Jim Anderson's research concentrates on applications of neural networks to Cognitive Science. An appropriately designed network can do many pattern recognition functions in ways reminiscent of human performance.
Neural networks have practical applications and can also serve as models for human behavior.
His group does research in several areas. Networks have been applied to models of human concept formation, to speech perception, and to models of low level vision, for example, the way local motion signals can be integrated to determine global object motion or the direction of self motion. A current project involves the study of elementary arithmetic, a problem that is surprisingly hard for both humans and neural networks. Study of elementary mathematics also raises questions about the way a neural network can be designed to perform effectively more general mathematical operations. Output of a neural network simulation of category formation.