harnad@mind.UUCP (Stevan Harnad) (06/28/87)
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
marty1@houdi.UUCP (M.BRILLIANT) (06/29/87)
In article <931@mind.UUCP>, harnad@mind.UUCP (Stevan Harnad) writes: > 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).... OK: once I classify a thing as a chair, there are no two ways about it: it's a chair. But there can be a stage when I can't decide. I vacillate: "I think it's a chair." "Are you sure?" "No, I'm not sure, maybe it's a bed." I would never say seriously that I'm 40 percent sure it's a chair, 50 percent sure it's a bed, and 10% sure it's an unfamiliar object I've never seen before. I think this is in agreement with Harnad when he says: > 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.... > 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 ..... it is always possible that > the categorizer will encounter an anomalous instance that he cannot classify > according to his current representation..... > ...... This still does not imply that membership is > fuzzy or a matter of degree..... So to pass the Total Turing Test, a machine should respond the way a human does when faced with inadequate or paradoxical sensory data: it should vacillate (or bluff, as some people do). In the presence of uncertainty it will not make self-consistent statements about uncertainty, but uncertain and possibly inconsistent statements about absolute membership. M. B. Brilliant Marty AT&T-BL HO 3D-520 (201)-949-1858 Holmdel, NJ 07733 ihnp4!houdi!marty1
dgordon@teknowledge-vaxc.ARPA (Dan Gordon) (06/30/87)
In article <931@mind.UUCP> harnad@mind.UUCP (Stevan Harnad) writes: >(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 Is this so? There is no reliable, consensual all-or-none categorization performance without a set of underlying features? That sounds like a restatement of the categorization theorist's credo rather than a thing that is so. Dan Gordon
harnad@mind.UUCP (Stevan Harnad) (07/01/87)
dgordon@teknowledge-vaxc.ARPA (Dan Gordon) of Teknowledge, Inc., Palo Alto CA writes: > There is no reliable, consensual all-or-none categorization performance > without a set of underlying features? That sounds like a restatement of > the categorization theorist's credo rather than a thing that is so. If not, what is the objective basis for the performance? And how would you get a device to do it given the same inputs? -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Princeton.EDU
aweinste@Diamond.BBN.COM (Anders Weinstein) (07/01/87)
In article <949@mind.UUCP> harnad@mind.UUCP (Stevan Harnad) writes: > >> There is no reliable, consensual all-or-none categorization performance >> without a set of underlying features? That sounds like a restatement of >> the categorization theorist's credo rather than a thing that is so. > >If not, what is the objective basis for the performance? And how would >you get a device to do it given the same inputs? I think there's some confusion as to whether Harnad's claim is just an empty tautology or a significant empirical claim. To wit: it's clear that we can reliably recognize chairs from sensory input, and we don't do this by magic. Hence, we can perhaps take it as trivially true that there are some "features" of the input that are being detected. If we are taking this line however, we have remember that it doesn't really say *anything* about the operation of the mechanism -- it's just a fancy way of saying we can recognize chairs. On the other hand, it might be taken as a significant claim about the nature of the chair-recognition device, viz., that we can understand its workings as a process of actually parsing the input into a set of features and actually comparing these against what is essentially some logical formula in featurese. This *is* an empirical claim, and it is certainly dubitable: there could be pattern recognition devices (holograms are one speculative suggestion) which cannot be interestingly broken down into feature-detecting parts. Anders Weinstein BBN Labs
dgordon@teknowledge-vaxc.ARPA (Dan Gordon) (07/02/87)
In article <949@mind.UUCP> harnad@mind.UUCP (Stevan Harnad) writes: > > >dgordon@teknowledge-vaxc.ARPA (Dan Gordon) >of Teknowledge, Inc., Palo Alto CA writes: > >> There is no reliable, consensual all-or-none categorization performance >> without a set of underlying features? That sounds like a restatement of >> the categorization theorist's credo rather than a thing that is so. > >If not, what is the objective basis for the performance? And how would >you get a device to do it given the same inputs? Not a riposte, but some observations: 1) finding an objective basis for a performance and getting a device to do it given the same inputs are two different things. We may be able to find an objective basis for a performance but be unable (for merely contingent reasons, like engineering problems, etc., or for more funda- mental reasons) to get a device to exhibit the same performance. And, I suppose, the converse is true: we may be able to get a device to mimic a performance without understanding the objective basis for the model (chess programs seem to me to fall into this class). 2) There may in fact be categorization performances that a) do not use a set of underlying features; b) have an objective basis which is not feature-driven; and c) can only be simulated (in the strong sense) by a device which likewise does not use features. This is one of the central prongs of Wittgenstein's attack on the positivist approach to language, and although I am not completely convinced by his criticisms, I haven't run across any very convincing rejoinder. Maybe more later, Dan Gordon
harnad@mind.UUCP (Stevan Harnad) (07/02/87)
