Testing The Psychological Reality 
of a Representational Model 
Dedre Gentner 
Bolt Beranek and Newman Inc. 
ABSTRACT 
A research program is described in which 
a particular representational format for 
meaning is tested as broadly as possible. In 
this format, developed by the LNR research 
group at The University of California at San 
Diego, verbs are represented as interconnected 
sets of subpredicates. These subpredicates 
may be thought of as the almost inevitable 
inferences that a listener makes when a verb 
is used in a sentence. They confer a meaning 
structure on the sentence in which the verb is 
used. To be psychologically valid, these 
representations should capture (at least): 
I. Similarity of meaning 
The more similar two verbs seem in 
meaning to people, the more their 
representations should overlap. 
2. Confusability 
The more confusable two verb meanings 
are, the more their representations 
should overlap. 
3. Memory for sentences containing the 
verb 
The sentence structures set up by the 
verb's meaning should in part 
determine the way in which sentences 
are remembered. 
4. Semantic integration 
The representations should allow for 
the integration of information from 
different sentences into discourse 
structure 
5. Acquisition patterns 
The structural partitions in the 
representations should correspond to 
the structures children acquire when 
they are learning the meanings of the 
verbs. 
6. Patterns of extension 
The representations should be 
extendible so as to reflect the ways 
in which people interpret verb 
meanings when the verbs are used 
outside their normal context. 
7. Reaction times 
The time taken to comprehend a 
sentence using a given verb should 
reflect the structural complexity of 
the verb meaning. 
Experiments concerned with predictions 
I-5 are described here. The results are 
promising for a general approach of 
representation of meaning in terms of 
interrelated subpredicates, but do not clearly 
distinguish between several similar 
representations. For example, to test 
prediction (2), I read people sentences 
containing verbs with similar meanings, and 
asked them to recall the sentences. The 
degree of overlap in the semantic structures 
was a good predictor of the number of 
confusions between sentences. In another 
sentence-memory experiment (prediction (3)), 
semantically complex verbs that provided more 
underlying interconnections between the nouns 
in a sentence led to better memory for the 
nouns in the sentence than simple general 
verbs, or than other complex verbs that did 
not provide such extra interconnections. To 
test prediction (5), I tested children's 
comprehension of a set of possession verbs. 
Both the order of acquisition among the verbs 
and the kinds of errors fitted well with an 
account of the acquisition of verb meaning in 
terms of interconnected subpredicates. 
This research illustrates a breadth-first 
approach to testing a representation. In the 
breadth-first approach, many different 
psychological predictions are made. Each 
different area of prediction requires a set of 
process assumptions, and in each case the 
process assumptions used are those that seem 
most plausible given previous research in the 
field. If one representational format can 
make correct predictions about a number of 
different kinds of psychological phenomena, 
then that representation stands a greater 
chance of being generally useful than one 
which was tested in only one depth-first way. 
This paper describes a program of 
research that tests a representational format 
for verb meaning. This research grew out of 
the LNR (Footnote I) attempt to the represent 
the meanings of words in a psychologically 
satisfying way. Verb meaning seemed a natural 
place to start for two reasons: (I) verbs are 
important: it is arguable that they provide 
the central organizing semantic structures in 
sentence meanings; and (2) verbs are 
tractable: their meanings are more easily 
analyzed than those of, for example, common 
nouns. 
Since different disciplines look at 
meaning in different ways, it may be 
worthwhile to describe the stance we took. 
What we wanted was a system of representation 
in which we could capture our intuitions about 
what a word typically conveys; or more 
specifically about the inferences a person 
normally makes (or believes should be made) 
when a word is used. The assumption is that 
the same representations operate when a person 
uses the word in speech as when the person 
comprehends it; however the methodology of 
experimental psychology makes it natural to 
spend more time pondering the input process 
than the output process. This approach 
differs from thinking of meaning in terms of 
necessary and sufficient truth-conditions, as 
many philosophers have done, or from thinking 
about meaning in generation rather than in 
comprehension, as many linguists have done. 
