@misc{cogprints1647,
          volume = {12},
           title = {From Robotic Toil to Symbolic Theft: Grounding Transfer from Entry-Level to Higher-Level Categories},
          author = {Angelo Cangelosi and Alberto Greco and Stevan Harnad},
            year = {2000},
           pages = {143--162},
         journal = {Connection Science},
        keywords = {symbol grounding, categorical perception, neural networks, robotics, language, perceptual learning
recognition},
             url = {http://cogprints.org/1647/},
        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.}
}

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