Neo-structuralism: A commentary on the correlations between the work of Zelig Harris and Jeffrey Elman

Peter A. Bensch

Department of Computer Science, UCSD


Elman's current connectionist models are designed to extract
significant regularities from "streams" of input data. A key
component of this work is the inclusion of time. That is, these
networks use both the current input and the previous network
state to determine their output.

     This inclusion of time has allowed his networks to recognize
regularities that were beyond the reach of earlier network
designs. Significantly, his networks' outputs very closely follow
the predictions of Harris [HARRIS82]. (Harris is one of the last
remaining practitioners of pre-Chomskyan structuralism.) The
Chomskyan revolution was to some extent precipitated by the lack
of sufficient computational tools to meet the goals of linguistic
structuralism. Chomsky proposed that the structuralist program
of inducing general principles from empirical data would never
succeed. As part of his revolution, he advocated a research
program based on deduction from general principles to empirical

     With the emergence of the computational tools being
developed by Elman, structuralism may again become a viable
research program. Further support for this conjecture is provided
by the continuing problems encountered by linguists attempting to
deduce empirical data from base principles. Thus, a
connectionist revolution seems to be emerging. And, this
revolution may be fittingly called "neo-structuralism."


In the past, connectionist models have concentrated on developing
a cause and effect relationship between the model's inputs and
its outputs. When a model was successful, analysis would reveal
that it had extracted key components (features) from the inputs
that were good predictors of the output. This program was
successful as long as the inputs were causal determiners of the

     The program was successfully challenged by Pinker and Prince
[PINKER88]. In that paper, they identified many problems in the
Rumelhart and McClelland [RUMEL86a]model of the acquisition of
verbal past-tense forms. Among those problems was the fact that
the root form of the verb was not a sufficient predictor of its
past-tense form. Much recent connectionist work has been
directed toward overcoming the problems identified by Pinker and

     As part of this recent work, Elman has been extending the
work of Jordan [JORDAN86]. This work includes time as an element
in the causal relationship between inputs and outputs (that is,
the connectionist network uses both the input and its own
previous state to determine its output). In particular, Elman has
found that having the network predict the next input has been a
successful method for identifying significant regularities in the
input data. In [ELMAN90], he reports on a model that can identify
word boundaries when the input is a stream of letters that spell
out the words (with no indication of where one word ends and the
next begins). Another model can identify word categories when
given a stream of input consisting of words that form sentences
(with no indication of where one sentence ends and the next
begins). In both of these models, the input is not a sufficient
predictor of the output. However, the inclusion of the network's
state as part of the input allows the network to successfully
extract relevant statistical properties from the input. Although
this does not directly address the prediction of the verbal
past-tense form, it does open a new avenue of research that might
aid in solving the problem.

     Elman's work is reminiscent of the work performed by the
pre-Chomskyan linguists. These "structuralists" believed that the
correct way to study language was to look for regularities in
actual usage. That is, they believed that word categories,
grammar, etc. could be identified by direct observation of the
"word streams" produced by native speakers of the language.
Unfortunately, the computational tools available to the linguists
of the early-1950's were not sufficient to make this program

     In the mid-1950's, Chomsky broke from the "structuralist"
tradition, starting his revolution in linguistics. His break
included a key change in methodology. He believed that actual
language was an aberration. It contained far too much "noise" to
ever allow the induction of general principles. Instead, he
postulated two levels of language: performance and competence.
Whereas the "structuralists" were committed to the study of
performance, he believed that there was a Platonic, internal
language competence. He felt that this internal language must be
very orderly, free from the noise present in language user's
"word streams." Thus, Chomsky's research program involved: (1)
postulating the principles underlying the internal language, (2)
deducing grammar, etc. of the internal language from these basic
language principles, and (3) describing how the "noisy" language
performance arises from the internal language competence. Thus,
Chomsky's revolution involved a very significant shift in
methodology. Post-Chomsky linguists perform deduction from basic
principles to the empirical language data. Pre-Chomsky linguists
performed induction from the empirical language data to basic