Each of those stances leads to useful 
intuitions. Overall, there has been a 
reassuring degree of convergence between the 
representations proposed. 
Representation of Verb Me~ning 
There are many notational systems for 
representation of verb meaning (e.g., 
Abrahamson, 1975; Chafe, 1970; Fillmore, 1971; 
Gentner, 1975; Lakoff, 1970; McCawley, 1968; 
Rumelhart & Levin, 1975; Schank, 1972, 1975; 
Talmy, 1975). These models of verb meaning 
differ from one another in detail, but there 
is widespread agreement on the idea that verb 
meanings can be represented in terms of 
interrelated sets of subpredicates, such as 
CAUSE or CHANGE. These subpredicates are not 
merely concatenated within a word's 
representation. Rather, they are 
interrelated, in specific ways. 
Representations of verb meaning include 
notation for specifying the relationships 
among the subpredicates that make up a word's 
meaning. The notation developed by the LNR 
Group is a network format. In this system of 
representation, verb meanings are expressed 
in terms of subpredicates that stand for 
states, changes of state, aetionals, etc. 
The El~ents of Verb Meaning. Verbs 
provide a system in which people can talk 
about happenings in the world, implicitly 
distinguishing several types of conceptual 
possibilities. The simplest of these is the 
state. A stative predicate conveys a 
relationship that endures for a period of time 
between two arguments, normally an object (or 
person) and an object or value within the 
conceptual field specified by the stative. 
For example, consider the sentence shown in 
Figure I. 
Ida owned a Cadillac from 1970 to 1977. 
The verb o~n conveys that a relationship of 
possession existed between Ida and the 
Cadillac for some duration. Besides statives 
for possession, there are a large number of 
other statives, including location (to be a~, 
$o remain a~, etc.) and emotion (to hate, to 
~, etc.) . 
In addition to simple stative 
relationships, verbs can be used to convey 
changes of state. Following Chafe (1970) I 
will refer to a change of state as a oroces~. 
For example, the sentence 
Ida receives $10.00. 
tells us 
(I) that Ida now has $10.00 
(2) that someone else had the $10.00 before; 
(3) that a change has taken place from this 
previous state of possession to the 
present state. 
More commonly, verbs express not simple 
changes of state but causal changes of state. 
We seem to be very interested in processes 
that are volitionally caused by humans and 
other sentient beings. Figure 2 shows the 
representation of the sentence: 
Ida gives Sam a rose. 
An agent may cause a change of state that 
relates to another object. Or the same person 
may act on both agent and experiencer of the 
change of state. The locational verb move can 
be used in either way, as in the following 
examples: 
a. Ida moved the car. 
b. Ida moved to the front seat. 
In both these casesthe action taken by Ida is 
unspecified. We often don't care exactly what 
someone did to cause some process to occur. 
However, there are also verbs in which the 
causal action is partially or wholly 
specified: e.g., ~L~!_k, saunte~r, meander, 
stride, ru~, sprint, race, ~rob, log. (See 
Miller (1972) and Miller & Johnson-Laird 
(1976) for a more extensive discussion of the 
verbs of location.) 
Thus, this system allows for the 
representation of verbs as states, changes of 
state, causal changes of state, simple 
actions, and complex cases in which specific 
actions cause changes of state. Further 
discussion of the LNR system of verb semantics 
can be found in the articles by Abrahamson, 
Gentner, Munro, Rumelhart & Levin, and 
Rumelhart & Norman in the Norman & Rumelhart 
(1975) volume. 
There are certainly gaps in the system, 
and aspects of verb meaning that are not 
expressible in this simple vocabulary. Some 
unresolved issues are discussed later in the 
paper. However, the system seems plausible at 
the first level, and allows a fair range of 
verb meanings to be captured at least roughly. 
