MLK b-day

A message from Nancy S. Taylor, Senior Minister of Old South Church in Boston:

Today would be the Reverend Doctor Martin Luther King’s 90th birthday. To honor the day and to honor the man – truly among the great prophets of Christianity and of America – Old South Church is airing his sermons and speeches all day, both out of doors onto Boylston Street where every single passerby cannot but hear his voice, and within the Sanctuary, where all who enter for respite or prayer, to wonder and wander, will be held captive by King’s remarkable voice, intonation and message.

This location at the Hub of the Hub of the Universe, this special platform for ministry, will proclaim from sidewalk and sanctuary: Let justice roll down like waters, and righteousness like a mighty stream. In posters that accompany our broadcasting of King’s sermons and speeches we ask the question: How can you take up the cause? May this be your question today and tomorrow and in all of your tomorrows.

Every year on the anniversary of Dr. King’s birthday, I make it a practice to read or re-read at least one of his speeches, sermons, or letters. He was a remarkable intellect, as well as a moral giant.

Here are some options for you to consider: The Old South Gospel Choir is performing at the Martin Luther King, Jr. Breakfast in Boston again this year. This is the nation’s longest-running, and Boston’s premier event, dedicated to honoring the legacy of Dr. King. Senior Deacon Ralph Watson will be running an MLK Day Mobile Soup Kitchen. Come to the church at 10:30am, help prepare a warm, home cooked meal, pack it up and take to the streets to share it with our unhoused neighbors for whom the streets of Copley Square are home.

Did you know that the young Martin Luther King, while a student in Boston, sometimes worshipped with us? He listened to the preaching of our 18th Senior Minister, Dr. Frederick Meek, took notes of Meek’s sermons and fashioned some of his own sermons with reference to Dr. Meek’s. These references are noted in the King Papers. Did you know Coretta Scott once sang in our sanctuary with a Boston University Chorus?

How then, can you and I fail to rise to the dream he painted for us, the United States he imagined for us, the country he died trying make live up to the true meaning of our creed?

King did his work. Now it’s our turn. Let’s get on with it.


BUCLD 2018

Here are my slides for BUCLD 2018.

A number of recent theories propose that prediction facilitates efficient language processing. Supporting this idea are findings that listeners can use verb semantics and number markings to predict upcoming referents (Mani & Huettig, 2012; Lukyanenko & Fisher, 2016). However, precisely how prediction occurs during language processing remains uncertain. One prominent theory is prediction via simulation (Pickering & Garrod, 2013): Listeners use language production mechanisms to simulate the speaker’s upcoming production, which is contingent, at least in part, on perspective-taking and on well-developed language production mechanisms.

In the present study, we tested whether prediction occurs via simulation by evaluating whether listeners can use spatial deixis (this, that, these, and those) to predict the plurality and proximity of a speaker’s referent. In two eye-tracking tasks, English L1 adults, English L1 5-year-olds, and English L2 adults viewed scenes that included a speaker and four referents (experiment 1) or two referents (experiment 2). Participants listened to deictic sentences (e.g., Look at that wonderful cookie) and neutral sentences (e.g., Look at the wonderful cookie). Data collection for experiment 2 is ongoing, but preliminary findings suggest that only L1 adults are capable of prediction via simulation.

The present pattern of results suggests that prediction via simulation (Pickering & Garrod, 2013) supports processing for the mature, native speaker, but that extensive experience with cues in a language may be required before listeners can use this route for prediction. This three-group investigation goes beyond the empirical goal of assessing whether prediction occurs and evaluates how prediction occurs – a crucial goal for defining prediction’s role in language processing and learning.

We’re grateful to all participant families, to Claire Robertson for her assistance with stimuli, and to Mia Sullivan to her assistance with data collection and CHILDES coding.

she roars

This past week, I was honored to give a research talk at She Roars – a conference celebrating women at Princeton.

This conference was a unique opportunity to reflect on all the amazing women who have mentored me: Jenny Saffran, Maryellen MacDonald, Melanie Jones, Alexa Romberg, Jessica Willits, Jill Lany, Jess Hay, Jesse Snedeker, Manizeh Kahn, Melissa Kline, Lauren Emberson, Chris Potter, Elise Piazza… The list goes on and on! I’m so fortunate to be surrounded by all of these female scholars.

I left feeling a sense of camaraderie and inspiration, and can’t wait to return to Princeton for the next She Roars conference!


CogSci 2018

Here is my  poster for CogSci 2018.

