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--- 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. altloc: - http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/cangelosi-connsci2.ps chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: [] creators_name: - family: Cangelosi given: Angelo honourific: '' lineage: '' - family: Greco given: Alberto honourific: '' lineage: '' - family: Harnad given: Stevan honourific: '' lineage: '' date: 2000 date_type: published datestamp: 2001-06-26 department: ~ dir: disk0/00/00/16/47 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 1647 fileinfo: /style/images/fileicons/application_postscript.png;/1647/2/cangelosi%2Dconnsci2.ps full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: |- symbol grounding, categorical perception, neural networks, robotics, language, perceptual learning recognition lastmod: 2011-03-11 08:54:43 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 143-162 pubdom: FALSE publication: Connection Science publisher: ~ refereed: TRUE referencetext: | Andrews, J., Livingston, K. & Harnad, S. 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Cambouropolos, & H. Pain (Eds.) Proceedings of SimCat 1997:Interdisciplinary Workshop on Similarity and Categorization. Department of Artificial Intelligence, Edinburgh University, pp. 263- 269. http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad97.cpnets.html relation_type: [] relation_uri: [] reportno: ~ rev_number: 10 series: ~ source: ~ status_changed: 2007-09-12 16:39:23 subjects: - cog-psy - comp-sci-neural-nets succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: 'From Robotic Toil to Symbolic Theft: Grounding Transfer from Entry-Level to Higher-Level Categories' type: journalp userid: 63 volume: 12