At this point in the research it seemed 
appropriate to begin testing the psychological 
rightness of the system as so far stated 
before going on to refine it. 
Psychological Tests of ~he Model 
One advantage of psychological 
experimentation (or of computer 
implementation) is that it forces one to make 
explicit the assumptions underlying 
representation and process. At least some of 
the choices made can then be tested as 
hypotheses. Some important assumptions are 
(I) a verb's representation captures the 
set of immediate inferences that people 
normally make when they hear or read a 
sentence containing the verb; 
(2) in general, one verb leads to many 
inferences 
(3) these networks of meaning components 
are accessed during comprehension, by an 
immediate and largely automatic process 
(4) the set of components associated with 
a given word is reasonably stable across 
tasks and contexts 
(5) surface memory for exact words fades 
quite rapidly, so that after a short time, 
only the representational network remains. 
In testing these representations, I 
took a very literal interpretation of the 
notion of representation -- namely that 
the nodes and arrows in a representation 
correspond to the concepts and 
relationships that are stored when a 
person comprehends a sentence containing a 
verb. The more ferociously literal the 
interpretation, the better the chances of 
discovering counter-evidence. 
Semantic overlao. One psychological 
criterion is that the representations should 
agree with people's intuitive notions of 
synonymity and similarity in meaning. One 
straightforward measure of this overlap is the 
degree to which people rate verbs as similar 
in meaning. In a study of about 60 selected 
verbs, I found that people's average rating of 
the semantic similarity between two verbs 
agreed very closely with the degree of 
semantic overlap between their 
representations. 
A more subtle measure of psychological 
similarity is the degree to which people 
unconsciously confuse things in memory. 
People in a sentence-memory experiment 
probably try to keep their sentence traces 
clear. But, suppose that within a short time 
after hearing a verb in a sentence, a person 
has only the representational network of 
concepts and relationships, and not the 
surface verb. Assume further that some pieces 
of the memory representation may be lost or 
unaccessible at any time (the "fallibility of 
human memory" assumption). Then the more two 
verb representations overlap, the more likely 
it is that sentences containing the two verbs 
will be confused in memory, despite people's 
attempts to keep them straight. In an 
experiment in sentence memory, using verbs of 
varying semantic overlap, I found that 
subjects did indeed confuse the verbs in 
exactly the way predicted by the theory 
(Gentner, 1974). The correlation between the 
number of confusions subjects made between two 
verbs and the semantic overlap between the 
verbs, as predicated from the representations, 
was quite high. In fact, the correlation 
between representational overlap and number of 
confusions was slightly higher (though not 
significantly so) than the correlation between 
the number of confusions and the rated 
similarity between the verbs. (The similarity 
ratings were taken from the first-mentioned 
study, with a different set of subjects). 
Semantic complexity. Semantic complexity 
refers to the number of underlying 
subpredicates and interconnections that make 
up the basic meaning of a verb. More complex 
meanings correspond to more specific actions 
or events. For example, stride is more 
specific than go. Its meaning contains more 
subpredicates. We know more having heard 
sentence (a) than sentence (b). 
(a) Ida strode across the field. 
(b) Ida went across the field. 
Various researchers have looked for evidence 
that semantic complexity ~ay affect 
comprehensibility, generally on the assumption 
that more complex semantic structures are 
harder to process (Kintsch 1974; Thorndyke, 
1977). However, the results have been 
negative. There is no evidence that more 
complex words lead either to longer 
reaction-times or to greater processing loads 
than do simpler words. I believe that it's 
incorrect to assume across the board that 
complexity is psychologically hard. Some 
research of mine suggests that the effects of 
semantic complexity in memory are more 
particular. 
Semantic Complexity and Connectivity. 
Although the view that semantic complexity 
leads to difficulty has not been supported, 
there is another side to the complexity issue. 