Our study evaluated whether listeners can use spatial deixis (e.g., this, that, these, and those) to predict the plurality and proximity of a speaker’s referent. In an eye-tracking task, L1 adults, L1 children and L2 adults viewed scenes while listening to deictic sentences (e.g., Look at that beautiful baby) and neutral sentences (e.g., Look at the beautiful baby). We found that L1 adults, L1 children and L2 adults all used deixis to predict the plurality of the referent (e.g., using this to anticipate a singular referent). However, only L1 adults used deixis to predict the proximity of the referent to the speaker (e.g., using this to anticipate a referent proximal to the speaker). Thus, our findings suggest that language processing experience influences verbal prediction. We argue that, beyond determining whether listeners predict, determining how listeners predict is crucial to understand prediction’s role in language processing and learning.

Big thanks to all participant families, to Claire Robertson for her assistance with stimuli, and to Mia Sullivan to her assistance with ongoing data collection and CHILDES coding!

I’m looking forward to spending time at my alma mater and catching up with family and friends in Madison!

ICIS 2018

Here are my abstracts and posters for ICIS 2018.

Our first study evaluated the developmental emergence of verbal prediction and language comprehension. We find that prediction and comprehension emerge concurrently over the second postnatal year. These findings add to a growing body of literature suggesting that prediction is a language learning mechanism, and further suggest that prediction supports language development from the earliest stages, as infants learn their first words. Here’s the abstract and the poster.

Our second study evaluated whether variation in home language input influences children’s verbal prediction abilities. We found that children who hear more language input from caregivers generate predictions, but children who receive less input do not do so robustly. This pattern of results suggests that the quantity/quality of language experience learners receive influences the extent to which they generate predictions during language processing. Here’s the abstract and the poster.

Looking forward to seeing lots of exciting talks and posters and catching up with old friends from the UW Infant Learning Lab!

online course: intro to stats with R

This Summer, Felicia Zhang and I are developing an online course with the Princeton McGraw Center for Teaching and Learning. Below is an overview of the course, which will be accessible in Fall, 2018:

Two of the biggest challenges for undergraduates in psychology are understanding key concepts in statistics and applying those concepts to analyze data and interpret findings. These challenges not only make it difficult for students to understand material in lectures and labs, but also difficult for instructors to help the students because students feel defeated and become unwilling to engage with the course. Therefore, we are designing an online course to introduce statistics and R programming. Integrating statistics and R programming in one course is ideal for learning: The former is essential for students to understand research more broadly and the latter is an important tool for students to engage with research directly. For example, a psychology student must interpret statistical results from prior experiments as well as analyze their own data for their senior thesis. In sum, our course is designed to help students who have minimal prior experience to understand key concepts in statistics and to apply those concepts to realistic problems in psychology research.

The course will include 6 modules (i.e., getting started with statistics; getting started with R; descriptive statistics; correlation; one-sample t-test and binomial test; two-sample t-test) and each module will have the same basic structure: The first portion helps students to understand key concepts. Students will watch narrated text, live drawings, or videos. To assess students’ understanding, students will complete multiple choice questions. The next portion of the module helps students to apply key concepts via R programming. First, we will pose realistic psychology research questions (e.g., Do toddlers who hear more language from caregivers tend to have larger vocabularies?). Students will observe how we answer each question with the appropriate statistical test (e.g., correlation) and R syntax (e.g., cor.test(data$language, data$vocabulary)). Next, we will pose new, similar questions (e.g., Do toddlers who have more books at home tend to hear more language from caregivers?) and students will attempt to answer these questions. To assess students’ understanding, students will input their answers and receive feedback. The last portion of the module helps students to review key concepts. Students will watch narrated text, live drawings, or videos. Finally, to assess students’ overall understanding, students will complete a module quiz.

After participating in our course, students will have fundamental knowledge of statistics and R programming. Although both are extremely important for students in psychology, students need more resources to understand key concepts in statistics and to apply those concepts to real research (e.g., their senior thesis). Our course will provide these resources, with two notable strengths: First, unlike other online R programming courses, we will use realistic, psychology-specific examples. This design enables direct connections between what students learn in lecture, in lab, and in our online course. Second, although our course is tailored to the needs of psychology students, having basic knowledge of statistics and R programming is applicable to a growing number of fields (e.g., sociology, politics, etc.). In sum, our online course will support learning among undergraduates in psychology and could have wide-reaching impact among undergraduates in science, more broadly.