GRAMMAR VIEWED AS A FUNCTIONING PART OF A COGNITIVE SYSTEM 
Helen M. Gigley 
Department of Computer Science 
University of New Hampshire 
Durham, NH 03824 
ABSTRACT 
How can grammar be viewed as a functional 
part of a cognitive system) Given a neural basis 
for the processing control paradigm of language 
performance, what roles does 'Sgrammar" play? Is 
there evidence to suggest that grammatical pro- 
cessing can be independent from other aspects of 
language processing? 
This paper will focus on these issues and 
suggest answers within the context of one com- 
putational solution. The example model of sen- 
tence comprehension, HOPE, is intended to demon- 
strate both representational considerations for a 
grammar within such a system as well as to illus- 
trate that by interpreting a grammar as a feedback 
control mechanism of a "neural-like" process, 
additional insights into language processing can 
be obtained. 
1. Introduction 
The role of grammar in defining cognitive 
models that are neurally plausible and psycho- 
logically valid will be the focus of this paper. 
While inguistic theory greatly influences the 
actual representation that is included in any such 
model, there are vast differences in how any 
grammar selected is "processed" within a "natural 
computation" paradigm. The processing does not 
grow trees explicitly; it does not transform trees 
explicitly; nor does it move constituents. 
In this type of model, a grammar is an ex- 
plicit encoded representation that coordinates the 
integrated parallel process. It provides the 
interfaces between parallel processes that can be 
interpreted within semantic and syntactic levels 
separately. It furthermore acts as a "conductor" 
of a time-synchronized process. Aspects of how a 
grammar might be processed within a cognitive view 
of sentence comprehension will be demonstrated 
within an implemented model of such processing, 
HOPE (Gigley, 1981; 1982a; 1982b; 1983; 1984; 
1985). This view of grammatical "process" sug- 
gests that neural processing should be included as 
a basis for defining what is universal in lan- 
guage. 
2. Background 
There are currently several approaches to 
developing cognitive models of linguistic function 
(Cottrell, 1984; Cottrell and Small, 1983; Gigley, 
1981; 1982a; 1982b; 1983; 1984; 1985; Small, 
Cottrell and Shastri, 1982; Waltz and Pollack, in 
press). These models include assumptions about 
memory processing within a spreading activation 
framework (Collins and Loftus, 1975; Hinton, 1981; 
Quillian, 1968/1980), and a parallel, interactive 
control paradigm for the processing. They differ 
in the explicit implementations of these theories 
and the degree to which they claim to be psycho- 
logically valid. 
Computational Neurolinguistics (CN), first 
suggested as a problem domain by Arbib and Caplan 
(1979), is an Artificial Intelligence (AI) ap- 
proach to modelling neural processes which sub- 
serve natural language performance. As CN has 
developed, such models are highly constrained by 
behavioral evidence, both normal and pathological. 
CN provides a framework for defining cognitive 
models of natural language performance of behavior 
that includes claims of validity at two levels, 
the natural computation or neural-like processing 
level, and at the system result or behavioral 
level. 
Using one implementation of a CN model, HOPE 
(Gigley, 1981; 1982a; 1982b; 1983) a model of 
single sentence comprehension, the remainder of 
the paper will illustrate how the role of grammar 
can be integrated into the design of such a model. 
It will emphasize the importance of the parallel 
control assumptions in constraining the repre- 
sentation in which the grammar is encoded. It 
will demonstrate how the grammar contributes to 
control the coordination of the parallel, asyn- 
chronous processes included in the model. 
The HOPE model is chosen explicitly because 
the underlying assumptions in its design are 
intended to be psychologically valid on two 
levels, while the other referenced models do not 
make such claims. The complete model is discussed 
in Gigley (1982a; 1982b; 1983) and will be sum- 
marized here to illustrate the role of the grammar 
in its function. The suggested implications and 
goals for including neurophysiological evidence in 
designing such models have been discussed else- 
324 
where in Lavorel and Gigley (1983) and will be 
included only as they relate to the role and 
function of the grammar. 
