Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!utgpu!water!watmath!clyde!rutgers!princeton!mind!harnad From: harnad@mind.UUCP Newsgroups: comp.ai,comp.cog-eng Subject: The symbol grounding problem Message-ID: <764@mind.UUCP> Date: Tue, 19-May-87 23:21:31 EDT Article-I.D.: mind.764 Posted: Tue May 19 23:21:31 1987 Date-Received: Sat, 23-May-87 00:43:21 EDT Organization: Cognitive Science, Princeton University Lines: 54 Keywords: Color categories, A/D filters, learning Xref: utgpu comp.ai:405 comp.cog-eng:95 John X. Laporta Apollo Computer, Chelmsford, MA wrote: > You say that symbols are grounded in nonsymbolic sensory input. > You propose a model of segmentation... by which discontinuities > in the input map to segment boundaries... I wonder what you do with > the problem of segmentation of the visual spectrum. > ...spectral segmentations differ widely across cultures. > The problem is that these breaks and their number vary widely... > what system intervenes to choose the set a particular culture favors > and asserts as obvious? What is the filter in the A/D converter? More recent evidence seems to suggest that color segmentation does not vary nearly as widely as had been believed (see M. Bornstein's work). There may be some variability in the tuning of color boundaries, and some sub-boundaries may be added sometimes, but the focal colors are governed by our innate color receptor apparatus and they seem to be universal. The partial flexibility of the boundaries -- short and long term -- must be governed by learning, and the learning must consist of readjustment of boundary locations as a function of color naming experience and feedback, or perhaps even the formation of new sub-boundaries where there are none. The innate color-detector mechanism would be the A/D filter in the default case, and learning may set some of the boundary fine-tuning parameters. The really interesting case, though, and one that has not been tested directly yet, is the one where boundary formation occurs de novo purely as a result of learning. This does not happen with evolutionarily "prepared" categories such as colors (although it may have happened in phylogeny), but it may happen with arbitrary learned ones (e.g., perhaps musical semitones). Here the A/D filter would be acquired from categorization training alone: labeling with feedback. In simple one-dimensional continua, what would be acquired would simply be some sort of a threshold detector, but with more complex multidimensional stimuli the feature-filter would have to be constructed by a more active inductive process. This may be where connectionist algorithms come in. Another important factor in the selectivity of the A/D feature-filter is the "context" of alternatives: the sample of confusable members and nonmembers of the categories in question on the basis of which the features must be extracted; these also focus the uncertainty that the filter must resolve if it is to generate reliable categorization performance. All this is described in the book under discussion (Categorical Perception: The Groundwork of Cognition, Cambridge University Press 1987, S. Harnad, Ed.). -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Princeton.EDU