Neural network models of categorical perception can help solve the symbolgrounding problem [5,6] by connecting analog sensory projections to symbolic representations through learned category-invariance detectors in a hybrid symbolic/nonsymbolic system. Our nets learn to categorize and name geometric shapes. The nets first learn to do prototype matching and then entry-level naming, grounding the shape names directly int he input patterns via hidden-unit representations. Next, a higher-level categorization is learned indirectly from combinations of the grounded category names (symbols). We analyze the architectures and input conditions that allow grounding to be "transferred" from directly grounded entry-level category names to higher-order category names.
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