2. I Summary of Included Knowledge and its Repre- 
sentation 
Types of representations included in the HOPE 
model, phonetic, categorially accessed meanings, 
grammar, and pragmatic or local context, receive 
support as separately definable knowledge within 
studies of aphasia. There is a vast literature 
concerning what aspects of language are indepen- 
dently affected in aphasia that has been used as a 
basis for deciding these representations. (See 
Gigley, 1982b for complete documentation.) 
Information that is defined within the HOPE 
model is presented at a phonological level as 
phonetic representations of words (a stub for a 
similar interactive process underlying word re- 
cognition). Information at the word meaning level 
is represented as multiple representations, each 
of which has a designated syntactic category type 
and orthographic spelling associate to represent 
the phonetic word's meaning (also a stub). The 
grammatical representation has two components. 
One is strictly a local representation of the 
grammatical structural co-occurrences in normal 
language. The other is a functional repre- 
sentation, related to interpretation, that is 
unique for each syntactic category type. Please 
note that ~ ~ not used in the strictest sense 
of its use wlthln a t_~ semantic system. 
~TIF be des~ l~n detaiaT'-Ta't-e~T. Finally, the 
pragmatic interpretation is assumed to reflect the 
sentential context of the utterance. 
Each piece of information is a thresholding 
device with memory. Associational interconnec- 
tions are made by using an hierarchical graph 
which includes a hypergraph facility that permits 
simultaneous multiple interpretations for any 
active information in the process. Using this 
concept, an active node can be ambiguous, repre- 
senting information that is shared among many 
interpretations. Sentence comprehension is viewed 
as the resolution of the ambiguities that are 
activated over the time course of the process. 
Within our implementation, graphs can repre- 
sent an aspect of the problem representation by 
name. Any name can be attached to a node, or an 
edge, or a space (hypergraph) of the graph. There 
are some naming constraints required due to the 
graph processing system implementation, but they 
do not affect the conceptual representation on 
which the encoding of the cognitive linguistic 
knowledge relies. 
Any name can have multiple meanings asso- 
ciated with it. These meanings can be interpreted 
differently by viewing each space in which the 
name is referencea as a different viewpoint for 
the same information. This means that whenever 
the name is the same for any information, it is 
indeed the same information, although it may mean 
several things simultaneously. An example related 
to the grammatical representation is that the 
syntactic category aspect of each meaning of a 
phonetic word is also a part of the grammatical 
representation where it makes associations with 
other syntactic categories. The associations 
visible in the grammatical representation and 
interpreted as grammatical "meanings" are not 
viewable within the phonetic word meaning per- 
spective. 
However, any information associated with a 
name, for instance, an activity value, is viewable 
from any spaces in which the name exists. This 
means that any interpreted meaning associated with 
a name can only be evaluated within the context, 
or contexts, in which the name occurs. Meaning 
for any name is contextually evaluable. The 
explicit meaning within any space depends on the 
rest of the state of the space, which furthermore 
depends on what previous processing has occurred 
to affect the state of that space. 
2.2 Summary of the Processing Paradigm 
The development of CN models emphasizes 
process. A primary assumption of this approach is 
that neural-like computations must be included in 
models which attempt to simulate any cognitive 
behavior (Of Lavorel and Gigley, 1983), speci- 
fically natural language processing in this case. 
Furthermore, CN includes the assumption that time 
is a critical factor in neural processin~ 
mechanlsms an-~-d--that it can be a slgnlflcant factor 
in language behavior in its degraded or "lesioned" 
state. 
Simulation of a process paradigm for natural 
language comprehension in HOPE is achieved by 
incorporating a neurally plausible control that is 
internal to the processing mechanism. There is no 
external process that decides which path or pro- 
cess to execute next based on the current state of 
the solution space. The process is time-locked; 
at each process time interval. There are six 
types of serial-order computations that can occur. 