dgordon@teknowledge-vaxc.ARPA (Dan Gordon) of Teknowledge, Inc., Palo Alto CA writes: > finding an objective basis for a performance and getting a device to > do it given the same inputs are two different things. We may be able > to find an objective basis for a performance but be unable...to get a > device to exhibit the same performance. And, I suppose, the converse > is true: we may be able to get a device to mimic a performance without > understanding the objective basis for the model I agree with part of this. J.J. Gibson argued that the objective basis of much of our sensorimotor performance is in stimulus invariants, but this does not explain how we get a device (like ourselves) to find and use those invariants and thereby generate the performance. I also agree that a device (e.g., a connectionist network) may generate a performance without our understanding quite how it does it (apart from the general statistical algorithm it's using, in the case of nets). But the point I am making is neither of these. It concerns whether performance (correct all-or-none categorization) can be generated without an objective basis (in the form of "defining" features) (a) existing and (b) being used by any device that successfully generates the performance. Whether or not we know know what the objective basis is and how it's used is another matter. > There may in fact be categorization performances that a) do not use > a set of underlying features; b) have an objective basis which is not > feature-driven; and c) can only be simulated (in the strong sense) by > a device which likewise does not use features. This is one of the > central prongs of Wittgenstein's attack on the positivist approach to > language, and although I am not completely convinced by his criticisms, > I haven't run across any very convincing rejoinder. Let's say I'm trying to provide the requisite rejoinder (in the special case of all-or-none categorization, which is not unrelated to the problems of language: naming and description). Wittgenstein's arguments were not governed by a thoroughly modern constraint that has arisen from the possibility of computer simulation and cognitive modeling. He was introspecting on what the features defining, say, "games" might be, and he failed to find a necessary and sufficient set, so he said there wasn't one. If he had instead asked: "How, in principle, could a device categorize "games" and "nongames" successfully in every instance?" he would have had to conclude that the inputs must provide an objective basis which the device must find and use. Whether or not the device can introspect and report what the objective basis is is another matter. Another red herring in Wittegenstein's "family resemblance" metaphor was the issue of negative and disjunctive features. Not-F is a perfectly good feature. So is Not-F & Not-G. Which quite naturally yields the disjunctive feature F-or-G. None of this is tautologous. It just shows up a certain arbitrary myopia there has been about what a "feature" is. There's absolutely no reason to restrict "features" to monadic, conjunctive features that subjects can report by introspection. The problem in principle is whether there are any logical (and nonmagical) alternatives to a feature-set sufficient to sort the confusable alternatives correctly. I would argue that -- apart from contrived, gerrymandered cases that no one would want to argue formed the real basis of our ability to categorize -- there are none. Finally, in the special case of categorization, the criterion of "defining" features also turns out to be a red herring. According to my own model, categorization is always provisional and context-dependent (it depends on what's needed to successfully sort the confusable alternatives sampled to date). Hence an exhaustive "definition," good till doomsday and formulated from the God's-eye viewpoint is not at issue, only an approximation that works now, and can be revised and tightened if the context is ever widened by further confusable alternatives that the current feature set would not be able to sort correctly. The conflation of (1) features sufficient to generate the current provisional (but successful) approximation and (2) some nebulous "eternal," ontologically exact "defining" set (which I agree does not exist, and may not even make sense, since categorization is always a relative, "compared-to-what?" matter) has led to a multitude of spurious misunderstandings -- foremost among them being the misconception that our categories are all graded or fuzzy. -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Pobjectivjohch
harnad@mind.UUCP (Stevan Harnad) (07/04/87)
aweinste@Diamond.BBN.COM (Anders Weinstein) of BBN Laboratories, Inc., Cambridge, MA writes: > [It's] tempting to suppose that all complex concepts *must* have implicit > definitions in terms of some atomic ones, even if...largely > unconscious... [but] philosophy has spent two thousand years searching > for implicit definitions of concepts without any conspicuous success. First of all, let me say that this rejoinder of Weinstein's is excellent. It portrays the standard Quinean view on these matters, so far as I can tell, faithfully and resourcefully. It will be a pleasure attempting to refute this sophisticated position, and if I succeed, I hope that the outcome cannot fail to be informative to all who have been following these exchanges, particularly in view of the influential status of the Quinean view. In replying, however, I have been obliged to quote extensively from Weinstein's articulate statements, despite Ken Laws's valid request that we minimize quotes (and my sincere efforts to comply with it). The facility of quoting is one of the unique powers of electronic communication, though, and I think in this case paraphrase or cross-reference would have caused more confusion and discontinuity than it was worth. Now my response: It is important to note that -- even in this ecumenical age of "cognitive science" -- the concerns of philosophy and of empirical psychology