Reprinted from Machine Intelligence 1, edited
by N. L. Collins and D. Michie, Oliver & Boyd,
Edinburgh and London, 1967.
Printed in Great Britain by T. & A. Constable Ltd., Edinburgh
I
10
NETWORKS AS MODELS
OF WORD STORAGE
G. R. KISS
BIRKBECK COLLEGE
UNIVERSITY OF LONDON
This paper is an attempt at the formulation of the processes which could
account for the behaviour of human beings when they use meaningful words
to communicate with each other. The two main aspects of this problem
are the selection and recognition of words. This paper will suggest a possible
model of these processes under certain restricted conditions. Ultimately
the word storage problem will have to be put into its proper perspective in
the setting of continuous language behaviour: the generation and reception
of connected human discourse. It is needless to emphasise that we are far
from achieving this aim. Correspondingly, the research reported here deals
with the simplestkinds of situations in which word meanings play a significant
role.
These simple situations have been much studied in a field of psychology
which is now generally called verbal learning. In particular, the basic, free
word association experiment could be regarded as perhaps the simplest
paradigm of a situation in which most ofthe important semantic and pragmatic aspects of word handling are involved. One of the later sections will
give a description of the word association process in terms of the model
suggested.
The main motivation behind the research reported here is psychological.
Although the model to be described may lead to mechanisms that may prove
useful in constructing intelligent machines, this is accidental. The main aim
has been to deepen our understanding of how human beings understand
words. In a sense, this is a more stringent requirement than the construction
of a machine which could understand words. For it is not sufficient to
build a mechanism which can fulfil a certain function, but it is necessary to
enquire whether that mechanism is similar to the one employed by humans.
155
COGNITIVE PROCESSES: METHODS AND MODELS
To that extent, where artificial intelligence ends, the simulation ofbehaviour
begins. The achievement of a working model is only the starting point.
VERBAL LEARNING AND VERBAL BEHAVIOUR
It would be out of place here to attempt a general survey of this field on
which there is a vast and scattered literature. We shall confine ourselves to
mentioning a sample of interesting findings in order to illustrate the nature
ofthe phenomena to which the model is assumed to be relevant.
Mediated transfer of learning is the name given to a class of phenomena
in which the effects of existing associative connections on the transfer of
associative learning manifest themselves. Jenkins (1963) reviews a large
number of experiments of this kind. To give a simple example, suppose a
nonsense syllabic, dax, is learned as a paired associate to the word war.
We know from word association frequency data collected from large samples
of subjects that a frequent association to war is peace, and that in turn
peace elicits justice with a high frequency. It is also known that war docs
not elicit justice. If now dax is learned as a paired associate with justice,
we find that there is a considerable saving compared with a control group of
subjects who did not learn the dax-war pair. Apparently the existing
associative chain, war-peace-justice, transfers the effects of learning through
the mediating word, peace. A criticism which can be directed towards most
of these experiments is that in selecting specific associative chains for experimentation they fail to recognise the fact that these chains are embedded in
a network of associative connections, so that many alternative paths may
exist, possibly working against the desired experimental effect.
A number of studies have been done by Razran (1939) and others on
semantic generalisation. Semantic generalisationrefers to the phenomenon
that a conditioned response (e.g., salivation) will generalise from one word
to other words which have a similar meaning.
Interesting clustering phenomena have been discovered by Bousfield,
Cohen & Whitmarsh (1958) in the free recall of lists composed of taxonomically related words. The words were presented in a randomised order, but
during free recall it was found that the words belonging to the same taxonomic
group (e.g., names of professions) tended to occur together. Similar clustering effects were found also with lists composed of associatively related
words (Jenkins, Mink & Russell 1958). A related clustering effect was
found by Bousfield & Sedgewick (1944) in the production of restricted
associations, like naming as many quadruped animals as possible. They
found that associations tend to occur in 'bursts' separated by longer time
intervals, and that the clusters produced in this way consist of semantically
related items. The curve showing the cumulative number ofresponses against
time is well approximated by an exponential function.
The influence of associative connections between words of a list presented
for free recall on the number of items recalled has been studied by Dccsc
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(1959). It was found that the greater the associative cohesion of a list, the
better the recall performance. The intrusion of words not on the original
list was also related to cohesion.
