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abstract: 'Neural network models of categorical perception can help solve the symbol-grounding problem [Harnad, 1990; 1993] 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 50x50 pixel images of circles, ellipses, squares and rectangles projected onto the receptive field of a 7x7 retina. The nets 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. Next, a higher-order categorization (symmetric vs. asymmetric) is learned, either directly from the input, as with the entry- level categories, or 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. Implications of such hybrid models for the evolution and learning of language are discussed.'
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conference: 'ICANN98 - International Conference on Artificial Neural Networks'
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creators_name:
  - family: Greco
    given: Alberto
    honourific: ''
    lineage: ''
  - family: Cangelosi
    given: Angelo
    honourific: ''
    lineage: ''
  - family: Harnad
    given: Stevan
    honourific: ''
    lineage: ''
date: 1998
date_type: published
datestamp: 1998-03-31
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dir: disk0/00/00/06/22
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eprintid: 622
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full_text_status: public
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keywords: 'symbol grounding, categorical perception, categorisation, language evolution modelling, neural networks, backpropagation, geometric shapes'
lastmod: 2011-03-11 08:54:07
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rev_number: 8
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status_changed: 2007-09-12 16:32:15
subjects:
  - cog-psy
  - comp-sci-neural-nets
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title: A connectionist model for categorical perception and symbol grounding
type: confpaper
userid: 24
volume: ~
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