Reverse Engineering Object Recognition in the Human Mind
Project Description
This project explores how human across life span (with a focus on infants) learn to recognize the objects around them. Object recognition is a key building block of learning. By integrating principles from cognitive psychology, neuroscience, and artificial intelligence, the project aims to uncover the mental and algorithmic processes that underlie object recognition. Through behavioral experiments and/or computational modeling, we will explore how attention, prior experience, and context shape visual understanding. The findings are expected to inform not only cognitive theory but also applications in user experience, marketing, and human–AI interaction design.
Supervisor
CHENG Chen
Quota
2
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
Students will help design and set up simple infant experiments, prepare visual stimuli (e.g., pictures or videos of everyday objects), and assist in data collection and coding of infants’ behavior and eye movements. Depending on their background, students may also analyze data using software tools (e.g., Python, R, SPSS) or contribute to computational modeling and pattern analysis using machine learning tools. There will be opportunities to participate in research discussions, present results, and contribute to manuscript preparation or conference submissions.
Applicant's Learning Objectives
By joining this project, students will gain hands-on experience in cognitive and experimental research design, data analysis, and interdisciplinary collaboration. They will develop an understanding of how computational and psychological frameworks can jointly explain visual cognition. Students will also strengthen their critical thinking and communication skills by interpreting data and linking theory with real-world applications in technology, design, and consumer research.
Complexity of the project
Challenging