WORD
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ASSOCIATIONS
The basic word association experiment consists of the presentation of a
stimulus word to which the subject has to respond with the first word that
comes to his mind. There are many variations on this basic theme. The
responses can be restricted to certain classes of words. Several responses
in a series may be requested instead of a single word. Several words may be
presented instead of a single stimulus. The method of presentation can be
auditory or visual, the response oral or written, and so on.
When the experiment is repeated with a sample of subjects a striking
degree of agreement is observed as to the most frequent response words.
The frequency distribution is usually of a very steep, highly skewed form. An
example, taken from Russell & Jenkins (1954), is shown in Table 1. Tables
like this, which are now widely collected from extensive samples of subjects,
are obtained by presenting subjects with lists of stimulus words and asking
them to respond to each word with the first word that comes to their mind.
The form of this frequency distribution has been shown by Skinner (1937)
to conform to Zipf's law which states that if the frequencies of words in a
sample are plotted on logarithmic scales against the ranks of the words, a
straight line results. Howes (1957) has also shown that the sums of the
associative frequencies taken over a hundred different stimulus words
correlate highly with the frequencies of those words in general usage. These
results seem to indicate that the word association experiment is a valid
approach to a study of general language behaviour, at least in these overall
statistical aspects.
Table 1
STIMULUS: Butterfly
RESPONSES:
1
144 moth
117 insect
104 wing(s)
84 bird
78 fly
62 yellow
42 net
9 worm, beauty
7 flying
6 animal, beautiful
5 blue
4 flight, caterpillar, Madam, opera
3 ant, spring, butter, catch, dance, flutter
2 light, warp, jet plane, sky, soft, mouse, colourful, song, dragonfly, Lepidoptcra, dog
orange, spots, warm, lagoon, sunshine
1 roach, tree, feather, delicate, thing, grace, car, canary, butterfly's, fragile, flap, swim,
curtain, maggot, girl, air, morn, hobby, smooth, felt, hornet, dainty, robin, grass,
mosquito, buttercup, silk, Orthoptera, wheat, game, collection, field, louse, tiger,
California, blind, cow, chase, watch, Cecropia, outside, woods, dust, Spanish fly,
freedom, nothing, nature, flea, sun, zoology, garden, Japan, look, dandelion, crazy
157
34 pretty
33 flowcr(s)
27 bug
.
23 cocoon
22 summer
21 colour(s)
20 monarch, bce(s)
14 stomach
COGNITIVE processes: METHODS AND models
Another stable relationship which has been observed by several investiga-
tors is expressed by Marbe's law. It states that the time taken by subjects
to
produce an association is a logarithmic function ofthe associative
frequency
ofthe response word.
Semantic satiation is-another phenomenon which is of considerable interest
for any theory of connotative meaning. When a word is repeated or inspected
for a prolonged period of time, the subject usually reports that the word
loses or changes its meaning. Associative satiation refers to the fact that the
satiated word will elicit a larger number of irrelevant and rare associations.
ELEMENTS OF AN INFORMATION PROCESSING THEORY FOR WORD
STORAGE
The aim of this theory is to specify a set of hypothetical processes
and
structures which are capable of accounting for the kinds of behaviour
described in the previous sections. The method used for the formulation of
this theory is the construction of an information processing model which can
be programmed for a digital computer. Then its behaviour will be studied
and compared with human behaviour, leading to a successive refinement and
elaboration of the model.
A novel feature of this model is that it is designed with a view to on-line
computer-controlled experimentation in which the model will interact
directly with human beings. This will serve two purposes. First, the
machine can be used as an automated data collector. Second, psychological
experiments can be set up in which the subject is put into a dynamic situation
where the changes arc made dependent on the subject's previous behaviour.
These two points will be further discussed later.
THE POSTULATES
1. Human beings have two qualitatively different resources for information
processing: (a) serial (a central processor), (b) parallel (a network of interconnected elements).
2. The serial and parallel processors interact. The serial processor can
create, eliminate and activate the elements ofthe network. It can 'read' the
current status of these elements, and it can create or eliminate the
links
between them. The central processor can gain access to information stored
or associated with these elements.
3. The structure and functions of the serial processor closely resemble
the central processors of digital computers. It has a limited-capacity fast
access memory. It is sequential in operation and can do only one thing at
a
time. It is capable of executing a set of elementary operations and programs
composed out of such operations. This postulate reflects the
of
higher levels of human consciousness and attention.
