Xref: utzoo comp.ai:3066 talk.philosophy.misc:1818 sci.lang:3911 Path: utzoo!attcan!uunet!lll-winken!lll-ncis!helios.ee.lbl.gov!nosc!ucsd!rutgers!elbereth.rutgers.edu!harnad From: harnad@elbereth.rutgers.edu (Stevan Harnad) Newsgroups: comp.ai,talk.philosophy.misc,sci.lang Subject: Re: Categorization Summary: On the priority of all-or-none categorization Message-ID: Date: 12 Jan 89 15:44:01 GMT References: <24080@cornell.UUCP> Organization: Rutgers Univ., New Brunswick, N.J. Lines: 49 turney@svax.cs.cornell.edu (Jenn Turney) of Cornell Univ. CS Dept, Ithaca NY wrote: " Deriving concept membership (categorization) from typicality ratings is " not automatic. It is entirely possible for something to receive a " typicality rating for a category without any knowledge about whether it " actually belongs to the category. No one said it was automatically derivable. I even denied it was derivable at all. I said it was simply PRESUPPOSED (taken for granted) by the typicality theorist that there WERE categories that the typicality ratings reflected the typicality OF. I also denied that much can be learned about category representation from typicality ratings; it's simply a different problem. And of course I agree that there can be spurious typicality ratings, dissociated from knowledge about categories. That's why I think I think that typicality ratings are about as informative about category representation as Osgood's "semantic differential" is about meaning (and for just about the same reason)! " It may still be possible to derive C from typicality ratings... " Perhaps all instances with typicality ratings higher than 15% are " members of the category. "C" referred to how the category was represented in the head. I deny that you can derive (or infer or reconstruct) this from typicality ratings. All you can get out of typicality ratings is what goes into them, and they are not judgments about membership, they are judgments about "degree of membership." (Perhaps instances with typicality ratings higher than 15% ARE members of the category; perhaps not. So what? What we need to know is what the category-detecting representation that does the correct, reliable, all-or-none categorizing is.) " As regards the statement that categories are either 100% or incoherent... No one made such a statement. First, I said that, besides the reliable, all-or-none categories that we can sort correctly (virtually) 100% of the time (like bird), there are graded categories too (like big). What I said was incoherent was the idea that one could derive category representations (the ones that subserve our all-or-none categorization performance) from typicality judgments and nearness-to-prototype notions. (The reverse may well be possible.) -- Stevan Harnad INTERNET: harnad@confidence.princeton.edu harnad@princeton.edu srh@flash.bellcore.com harnad@elbereth.rutgers.edu harnad@princeton.uucp BITNET: harnad@pucc.bitnet CSNET: harnad%princeton.edu@relay.cs.net (609)-921-7771