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The Appeal of Parallel Distributed Processing  75

               no close partial matches which include the letter F in the second position, so
               this letter receives no feedback support.As a result, E comes to dominate, and
               eventually suppress, the F in the second position.
                 The fact that the word perception model exhibits perceptual facilitation to
               pronounceable nonwords as well as words illustrates once again how behavior
               in accordance with general principles or rules can emerge from the interactions
               of simple processing elements.Of course, the behavior of the word perception
               model does not implement exactly any of the systems of orthographic rules that
               have been proposed by linguists (Chomsky & Halle, 1968; Venesky, 1970) or
               psychologists (Spoehr & Smith, 1975).In this regard, it only approximates such
               rule-based descriptions of perceptual processing.However, rule systems such
               as Chomsky and Halle’s or Venesky’s appear to be only approximately hon-
               ored in human performance as well (Smith & Baker, 1976).Indeed, some of the
               discrepancies between human performance data and rule systems occur in ex-
               actly the ways that we would predict from the word perception model (Rumel-
               hart & McClelland, 1982).This illustrates the possibility that PDP models may
               provide more accurate accounts of the details of human performance than
               models based on a set of rules representing human competence—at least in
               some domains.

               Retrieving Information from Memory
               Content Addressability  One very prominent feature of human memory is that it
               is content addressable.It seems fairly clear that we can access information in
               memory based on nearly any attribute of the representation we are trying to
               retrieve.
                 Of course, some cues are much better than others.An attribute which is
               shared by a very large number of things we know about is not a very effective
               retrieval cue, since it does not accurately pick out a particular memory repre-
               sentation.But, several such cues, in conjunction, can do the job.Thus, if we ask
               a friend who goes out with several women, ‘‘Who was that woman I saw you
               with?’’hemay notknowwhich onewemean—butifwe specify somethingelse
               about her—say the color of her hair, what she was wearing (in so far as he
               remembers this at all), where we saw him with her—he will likely be able to hit
               upon the right one.
                 It is, of course, possible to implement some kind of content addressability of
               memory on a standard computer in a variety of different ways.One way is to
               search sequentially, examining each memory in the system to find the memory
               or the set of memories which has the particular content specified in the cue.An
               alternative, somewhat more efficient, scheme involves some form of indexing—
               keeping a list, for every content a memory might have, of which memories have
               that content.
                 Such an indexing scheme can be made to work with error-free probes, but
               it will break down if there is an error in the specification of the retrieval cue.
               There are possible ways of recovering from such errors, but they lead to the kind
               of combinatorial explosions which plague this kind of computer implementation.
                 But suppose that we imagine that each memory is represented by a unit
               which has mutually excitatory interactions with units standing for each of its
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