4. The parallel processor consists of a set of elements which arc interconnected by a network of transmission links. The elements arejntcn__r
representations of words. ' They consist of a set of storageccfis which contain
158
properties
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KISS
all the information pertaining to an element. This information includes the
following items: (a) excitation level' (long-term and short term), (_.) a set of I
pointers to a relational store (sec below), (c) a set of pointers to other
elements of the network.
The transmission links are physical linkages having a certain capacity for
transmitting excitation to other elements. This transmittancc value is
variable!.
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5. The spread of excitation along the transmission links is governed by
the rules of signal flow graphs. Following the conventions first described
by Mason (1953), it is assumed that the elements ofthe network are 'ideal'
repeater stations which sum the incoming excitations and transmit the resulting signal along the outgoing links. In general a signal flow graph is a
directed graph which is a graphical representation of a set of linear algebraic
equations for the variablesrepresented by the nodes ofthe graph. From this
concept a basic set of transformation rules can be derived. These are shown
in Fig. 1(a).
159
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,
/
COGNITIVE PROCESSES: METHODS AND MODELS
<
»
These elementary transformations, together with the elimination of a
self-loop, shown in Fig. 1(/;), can be used for the reduction of a flow graph
to a form in which only the variables of interest arc retained.
This postulate reflects our current ignorance ofthe nature ofthe interaction
between the elements of the parallel processor. Since this theory is not
primarily concerned with ncurophysiological mechanisms, it does not seem
necessary to assume that the elements of the parallel processor have the
properties of neurons. However, for the convenience of digital simulation,
the linear elements assumed by this postulate may have to be replaced by
neuron-like elements.
6. There is a long-term relational store of images and concepts. The
contents of this store represent the knowledge of the organism about the
environment. It is called relational, because in contrast to the parallel
processor it contains information on the various logical and other relationships between items. The parallel processor is an 'indexing structure', or
thesaurus, for this relational store, and it is concerned with ojjly one kind of
relationship, that of the relevance of one clement to another. The nature of
the relational store is not discussed in detail in this paper, but we can point
to Raphael's (1964) information retrieval system to indicate the kind of
organisation we have in mind.
7. Word recognition. The parallel processor resolves the connotativc
meaning of a word into an excitation pattern produced over the network by
<"
3 thc_activation ofthe corresponding element. The recognition of connotativc
"\ meaning implies the recognition of this excitation pattern. This pattern is
/ a representation of aspects ofthe word's meaning. To the extent that clusters
related to concepts and qualities exist in the network, excitation levels in
those clusters reflect the relevance of these concepts to the activated word.
The network of elements thus acts as a transformation, mapping v. ords.into
a semantic space. The central processor can obtain the coordinates of a
"'point in this space by reading off the excitation levels of a set of selected
elements in the vicinity of the activated element. The selection of the
coordinate system can therefore be made dependent on the 'point of view'
from which the word meaning is regarded. Since the structure ofthe network
is likely to be hierarchical (corresponding to conceptual organisation), the
description of the pattern which can be obtained in this way can represent
meaning to a varying degree of detail, and can bring into focus some facets
of meaning at the expense of others.
It now becomes possible to compare meanings and define degrees of
similarities in meaning, or synonymy, by comparing patterns of excitation.
8. Word selection. This involves a search process executed by the central
processor over the elements of the network. The specification of the desired
word, either by its properties or by the activation of stimulus words, will
produce a region of high excitation in the network. The search is confined
to the region where the excitation level is above some threshold value.
High levels of excitation indicate high relevance. In general the search
160
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KISS
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7
proceeds from high excitation levels to lower ones. This implies that in
choosing among the alternatives at any given node, the path leading to the
highest excitation is taken first.
Various search strategics arc conceivable, and it is assumed that this is a
characteristic of the individual. For example, in the word association
experiment a 'width first' strategy would at first generate the elements
connected to the stimulus node, then the elements connected to these 'first
generation' elements, and so on. On the other hand, a 'depth first' strategy
would follow any given chain of links to its limit, then the next such path
would be selected, and so on. Clearly, these different strategies can be
expected to have different behavioural consequences. A depth first strategy
may be expected to produce more distant associates if the time allowed for
giving an associate is limited.
The elements generated during this search are tested for acceptability.
The nature of these tests will vary with the nature of the task. For example,
in the word association situation it is known that coordinate words (of similar
meaning) and contrasting words (oppositcs) arc the most frequent responses.