are not the same. In other words, it may be that philosophy was searching for 2000 years in the wrong way or for the wrong thing. Probably both. This will become clearer in my response, but what I claim is that (i) the only way to find out how far a bottom-up approach to concepts grounded in sensory features can get you is actually to model it -- to see what performance you can get out of a device that functions that way. (Even what I'm doing is just prolegomena to such modeling, by the way, but I think I've got the methodological constraints right and some hints as to how one might start.) Philosophy has certainly not been doing that. (ii) "Definitions" (implicit or otherwise) are not what we're looking for in modeling our use of concepts. We're looking for what kinds of internal structures and processes a device must have in order to be able to do what we can do. The fact that philosophers have failed to introspect exhaustive definitions of concepts is not evidence about what internal representations may or may not actually underlie concepts. Not only is it possible that explicit, verbalizable definitions are not what these representations consist of, but it is unlikely that even their "implicit" counterparts will be "definitions" at all. According to my own model, for example, the features that pick out a category named "X" will never be the real, "essential" features that define X's ontologically, from the eternal, omniscient point of view. They will only be the local, context-dependent features that allow a categorizer to sort X's and non-X's CORRECTLY (sic -- I'll return to this) on the basis of the sample of interconfusable X's he has encountered to date. We're not defining X's. We're picking out the features available from the sensory projection that will reliably sort the X's and non-X's we encounter. This provisional, approximate, context-dependent representation then allows us to use "X" in grounded composite symbolic descriptions of higher-order objects not so closely tied to our sense experience. These descriptions too merely provisionally pick out rather than definitively define. Nor is it some exact object that's being picked out; just the best current approximation on the data available. > [Why the psychology of categorization won't dent the problem of meaning:] > "Angry gods nearby" is composite in *English*, but it need not be > composite in native, or, more to the point, in the supposed inner > language of the native's categorical mechanisms. They may have a single > word, say "gog", which we would want to translate as "god-noise" or some> such. Perhaps they train their children to detect gog in precisely the > same way we train children to detect thunder -- our internal > thunder-detectors are identical. Nevertheless, the output of their > thunder-detector does not *mean* "thunder". Besides the obvious rejoinder that -- in a very real sense -- "gog" and "thunder" ARE picking out the same thing to an approximation, I think you are understimating the complexity and resources of compositeness versus atomicity (i.e., descriptions versus names). Utterances are not infinitely decomposable; there are elementary labels that simply refer to an object or a state of affairs, rather than predicate something more complex about it, and some of these objects will be sensory, and picked out by sensory attributes alone. The rest, I claim, can be grounded in combinations of these labels (stating category inclusion relations, to begin with). If "gog" is really a holophrastic description rather than an atomic name, then there must be a way of decomposing it into its components ("angry," "gods," etc.), which will then themselves either be composite or atomic, and if atomic (and sensory), then the grounding can start there. "Thunder," on the other hand, need not be decomposable in that way, and its representation need not presuppose a similar set of interrelations with other representations. (This is not to say that it does not have interrelations with other names and descriptions and their underlying features; just that it does not have the ones the composite holophrastic "gog" must have.) I'll return to the issue of training below. For now, let me say that although I myself introduced the problem of "meaning" ("intentionality" etc.) in formulating the symbol grounding in the first place, I was appealing mainly to the informal, intuitive "folk-psychological" meaning of meaning. We all know what "meaningful" vs. "meaningless" means. We all know what it's like to mean something, and what it's like not to know what something means. That's really all I want to take on. On the other hand, the long line of intentionality conundrums -- beginning with Frege's "morning star" and "evening star" and passing through puzzles about referential opacity and culminating in Putnam's "water/twin-water" koans (and related Quinean "gavagai" problems and even Goodmanian "green/grue" and Kuhnian incommensurability) -- I would rather keep my distance from, as not a helpful legacy from philosophers' 2000-year unsuccessful struggle with meaning. > there are two reasons why meaning resists explication by this kind > of psychology: (1) holism: the meaning of even a "grounded" symbol will > still depend on the rest of the cognitive system; and (2) normativity: > meaning is dependent upon a determination of what is a *correct* > response, and you can't simply read such a norm off from a description > of how the mechanism in fact performs. (1) "Holism" is a vague notion, but I take it that Quine has in mind that the meanings of words are intimately interrelated, and that a change of meaning in one may require adjustments, perhaps even radical ones, throughout the entire system. I think this is something that the kind of bottom-up grounding scheme I'm proposing is particularly well suited to handle, and I discuss it