Welcome to the Human-Machine Cognition Lab!
Our team aims to uncover the computational dynamics that differentiate high-level cognition in humans and machines. We explore the computational principles and neural mechanisms in the brain that enable effortless interaction with our environment and leverage these insights to advance machine models. As machines become integral to daily life, the demand for trustworthy, human-compatible models cannot be overstated. We envision a future where humans and machines work together seamlessly, with technology augmenting human tasks. If our vision aligns with your goals, please contact(✉️) the PI to discuss collaboration or joining our team.
고려대학교 인간-기계 인지 연구실은 인간과 기계의 고차원 인지 과정의 차이를 심층적으로 연구합니다. 최근 딥러닝의 발전으로 인한 기계의 인지 능력 향상은 표면적으로 인간의 인지 과정과 유사해 보이나, 본질적인 차이가 존재합니다. 본 연구실은 심리학과 뇌공학의 학제간 융합 접근법을 통해 인지 행동 및 신경학적 메커니즘을 규명하고, 이를 기반으로 신뢰성 높은 인간 중심 인공지능 모델 개발을 목표로 합니다. 주요 연구 주제는 시지각 및 시각 인지, 인간-기계 인지, 인간 중심 인공지능 모델, 브레인 디코딩입니다. 인문학적 통찰력과 공학적 전문성을 결합하고자 하는 대학원생 및 학부 연구생 모두 환영합니다. 연구 경험이나 연구실 참여에 관심이 있으신 분은 연구 책임자에게 이메일(✉️)로 문의해 주시기 바랍니다.
Research Interests
Visual Perception and Cognition
Vision is arguably the most important sense for perceiving information from the world. Despite significant variations in the external environment, our visual system remains stable, consistent, and precise. What mechanisms enable our sophisticated visual system, and how can they be understood? Answering these questions is also crucial for developing reliable machine vision models.
Human-Machine Cognition
Recent advances in deep learning have enabled novel approaches to the study of human cognition. By systematically comparing human and machine cognitive systems, we gain a deeper understanding of the computational principles underlying complex, high-level cognition. This understanding becomes increasingly important as machine systems become integral to daily life.
Human-Inspired Models
Exploring how psychological and neuroscientific knowledge can advance machine vision models presents a promising research direction. Our group is interested in investigating machine models that not only mirror biological systems but also provide tangible advantages for applications in the real world.
Brain Decoding
Brain decoding techniques offer a novel approach to accessing and interpreting the mental processes of perception and cognition. These methods allow researchers to decode the complex mental processes without the need for individuals to verbally articulate their thoughts, thus often referred to as “neural mind reading.”
Latest News
- Our paper "Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks" has been published in Nature Communications!
- Hojin Jang has been appointed to the position of Assistant Professor in the Department of Brain and Cognitive Engineering at Korea University and the Human-Machine Cognition Lab's website has launched.