One test therefore consists of determining the similarity between the word
generated and the stimulus word. Similars would be defined by highrelevance
and high similarity, while oppositcs would have high relevance and low
similarity.
Other acceptability tests will be concerned with the determination of the
relational operators connecting the stimulus element to the clement under
testing. These tests arc based on the relational store. The nature of the
relational operator which is acceptable to the subject will again depend on
the interpretation of the task by the subject (Moran, McfTerd & Kimble
1964).
'
'
IMPLEMENTATION OF THE MODEL AS A COMPUTER PROGRAM
A computer program, WORSE (YV'ORd Selection), is at present under
construction, which will incorporate the Postulates outlined in the previous
section. The. main aim in writing this program is not the reproduction of
word association norms, or associative responses for individual subjects. It
is intended rather as a work-bench for testing various hypotheses in relation
to the word selection problem, and also to experiment with direct manmachine interaction in using words.
The program is written in Information Processing Language 5, which has
been implemented for the KDF-9 computer.
In WORSE each word is represented by.a.list structure. It contains all ]
information that is pertinent to that word, such as its current .excitation level,
the names ofthe other words to which it is linked, the transmittance values
of these links, the externa! 'printnamc' ofthe word, and so on.
'*""""
WORSE can acquire word association network data in two ways. It can
either read from paper tape a suitably prepared set of data representing a
161
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cognitive
1
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processes: methods and MODELS
network derived from a sample of subjects, or it can build up such a network
through on-line interaction with an individual subject.
The flow graph reduction part of the program is at the moment implemented in the form of a separate ALGOL program along the lines indicated
by Robichaud, Boisvert & Robert (1962). This program iterates over all
nodes of a network, first eliminating self-loops and then removing all outgoing
branches, thereby reducing the graph to sources and sinks.
In terms of a word association network, a set of stimulus words is
designated, and the program derives the transmittancc values leading from
...any stimulus word to all other stimulus words and to a set ofresponse words.
When WORSE is run with network data obtained from a sample of subjects
by means ofthe word association experiment, these transmittancc values are
input to the program. Since IPL-5 is rather inefficient for numerical calculations, it will probably not be possible to include in WORSE a complete
'
flow graph solving routine, unless it is programmed in machine code as a
special 'J-process'. However, some approximation to a complete reduction
may be possible. Instead of taking into account all possible paths in the
'graph, the reduction process could be limited to paths up to a certain maximum length.
f. The program contains a process which represents the 'activation' of a
| (jl word by the central processor. When this happens, the excitation level of
j the word is increased by one, and all other words within a specified topological
"
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ofthe path leading to them from the activated word.
The pattern recognition part of the program is not yet implemented, nor
is the search process for a word satisfying some specific conditions.
An j
1
SOME
SIMPLE ON-LINE EXPERIMENTS
Considerable interest is attached to the use of WORSE as a man-machine
experimental system. In the usual word association or verbal learning
experiments the situation is always fixed in advance, and no account can be
taken of a subject's individual characteristics or actual behaviour during
an experimental session. "Messick & Radoport (1964) have recently
summarised the potentialities of computer-controlled experimentation in
psychology and pointed out the great flexibility which can be obtained in
experimental set-up, the economies of data processing during and immediately
after the experiment, the improved standardisation of experimental conditions
and the unlimited complexity in making the experimental situation a dynamic
one.
.
In order to explore the feasibility of using IPL-5 on the KDE-9 computer
for this kind of work, two 'J-proccsses' have been added to the system to
enable the reading and printing of character strings through the on-line
Flexowritcr. Internally the character string is represented as an IPL list
containing internal symbols which are the numerical representations of the
characters.
162
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KISS
The man-machine system part of WORSE contains an experimenter
executiveroutine which itcratively presents a stimulus to the subject, identifies
the response (in terms ofthe words which have already occurred during the
experiment), processes the response, and then determines and presents the
next stimulus. The routine is written in a general form so that different
strategies of determining the next stimulus can easily be set up.
Strategy I, which is currently running on the computer, investigates the
relationship between the excitation level of a word and the probability that
it will be chosen as a response. The technique used has been taken from
Baldridge & Hustmycr (1965). The subject is initially presented with a list
of 15 words for free association. Then the 15 responses arc given back
to the subject as stimuli, and the same process is repeated over several
cycles.
