SRCD 2019: language input and prediction

Here are my slides for SRCD 2019.

A large body of research indicates that environmental factors like SES play a role in children’s language development (for review see Fernald & Weisleder, 2011). These findings reveal that children’s day-to-day language experiences vary tremendously, as do their developmental trajectories. Factors like SES, language input, and language processing abilities interact to shape children’s emerging linguistic abilities (e.g., Weisleder & Fernald, 2013).

At the same time, a related body of work has investigated whether a particular aspect of language processing – prediction – supports development, and a number of findings are in line with this view (e.g., Reuter et al., under revision). However, the origins of individual differences in prediction are unknown. Given prior findings linking language input and processing efficiency, it seems likely that language input also supports predictive language processing. Importantly, evaluating the link between input and prediction is essential for evaluating prediction-based theories of learning: These theories claim that accurate predictions are the result of accumulated language processing experiences. Children who receive more input have more processing experiences and therefore should be able to generate more accurate predictions during language processing (e.g., Dell & Chang, 2014).

We therefore hypothesized that home language input supports children’s emerging language processing abilities, including predictive language processing. Using a combination of eye-tracking tasks, LENA recordings, and vocabulary measures (PPVT), we find that:  High-SES toddlers receive marginally more language input from caregivers. High-input toddlers have larger vocabularies, and, importantly, high-input toddlers generate more robust predictions during language processing. These findings add to a body of literature linking SES-based disparities in input and processing (e.g., Weisleder & Fernald, 2013) and provide further support for prediction-based theories of language development (e.g., Dell & Chang, 2014).

We thank all participant families and we’re grateful to all Princeton Baby Lab research assistants for their help with data collection. We also thank Monica Ellwood-Lowe and Mahesh Srinivasan for organizing our SRCD 2019 symposium on SES-based disparities in language development.

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!

 

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.

princeton teaching evaluations

Here’s what my Introductory Psychology students (mostly freshmen) had to say:

“Tracy Reuter was absolutely fantastic. She made the topics really interesting and relevant, and she was ALWAYS within reach (email, phone, in-person) if we ever had any questions. She is a fantastic teacher and anyone would be lucky to have her. I can’t stress enough how much she MADE this lab fantastic.”

“tracy rocks!!!”

“it was pretty fun”

“It was very fun and helpful and informative, keep it that way!”

“TRACY IS THE BEST PERSON EVER AND MADE LAB SO FUN I’M SO HAPPY I HAD HER AND SHE MADE LAB SO FUN”

“tracy is fantastic. she’s an absolutely fantastic teacher.”

And here’s what our head AI had to say to my advisor (who then immediately shared it with me, because he was so pleased):

“I thought you might be happy to hear that Tracy is a rockstar instructor. Her lab had the highest ratings — and the ratings were so high that I still felt pretty good about myself coming in second place. Also, Joel and I sat in on all of the 101 labs this week as audience members for the student presentations, and we were both impressed by how much enthusiasm Tracy elicited from her students. I expect that some of her virtuosity is due to dedication and personality, and some is due to your mentorship and example of outstanding teaching.”

It’s not every day that you get such positive affirmation, especially as a teacher! I’m glad to hear that my students learned a lot from the course and had fun in the process too. More importantly, I learned which teaching methods worked well and which methods I can improve for next time. For example, our ice-breaker activities on the first day really helped my students get to know each other. As a result, it was always easy to start a discussion on any topic and my students all collaborated beautifully for group projects. I learned that introducing new material via PowerPoint slides could help to lay the groundwork for the lab (e.g., What is cognitive control?) but this needed to include lots of engaging material and active participation (e.g., students responding aloud for a classic Stroop task). And I remembered how much I love working with first-year students. It’s a lot of work, but it’s worth it! Overall, I had a wonderful semester and am looking forward to teaching Developmental Psychology next year.

PSURE

This year, I’m also serving as a graduate student mentor for PSURE – the Princeton Summer Undergraduate Research Experience.

Here’s an overview of the program, from the PSURE website:

“The Graduate School offers an eight-week summer research experience for undergraduates who express a serious interest in pursuing a Ph.D. and following a career in college or university teaching and research. The purpose of the program is to motivate and prepare students to make competitive applications to research doctoral programs, with a view toward completing the Ph.D. and going on to teach and conduct original research.

Princeton is a member of The Leadership Alliance, a consortium of 32 institutions of higher learning dedicated to increasing diversity in doctoral programs and on college and university faculties. The Alliance collaborates on a number of programs, from undergraduate research, to faculty development, to national symposia, to develop underrepresented students into outstanding leaders and role models in academia, business and the public sector. The Summer Research Early Identification Program (SR-EIP) is the keystone of Princeton’s participation in the Alliance.”

Specifically, I’ll be working on the following aspects of the program:

  • Conduct weekly meetings with students to mentor and advise on the research process, writing and presenting research, and general questions and concerns of students.
  • Assist with academic support programs developed in collaboration with the McGraw Center for Teaching and Learning, the Writing Center, and other on-campus resources.
  • Attend and periodically facilitate lunchtime speaker series and other weekly events.
  • Collaborate with the Director of the PSURE program, Associate/Assistant Dean for Diversity and Inclusion, the Post-Doctoral Assistant, and Graduate School staff.

ReMatch

This year, I’m participating in the Princeton ReMatch research mentoring program. My mentee and I will work together on a research project this summer. (Stay tuned for more posts on that!)

Here’s the mission statement, from the ReMatch website:

Mission: To foster meaningful research collaborations between first and second year undergraduate students and graduate students from across all departments; support a diverse and inclusive research community at Princeton and beyond; provide undergraduates with early hands-on opportunities for mentored research to ignite and sustain their interest in research and prepare them for junior and senior independent work; train and support graduate students in becoming effective mentors and educators; strengthen Princeton’s research community.

ReMatch is a collaborative initiative between the Office of the Dean of the College (ODOC) and the Office of the Dean of the Graduate School (ODGS).”

classroom conflicts

Here’s another wonderful excerpt from Reaching all Students:

  • Respond to classroom conflict in a manner that helps students become aware of the learning moment this conflict provides. Heated discussions need to be facilitated in a manner that does not result in hostility among class members and a sustained bad feeling in the room. You can avoid these outcomes by encouraging students to tie their feelings and conflicts to the course material and by looking for underlying meanings and principles that might get buried in the process of class conflict. Students appreciate tensions between groups in the class being recognized and effectively addressed.
  • Recognize student fears and concerns about conflict. Students enter a class with different levels of experience and comfort with conflict. It is important to normalize the experience of conflict in the classroom, particularly in classes that focus on controversial topics. This can be accomplished through explicit discussion of student experiences with conflict, and through the use of structured discussion exercises.
  • Maintain the role of facilitator. One of the challenges of teaching is maintaining the role of instructor under a variety of conditions. For example, you can get caught up in expressing your own perspective in heated discussions, or can become overly silent in discussions that go beyond your own knowledge base or experience. While these responses are understandable, such role abdication can create chaos in the classroom or force students to fill the abdicated facilitator role. In order to avoid this outcome, you should examine your typical responses to conflict. It can also be useful to find ways that you may admit your limits with respect to content areas while maintaining responsibility for the group process.