Cogprints

Learned Categorical Perception in Neural Nets: Implications for Symbol Grounding

Harnad, Stevan and Hanson, Stephen J. and Lubin, Joseph (1995) Learned Categorical Perception in Neural Nets: Implications for Symbol Grounding. [Book Chapter]

Full text available as:

[img] HTML
23Kb

Abstract

After people learn to sort objects into categories they see them differently. Members of the same category look more alike and members of different categories look more different. This phenomenon of within-category compression and between-category separation in similarity space is called categorical perception (CP). It is exhibited by human subjects, animals and neural net models. In backpropagation nets trained first to auto-associate 12 stimuli varying along a one-dimensional continuum and then to sort them into 3 categories, CP arises as a natural side-effect because of four factors: (1) Maximal interstimulus separation in hidden-unit space during auto-association learning, (2) movement toward linear separability during categorization learning, (3) inverse-distance repulsive force exerted by the between-category boundary, and (4) the modulating effects of input iconicity, especially in interpolating CP to untrained regions of the continuum. Once similarity space has been "warped" in this way, the compressed and separated "chunks" have symbolic labels which could then be combined into symbol strings that constitute propositions about objects. The meanings of such symbolic representations would be "grounded" in the system's capacity to pick out from their sensory projections the object categories that the propositions were about.

Item Type:Book Chapter
Keywords:Neural nets, symbol grounding, categorical perception
Subjects:Computer Science > Dynamical Systems
Computer Science > Neural Nets
Psychology > Perceptual Cognitive Psychology
ID Code:1596
Deposited By: Harnad, Stevan
Deposited On:19 Jun 2001
Last Modified:11 Mar 2011 08:54

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Andrews, J., Livingston, K., Harnad, S. & Fischer, U. (1992) Learned Categorical Perception in

Human Subjects: Implications for Symbol Grounding.

Berlin, B. & Kay, P. (1969) Basic color terms: Their universality and evolution. Berkeley: University

of California Press

Bornstein, M. H. (1987) Perceptual Categories in Vision and Audition. In: Harnad (1987)

Boynton, R. M. (1979) Human color vision. New York: Holt, Rinehart, Winston

Cottrell, Munro & Zipser (1987) Image compression by back propagation: an example of extensional

programming. ICS Report 8702, Institute for Cognitive Science, UCSD.

Etcoff, N.L. & Magee, J.M. (1992) Categorical perception of facial expressions. Cognition 44: 227 -

240.

Gibson, E. J. (1969) Principles of perceptual learning and development. Engelwood Cliffs NJ: Prentice

Hall

Gluck, M.A. & Myers, C.E. (1993) Hippocampal mediation of stimulus representation: A

computational theory. Hippocampus (in press)

Carpenter G.A., Grossberg S. & Reynolds J.H. (1991) ARTMAP - Supervised real-time learning and

classification of nonstationary data by a self-organizing neural network. Neural Networks 4:565-588.

Hanson S.J. & Burr D.J. (1990) What connectionist models learn: Learning and Representation in

connectionist networks. Behavioral and Brain Sciences 13: 471-518.

Hanson, S. J. and Kegl, J. (1987) Parsnip: A Connectionist Model that Learns Natural Language

Grammar from Exposure to Natural Language Sentences. Proceedings of the Ninth Annual Cognitive

Science Conference Seattle.

Harnad, S. (ed.) (1987) Categorical Perception: The Groundwork of Cognition. New York: Cambridge

University Press.

Harnad, S. (1990a) The Symbol Grounding Problem. Physica D 42: 335-346.

Harnad, S. (1990b) Symbols and Nets: Cooperation vs. Competition. Review of: S. Pinker and J.

Mehler (Eds.) (1988) Connections and Symbols Connection Science 2: 257-260.

Harnad, S. (1992) Connecting Object to Symbol in Modeling Cognition. In: A. Clarke and R. Lutz

(Eds) Connectionism in Context. Springer Verlag.

Harnad, S., Hanson, S.J. & Lubin, J. (1991) Categorical Perception and the Evolution of Supervised

Learning in Neural Nets. In: Working Papers of the AAAI Spring Symposium on Machine Learning of

Natural Language and Ontology (DW Powers & L Reeker, Eds.) pp. 65-74. Presented at Symposium

on Symbol Grounding: Problems and Practice, Stanford University, March 1991.

Howell P. & Rosen, S. (1984) Natural auditory sensitivities as universal determiners of phonemic

contrasts. Linguistics 21: 205-235.

Lane, H. (1965) The motor theory of speech perception: A critical review. Psychological Review 72:

275 - 309.

Lawrence, D. H. (1950) Acquired distinctiveness of cues: II. Selective association in a constant

stimulus situation. Journal of Experimental Psychology 40: 175 - 188.

Lubin, J., Hanson, S. & Harnad, S. (in preparation) Categorical Perception in ARTMAP Neural

Networks.

McClelland, J.L., Rumelhart, D. E., and the PDP Research Group (1986) Parallel distributed

processing: Explorations in the microstructure of cognition, Volume 1. Cambridge MA: MIT/Bradford.

Newell, A. (1980) Physical Symbol Systems. Cognitive Science 4: 135 - 83.

Metadata

Repository Staff Only: item control page

Cogprints is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.