, Research Paper Theories of Knowledge and Psychological Applications Robin A. Finlayson University of Saskatchewan Ed.Psy: 855.3: Advanced Educational Psychology
, Research Paper
Theories of Knowledge and Psychological Applications
Robin A. Finlayson
University of Saskatchewan
Ed.Psy: 855.3: Advanced Educational Psychology
October 16, 1996
How individuals are able to obtain knowledge is something that
psychologists have studied for a number of years. The ability to store and
retrieve knowledge provides individuals with the propensity to form logical
thought, express emotions and internalize the world around them. In order for a
psychologist to understand the theories of knowledge it is necessary to
investigate the aspects of the theories. In this paper we examine the history ,
the basic construct, the similarities of the theories and how those theories
relate to psychological therapies. History of the theories
The neural network model attempts to explain that which is known about
the retention and retrieval of knowledge. Neural network models have been
examined for a number of years. In the mid 1940’s and 1950’s the first of the
network models began to appear. These publications introduced the first models
of neural networks as computing machines, the basic model of a self-organizing
network (Arbib, 1995).
In 1943 McCulloch and Pitts published their model theory ( Arbib, 1995). In
1948 Rashevsky proposed a number of neural network models to explain
psychological phenomena. During this era not enough was known about the brain,
subsequently he was considered ahead of his time. Rashevsky relied heavily upon
complex mathematical equations within his model, consequently many people simply
did not understand his theoretical perspective ( Martindale, 1991). In 1958
Rosenblatt proposed his theory on neural network models which focused on
perception. The theory elicited a great deal of interest; however it was
considered too simple to sufficiently explain all aspects of perception (Arbib,
As a result of the lack of acceptance, neural network models “fell out
of fashion”(Martindale, 1991, P.12). For a nine year lapse no neural network
model theories were developed. In 1967 the network approach was again examined.
Konorski developed a useful network model that focused primarily on Pavlovian
conditioning as opposed to cognition. Grossberg developed his neural network
theory during the years of 1969, 1980, 1987, and 1988. Grossberg developed a
powerful network theory of the mind but, like the Rashevsky model, Grossberg’s
theory was comprised of complex mathematical terms and was therefore extremely
difficult to understand. His neural network models are only now being recognized
as truly revolutionary (Martindale, 1991).
Many new theorists would enter the field of neural network models, but
it was the work of Rumelhart, Hinton, and McClelland that would simplify the way
we would view such models (Arbib, 1995). It was in 1986 that Rumelhart, Hinton,
and McClelland developed their network model. It was and still is regarded as
one of the most notable network theories. This is true because they structured
their theory in a clear, concise, and intelligible manner (Martindale ,1991).
Neural network models have evolved during the past sixty years. The
initial theories were extremely difficult to comprehend and they were not
interchangeable with a broad range of topics. Today’s theories are simpler to
understand because they are less complex. The theories are capable of
encompassing numerous topics.
The dual coding approach is one that believes that knowledge is a series
of complex associative networks. Within these networks we find imaginal and
verbal representations. These verbal and nonverbal representations are means
that facilitate the retrieval and storage of knowledge (Paivio, 1986).
The individual who was at the fore front of the development of the dual
coding theory was Allan Paivio. He did research in the area of verbal and
nonverbal representations during the 1960’s. Research papers that dealt with
topics of verbal and imaginal processes were: Abstractness, imagery, and
meaningfulness in paired-associated learning (1965) ; Latency of verbal
associations and imagery to noun stimuli as a function abstractness and
generality (1966) and; Mental imagery in associative learning and memory (1969),
( Paivio, 1986). In 1971 Allan Paivio presented his revolutionary paper, Imagery
and Verbal Processes. As a result of this paper the concept of a dual coding
process was conceived.
Paivio’s subsequent paper in 1985, Mental Representations, retained the
same constructive empiricism and the same basic theoretical assumptions as the
earlier paper, Imagery and Verbal Processes. In this paper Paivio demonstrated
that the fundamentals of a dual coding approach have stood up well to challenges
over the years ( Paivio, 1986).
The dual coding process offers a clear explanation of how individuals
are able to store and retrieve knowledge. Through Paivio’s dual coding approach
we are able to see how internal networks of verbal and imaginal representations
are capable of logging and retrieving information both nonverbally and verbally.
Construct of the theories
There are a number of theories that explain how it is the human brain is
capable of storing and retrieving information. A neural network model of
cognition aims at explaining how and why we experience such mental phenomena.
The metaphor “the mind works like a computer” has been heard by everyone
at one time or another. Recently cognitive psychologists have considered that
the mind does not work like a conventional computer. They have replaced the
computer metaphor with a brain metaphor (Martindale, 1991).
The logic for the rebuttal of the computer metaphor is that a computer
has a central processing unit that is only capable of doing one thing at a time.
It processes very quickly and in fact, operates at a million times faster than
the average neuron (Arbib, 1995). A computer can thus do long division problems
quicker than you or I can, but there are some tasks-for example, perceiving and
understanding a visual scene- that the brain can perform faster than a computer.
In such a case, the brain could not possibly work like a computer. The brain
therefore solves the problem of vision differently than a computer (Martindale,
Martindlae (1991) states that “The brain does not have anything we
could really call a central processing unit, and the brain does not work in a
serial fashion. The brain is therefor more like a large number of very slow
computers all operating at the same time and each dedicated to a fairly specific
task” (p. 10).
Since the computer metaphor was replaced with the brain metaphor, a
cognition model was needed to explain how and why we experience mental phenomena.
One such theory is the neural network model.
A neural network model is composed of several components:
1. A set of possessing units, referred to as “nodes” or “cognitive
2. A state of activation. Nodes can be activated to varying degrees. The
set of these activated nodes corresponds to the contents of consciousness. The
most active nodes represent what is being done at the time, all other deals with
motor function at the unconscious level.
3. A pattern of connections among nodes. Nodes are connected to one
another by either excitatory or inhibitory connections that differ in strength.
The strength of these connections constitutes long-term memory.
4. Activation rules for the nodes. These rules specify such things as
exactly how a node “adds up”its inputs, how it combines inputs with its current
state of activation, the rate at which its activation decays, and so on.
5. Output functions for the node. We assign thresholds or make output a
nonlinear function of the node’s activation, we get useful results.
6. A learning rule. We need to explain how learning occurs; in a network
model, learning means strengthening the connections between nodes. The
connection between two nodes are strengthened if they are simultaneously
7. An environment for the system. Neural network modules are massively
interconnected. The nodes in any analyzer are organized into several layers.
Connections among nodes on different layers are generally excitatory, and
connections among nodes on the same layer are usually inhibitory. (Martindale,
An interactive and competitive network consists of processing nodes
gathered into a number of competitive pools. There are excitatory connections
between pools and they are generally bidirectional. Within the pool, the
inhibitory connections are assumed to run from one node in the pool to all the
other nodes in that pool, therefore they will not be activated (
McClelland & Rumelhart, 1988).
The easiest way to comprehend how a neural network model works is to
examine a simple neural network model. Figure 1 is an interactive and
competition model based on the works of McClelland (1991). The network model
concerns knowledge about five people, this is represented by the five nodes
in the center circle. There is nothing stored in these nodes. Knowledge about
what they represent lie in their connections to the other nodes. The
attributes of the five Figure 1 (Martindale, 1991,
p. 15) people are represented by nodes in the circles surrounding
the center circle. