2001-06-26Z2011-03-11T08:54:43Zhttp://cogprints.org/id/eprint/1647This item is in the repository with the URL: http://cogprints.org/id/eprint/16472001-06-26ZFrom Robotic Toil to Symbolic Theft: Grounding Transfer from Entry-Level to Higher-Level CategoriesNeural 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.Angelo CangelosiAlberto GrecoStevan Harnad