|Social Interaction Network Labs|
Machine Perception Lab, UCSD
Javier Movellan, PI
The goal of the MPLab is to gain insights into how the brain works by developing systems that perceive and interact with humans in real time using natural communication channels. To this effect we are developing perceptual primitives to detect and track human faces and to recognize facial expressions. We are also developing algorithms for robots that develop and learn to interact with people on their own. Applications include personal robots, perceptive tutoring systems, and system for clinical assessment, monitoring, and intervention.
Cognitive Development Lab, UCSD
Gedeon Deak, PI
The Cognitive Development Laboratory at the University of Califironia, San Diego, is composed of faculty, graduate and undergraduate students, and research assistants who share an interest in studying cognitive and language development in infants and preschool children. Some current projects include: how babies and parents share interest and attention – an effort to increase our understanding of how new attention-sharing skills and behaviors can be acquired, how young children learn words, facts, and symbols, and how children develop the ability to "switch gears" - flexible cognition describes our abilities to adapt to new or unpredictable situations by altering our representations, problem-solving strategies, or patterns of information-seeking.
Swartz Center for Computational Neuroscience, UCSD
Scott Makeig, PI
The goal of the Swartz Center for Computational Neuroscience is to observe and model how functional activities in multiple brain areas interact dynamically to support human awareness, interaction and creativity.
Complex Systems and Cognition Lab, UCSD
Jochen Triesch, PI
Our goal is to further our understanding of how cognitive phenomena can arise from the collective interactions of relatively simple neural elements. Particular emphasis is put on active visual perception and learning. The lab closely integrates two complementary methodologies. First, we build computational and robotic models of various aspects of visual cognition and learning. Second, we study and analyze visual cognition in human subjects in controlled psychophysical experiments. The close integration of analytic and synthetic approaches to cognition (studying real brains and building artificial ones) helps us better understand the computational principles underlying intelligent behavior.