     I believe that Elman's recent connectionist models are built
in the pre-Chomskyan tradition. His goal is to have his networks
discover linguistic properties from empirical data. In a recent
critique of the connectionist research program, Fodor and
Pylyshyn [FODOR88] argued forcefully that connectionism was only
valid as a computational tool being used to implement
post-Chomsky linguistic theories [see NOTE 1]. This conviction is
firmly grounded in their commitment to a deductive methodology.
They can see no "revolution" in the connectionist program.
However, when viewed the way I am proposing, there is a very
definite connectionist revolution -- or, better, connectionist
counter-revolution. If Elman is successful in his current
research program, he will indeed have created a powerful
computational tool. It may be just the tool that the
"structuralists" of the early-1950's lacked. If so, we may be
witnessing the origin of a neo-structuralism.

     In the following, I will discuss the implications of a
neo-structuralist revolution. Then I will discuss in detail two
of Elman's recent models, and contrast them with the current work
of Zelig Harris -- a linguist that has continued to follow the
structuralist methodology.


Among the problems that will face the modellers following Elman's
research program will be the selection of the appropriate kind of
data to use in training their models. There are two possible
choices for training data: (1) actual examples from a human
language, or (2) an artificial sample generated from a standard
grammar. The problem here is that using the latter presupposes
the deductive base principles underlying the artificial grammar.

     Assume a connectionist modeller begins an experiment by
generating training sentences from a grammar. Then these
sentences are used to train the model. Finally, the model is
tested by using actual sentences from the language. This seems
like a perfectly valid research program, but it is fatally
flawed. The problem is that the model will learn the formal
system, not the language. If the model performs perfectly well in
the test sentences given it, this says nothing about the validity
of the model as a whole. At any time, we might encounter a series
of sentences that the model could not process correctly. The only
value in this model would be in refuting the formal system under
study. Since this kind of negative evidence could easily be
generated without the construction of such a model, this seems to
be a fruitless enterprise.

     To be fair, I should note possible alternative motives for
creating such a model. For classical (i.e., Chomskyan) linguists,
such a model would show that the formal system under study could
in fact be created using "neuron-like" machinery. This could aid
in establishing the psychological reality of the model. Further,
such a simulation could be used to show that the behavioral
consequences of the formal system parallel human behavior with
the target language. But, again, the object of study is the
formal system, not the language itself. On the other hand, this
kind of model could be used by a connectionist to show that
his/her model can do at least as much as a classical model can.

     The other alternative is to use actual language sentences to
train the connectionist model. The problem here is that the
classical Chomskyan linguists have nothing to offer our modeller
in this case. One of their key tenets is that language principles
are not induced from the empirical language data. Thus, our
modeller is left without any theorectical underpinnings to aid
his/her research. He/she will be like Thomas Kuhn's pre-paradigm

In the absence of a paradigm or some candidate for
paradigm, all of the facts that could possibly pertain
to the development of a given science are likely to
seem equally relevant. As a result, early
fact-gathering is usually restricted to the wealth of
data that lie ready to hand. The resulting pool of
facts contains those accessible to casual observation
and experiment together with some of the more esoteric
data retrievable from established crafts ...
                                         [KUHN, pp.15]

In this case the craft will be connectionist modelling, and the
data that has emerged thus far from sentence processing models
has been diverse and difficult to analyze.

     But, we must recall, that linguistics is not a pre-paradigm
science. It is, in fact, a science with its paradigm --
generative grammar -- in crisis. But paradigms under attack can
prove to be quite resilient. Kuhn says the following about
scientists struggling through such a crisis:

     Though they may begin to lose faith and then to
consider alternatives, they do not renounce the
paradigm that has led them into crisis.  They do not,
that is, treat anomalies as counterinstances, though in
the vocabulary of philosophy of science that is what
they are. ... [O]nce it has achieved the status of
paradigm, a scientific theory is declared invalid only
if an alternative candidate is available to take its
place. ... The decision to reject one paradigm is
always simultaneously the decision to accept another,
and the judgement leading to that decision involves the
comparison of both paradigms with nature and with each
                                          [KUHN, pg.77]