The additional semantic components in a 
complex verb may set up additional connections 
among the nouns in the sentence. In this 
case, more complex verbs should lead to a 
richer and more highly interwoven sentence 
representation, and thus to better memory for 
the nouns in the sentence. 
Notice that this prediction derives from 
a fanatically literal interpretation of the 
verb representations: more paths in the 
representation means more conceptual paths in 
memory. This prediction is quite specific. 
It is not simply a question of certain complex 
versus simple verbs having some overall 
effect, but rather of complex verbs providing 
extra connections between the particular nouns 
in question. This is clearly true for Ida and 
her tenants in the case of sell versus give, 
as can be seen in Fig 3a and 3b. 
I tested for this kind of improvement in 
connectivity in a series of experiments in 
sentence memory (Gentner, 1977). I read 
people sentences that differed in the semantic 
connectivity of their verbs, such as the 
following pair of sentences: 
Ida gave her tenants a clock. (simple) 
Ida sold her tenants a clock. (complex 
connective) 
Then I gave the people the names of the 
characters and asked them to recall the 
sentences. As predicted, they were better 
able to recall the noun tenants when the 
complex connective verb sell was used then 
when the simple verb give was used. More 
semantic connections between the two nouns led 
to stronger memory connections. 
To see the specificity of the prediction, 
consider a complex verb that merely amplifies 
the simple verb and does not add connections 
between the key nouns. For example, the verb 
mail (Fig 3c) adds the information that the 
method of transfer was by mailing or some such 
long-distance transfer. Using mail leads to 
more inferences (a more specific event 
description) than using give. However, the 
knowledge that the object was mailed leads to 
few, if any, additional connections between 
the agent, Ida, and the recipient, tenants. 
Therefore, the prediction was that use of such 
non-connecting specific verbs would lead to no 
improvement over use of general verbs in 
memory between the nouns. 
The results were exactly as predicted: 
The object nouns of complex connective verbs 
were recalled better than those of general 
verbs and non-connecting complex verbs. These 
differences were not traceable to differences 
in imagery or word-frequency. Thus 
connectivity is beneficial to sentence memory 
in a very specific way. 
Accuis~ion. There may be a more direct 
relationship between complexity and difficulty 
in children than in adults. Young children 
often fail to comprehend the full meanings of 
semantically complex terms (e.g., Bowerman, 
1975; Clark, 1973; Gentner, 1975, in press). 
Working with the verbs of possession, I have 
observed that children act out the simple 
verbs gLve and takacorrectly before they act 
out the more complex verbs b~¥ and trade. 
Still later they learn the yet more complex 
verbs bu¥, sell and spend. The order in which 
the verbs are learned is exactly the order of 
increasing semantic complexity. This 
complexity ordering can be made quite precise, 
since the verbs are closely related in 
meaning. The representation of a verb at the 
nth level of simplicity is properly nested 
within the representation of a verb at the 
(n+1)th level. Further, when children around 
4-6 years are asked to act out sell (as in 
"Make Ernie sell Bert a boat.") they act out 
give instead (A boat is transferred from Ernie 
to Bert). Similarly, bu~ is acted out as 
take. They systematically act out complex 
verbs like simple verbs; and more 
surprisingly, they choose the appropriate 
simple verb. My interpretation, consistent 
with Clark's (1973) semantic features 
analysis, is that children learn these complex 
verb meanings gradually, by adding components 
to their partially correct representations. 
At any given time, the child comprehends 
language in terms of the components that he 
has so far acquired. 
Semantic ~ntegration. Another important 
psychological requirement is combinability. 
The basic notions of state, change of state, 
cause, and so on must be combinable into 
networks larger than the individual sentence. 
When two verbs share parts of their underlying 
structure, this redundancy should be utilized 
to combine the two representations into one 
discourse structure. How can we test whether 
this happens? One way is to arrange things so 
that collapsing the redundancies between two 
verbs should create the representation of a 
third verb. Then the prediction is that 
people should use this third verb in recall. 