They apply to all representation viewpoints or 
spaces simultaneously, and uniformly. Threshold 
firing can affect multiple spaces, and has a local 
effect within the space of firing. 
Each of these serial-order computations is 
intended to represent an aspect of "natural compu- 
tation" as defined in Lavorel and Gigley, 1983. A 
natural computation, as opposed to a mechanistic 
one, is a "computation" that is achieved by neural 
processing components, such as threshold devices 
and energy transducers, rather than by components 
such as are found in digital devices. The most 
important aspect of the control is that all of the 
serial order computations can occur simultaneously 
and can affect any info'~m-atTo~-'that has been 
defined in the instantiated model. 
Processing control is achieved using activity 
values on information. As there is no preset 
context in the current implementation, all in- 
formation initially has a resting activity value. 
This activity value can be modified over time 
depending on the sentential input. Furthermore, 
there is an automatic activity decay scheme in- 
tended to represent memory processing which is 
325 
based on the state of the information, whether it 
has reached threshold and fired or not. 
Activity is propagated in a fixed-time scheme 
to all "connected" aspects of the meaning of the 
words by spreading activation (Collins and Loftus, 
1975; 1983; Hinton, 1981; Quillian, 1968/1980). 
Simultaneously, information interacts 
asynchronously due to threshold firing. A state 
of threshold firing is realized as a result of 
summed inputs over time that are the result of the 
fixed-time spreading activation, other threshold 
firing or memory decay effects in combination. 
The time course of new information introduction, 
which initiates activity spread and automatic 
memory decay is parameterized due to the under- 
lying reason for designing such models (Gigley, 
1982b; 1983; 1985). 
The exact serial-order processes that occur 
at any time-slice of the process depend on the 
"current state" of the global information; they 
are context dependent. The serial-order processes 
include: 
(1) NEW-WORD-RECOGNITION: Introduction of the 
next phonetically recognized word in the 
sentence. 
(2) DECAY: Automatic memory decay exponentially 
re-e'du'ces the activity of all active informa- 
tion that does not receive additional input. 
It is an important part of the neural pro- 
cesses that occur during memory processing. 
(3) REFRACTORY-STATE-ACTIVATION: ~-- _ -~ An auto- 
matic change of state that occurs after 
active information has reached threshold and 
fired. In this state, the information can 
not affect or be affected by other informa- 
tion in the system. 
(4) POST-REFRACTORY-STATE-ACTIVATION: ~ An 
automatic change of state which all fired in- 
formation enters after it has existed in the 
REFRACTORY-STATE. The decay rate is dif- 
ferent than before firing, although still 
exponential. 
(5) MEANING-PROPAGATION: Fixed-time spreading 
activation to the distributed parts of 
recognized words' meanings. 
(6) FIRING-INFORMATION-PROPAGATION: 
Asynchronous activity propagation that occurs 
when information reaches threshold and fires. 
It can be INHIBITORY and EXCITATORY in its 
effect. INTERPRETATION results in activation 
of a pragmatic representation of a dis- 
ambiguated word meaning. 
Processes (2) through (6) are applicable to 
all active information in the global representa- 
tion, while process (1) provides the interface 
with the external input of the sentence to be 
understood. The state of the grammar representa- 
tion affects inhibitory and excitatory firing 
propagation, as well as coordinates "meaning" 
interpretation with on-going "input" processing. 
It is in the interaction of the results of these 
asychronous processes that the process of compre- 
hension is simulated. 
3. The Role of a Grammar in Cognitive Processing 
Models 
Within our behavioral approach to studying 
natural language processing, several considera- 
tions must be met. Justification must be made for 
separate representations of information and, when- 
ever possible, neural processing support must be 
found. 
3.1 Evidence for a Separate Representation of 
Grammar 
Neurolinguistic and psycholinguistic evidence 
supports a separately interpretable representation 
for a grammar. The neurolinguistic literature 
demonstrates that the grammar can be affected in 
isolation from other aspects of language function. 