If the generative grammar paradigm has no assistance for our
connectionist modellers, is there any other source that might
help? The answer is yes. As Kuhn has noted, when paradigms begin
to lose their dominance, the research they guide increasingly
resembles "that conducted under the competing schools of the
pre-paradigm period" [KUHN, pg.72]. The school of linguistics
that preceded the Chomskyan paradigm was post-Bloomfieldian
structuralism, and one of its foremost practitioners was Zelig
Harris. In fact, Harris has continued to practice and refine
structural linguistics throughout the Chomskyan revolution. 
S.-Y. Kuroda notes:

... the difference between Harris and Chomsky turns on
the notion of grammar. Harris was one of the foremost
methodologists in post-Bloomfieldian taxonomic
structuralism; he brought it to a completion by his
work Methods in Structural Linguistics in 1947. Harris
attempted to extend the taxonomic methodology of
descriptive linguistics to discourse analysis around
1950, but by 1960 he had virtually returned to the
study of grammar by developing [his] transformational
theory, without explicitly dissociating himself from
his past methodological stance. Chomsky, in the
meantime, abandoned taxonomic methods of structural
linguistics in the early 50's and launched into the
construction of the theory of transformational
generative grammar under a "realist" and pyschological
interpretation of linguistic theory.
                                       [KURODA, pg.45]

Expounding on the differences between Harris and Chomsky, Kuroda

Harris's [transformational] theory is directed to the
structure of correspondence that underlies the
syntactic design of language. ... Correspondence and
derivation are two dynamic forces that shape the
formal design of human language, and it is a major task
imposed on linguistic theory how to determine the
sphere of influence of these contending forces. Harris'
transformational theory took the form it did to respond
primarily to the former, and Chomsky's initial
formulation of transformational generative grammar, to
the latter. The later development of transformational
generative grammar may to a large measure be looked
upon as testimony to the linguist's response to a
tension produced by two contending forces.
                                         [KURODA, pg.6]

In further examining the history of generative grammar, Kuroda

Chomsky is reported to have ... expressed the opinion
that "the history of transformational grammar would
have been more 'rational' if generative semantics had
been the original position ..." ... [A] development
from generative semantics through the Standard Theory
and then to the Government and Binding Theory is easy
to imagine as a rational history of transformational
grammar ... If what interests us is a conceivable ideal
history, ... one might be able to imagine a path from
Harris' ... conception of transformational theory to
the present [i.e., Government and Binding Theory] and
to the future, without going through the idea of
transformational generative grammar ...
                                        [KURODA, pg.47]

Thus, it appears that Chomsky's theory, emphasizing derivation,
and Harris' theory, emphasizing correspondence, are two possible
trails leading to the same end. What is important here for
connectionist modellers is that Harris' theory gets to the common
goal via the study of actual language performance. Thus, Harris'
theory may provide connectionist modellers the appropriate
guidance to be successful in developing their grammar models.
Further, Harris' theory provides specific guidance as to what
types of internal representation might be expected to emerge from
these models. This should guide the modellers as they attempt to
analyze their models' performance.

     Below, I will examine two specific Elman models. Both of
these models take sentences, represented by a stream of words, as
their input. The "simulations address problems in the distinction
between type and token, the representation of lexical categories,
and the representation of grammatical structure." [ELMAN89, pg.1]

     At the core of both simulations is the way words interact in
the sentences of a language. Harris' theory is also built on a
foundation of word interactions. Harris' theory postulates the
emergence of "grammar-like" behavior from these low-level
interactions. This too, appears to be happening in the models
under review. I will have more to say about each model in turn.


