creators_name: Cangelosi, Angelo creators_name: Greco, Alberto creators_name: Harnad, Stevan type: journalp datestamp: 2001-06-26 lastmod: 2011-03-11 08:54:43 metadata_visibility: show title: From Robotic Toil to Symbolic Theft: Grounding Transfer from Entry-Level to Higher-Level Categories ispublished: pub subjects: cog-psy subjects: comp-sci-neural-nets full_text_status: public keywords: symbol grounding, categorical perception, neural networks, robotics, language, perceptual learning recognition abstract: Neural network models of categorical perception (compression of within-category similarity and dilation of between-category differences) are applied to the symbol-grounding problem (of how to connect symbols with meanings) by connecting analog sensorimotor projections to arbitrary symbolic representations via learned category-invariance detectors in a hybrid symbolic/nonsymbolic system. Our nets are trained to categorize and name 50x50 pixel images (e.g., circles, ellipses, squares and rectangles) projected onto the receptive field of a 7x7 retina. They first learn to do prototype matching and then entry-level naming for the four kinds of stimuli, grounding their names directly in the input patterns via hidden-unit representations ("sensorimotor toil"). We show that a higher-level categorization (e.g., "symmetric" vs. "asymmetric") can learned in two very different ways: either (1) directly from the input, just as with the entry-level categories (i.e., by toil), or (2) indirectly, from boolean combinations of the grounded category names in the form of propositions describing the higher-order category ("symbolic theft"). We analyze the architectures and input conditions that allow grounding (in the form of compression/separation in internal similarity space) to be "transferred" in this second way from directly grounded entry-level category names to higher-order category names. Such hybrid models have implications for the evolution and learning of language. date: 2000 date_type: published publication: Connection Science volume: 12 pagerange: 143-162 refereed: TRUE referencetext: Andrews, J., Livingston, K. & Harnad, S. (1998) Categorical perception effects induced by category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 24(3), 732-753. Cangelosi, A. & Harnad, S. (1998) The adaptive advantage of symbolic Theft over sensorimotor Toil: Grounding language in perceptual categories. Presented at the 2 nd International Conference on the Evolution of Language, London, April 1998. 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Department of Artificial Intelligence, Edinburgh University, pp. 263- 269. http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad97.cpnets.html citation: Cangelosi, Angelo and Greco, Alberto and Harnad, Stevan (2000) From Robotic Toil to Symbolic Theft: Grounding Transfer from Entry-Level to Higher-Level Categories. [Journal (Paginated)] document_url: http://cogprints.org/1647/2/cangelosi-connsci2.ps