It may be thought at first that this procedure will generate 15 stimulusresponse chains which will have little to do with each other. This turns out
not to be the case. Even when the original 15 words are unrelated, the
associative responses will soon converge on to a fairly small set of words
and form a connected graph. The rate at which new words are introduced is
at first high and then decreases and approaches an asymptotic value. Strategy
I monitors the ratio of the number of new words to the total number of
responses, and when a limiting value is reached it switches the subject to a
new set of words.
In the experiments run so far the first set of 15 words are closely interrelated owing to the fact that they are all high-frequency associates to
butterely. The members of this list are: bees, bug, yellow, kly, moth,
NET, COLOUR, BUTTERFLY, COCOON, PRETTY, WING, INSECT, FLOWER, BIRD and
blue. The members of the other list of 15 words are relatively unrelated
words (the same list as the one in the experiments by Baldridge): animal,
PACKAGE, TOWN, HOOK, TASK, SOUND, DREAM, PAPER, HOUSE, BOTTLE, PLANT,
word, colour, picture and letter. The word colour is repeated in the
second list.
The behaviour of one of the subjects is shown in Fig. 2. The underlined
words are members of the two starting lists of stimuli. The numbers show
their order of introduction. The arrows represent a stimulus-response
connection.
Although no quantitative evaluation of this data has been attempted so
far, the qualitative picture is quite clear. There is a strong tendency to
repeat words which occurred previously in the experiment. The path taken
by the stimulus-response chains seems to depend on the bias. created over
the network by the previous words. Consider for example the chain
pretty-sour-ointment-ely.
The occurrence of OINTMENT is probably due
to the previous occurrences of words like bees, p.ug, wasp, hornet and FLY.
If the experiment is earn. ' on long enough, the subject finds it difficult to
think of new words and gets trapped into endlessly repeating cycles.
The introduction of new words against time is shown in Fig. 3 for the
163
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same subject. The limiting ratio of the number of different words to the
total number of words was set in this experiment to 055. For the first
group of words (interrelated items) the subject took 87 trials to reach this
limit, while 151 trials were required in the case of the unrelated group.
The break point A on this curve indicates the switch to the second (unrelated)
group of words. At point B, during the second half of the experiment, the
subject stopped and asked 'Am I supposed to be creative? I seem to repeat
the same words too much.' No information v ■■... forthcoming from the
experimenter, who referred the subject to the original instructions ('always
give the first word which occurs to you .'). Nevertheless, there is clearly a
shift in the subject's task-set here.
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Fig. 3.
All five subjects used so far reported that they enjoyed the experience of
working with the machine. Although this experiment is a fairly simple one,
it shows the feasibility of using the machine in this way. For more complex
strategics it will probably be necessary to rewrite the more time-consuming
parts of the program in machine code. With Strategy I, the program
takes
3-5 seconds to produce the next stimulus after the subject's response. Even
without resorting to machine code, it would be possible to improve on this
by more sophisticated search procedures in the word-identification routine.
SUMMARY
.
Ihe elements of an information processing theory of word storage in
humans were outlined. Eight postulates were formulated: (1) There arc
serial and parallel information processing resources in humans. (2) These
interact with each other. (3) The serial processor is a limited-capacity
sequential mechanism. (4) The parallel processor consists of
a network of
. interconnected elements. (5) The links connecting the elements transmit
165
cognitive
processes: methods and models
excitation according to a flow graph model. (6) There is a relational store
of images and concepts indexed by the parallel processor. (7) Word recognition implies the recognition ofthe pattern of excitation produced by the
activation of an clement. (8) Word finding involves a search process over
the elements ofthe network.
The progress with the implementation of this theory in the form of a
computer program was reported and some simple on-line computer-controlled
experiments were described.
■
.
ACKNOWLEDGEMENTS
»*
In addition to the sources mentioned in the paper, I should mention that
I have been much influenced by current developments in information
retrieval, especially Giuliano (1963), and by Bousfield's (generally) and
Dccse's (1962) theorising in connection with word associations. Although
working in a different context, Broadbent (1965) and Morton (1964) arc
thinking about word storage along similar lines.
I had many stimulating discussions with Phi] Levy at Birmingham and with
Rod Biirstall ofthe Experimental Programming Unit at Edinburgh.
The work reported in this paper has been supported by a grant from
D.S.I.R.
>
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