explicitly in the theoretical chapter of the book under discussion here ("Categorical Perception"). One of the most important features of this approach is what I've dubbed "approximationism": All category representations are provisional and approximate, depending on the confusable alternatives sampled to date. This means that feature-sets are open to revision, perhaps even radical revision, if the existing context is too narrow or unrepresentative. The only constraint is that all prior contexts must be subsumed as special cases; in other words, the updating is convergent. Grounding is itself a "holistic" relation, and any ground-level change in the representation will ramify bottom-up to everything that's grounded in it (for example, gog's meaning changes if gods turn out not to exist). This is not to say, however, that incoherencies can't make their way into such a system, or that it will always behave optimally or rationally. (2) "Normativity" is no problem for an approximationist device whose internal principles of function have nothing at all to do with questions about what things "really" are (or "really mean"). These principles only concern what you can reliably sort and label on the evidence available: Every category learning task has a source of feedback about "right" and "wrong." If you are in the wild and you're hungry and only mushrooms are available, there's a distinct ecological constraint to guide you in sorting "edibles" from "inedibles." Less radically, most of our transactions with objects and events that require categorization are attended by feedback from the consequences of MIScategorization (otherwise why bother?). And often the feedback source is good old-fashioned instruction, some of it based on preemptive ecological experience, some of it just based on arbitrary convention. The trick for the theorist is to forget about what a label "really" picks out and just worry about the actually sample a device sorts, and how. So neither holism nor "norms" seem to be a problem for the categorization model I am describing. And whether some of its internal representations are justifiably interpreted as "meanings" depends ultimately on whether or not its performance is TTT-indistinguishable (Total Turing Test) from ours. (Let's not get into another round about whether this is the ONLY criterion again...) > The fact that a subject's brain reliably asserts the symbol "foo" when > and only when thunder is presented in no way "fixes" the meaning of > "foo". Of course it is obviously a *constraint* on what "foo" may > mean: it is in fact part of what Quine called the "stimulus meaning" > of "foo", his first constraint on acceptable translation. Nevertheless, > by itself it is still way too weak to do the whole job, for in different > contexts the positive output of a reliable thunder-detector could mean > "thunder", something co-extensive but non-synonymous with "thunder", > "god-noise", or just about anything else. Indeed, it might not *mean* > anything at all, if it were only part of a mechanical thunder-detector > which couldn't do anything else... I wonder if you disagree with this? I agree with most of this. I certainly agree about context-dependence and what sounds like approximateness. I don't really know what Quine's "stimulus meaning" is, but perhaps it could be cashed in by coming up with the right performance model. That theoretical task, however, is anything but trivial, and the real work seems to begin where Quine's vague descriptor leaves off. (Same for "behavioral dispositions.") I also agree that a sub-TTT device may have nothing worthy of being interpreted as "meaning" at all. Hence much of meaning must have to do with the interrelations among the representations subserving our total sorting, labeling and describing capacity; and it of course depends on the context of interconfusable alternatives that any given device can successfully sort and describe -- the "compared to what?" factor. Widen the context and you narrow the options on what an isolated act of stimulus-naming (and the underlying structures and processes generating it) might "mean." (I've always felt that radical alternative translations are unlikely to exist because of constraints on the permutations and combinations that will still yield a coherently decryptable story. In the propositional calculus, conjunction/negation and disjunction/negation may be "duals," but it's not clear that more complex alternative permutations are possible in the semantics of natural language. They may leave no degrees of freedom. See also the contributions of Dan Berleant to this discussion on that topic. I think similar considerations may apply to inverted-spectrum thought-experiments regarding qualia, i.e., swapping red and green, etc.) > As to normativity, the force of problem (2) is particularly acute when > talking about the supposed intentionality of animals, since there aren't > any obvious linguistic or intellectual norms that they are trying to > adhere to. Although the mechanics of a frog's prey-detector may be > crystal clear, I am convinced that we could easily get into an endless > debate about what, if anything, the output of this detector really > *means*. I agree, although that may partly be a problem with the weakness of our ecological knowledge and cross-species intuitions with respect to the infrahuman-TTT. It may also be a consequence of the preeminent role language plays in our judgments (as perhaps it should). But one can certainly speak about "right" and "wrong" in an animal's categorization performance, both with respect to evolutionary adaptation and learning. And approximationism relieves us of having to decide the fact of the matter about what EXACTLY the frog's bug-detector is picking out. To an approximation it might be the same thing ours is picking out... But, not being TTT-equivalent to us, frogs