(Cf Studies of agrammatic and Broca's aphasia as 
described in Goodenough, Zurif, and Weintraub, 
1977; Goodglass, 1976; Goodglass and Berko, 1960; 
Goodglass, Gleason, Bernholtz, and Hyde, 1970; 
Zurif and Blumstein, 1978). 
In the HOPE model, this separation is 
achieved by including all relevant grammatical 
information within a space or hypergraph called 
the grammar. The associated interpretation func- 
tions for each grammatical type provide the in- 
terface with the pragmatic representation. Before 
describing the nature of the local representation 
of the currently included grammar, a brief dis- 
cussion of the structure of the grammar and the 
role of the grammar in the global nature of the 
control must be given. 
3.2 The Local Representation of the Grammar 
The grammar space contains the locally de- 
fined grammar for the process. The current model 
defined within the HOPE system includes a form of 
a Categorial Grammar (Ajdukiewicz, 1935; Lewis, 
1972). Although the original use of the grammar 
is not heeded, the relationship that ensues be- 
tween a well defined syntactic form and a "final 
state" meaning representation is borrowed. 
Validity of the "final state" meaning is not the 
issue. Final state here means, at the end of the 
process. As previously mentioned, typed semantics 
is also not rigidly enforced in the current model. 
HOPE a11ows one to define a lexicon within 
user selecte~ syntactic types, and a11ows one to 
define a suitable grammar of the selected types in 
the prescribed form as well. The grammar may be 
defined to suit the aspects of language per- 
formance being modelled. 
There are two parts to the grammatical aspect 
of the HOPE model. One is a form of the struc- 
tural co-occurrences that constitute context free 
phrase structure representations of grammar. 
However, these specifications only make one "con- 
stituent" predictions for subsequent input types 
where each constituent may have additional sub- 
structure. 
326 
Predictions at this time do not spread to 
substructures because of the "time" factor between 
computational updates that is used. A spread to 
substructures will require a refinement in time- 
sequence specifications. 
The second aspect of the representation is an 
interpretation function, for each specified syn- 
tactic type in the grammar definition. Each 
interpretation function is activated when a word 
meaning fires for whatever reason. The inter- 
pretation function represents a firing activation 
level for the "concept" of the meaning and in- 
cludes its syntactic form. For this reason, each 
syntactic form has a unique functional description 
that uses the instantiated meaning that is firing 
(presently, the spelling notation) to activate 
structures and relations in the pragmatic space 
that represent the "meaning understood." 
Each function activates different types of 
structures and relations, some of which depend on 
prior activation of other types to complete the 
process correctly. These functions can trigger 
semantic feature checks and morphological matches 
where appropriate. 
Syntactic types in the HOPE system are of two 
forms, lexical and derived. A lexical cateqory 
te~xle is one which can be a category type of a 
cal item. A derived cate_~o type is one 
which is "composed.-a~"-~erlved category types 
represent the occurrence of proper "meaning" 
interpretation in the pragmatic space. 
The current represented grammar in HOPE 
contains the following lexical categories: OET 
for determiner, ENOCONT for end of sentence in- 
tonation, NOUN for common noun, PAUSE for end of 
clause intonation, TERM for proper nouns, VIP for 
intrasitive verb, VTP for transitive verb. As is 
seen, the lexical "categories" relate 
"grammatical" structure to aspects of the input 
signal, hence in this sense ENDCONT and PAUSE are 
categories. 
The derived categories in the current in- 
stantiated model include: SENTENCE, representing 
a composition of agent determination of a TERM for 
an appropriate verb phrase, TERM, representing a 
composed designated DET NOUN referent, and VIP, 
representing the state of proper composition of a 
TERM object with a VTP, transitive verb sense. 
TERM and VIP are examples of category types in 
this model that are both lexical and derived. 
