Microcognition by Andy Clark

Abstract

Reviews lssues in Applied Linguistics Vol. 1 No. 1 As an introductory linguistics text, one expects the goal of Contemporary Linguistics to be to get people started in the field of linguistics and related disciplines. In spite of a few minor problems the authors have not only achieved this goal in a more-than-adequate fashion, they have also demonstrated that an introductory text can be comprehensive enough for the non-initiated to become acquainted with the complexities which the study of language entails. For the practicing linguist, Contemporary Linguistics could be a good reference source. For the linguistics teacher, it should serve as an excellent course text. For the student, it is particularly helpful because of the straightforward, explanatory style the authors have adopted. In all, Contemporary Linguistics fills a major gap, since state-of-the-art introductory linguistics texts are not plentifufon the market. REFERENCES Yule, G. (1985). The study of language: An introduction. Cambridge: Cambridge University Press. Stephen Adewole is a Ph.D. student in the Department of Linguistics at UCLA. His interests include the teaching and analysis of African languages. (Received April 25, 1990) Microcognition 1989. 226 pp. by Andy Clark. Cambridge, M A : M I T Press Reviewed by Cheryl Fantuzzi University of California, Los Angeles In the true spirit of integrative, interdisciplinary cognitive science research, philosopher Andy Clark seeks to call a truce to what he aptly refers to as the holy war of cognitive modeling. In complex matters of the mind, it is Clark's contention that since the brain may simply need and use more than one mode of information- processing to perform such tasks as comprehending and producing human language, a more integrative theory of cognition is therefore needed. In Microcognition, Clark introduces the reader to some of the major issues and conundrums of 20th-century philosophy and cognitive science: What is the mind, and what is its relationship to the brain? How does the mind work, and how does it affect behavior? How do seemingly abstract entities, such as personal beliefs and desires, influence bodily movement, and how might sentential propositions actually be instantiated in neural stuff? Clark gives us a quick overview of the modern answer to the mind/body dilemma: the computational model of mind. Most interestingly, he brings us into the heart of the currently lively debate between two competing computer models of the mind: the classical symbol systems of AI (Artificial Intelligence) and the new connectionist movement, also known as PDP (Parallel Distributed Processing). This new debate in cognitive science is reminiscent of the heated discussions between Chomsky and the behaviorists some 30 years ago. One central issue, now as then, concerns the systematicity of thought and language and the explanatory adequacy of a generative grammar for language, as opposed to a 'subsymbolic,' or associationist, view of both cognition and language. The symbolic computational approach of classical AI, in fact, represents the mathematical-syntactic approach of Chomskian generative grammar par excellence, since it conceptualizes the mind as a type of formal logic machine with mental rules operating on abstract, symbolic representations. The subsymbolic connectionist approach, on the other hand, operates without rules and without symbols. It emphasizes the messy, biological substrate of cognition, rather than the rational, logical nature of thinking, and uses a computational architecture closer to the distributed, parallel nature of processing in the brain, rather than the conventional sequential architecture of a digital computer. Clark's thesis is that cognitive modeling may actually require both classical and connectionist 'cognitive architecture' to model two different kinds of information processing: for some tasks, our thinking does appear to be slow, deliberate and serial, while for others, such as visual perception, it is fast, automatic and parallel. Microcognition is divided into two parts: the mind's-eye view, which describes classical AI and some of the philosophical criticisms against it, and the brain's-eye view, which focuses on