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.

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.

Project 1: How does exploration shape problem solving? How do children (ages 6-12) and adults figure out what unfamiliar objects can do? In this project, we design virtual environments where participants freely play with novel objects (with hidden features!) before trying to solve tasks with them. We are especially interested in what motivates exploration, and how it helps people flexibly use tools in new ways.

Project 2: Exploring information: when is curiosity useful? Children are famously curious, but do they prefer novel information that is useful and predictable, or any kind of novel information novel that can also be random? We are running experiments with toddlers (ages 2-3) and young children (ages 5–7) to test how much they are willing to learn different kinds of information, and whether they can distinguish signal from noise.

Project 3: How is analogical reasoning developed and can children’s reasoning inspire AI? This project builds a large-scale database of how children (ages 3–12) solve visual analogy puzzles. Unlike traditional IQ-style tests, our puzzles are designed to capture real cognitive capacities—such as reasoning about size, number, or symmetry—and to reveal where even the most advanced AI models fail. The work is already underway and has been published at ICLR, with a major AI benchmark challenge planned for ICCV 2025. We’re now expanding the database to track how children’s reasoning develops with age and increasing puzzle complexity. The goal is to create an informative resource on human analogical reasoning.

Care and caregiving relationships are central to children’s everyday lives. How do children mentally represent the nature of caregiving relationships? What are the unique expectations that structure children’s understandings of these relationships? We are currently addressing these questions empirically with experiments involving 3- to 9-year-old children.

We are looking for undergraduate research assistants who are collaborative, reliable, and enthusiastic about psychological research. Research assistants will receive structured mentorship from a postdoctoral scholar and lab manager, and will gain hands-on research experience in a supportive environment. Responsibilities include data collection with children in museums and schools, study material development (stimuli and surveys), data preparation and analysis, and desk research.

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!