"Rules" in the independently represented 
grammar are intended to represent what is con- 
sidered in HOPE as the "syntactic meaning" of the 
respective category. They are expressed as local 
interactions, not global ones. Global effects of 
grammar, the concern of many rule based systems, 
can only be studied as the result of the time 
sequenced processing of an "input". Table l 
contains examples of "rules" in our current model. 
Other categories may be defined; other lexical 
items defined; other interpretations defined 
within the HOPE paradigm. 
Table l: Category specification 
DET: = TERM / NOUN 
VIP: = SENTENCE / ENDCOUNT 
VTP: = VIP / TERM 
In Table l, the "numerator" of the specifi- 
cation is the derived type which results from 
composition of the "denominator" type interpre- 
tation with the interpretation of the category 
whose meaning is being defined. For example, 
DETerminer, the defined category, combines with a 
NOUN category type to produce an interpretation 
which is a TERM type. When a category occurs in 
more than one place, any interpretation and re- 
sultant activity propagation of the correct type 
may affect any "rule" in which it appears. Ef- 
fects are in parallel and simultaneous. Inter- 
pretation can be blocked for composition by un- 
successful matches on designated attribute fea- 
tures or morphological inconsistencies. 
Successful completion of function execution 
results in a pragmatic representation that will 
either fire immediately if it is non-compositional 
or in one time delay if the "meaning" is composed. 
Firing is of the syntactic type that represents 
the correctly "understood" entity. This "top- 
down" firing produces feedback activity whose 
effect is "directed" by the state of the grammar, 
space, i.e. what information is active and its 
degree of activity. 
The nature of the research in its present 
state has not addressed the generality of the lin- 
guistic structures it can process. This is left 
to future work. The concentration at this time is 
on initial validation of model produced simulation 
results before any additional effort on expansion 
is undertaken. With so many assumptions included 
in the design of such models, initial assessment 
of the model's performance was felt to be more 
critical than its immediate expansion along any of 
the possible dimensions previously noted as stubs. 
The initial investigation is also intended to 
suggest how to expand these stubs. 
3.3 The Grammar as a Feedback Control System 
The role of the grammar as it is encoded in 
HOPE is to function in a systems theoretic manner. 
It provides the representation of the feedforward, 
or prediction, and feedback, or confirmation 
interconnections among syntactic entities which 
have produced appropriate entities as pragmatic 
interpretations. It coordinates the serial or- 
dered expectations, with what actually occurs in 
the input signal, with any suitable meaning in- 
terpretations that can affect the state of the 
process in a top-down sense. It represents the 
interface between the serial-order input and the 
parallel functioning system. 
Grammatical categories are activated via 
spreading activation that is the result of word 
meaning activation as words are recognized. 
Firing of an instance of a grammatical type acti- 
vates that type's interpretation function which 
327 
results in the appropriate pragmatic interpreta- 
tion for it, including the specific meaning that 
was fired. 
Interpretation function~ are defined for 
syntactic types not specific items within each 
type. Each type interpretation has one form with 
specific lexical "parameters"L A11 nouns are 
interpreted the same; a11 intransitive verbs the 
same. What differs in interpretation is the 
attributes that occur for the lexical item being 
interpreted. These also affect the interpreta- 
tion. 
The meaning representation for a11 instances 
of a certain category have the same meta- 
structure. General nouns (NOUN) are presently 
depicted as nodes in the pragmatic space. The 
node name is the "noun meaning." For transitive 
verbs, nodes named as the verb stem are produced 
with a directed edge designating the appropriate 
TERM category as agent. The effect of firing of a 
grammatical category can trigger feature propaga- 
tions or morphological checks depending on which 
category fires and the current pragmatic state of 
the on-going interpretation. 
Successful interpretation results in thres- 
hold firing of the "meaning." This "meaning" has 
a syntactic component which can affect grammatical 
representations that have an activity value. This 
process is time constrained depending on whether 
the syntactic type of the interpretation is lexi- 
cal or derived. 