In his first model, Elman sought to demonstrate that "a network
could learn the lexical category structure which was implicit in
a language corpus." [ELMAN89, pg.3] A key assumption behind this
model was:

One of the consequences of lexical category structure
is word order. Not all classes of words may appear in
any position. Furthermore, certain classes of words ...
tend to cooccur with other words.
                                        [ELMAN89, pg.3]

The network was trained "to take successive words from the input
stream and to predict the subsequent word" [ELMAN89, pg.4]. After
being trained on 6 cycles through 10,000 two- and three-word
sentences, the network's internal representations were examined.
The hidden unit activations were averaged over all occurrences
for each word in the lexicon. Then, these "mean vectors" were
analyzed using "hierarchical clustering analysis." The resulting
similarity structure shows a grouping of the words by the
traditional lexical categories of verb and noun. The verbs are
further divided by their argument requirements. The nouns are
divided into animates and inanimates. And each of these
categories is further divided into groups based on the set of
verb argument roles they can fill.

     Elman summarizes the model's performance as follows:

The network is not able to predict the precise order of
specific words, but it recognizes that (in this corpus)
there is a class of inputs (viz., verbs) which
typically follow other inputs (viz., nouns). This
knowledge of class behavior is quite detailed; from
the fact that there is a class of items which always
precedes chase, break, and smash, it infers a category
of large animals (or possibly, aggressors).
                                        [ELMAN89, pg.7]

     In Harris' theory, the ability of words to enter together
into a sentence is based on their likelihood to co-occur. This
likelihood is a statistical relationship between words that can
be observed over time. In Harris' theory all words are operators.
Operators can take zero or more arguments, with the operator's
first argument always preceding it. Words that can start
sentences form a priviledged class as operators with no argument
predecessors. These "null" operators are labelled N. All other
operators are labelled O(x,y,...), where x, y, ... identify the
class of arguments that will co-occur with the operator. X and y
are always chosen from n (for "null" operators) or o (for all
other kinds of operators). Thus, as Harris is gathering
co-occurrence statistics, he is also using the sequential
ordering of words to determine operator classes.

     When a corpus of language material has been analyzed in this
way, Harris predicts that all words will fall into large operator
classes. In this particular example, the nouns will be classified
as type N operators, and the verbs will be classified as either a
type O(n) operator -- intransitive -- or as a type O(n,n)
operator -- transitive. Note that Elman identified a third class
of verb with an optional direct object. Harris would eliminate
this class by making the verb transitive and claiming that its
object was "reduced" to zero. In fact, he would not have included
any of the sentences with the missing direct objects in his
"base" corpus on strictly theoretical grounds. Thus, this would
be a case where having an operational paradigm to follow would
have influenced the data selected to train the model.

     (Note that Harris would not object to having a missing
direct object in the test set for the model. He would predict
that the model would partially activate all of the possible
objects when the verb is presented. Then, when no object
occurred, the model should have a "very likely" object highly
activated. This object could be at the word level, or it could be
at a higher "word group" level. In both cases, the object would
be providing little or no information to the sentence and would
be a candidate for reduction -- see TRANSFORMATIONS below.)

     The main goal of Harris' analysis is to determine
co-occurence "likelihoods" between words in the target lexicon. A
further subdivision of the operator classes will occur based on
the similarity between the co-occurence sets associated with
individual words. From the fact that there is a class of items
within the N operator class which always acts as the first
argument for the O(n,n) operators chase, break, and smash, it
follows that a subdivision of "large animals" will occur in the N
operator class.

     Note that in Harris' theory these co-occurence sets are
"fuzzy". They are dynamic -- subject to change as the language
users vary the meaning of their words. Thus, at any point in
time, a word's co-occurence set would reflect all previous
experience with that word over time. In other words, the
co-occurence set is a direct analog of Elman's "mean vector" of
hidden unit activations. However, the "lexical" operator classes
that words belong to will remain constant over time. This again
is based on theoretical considerations. Harris attempts to
restrict all words to one operator class, leaving any appearance
of membership in multiple classes to be explained by grammatical


Elman also clustered the hidden unit activation patterns for each
word in the training data set. This "context-sensitive"
clustering of hidden unit patterns created groupings similar to
those obtained for the "mean vector" analysis.

