Abstract
Neural network models of categorical perception can help solve the symbol-grounding 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 in the 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.KeywordsReceptive FieldCategorical RepresentationCategory LearningCategorical PerceptionLearning StageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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