David E. Rumelhart: A Scientific Biography
David E. Rumelhart made many contributions to the formal analysis of human cognition, working primarily within the frameworks of mathematical psychology, symbolic artificial intelligence, and parallel distributed processing. He also admired formal linguistic approaches to cognition and explored the possibility of formulating a formal grammar to capture the structure of stories.
Rumelhart obtained his undergraduate education at the University of South Dakota, receiving a B.A. in psychology and mathematics in 1963. He studied mathematical psychology at Stanford University, receiving his Ph. D. in 1967. From 1967 to 1987 he served on the faculty of the Department of Psychology at the University of California, San Diego. In 1987 he moved to Stanford University, serving as Professor there until 1998. He became disabled by Pick’s disease, a progressive neurodegenerative illness, and died in March 2011.
Rumelhart developed models of a wide range of aspects of human cognition, ranging from motor control to story understanding to visual letter recognition to metaphor and analogy. He collaborated with Don Norman and the LNR Research Group to produce “Explorations in Cognition” in 1975 and with Jay McClelland and the PDP Research Group to produce “Parallel Distributed Processing: Explorations in the Microstructure of Cognition” in 1986. He mastered many formal approaches to human cognition, developing his own list processing language and formulating the powerful back-propagation learning algorithm for training networks of neuron-like processing units. Rumelhart was elected to the National Academy of Sciences in 1991 and received many prizes, including a MacArthur Fellowship, the Warren Medal of the Society of Experimental Psychologists, and the APA Distinguished Scientific Contribution Award.
Rumelhart articulated a clear view of what cognitive science, the discipline, is or ought to be. He felt that for cognitive science to be a science, it would have to have formal theories — and he often pointed to linguistic theories, as well as to mathematical and computational models, as examples of what he had in mind.