In this simulation, the context makes up an important
part of the internal representation of a word. ... [I]t
is literally the case that every occurrence of a
lexical item has a separate internal representation.
... The fact that these are all tokens of the same
type is not lost ... These tokens have representations
which are extremely close in space -- closer to each
other by far than to any other entity. Even more
interesting is that the spatial organization within the
token space is not random but reflects differences in
context which are also found among tokens of other
items. The tokens of boy which occur in subject
position tend to cluster together, as distinct from the
tokens of boy which occcur in object position. This
distinction is marked in the same way for tokens of
other nouns. Thus, the network has learned not only
about types and tokens, and categories and category
members; it also has learned a grammatical-role
distinction which cuts across lexical items.
                                      [ELMAN89, pp.7-8]

     Although Harris does not directly address this type-token
distinction, he does address the emergence of grammatical-role
from co-occuring words. The "fuzzy" sets of next words tend to
establish grammatical-roles. In essence, the likelihood
relationship between a word and its possible successors
partitions the appropriate operator space in a very specific
manner. In the context of a PDP schema model [RUMEL86b], each
word will adjust the "goodness-of-fit" landscape for the next
possible word. This distortion will place more likely words at
very high points, and less likely words at lower points.

     I believe that Elman's type-token distinction may well
correspond to a word's adjusting positions in "likelihood" space
based on the word(s) that preceded it. Note that subjects, which
precede their verbs, would have a distinctly different position
in "likelihood" space from objects, which follow the verb. Thus,
it appears that Elman's type-token distinction is also
consistent with Harris' language theory.


Although lexical information plays an important role in
language, it actually accounts for only a small range
of facts. Words are processed in the contexts of other
words; they inherit properties from the specific
grammatical structure in which they occur.
                                        [ELMAN89, pg.8]

     Up to now we have been looking at low-level relationships
between Harris' word categories. But, at the next higher level,
we can examine words which yield the same "fuzzy" sets for next
words. These words can be considered "equivalent" to the extent
that their word groups establish the same "context" for the next
word. Thus, we can identify groups of subjects that are
associated with the same "likelihood" space of verbs. Or, groups
of verbs that are associated with the same "likelihood" space of
objects. This equivalence relation between words, when correlated
with the lexical definition of the words, can be used to identify
word sequences that are paraphrases of each other. In fact, a
necessary condition for Harris to consider two word sequences
"paraphrastic" to each other is that they have the same next word
"likelihood" space.

     Harris considers word groups belonging to the same
paraphrastic equivalence class to be related by linguistic
transformations. He attempts to locate the core of a language by
finding the one "kernel" word group for each class. This "kernel"
word group must generate the whole class using as few
transformations as possible. The analysis at this level will
yield a set of transformation domains. Such a domain includes the
words that terminate each word group on which the transformation
can act. It should be noted that most of the transformations will
be "reductions" -- that is, the elimination of redundant or
low-information words. Further, these reductions are based on the
"likelihood properties" of the component words of each word

     A key point here is the need to include a "semantic"
component to guide the network's search for transformations.
Elman voices a similar sentiment:

The network has no information available which would
"ground" the structural information in the real world.
In this respect, the network has much less information
to work with than is available to real language
learners. In a more realistic model of acquisition,
one might imagine that the utterance provides one
source of information about the nature of lexical
categories; the world itself provides another source.
One might model this by embedding the "linguistic" task
in an environment; the network would have the dual task
of extracting structural information contained in the
utterance, and structural information about the
environment. Lexical meaning would grow out of the
associations of these two types of input.
                                      [ELMAN90, pg.201]

     The appeal of a Chomskyan-style formal system is the ability
to isolate syntax from semantics. However, as Chomsky has said,
such a formal system cannot emerge by induction from the actual
sentences of the language. Harris offers a theory that will allow
grammar to arise from the actual linguistic data, but it requires
the mixing of semantics with syntax.