may well be "meaning" nothing at all. > in doing this sort of psychology, we probably won't care about the > difference between correctly identifying a duck and mis-identifying > a good decoy -- we're interested in the perceptual mechanisms that are > the same in both cases. In effect, we are limiting our notion of > "categorization" to something like "quick and largely automatic > classification by observation alone". Whether duck/decoy is a good enough approximation for duck depends on context and consequences. (For the unfortunate hunted duck, it matters.) But there are big differences between innate and learned categories (the former are not revisable in an indivdual lifetime) and not all categories are sensory. They're simple all GROUNDED in sensory categories. > We pretty much *have* to restrict ourselves in this way, because, in the > general case, there's just no limit to the amount of cognitive activity > that might be required in order to positively classify something. > Consider what might go into deciding whether a dolphin ought to be > classified as a fish, whether a fetus ought to be classified as a > person, etc. These decisions potentially call for the full range of > science and philosophy, and a psychology which tries to encompass such > decisions has just bitten off more than it can chew: it would have to > provide a comprehensive theory of rationality, and such an ambitious > theory has eluded philosophers for some time now... we seem committed > to the notion that we are limiting ourselves to particular *modules* > as explained in Fodor's modularity book. Unfortunately... these > normative distinctions *are* significant for the *meaning* of symbols. > ("Duck" doesn't *mean* the same thing as "decoy"). I'd like to try having that bite and chewing it too. As I suggested before, philosophers may have failed because they never really tried. And holism is not a problem for my kind of model, for example, because there's no restriction on how much of a grounded hybrid system is used to form one categorization, concrete or abstract. And, as I mentioned, the current provisional approximation is always open to updating (say, on the basis of new scientific findings) by widening the context. Nor are "norms" a problem; category formation is always guided by feedback -- either ecological or social -- about what labels and descriptions are right and wrong. I disagree, though, that a successful model calls for a comprehensive theory of rationality (any more than it needs a periscope on ontic reality): It need only be able to make the fallible practical inferences we can and do make. I also see nothing that commits a grounded bottom-up system of the kind I'm describing to any kind of modularity, on the contrary. And "duck" doesn't mean the same as "decoy" only because there are ways we can and do tell them apart. > I think there's some confusion as to whether Harnad's claim [about > the necessity of a sufficient feature-set] is just an empty tautology > or a significant empirical claim. To wit: it's clear that we can > reliably recognize chairs from sensory input, and we don't do this by > magic. Hence, we can perhaps take it as trivially true that there are > some "features" of the input that are being detected. If we are taking > this line however, we have to remember that it doesn't really say > *anything* about the operation of the mechanism -- it's just a fancy > way of saying we can recognize chairs. I agree that I haven't provided a feature-learning mechanism (although I've suggested some candidates, such as connectionist nets or some other inductive statistical algorithm). I've just argued that one must exist. But those who were disagreeing were suggesting that category membership is really graded, not all-or-none, and that sufficient feature-sets do not and need not exist. Be it ever so fancy, it matters whether we categorize chairs as chairs on an all-or-none featural basis or as a matter of degree (of similarity to a "template," say). I think the whole line of research based on "family resemblances" and protoype-matching is wrong-headed and based on misunderstandings about what features and feature-detectors are; moreover, it begs most of the questions involved in trying to get a device to perform successful all-or-none categorization at all. If the existence and use of sufficient feature-sets is so certain that it's tautologous, tell that to the ones who seem to be denying it! > On the other hand, it might be taken as a significant claim about the > nature of the chair-recognition device, viz., that we can understand > its workings as a process of actually parsing the input into a set of > features and actually comparing these against what is essentially some > logical formula in featurese. This *is* an empirical claim, and it is > certainly dubitable: there could be pattern recognition devices > (holograms are one speculative suggestion) which cannot be > interestingly broken down into feature-detecting parts. In another response I argue that holograms and other iconic representations cannot do nontrivial categorization (i.e., problems in which there are no obvious gaps in the variation and the feature-set is complex and underdetermined). I also do not favor "logical formulas in featurese" (which sounds as if it has gone symbolic prematurely). A disjunctive feature-detector need not have any explicit formulas. It could be a selective filter that only passes input that is, say, red or green; i.e., it could be "micro-iconic," -- invertible only in red-or-green-ness. I also don't think the representation of "chair" is likely to be purely sensory; it's probably a higher-order category grounded in sensory categories. I think there's plenty in what I claim that is dubitable (hence empirical), if not dubious. -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Princeton.EDU