In a study of semantic integration, I 
read people short passages and tested their 
memory by having them fill in blanks (Gentner, 
1978). Every passage contained a general 
verb, such as give. Half the passages also 
contained additional semantic information, 
such as the fact that the giver actually owed 
the money he was giving. According to the 
representational model, the integration of the 
representation of give with that of owing 
should have created the structure of pay. If 
what people have in their minds after hearing 
the verbs is the network representations, and 
if these representations are integrated during 
discourse comprehension, then people who heard 
give and owe should end up with the 
representation of DaY. As predicted, subjects 
hearing the extra material falsely recalled 
the verb which best fit the composite 
structure (e.g. nay) rather than the verb 
actually presented. 
Further lssues 
I have made the assumption that a verb 
carries with it a set of inferences that are 
normally made during comprehension, as well as 
several supporting assumptions. This view has 
been fairly well supported by the research 
presented here, but nevertheless it seems to 
me an oversimplification. There remain a 
great many questions, some large and some 
small. 
(I) Where should the line be drawn around a 
word's meaning? As Clark and Clark (1977) 
have put it, is word meaning more like a 
dictionary or an encyclopedia? The extreme of 
the dictionary approach would be to take a 
minimal contrast approach, storing with a word 
only enough to distinguish it from all other 
words. The extreme of the encyclopedia 
approach would be to access the entire 
long-term memory whenever any word is used. 
The question is, how to define a reasonable 
middle ground. 
(2) What is the process of expansion into a 
semantic representation during comprehension? 
a) Are there invariable inferences? When 
an incoming word is processed, is there 
a set of inferences (such as the set I 
have called the "almost-inevitable 
inferences" that is always made during 
comprehension, or is there variation in 
which inferences get made? 
b) If there is variation, is it 
quantitative or qualitative? Do context 
and the person's interests and attention 
determine W~c~ inferences get made, so 
that there are qualitative differences 
in what inferences get made? Or is the 
difference merely quantitative, with the 
radius of expansion varying with the 
amount of attention (or energy, or 
interest) that the person brings to 
bear? 
The notion of at least quantitative 
variation a seems hard to avoid. It is a 
fairly strong intuition that we process word 
meanings with varying degrees of energy. 
Further, the phenomenon of in~tantiation 
(Anderson, R.C., Stevens, K.C., Shifrin, Z., & 
Osborn, J.; 1977) makes it clear that a model 
of sentence comprehension must allow for 
qualitative differences in the final set of 
inferences stored. For example, compare the 
sentences 
Rover ate his dinner. 
Mr. Pritchard ate his dinner. 
The verb eat conveys vastly different action 
sequences when used with different agents, 
though its causal change-of-state structure 
remains more-or-less constant. It is possible 
that this qualitative variation can be 
accounted for by simple underlying 
quantitative processes spreading activation. 
We may have to settle for a more complex 
model, in which some parts of a verb's meaning 
are almost always accessed while other 
inferences develop out of the interaction of 
the verb with its context, including its 
pragmatic context. In Hewitt's (1976) terms, 
there may be both if-added inferences and 
if-needed inferences. Where in this model 
(and whether) we want to draw a line between 
meaning and knowledge-of-the-world is not at 
all clear to me. (3) Carrying the notion of 
variable verb meaning still further, how does 
metaphorical extension work? Most common 
verbs can be used in several related ways. 
For example, consider the range of meanings 
that give can convey depending on the nouns it 
is used with: 
Ida gave Sam 
a rose. 
a job. 
an heir. 
an excuse. 
a talking to. 
all his best ideas. 
the time of his life. 
Clearly the subpredicate structure varies 
across these sentences, so much so that some 
might want to describe this as a collection of 
entirely different senses of the same word. 