     In the Elman model mentioned above, the corpus of sentences
is generated by a very simple sentence generator. It had a set of
simple two- and three-word sentence "templates" that it randomly
filled with words from the lexicon. This corpus was so
constrained that it would easily satisfy Harris' criterion for
forming a sublanguage [HARRIS89]. A sublanguage is a very
restricted subset of the language as a whole. The key restriction
is that the words assigned to the sublanguage only have a
"standard" usage. That is, the co-occurrence patterns are
sufficiently regular that sentence "formulas" can be identified
for those words. These sentence formulas perform the function of
a grammar in Harris' theory of language.

     Thus, Harris would predict that Elman should be able to
identify specific sentence formulas in his model. Elman does not
address this point in either of his two papers [ELMAN89,90]
covering the first model. However, analysis using principle
components (see below) identifies patterns in the hidden units
that might qualify as sentence formulas.

     Elman's second sentence processing model was a giant step
beyond the one mentioned above. It's primary goal was to
investigate a connectionist model's representation of grammatical
structures. To pursue that goal, he set up the following training
data set:

     The stimuli in this simulation were based on a
lexicon of 23 items. These included 8 nouns, 12 verbs,
the relative pronoun who, and an end of sentence
indicator, ".". Each item was represented by a randomly
assigned 26-bit vector in which a single bit was set to
1 (3 bits were reserved for another purpose). A phrase
structure grammar ... was used to generate sentences.
                                        [ELMAN89, pg.9]

As mentioned above, when a modeller uses grammar generated
sentences to train his/her model, the subject of study becomes
the grammar, not the language. In this case, the goal was to show
that a connection model could implement the rather complex system
represented by the grammar. In particular, the grammar allowed
the nesting of relative clauses. This made the tasks of
subject/verb agreement and verb argument determination far more
complex. Any of the words filling these roles might be separated
from their companion word by one or more relative clauses. Elman

... the network was unable to learn the task when given
the full range of complex data from the beginning of
training. However, when the network was permitted to
focus on the simpler data first, it was able to learn
the task quickly and then move on successfully to more
complex patterns. The important aspect to this was that
the earlier training constrained later learning in a
useful way; the early training forced the network to
focus on canonical versions of the problems which
apparently created a good basis for then solving the
more difficult forms of the same problem.
                                    [ELMAN89, pp.11-12]

     Since we are not talking about a corpus from actual language
here, Harris is not really applicable. However, the idea that the
model would learn the simpler patterns first is compatible with
Harris. He would hold that the complex sentences would be
"paraphrastically" equivalent to simpler sentences in the
"kernel" language. Since the simpler sentences are all "kernel"
sentences themselves, it would be easier to learn them. Learning
a complex sentence would require the language learner to: (1)
first acquire the "kernel" sentences that would be considered
equivalent to the complex sentence, and (2) then learn the
transformation(s) that relate the "kernel" and complex sentences
[see NOTE 2]. If the corpus was from actual language, the
frequency of occurrence of complex sentences would probably be
diminished enough so that the task could be accomplished without
resorting to the "staged learning" strategy used by Elman.

     The corpus used for training this model was sufficiently
simple so that the network could, in fact, learn its regularities
without resorting to transformations. Thus, Harris would
anticipate that sentence formulas should be stored within the
statistical information coded by the hidden units. Elman also
aniticipated that the grammatical structure must be coded in the
hidden units. Since the cluster analysis only yielded categorical
information, it was necessary to devise a different analysis
technique to look for the grammatical relations. The technique
that located this information was principle component analysis.

This involved passing the training set through the
trained network (with weights frozen) and saving the
hidden unit pattern produced in response to each new
input. The covariance matrix of the set of hidden unit
vectors is calculated, and then the eigenvectors for
the covariance matrix are found. The eigenvectors are
ordered by the magnitude of their eigenvalues, and are
used as the new basis for describing the original
hidden unit vectors. This new set of dimensions has the
effect of giving a somewhat more localized description
to the hidden unit patterns, because the new dimensions
now correspond to the location of meaningful activity
(defined in terms of variance) in the hyperspace.
Furthermore, since the dimensions are ordered in terms
of variance accounted for, we can now look at phase
state portraits of selected dimensions, starting with
those with the largest eigenvalues.
                                       [ELMAN89, pg.15]