3.4 Spreading Activation of the Grammar 
Input to HOPE is time-sequenced, as phone- 
tically recognized words, (a stub for future 
development). Each phonetic "word" activates all 
of its associated meanings. (HOPE uses homophones 
to access meanings.) Using spreading activation, 
the syntactic category aspect of each meaning in 
turn activates the category's meaning in the 
grammar space representation. 
Part of the grammatical meaning of any syn- 
tactic category is the meaning category that is 
expected to follow it in the input. The other 
part of the grammatical meaning for any category 
type, is the type it can derive by its correct 
interpretation within the context of a sentence. 
Because each of these predictions and interpreta- 
tions are encoded locally, one can observe inter- 
actions among the global "rules" of the grammar 
during the processing. This is one of the moti- 
vating factors for designing the neurally moti- 
vated model, as it provides insights into how 
processing deviations can produce degraded lan- 
guage performance. 
3.5 Grammar State and Its Effect on Processing 
Lexical category types have different effects 
than derived ones with respect to timing and 
pragmatic interpretation. However, both lexical 
and derived category types have the same effect on 
the subsequent input. This section will describe 
the currently represented grammar and provide 
example processing effects that arise due to its 
interactive activation. 
Through spreading activation, the state of 
the syntactic types represented in the grammar 
affects subsequent category biases in the input 
(feedforward) and on-going interpretation or 
disambiguation of previously "heard" words (feed- 
back). The order of processing of the input 
appears to be both right to left and left to 
right. Furthermore, each syntactic type, on 
firing, triggers the interpretation function that 
is particular to each syntactic type. 
Rules, as previously discussed, are activated 
during processing via spreading activation. Each 
recognized word activates all "meanings" in 
parallel. Each "meaning" contains a syntactic 
type. Spreading activation along "syntactic type 
associates" (defined in the grammar) predictively 
activates the "expected" subsequent categories in 
the input. 
In the HOPE model, spreading activation 
currently propagates this activity which is not at 
the "threshold" level. Propagated activity due to 
firing is always a parameter controlled percentage 
of the above threshold activity and in the pre- 
sently "tuned" simulations always propagates a 
value that is under threshold by a substantial 
amount. 
All activations occur in parallel and affect 
subsequent "meaning" activities of later words in 
the sentence. In addition, when composition 
succeeds (or pragmatic interpretation is 
finalized) the state of the grammar is affected to 
produce or changes in category aspects of all 
active meanings in the process. 
The remainder of this section will present 
instances of the feedforward and feedback effects 
of the grammar during simulation runs to illus- 
trate the role of grammar in the process. The 
last example will illustrate how a change in state 
of the grammar representation can affect the 
process. All examples will use snapshots of the 
sentence: "The boy saw the building." This is 
input phonetically as: (TH-UH B-OY S-AO TH-UH 
B-IH-L-D-IH-NG). 
3.5.1 An Example of Feedforward, Feedback, and 
Composition 
This example will illustrate the feedforward 
activation of NOUN for the DETerminer grammatical 
meaning during interpretation of the initial TERM 
or noun phrase of the sentence. At1 figures are 
labelled to correspond with the text. Each in- 
terval is labelled at the top, tl, t2, etc. The 
size of each node reflects the activity level, 
larger means more active. Threshold firing is 
represented as F~ Other changes of state that 
affect memory are are denoted (~ and~ and 
are shown for coa~leteness. They indicate 
serial-order changes of state described earlier, 
but are not critical to the following discussion. 
328 
II l| I$ 14 III 
r-a-,boy .... ~'z~i ( i ) 
/ ~/ . 
,' ~, ,~ ',~ 
l 1 'P / ~ 
t ,v 
(q) --', .' / 
PNO IIIT|~o I i (h) 
TH-UH B'<)¥ S-AO 
Figure 1 
On "hearing" /TII-UH/ (a) at tl, the repre- 
sented meaning "OET-the" is activated as the only 
meaning (b). At the next time interval, t2, the 
meaning of OET is activated - which spreads acti- 
vity to what OET predicts, a NOUN (c). A11 NOUN 
meanings are activated by spread in the next time 
interval, t3, in combination with new activity. 
