series of articles lays out the current thinking of myself and colleagues on the relation between traditional linguistic theories and theories in psycholinguistics.

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

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

Phillips, C., Gaston, P., Huang, N., & Muller, H. (2020). Theories all the way down: remarks on “theoretical” and “experimental” linguistics. In press: G. Goodall, ed., Cambridge Handbook of Experimental Syntax. 

Omaki, A. & Lidz, J. (2015). Linking parser development to acquisition of syntactic knowledgeLanguage Acquisition, 22, 158-192.


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?]

Hitczenko, K. & Feldman, N. (2022). Naturalistic speech supports distributional learning across contextsProceeding of the National Academies of Science, 119, e2123230119.

Feldman, N., Griffiths, T., & Morgan, J. (2009). Learning phonetic categories by learning a lexicon. Proceedings of the 31st Annual Meeting of the Cognitive Science Society.

Feldman, N, Goldwater, S., Dupoux, E., & Schatz, T. (2021). Do infants really learn phonetic categories? Open Mind, 5, 113-131.

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.

Pelzl, E., Lau, E., Guo, T., & DeKeyer, R. 2021. Even in the best case scenario L2 learners have persistent difficulty perceiving and utilizing tones in Mandarin. Studies in Second Language Acquisition, 43, 268-296.

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, T. & 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