Research Assistant Positions: Computational Cognitive Development (fall 2025)
Research Assistant Positions: Computational Cognitive Development
Prof. Elizabeth Bonawitz, Computational Cognitive Development Lab, Graduate School of Education
fall 2025
The Computational Cognitive Development (CoCoDev) Lab is looking to hire Research Assistants. Some of our ongoing projects are noted below. Responsibilities: Depending on project needs and student interest, students may help to design stimuli (e.g., videos, PPTs), program studies (Lookit studies, adult surveys, etc.), recruit child participants, run in-person or online experiments, annotate participant responses (such as categorizing actions and verbal explanations), or analyze and visualize data. Requirements and Expectations: Students are expected to work 10-12 hours a week. Student involvement includes attending (1) weekly project meetings and (2) lab meetings if aligned with the course schedule. Additional Information: Mentorship Provided to Students: Co-advising (i.e., working on two projects led by different researchers) is also possible, depending on the workload of each project. Students will meet weekly with mentors during their project meetings to set research goals and learn methods that allow them to reach those goals. In addition to project-specific training, students may do the following with their project mentors: read papers, run statistical analyses in R, design new studies for children or adults, discuss professional development, etc. Students will be invited to present brief updates on their research progress at the semester-end lab party and receive direct feedback from experts and professors. This holistic mentorship approach is designed to equip students with the skills and knowledge to flourish in their research endeavors. To Apply: Go to application here. (posted 9/25)
Counterfactual Reasoning (Led by Michelle Wong): We’re interested in how children consider different outcomes if they imagine that something they saw or experienced had been different. As adults, we use this skill all the time -- someone who is running late might think to themselves "if I left the house 5 minutes earlier, would I have been on time for my meeting?" In this case, this adult is imagining an alternative scenario (leaving earlier) to understand how it could have led to a different outcome (make the meeting). In this study, we want to better understand how this ability develops in children.
Pretend Play (Led by Michelle Wong): Recent work on play has suggested play is not just about information gain, but also about inventing and pursuing novel goals. However, this body of work maintains that we are still unsure how children themselves decide what goals are worth pursuing and make decisions as to what to play with. Here, we take a closer look at pretend play, or pretense (a type of play in which information gain and external rewards are no longer relevant), and what drives play preferences. With this account, we seek to uncover the signals that drive play preferences and to unite theoretical frameworks of “playing-to-learn” and “playing-for-fun”.
Science education in early childhood (Led by Igor Bascandziev): Dr. Bascandziev’s research investigates the cognitive and motivational mechanisms of conceptual development. In particular, Dr. Bascandziev is interested in the acquisition of scientific concepts, science learning, and science education. He is currently running an NSF-funded longitudinal study that investigates the effects of two different teaching styles, namely direct instruction and guided query, on the acquisition of scientific concepts in the domain of physics in the early elementary years. Dr. Bascandziev is looking for interns who will help him with data collection for the longitudinal study, which will include working with elementary school children at several schools in Massachusetts.
Children’s understanding of automatic reasoning in others (Led by Lonnie Bass): Decision-making may be fast, automatic, and rote; or it may be slow, contemplative, and reflective. While this dichotomy has been studied with regard to people’s own cognition, it is unknown whether people can recognize when others’ behavior is driven by rote or reflective decision-making. In these projects, we investigate children’s (and adults’) ability to recognize and reason about automatic reasoning processes in others, as well as potential effects on learning in pedagogical contexts. We will study this using a variety of approaches, including online and in-person studies with adults and children, leveraging both behavioral and EEG methods. If you are interested in this set of projects, it would be great to hear about any experience you have with EEG, as well as your general comfort working with technology and learning about new kinds of software / programming languages.
Children’s reasoning about uncertainty (Led by Lonnie Bass): Sensitivity to uncertainty directs even the youngest learners to preferentially explore where they have incomplete or inconsistent knowledge. However, Uncertainty Tolerance – children's willingness to make quick decisions when faced with incomplete information, as opposed to continuing to explore until their uncertainty is completely resolved – is an important and underexplored facet of cognitive development. For this project, I am looking for students who can help with data collection of this task at elementary schools in Massachusetts. If you are interested in working on this study, it would be great to hear about any experience you have interacting with school-aged children. It would also be helpful to know your availability during the after-school hours (e.g. 2:30pm-5:00pm), as well as whether you have a car.
Parent–Child Interaction in Digital Settings (Led by Isminur Yilar): In this study, we are interested in how joint and observed playful exploration between a parent and child shapes causal reasoning. Past work has demonstrated differences in how adults and children learn about certain kinds of causal relationships (i.e. how different objects might activate a machine). Here we are seeking to understand how the particular actions that adults (or children) take during causal exploratory play might shape learning and reasoning. Research assistants will work on coding videos, identifying potential patterns, and conducting data analysis.
AI & Questioning Project (Led by Blerim Jashari): Are children asking more questions about challenging science content to an AI or to a teacher? In this project, participants are presented with science material and then interact with either a teacher or an AI. We examine their asking question behavior and how this relates to their learning. Research Assistants will help with stimuli design, data collection, and transcribing children’s responses from videos. Depending on interest and progress, RAs may also contribute to data visualization, analysis, and literature review.