This produces a threshold which "fires" the 
"meaning" selected (d). At completion of inter- 
pretation (e), in t4, feedback occurs to a11 
instances of meaning with category types in the 
grammar associated as predictors of the inter- 
preted category. OET is the only active category 
that predicts NOUN so all active meanings of type 
OET will receive the feedback activity. In Figure 
I, OET-the is ready to fire (f). The increase or 
decrease in activity of a11 related types, 
competitive ones for the meaning (inhibitory) (g) 
as well as syntactic ones for composition (ex- 
citatory) (f) is propagated at the next interval 
after firing, shown in t3 and t4. In tS, /S-AO/ 
enters the process (h) with its associated mean- 
ings. 
The effect of OET-the firing is also seen in 
t5 where the compositional TERM is activated (i). 
NOTE: DETerminers are not physically represented 
as entities in the pragmatic space. Their meaning 
is only functional and has a "semantic" combosi- 
tional effect. Here 'tne' requires a "one and 
only one" NOUN that is unattached as a TERM to 
successfully denote the meaning of the boy as a 
proper TERM (i). As this is a compositional 
"meaning", the firing will affect t6. Because 
there is no active TERM prediction in the grammar 
space, and no competitive meanings, the top-down 
effect in t6 will be null and is not shown. The 
next exa~le will illustrate a top-down effect 
following TERM composition. 
3.5.2 An Example of Feedforward, Feedback, 
Composition, and Subsequent Feedback 
This ex~,nple, shown in Figure 2, will be very 
similar to the previous one. Only active informa- 
tion discussed is shown as otherwise the figures 
become cluttered. The grammar is in a different 
state in Figure Z when successful TERM interpre- 
tation occurs at all (a). This is due to the 
activation at tg of all meanings of B-UI-L-O-IH-NG 
(b). 
The VTP meanings of /S-AO/ and then 
/B-UI-L-O-IH-NG/ make a TERM prediction shown as 
it remains in tlO (c). After composition of "the 
building" (a) shown in tel, TERM will fire top- 
down. It subsequently, through feedback,- acti- 
vates all meanings of the category type which 
predicted the TERM, all VTP type meanings in this 
case. This excitatory feedback, raises both VTP 
meanings in t12, for saw (d), as well as, building 
(e). However, the activity level of "building 
does" not reach threshold because of previous 
disembiguation of its NOUN meaning. When the VTP 
meaning, saw, fires (d) in t\]2, additional 
comoosition occurs. The VTP interpretation 
composes with a suitable TERM (a), one which 
matches feature attribute specifications of saw, 
329 
/ 
.tl 0 
I I I " 
PRJU3~TZC 
bu£1dinq 
f 
/ /.,::.--'!: 
ill (:12 
Figure 2. 
t 'O "" ¢ • ~ " -"- 
~',--, I v ,_'.'" ~ ,,_., * # 
0=- ~d~------------~/~"--~"-- ----." "~ L " i -"- 
"" ~" "~ " ~ ~ I " ~ "--\ L~WJ,h -- 
TEIm 
(b) 
S-AO ~ ~ ., 
8-ZH-L-O-Zn-,~ _-- .. .... .('o "~,- .... %_. 
f 
-- ,m~ (e) 
um 
.L • ) m, 
tl 9.Z t3 t4 
P#AOMAI~C: 
S-A~ 
Figure 3. 
~I-Ull 
330 
to produce a VIP type at t13 this will sub- 
sequently produce feedback at t14, Neither are 
shown. 
3.5.3 Effect of a Oifferent Grammar State on 
Processing 
The final example, Figure 3, will use one of 
the "lesion" simulations using HOPE. The grammar 
representations remain intact. This example will 
present the understanding of the first three words 
of the sentence under the condition that they are 
presented faster than the system is processing. 