In particular, Elman found that principal components 1 and 11
appear to identify the sentence formulas for the following test

(10a) boy chases boy .
(10b) boy chases boy who chases boy .
(10c) boy who chases boy chases boy .
(10d) boy chases boy who chases boy who chases boy .
                                       [ELMAN89, pg.17]

The trajectories through state space for these four
sentences ... are shown in Figure 10 [pg.18]. Panel
(10a) shows the basic pattern associated with what is
in fact the matrix sentence for all four sentences. ...
[T]he matrix subject noun is in the lower left region
of state space, the matrix verb appears above it and to
the left, and the matrix object noun is near the upper
middle region. ... The relative clause appears to
involve a replication of this basic pattern, but
displaced toward the left and moved slightly downward,
relative to the matrix constituents. Moreover, the
exact position of the relative clause elements
indicates which of the matrix nouns are modified. ...
This trajectory pattern was found for all sentences
with the same grammatical form; the pattern is thus

Thus, it appears that another of Harris' predictions is being
fulfilled. It is possible to identify the underlying grammatical
structure for a simple corpus by induction from the empirical


The correlations between Elman's and Harris' work seem to be
quite strong. This implies that the models and analysis
techniques that Elman has been developing might prove very useful
for linguistic structuralists. ELman, himself, seems to have
become a methodological structuralist. Given that Harris' work
implies a realist position [KURODA], Elman may not be too upset
being characterized as a neo-structuralist.


I would like to thank Walt Savitch for many stimulating
discussions concerning the subject matter of this paper. I would
like to further thank Savitch for helpful critical comments on
earlier drafts of this paper.


1. The following comments by Fodor and Pylyshyn indicate their
strong feeling that connectionism should be considered an
implementation theory for classical cognitive theories.

... many of the arguments for Connectionism are best
construed as claiming that cognitive architecture is
implemented in a certain kind of network (of abstract
"units"). Understood this way, these arguments are
neutral on the question of what the cognitive
architecture is. ... 
                                       [FODOR88, pg.64]

... the implementation, and all properties associated
with the particular realization of the algorithm that
the theorist happens to use in a particular case, is
irrelevant to the psychological theory; only the
algorithm and the representations on which it operates
are intended as a psychological hypothesis. ...
     Given this principled distinction between a model
and its implementation, a theorist who is impressed by
the virtues of Connectionism has the option of
proposing PDP's as theories of implementation. But then
... these models are in principle neutral about the
nature of cognitive processes. In fact, they might be
viewed as advancing the goals of Classical information
processing psychology by attempting to explain how the
brain (or perhaps some idealized brain-like network)
might realize the types of processes that conventional
cognitive science has hypothesized.
                                       [FODOR88, pg.65]

2. Harris says the following about relative clauses:

English has a set of pronounings from which are derived
all the modifiers in the language -- attributive
adjectives, relative clauses, adverbs and PN phrases,
and also subordinate clauses. All of these originate in
relative clauses. The relative clause is a "secondary"
sentence S2 connected by [a] semicolon to a "primary"
sentence S1, where a word in S2 is reduced to a
wh-pronoun on the grounds that it is the same as a word
(the 'host') in S1. The wh-pronouning is carried out
primarily on a word that is first in S2 (in many cases
because of its front positioning as in section 3.11
[pp.109-115: Bill spoke to John; John knew Bill well
--> Bill spoke to John; Bill John knew well]), when S2
has interrupted S1 (3.13 [pp.116-117: Bill spoke to
John; Bill John knew well --> Bill -- Bill John knew
well -- spoke to John]) immediately after the host. In
this situation the two words that are the same are most
often next to each other as in Bill -- Bill John knew
well -- spoke to John --> Bill, whom John knew well,
spoke to John. Although some of the sentences with
front positioning may seem uncomfortable when standing
alone, they are natural when interrupting a primary
sentence after the same word as they placed in front
                                [HARRIS82, pp.120-121]


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