"1647","From Robotic Toil to Symbolic Theft: Grounding Transfer from Entry-Level to Higher-Level Categories","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.","http://cogprints.org/1647/","Cangelosi, Angelo and Greco, Alberto and Harnad, Stevan","UNSPECIFIED","       <span class=\"person_name\">Cangelosi, Angelo</span> and <span class=\"person_name\">Greco, Alberto</span> and <span class=\"person_name\">Harnad, Stevan</span>     (2000)  <a href=\"http://cogprints.org/1647/\"><em>From Robotic Toil to Symbolic Theft: Grounding Transfer from Entry-Level to Higher-Level Categories.</em></a>       [Journal (Paginated)]           ","","2000"