Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!mnetor!seismo!rutgers!princeton!mind!harnad From: harnad@mind.UUCP (Stevan Harnad) Newsgroups: comp.cog-eng,comp.ai Subject: The symbol grounding problem: Against Rosch & Wittgenstein Message-ID: <931@mind.UUCP> Date: Sun, 28-Jun-87 13:52:03 EDT Article-I.D.: mind.931 Posted: Sun Jun 28 13:52:03 1987 Date-Received: Sun, 28-Jun-87 19:36:53 EDT References: .... <6174@diamond.BBN.COM> <917@mind.UUCP> <1192@houdi.UUCP> Organization: Cognitive Science, Princeton University Lines: 103 Summary: All-or-none categorization performance requires defining features Xref: mnetor comp.cog-eng:155 comp.ai:585 marty1@houdi.UUCP (M.BRILLIANT) of AT&T Bell Laboratories, Holmdel asks: > Why require 100% accuracy in all-or-none categorizing?... I learned > recently that I can't categorize chairs with 100% accuracy. This is a misunderstanding. The "100% accuracy" refers to the all-or-none-ness of the kinds of categories in question. The rival theories in the Roschian tradition have claimed that many categories (including "bird" and "chair") do not have "defining" features. Instead, membership is either fuzzy or a matter of degree (i.e., percent), being based on degree of similarity to a prototype or to prior instances, or on "family resemblances" (as in Wittgenstein on "games"), etc.. I am directly challenging this family of theories as not really providing a model for categorization at all. The "100% accuracy" refers to the fact that, after all, we do succeed in performing all-or-none sorting and labeling, and that membership assignment in these categories is not graded or a matter of degree (although our speed and "typicality ratings" may be). I am not, of course, claiming that noise does not exist and that errors may not occur under certain conditions. Perhaps I should have put it this way: Categorization preformance (with all-or-none categories) is highly reliable (close to 100%) and MEMBERSHIP is 100%. Only speed/ease of categorization and typicality ratings are a matter of degree. The underlying representation must hence account for all-or-none categorization capacity itself first, then worry about its fine-tuning. This is not to deny that even all-or-none categorization may encounter regions of uncertainty. Since ALL category representations in my model are provisional and approximate (relative to the context of confusable alternatives that have been sampled to date), it is always possible that the categorizer will encounter an anomalous instance that he cannot classify according to his current representation. The representation must hence be revised and updated under these conditions, if ~100% accuracy is to be re-attained. This still does not imply that membership is fuzzy or a matter of degree, however, only that the (provisional "defining") features that will successfully sort the members must be revised or extended. The approximation must be tightened. (Perhaps this is what happened to you with your category "chair.") The models for the true graded (non-all-or-none) and fuzzy categories are, respectively, "big" and "beautiful." > The class ["chair," "bird"] is defined arbitrarily by inclusion > of specific members, not by features common to the class. It's not so > much a class of objects, as a class of classes.... If that is so, > then "bird" as a categorization of "penguin" is purely symbolic, and > hence is arbitrary, and once the arbitrariness is defined > out, that categorization is a logical, 100% accurate, deduction. > The class "penguin" is closer to the primitives that we infer > inductively [?] from sensory input... But the identification of > "penguin" in a picture, or in the field, is uncertain because the > outlines may be blurred, hidden, etc. So there is no place in the > pre-symbolic processing of sensory input where 100% accuracy is > essential. (This being so, there is no requirement for invertibility.) First, most categories are not arbitrary. Physical and ecological contraints govern them. (In the case of "chair," this includes the Gibsonian "affordance" of whether they're something that can be sat upon.) One of the constraints may be social convention (as in stipulations of what we call what, and why), but for a categorizer that must learn to sort and label correctly, that's just another constraint to be satisfied. Perhaps what counts as a "game" will turn out to depend largely on social stipulation, but that does not make its constraints on categorization arbitrary: Unless we stipulate that "gameness" is a matter of degree, or that there are uncertain cases that we have no way to classify as "game" or "nongame," this category is still an all-or-none one, governed by the features we stipulate. (And I must repeat: Whether or not we can introspectvely report the features we are actually using is irrelevant. As long as reliable, consensual, all-or-none categorization performance is going on, there must be a set of underlying features governing it -- both with sensory and more abstract categories. The categorization theorist's burden is to infer or guess what those features really are.) Nor is "symbolic" synonymous with arbitrary. In my grounding scheme, for example, the primitive categories are sensory, based on nonsymbolic representations. The primitive symbols are then the names of sensory categories; these can then can go on to enter into combinations in the form of symbolic descriptions. There is a very subtle "entry-point" problem in investigating this bottom-up quasi-hierarchy, however: Is a given input sensory or symbolic? And, somewhat independently, is its categorization mediated by a sensory representation or a symbolic one (or both, since there are complicated interrelations [especially inclusion relations] between them, including redundancies and sometimes even incoherencies)? The Roschian experimental and theoretical line of work I am criticizing does not attempt to sort any of this out, and no wonder, because it is not really modeling categorization performance in the first place, just its fine tuning. As to invertibility: I must again repeat, an iconic representation is only analog in the properties of the sensory projection that it preserves, not those it fails to preserve. Just as our successful all-or-none categorization performance dictates that a reliable feature set must have been selected, so our discrimination performance dictates the minimal resolution capacity and invertibility there must be in our iconic representations. -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Princeton.EDU