Effectively, a slow-down of activation spread to 
the grammar is assumed. Figures such as Figure 1 
and Figure 3 can be compared a to suggest possible 
language performance problems and to gain insights 
into their possible causes. 
In Figure 3, when /TH-UH/ is introduced at tl 
Ca), all meanings are activated (b) as in Figure 
1. The spread of activation to the grammar occurs 
in t2 (c). However, the second word, /8-OY/ (d) 
is '*heard" at the same time as the activity 
reaches the grammar. The predictive activation 
spread From the grammar takes effect at t3, when 
the new word /S-N)/ (e) is "heard." The immediate 
result is that the NOUN meaning, saw (f), fires 
and is interpreted at t4 (g). 
This shows in a very simple case, now the 
grammar can affect the processing states of an 
interactive parallel model. Timing can be seen to 
be critical. There are many more critical results 
that occur in such "lesion" simulations that 
better i11ustrate such grammatical affects, how- 
ever they are very difficult to present in a 
static form, other than within a behaviorial 
analysis of the overall linguistic performance of 
the entire made1. This is considered an hypo- 
thesized patient profile and is described in 
Gigley (1985). Other examDles of processing are 
presented in detail in Gigley (lg82b; 1983). 
3.6 Summary 
The above figures present a very simple 
examole of the interactive process. It is hoped 
that they provide an idea of the interactions and 
feedback, feedfor~ard processing that is cooP- 
dinated by the state of the grammar. Any pre- 
diction in the grammar that is not sufficiently 
active affects the process. Any decay that ac- 
cidently reduces a grammatical aspect can affect 
the process. The timing of activation, the cate- 
gorial content and the interactions between in- 
terpretation and prediction are imbortant Factors 
when one considers grammar as part of a func- 
tioning ~ynamic system. 
Finally, the Categorial Grammar is one form 
of a Context-Free (CF) grammar which provides a 
suitable integration of syntactic and semantic 
processing. In addition, it has been used in many 
studies of English so that instances of gr~ars 
sufficiently defined for the current implementa- 
tion level of processing could be found. Other 
forms of grammar, such as Lexical-Functional 
Grammar (Kaolan and Bresnan, 1982) or Generelized 
Phrase Structure Grammar (Gazdar, 1982; 1983) 
could be edually suitable. 
The criteria to be met all that they can be 
encoded as predictive mechanisms, not necessarily 
unamOiguous or deterministic, and also that they 
specify constraints on compositionality. The 
composition depends on adequate definition of 
interpretation constraints to assure that it is 
"computed" properly or else suitably marked for 
its deviation. 
4. Conclusion 
HOPE provides evidence for how one can view a 
grammar as an integrated part of a neuraIly- 
motivated processing model that is psychologically 
valid. ~uitable constraints on grammatical form 
that are relevant for using any grammar in the CN 
context are t~at the grammar make serial predic- 
tions and provide the synchronization information 
to coordinate toD-down effects of interpretation 
with the on-going process. 
This type of model suggests that universals 
of language are inseparable from how the are 
computed. Universals of language may only be 
definable within neural substrata and their pro- 
cesses. Furthermore, if this view of linguistic 
universals holds, then grammar becomes a control 
representation that synchronizes the kinds of 
signals that occur and when they get propagated. 
The states of the grammar in this suggested view 
of grammatical function are a form of the rewrite 
rules that are the focus of much linguistic 
theory. 
A neurally motivated processing paradigm for 
natural language processing, demonstrates now one 
can view an integrated process for language that 
employs integrated syntactic and semantic pro- 
cessing which relies on a suitable grammatical 
form that coordinates the processes. 
S. Acknowledgements 
The initial development of the reported 
research was supported by an Alfred P. Sloan 
Foundation Grant for "A Training Program in Cog- 
nitive Science" at the University of Massachusetts 
at Amherst. Continuing development is subported 
through a Biomedical Research Support Grant at the 
University of New Hamoshire. 
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