JAMES L. MCCLELLAND, DAVID E. RUMELHART, AND
GEOFFREY E. HINTON
James L. McClelland (1948-) received his Ph.D. in cognitive psychology from the Univer-
sity of Pennsylvania in 1975. He was an assistant and associate professor at University of
California, San Diego, before moving to his current positions as professor of psychology
and computer science at Carnegie Mellon University, adjunct professor of neuroscience at
the University of Pittsburgh, and codirector of the Center for Neural Basis of Cognition. His
continued interest in cognitive neuroscience and computational models is apparent from
recent coauthored publications, including “No Right to Speak? The Relationship Between
Object Naming and Semantic Impairment: Neuropsychological Evidence and a Computa-
tional Model” (Journal of Cognitive Neuroscience 13:341–356). David E. Rumelhart did
his undergraduate work at the University of South Dakota, majoring in psychology and
mathematics. He went on to Stanford University and received his Ph.D. in mathematical
psychology in 1967, at which point he joined the psychology faculty at the University of
California, San Diego. He returned to Stanford as a professor in 1987 and retired in 1998.
He has been diagnosed with Pick's disease, a neurodegenerative illness that involves the
slow atrophy of frontal and temporal lobes with symptoms including agnosia, aphasia, and
apraxia. Geoffrey E. Hinton is currently the director of the Gatsby Computational Neuro-
science Unit at University College, London. He earned a B.A. in experimental psychology
from Cambridge and a Ph.D. in artificial intelligence from Edinburgh. He worked at the
University of California, San Diego, as a postdoctoral fellow before joining the computer
science department of Carnegie Mellon University. Before he moved to London, he was
professor of psychology, professor of computer science, and fellow at the Canadian Insti-
tute for Advanced Research in Toronto. He maintains a diverse array of research interests,
and one of his many recent coauthored reports includes “A Mobile Robot that Learns Its
Place” (Neural Computation 9:683–699).
MICROSTRUCTURE OF COGNITION
What makes people smarter than machines? They certainly are not quicker or more precise. Yet people are far better at perceiving objects in natural scenes and noting their relations, at understanding language and retrieving contextually appropriate information from memory, at making plans and carrying out contextually appropriate actions, and at a wide range of other natural cognitive tasks. People are also far better at learning to do these things more accurately and fluently through processing experience.
What is the basis for these differences? One answer,
From J. L. McClelland, D. E. Rumelhart, and G. E. Hinton, “The Appeal of Parallel Distributed Processing.” In D. E. Rumelhart,
J. L. McClelland, and the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition
(pp. 3–13, 25–40). Cambridge: MIT Press, 1986.