BIOLOGICAL CONSTRAINTS AND THE REPRESENTATION OF
STRUCTURE IN VISION AND LANGUAGE
Book Review of Miikkulainen on Language-Network

Abstract

The computational building blocks of biological information processing systems are highly interconnected networks of simple units with graded overlapping receptive fields, arranged in maps. In view of this basic constraint, it is proposed that the present stage in the study of cognition should concentrate on gaining understanding of the cognitive system at the level of the distributed computational mechanism. The model of script understanding introduced in the Miikkulainen's (1993) book appears promising, both because it treats seriously the question of the architecture of the language processor, and because its architectural features resemble those used in modeling other cognitive modalities such as vision.

Keywords

References

• Adini, Y., Y. Moses and S. Ullman (1993) Face recognition: the problem of compensating for changes in illumination direction, Weizmann Institute of Science CS-TR 93-21.
• Aha,, D.W., D. Kibler and M.A. Albert (1991) Instance-based learning algorithms, Machine Learning, 6:37-66.
• Ajjanagadde, V. and L. Shastri (1991) Rules and variables in neural nets, Neural Computation, 3:121-134.
• Anderson, J.R. (1978) Arguments concerning representations for mental imagery, Psychological Review 85:249-277.
• Biederman, I. (1987) Recognition by components: a theory of human image understanding, Psychol. Review 94:115-147.
• Braitenberg, V. (1977) On the texture of brains, Springer-Verlag, New York.
• Carroll, J. and T. Bever (1976) Sentence comprehension: a case study in the relation of knowledge and perception, in Language and Speech, vol. 7 of Handbook of Perception, E. Carterette and M Friedman, eds., New York: Academic Press.
• Cheng, K., T. Hasegawa, K. Saleem, and K. Tanaka (1994) Comparison of neuronal selectivity for stimulus speed, length, and contrast in the prestriate visual cortical areas V4 and MT in the macaque monkey, J. of Neurophysiology, 71, in press.
• Edelman, S. (1994), Representation, Similarity, and the Chorus of Prototypes, Minds and Machines, in press.
• Finch, S. and N. Chater (1991) A hybrid approach to the automatic learning of linguistic categories, manuscript, available via ftp from archive.cis.ohio-state.edu as /pub/neuroprose/finch.hybrid.ps.Z
• Henderson, J. and M. Marcus (1994) Description Based Parsing in a Connectionist Network, U. of Pennsylvania TR IRCS 94-12.
• Henderson, L. (1989) On mental representation of morphology and its diagnosis by measures of visual access speed, pp. 357-391, in Lexical Representation and Process, W. Marslen-Wilson, ed., Cambridge, MA : MIT Press.
• Hummel, J.E. and I. Biederman (1992) Dynamic binding in a neural network for shape recognition, Psychological Review 99:480-517.
• Miller, G. (1962) Some psychological studies of grammar, American Psychologist 17:748-762.
• Miikkulainen. R. (1993) Subsymbolic Natural Language Processing: An Integrated Model of Scripts, Lexicon and Memory. Cambridge, MA: MIT Press.
• Miikkulainen. R. (1994) Precis of: Subsymbolic Natural Language Processing: An Integrated Model of Scripts, Lexicon and Memory. PSYCOLOQUY 5(46) language-network.1.miikkulainen.
• Omohundro, S.M. (1987) Efficient algorithms with neural network behavior, UIUCDCS report R-87-1331, Univ. of Illinois at Urbana-Champaign.
• Poggio, T. and S. Edelman (1990) A network that learns to recognize three-dimensional objects, Nature 343:263-266.
• Poincare, H. (1913/1963) Mathematics and Science: Last Essays, translated by J. W. Bolduc, New York: Dover.
• Putnam, H. (1988) Representation and reality, MIT Press, Cambridge, MA.
• Pylyshyn, Z. (1973) What the mind's eye tells the mind's brain: a critique of mental imagery, Psychological Bulletin 80:1-24.
• Shadlen, M.N. and W.T. Newsome (1994) Noise, neural codes and cortical organization, Current Opinion in Neurobiology 4:569-579.
• Snippe, H.P. and J.J. Koenderink (1992) Discrimination thresholds for channel-coded systems, Biological Cybernetics 66:543-551.
• Stanfill, C. and D. Waltz (1986) Toward memory-based reasoning, Comm. of the Assoc. for Computing Machinery 29:1213-1228.
• Treisman, A. (1985) Preattentive processing in vision, Computer Vision, Graphics, and Image Processing 31:156-177.
• Weiss, Y. and S. Edelman (1993) Representation with receptive fields: gearing up for recognition, Weizmann Institute of Science CS-TR 93-09.