Professor of Linguistics
Department of Linguistics
lakoff@berkeley.edu
(510) 643-7616

Research Expertise and Interest

mathematics, literature, philosophy, cognitive linguistics, the neural theory of language, conceptual systems, conceptual metaphor, syntax-semantics-pragmatics, the application of cognitive linguistics to politics

Description

George taught at Harvard University from 1965-69, at the University of Michigan from 1969-71, spent 1971-72 at the Center for Advanced Study in the Behavioral Sciences at Stanford, before coming to Berkeley in 1972. He is also a Senior Fellow at The Rockridge Institute and member of the advisory board of the Frameworks Institute. His current research covers many areas of Conceptual Analysis within Cognitive Linguistics: (i) The nature of human conceptual systems, especially metaphor systems for concepts such as time, events, causation, emotions, morality, the self, politics, etc. This also includes the study of such systems in other languages and their manifestations in linguistic form; (ii) The development of Cognitive Social Science, which applies ideas of Cognitive Semantics to the Social Sciences. It includes practical applications of cognitive linguistics to help social advocates reframe social and political issues; (iii) The implications of Cognitive Science for Philosophy, in collaboration with Mark Johnson, Chair of Philosophy at the University of Oregon; (iv) The cognitive structure, especially the metaphorical structure, of mathematics, in collaboration with Rafael Núñez of UC San Diego, Department of Cognitive Science; (v) Neural foundations of conceptual systems and language, in collaboration with Jerome Feldman, of the International Computer Science Institute, seeking to develop biologically-motivated structured connectionist systems to model both the learning of conceptual systems and their neural representations. This includes the development of a neural theory of grammar — embodied construction grammar — which precisely characterizes grammars in a notation suitable for both reduction to structured connectionist models and use in natural language processing.

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