For Undergraduates
The Gopnik Lab will be recruiting new research assistants in Spring 2025.
Google Form Link: https://forms.gle/rAZ1iE6NkV8PPVDZ9
Deadline: January 27, 2025 at 4PM PST.
Thank you for your interest! Please read the application requirements, criteria, and project descriptions completely before applying. Late or incomplete applications will not be considered.
Any questions can be directed to Maansi at gopniklabmanager@berkeley.edu
Application Requirements and Criteria
Please only apply if you are willing to make a commitment to work in our lab:
A minimum of 10 hours per week during the semester for a minimum of 2 semesters
Must be a UC Berkeley undergraduate student
Must be fully vaccinated and able to pass a background check in order to work with children
Be sure to apply both on the URAP portal and the Google Form linked on the portal! Individuals who do not follow these instructions will not be considered.
Requirements for
All Applicants
All research in the Gopnik Cognitive Development and Learning Lab is broadly focused on children's development of cause and effect reasoning and how they learn from and about other people. We are looking for dedicated and motivated undergraduate students interested in pursuing a graduate degree in developmental psychology or a related field.
RAs will work closely with a graduate student, post-doc, or lab manager assisting them on all aspects of the research process. RAs will help with experimental and stimuli design; recruiting adults and children ages 2-14 years old; and collecting, organizing, coding, and analyzing data. RAs will meet regularly with their mentors to discuss the theoretical motivations of the studies they are working on as well as the findings of other empirical papers both related to the studies in the lab and important to the field in general.
What We Are Looking For in an Applicant
Qualifications
* Must be excited about Cognitive Development research!
* Must be fully vaccinated against COVID-19 and able to pass a background check to work with children
* Organized, dependable, self-motivated, independent, and hard working
* Prior experience with children (both formal and informal experience is great!)
* Comfort acting silly around children (a bit of acting or improv experience is helpful but not essential)
* Prior research experience is appreciated but not required
* Artistic, mechanical, electrical engineering or programming experience is not necessary, but would be great!
* Weekend availability is desirable for museum testing, and daytime (8-11 am) is great for preschool testing, but we will try to work with your schedule
Current Project Descriptions
Experimental and Computational Exploration and Learning
Supervisor:
Annya Dahmani, PhD Student
In this line of work, we are interested in how children and artificial intelligence systems explore and learn in their environments.
The explore-exploit tradeoff is a well-known problem in reinforcement learning algorithms, where an agent can either stick to a safe but uninformative option or opt for a riskier but informative option. One solution to this tension is a protected period of childhood. In the first project, we are investigating how children explore within a social context. Specifically, how does the presence of a caregiver influence children’s risk-taking and exploration?
The second project focuses on automated curriculum learning. We are interested in how humans create a curriculum to navigate through the space of possible scenes of varying difficulty. Children and adults will play video-like games to see how they scaffold their own learning. We are interested in comparing such a curriculum to that of artificial intelligent agents.
The third project focuses on information and empowerment. How does knowledge of information gain influence exploration and learning? How do environmental cues (for example, controllability, predictability, safety) in an environment influence empowerment in agents and humans?
We are looking for undergraduate RAs that are passionate about research, artificial intelligence, and cognitive development. Tasks include: data collection in children and adults and LLMs, designing studies, literature review, analyzing data, computational modeling.
Our research investigates the ways in which children and adults explore their environments and build an understanding of cause and effect. We’re particularly interested in how children develop causal models of the world and how they apply past experiences to new situations. Our work seeks to uncover the cognitive mechanisms that drive effective exploration and learning.
Unlike AI systems, which often struggle with generalization, children excel at forming abstract concepts and applying them to novel situations. Our project aims to bridge this gap by studying the processes children use to build and refine causal models, with the goal of informing more adaptable AI systems that better reflect human learning. We will use games to uncover the strategy and patterns of exploration which enables causal learning in children and adults.
Undergraduates passionate about research and cognitive developmental psychology are welcomed to apply!