This misses the structural similarities. Some 
kind of metaphorical extension of meaning 
seems a necessary part of a theory of verb 
meaning, since it is generally the verb that 
does most of the adjusting. A series of 
studies by Albert Stevens and me suggests that 
people faced with an odd sentence assume that 
some of the subpredicates normally conveyed by 
the verb are not meant to apply in the 
sentence at hand. A current project is to 
model the rules for which subpredicates apply 
in different contexts. 
(4) I have so far treated nouns as nodes in 
the semantic representation. Clearly in order 
to analyze sentence interactions it is 
necessary to have a representation of noun 
meaning. Some progress been made with 
abstract nouns, such as kinship terms. But 
the truly nounlike nouns ---basic-level 
nouns--- resist analysis. I believe that 
these differences in amendability to analysis 
reflect differences in the kind of meaning 
that verbs and nouns have, and that a useful 
representation of concrete noun meaning may be 
quite different from that used for verbs, 
prepositions and even abstract nouns. 
(5) There are several aspects of the 
representational scheme that need further 
thought. To single out one issue, consider 
the notion of change of state. The LNR 
representation represents a verb like get as 
conveying a change from an initial state of 
possession to a final state of possession. 
Schank's Conceptual Dependency theory would 
represent the entire sequence as a primitive 
act. Many generative semanticists have 
represented only the inchoative part of the 
chain (the change to the final state) as 
belonging to the assertion of the verb, 
considering the initial state to be more in 
the nature of a presupposition (e.g. Fillmore, 
1966). All these positions seem to me to have 
merit. The LNR use of change from initial to 
final state allows a change-of-state verb to 
hook automatically with relevant state 
information. The use of acts as primitives 
captures the psychological wholeness of 
change. The use of the inchoative captures 
the intuition that people seem more interested 
in the results of an event --i.e. in the final 
state-- than in the setting state. The 
explicit change-of-state formats (LNR format 
and inchoative format) have a natural way of 
capturing some kinds of metaphorical 
extension: by substituting a different stative 
while preserving the rest of the verb's 
structure. 
Summary 
This work is just beginning. Neither the 
representations nor the processes that are 
assumed to operate on them come very close to 
capturing the subtlety of human language use. 
Still, the results of the experimental 
investigation are promising some kind of 
decompositional model along these lines. 
exper~ 
Ida Cadillac 1970 1977 
Figure i. Ida owned a Cadillac from 
1970-1977. 
event~result 
Experiencey Object~i #bjec, ~.periencer 
Ida rose Sam 
Figure 2. Ida gives Sam a rose. 
Ida mailed her tenants a clock 
. ....-" ............ ..o o '~'~,, .. 
*"" ',,/ ~ °'" ''.. 
,;o ,d~ o,o.; ,en~.s . ~o \,o:k ,e~onts 
SPECIFIC VERB (FEW CONNECTING P~HS) 
Figure 3e, 
Footnote 
i. The repres~,~t~Eional format shown here was 
developed by a group of researchers at the 
University.of California at San Diego: 
Adele A. Abrahamson, Dedre Gentner, James A, 
Levin, Stephen E. Palmer, and David E. 
Rumelhart. The system is explained in detail 
in Norman & Rumelhart, 1975. 
Ida gav.ee her tenants a clock 
E~ I! 
Ida clock tenants 
GENERAL VERB (FEW CONNECTING PATHS) 
Figure 3a. 
Ida sold her tenants a clock ~~, 
¢ ...... ~-" ~;( ~\ ,,. 
Ida ..-" ~ .... .. Ida tenants ~_Jus~ 
.'" EvenJ~ -- ~Result "",, Result/~ ~Even ." j ~', ".. y ~.~. 
~/ \o O/ E\i/~ '~o O,'---\S 
Ida clock tenani~': money Ida • ..., o. , ,, ,o. ,,,,° ........... ,.,-" 
SPECIFIC VERB (MANY CONNECTING PATHS) Figure 35. 
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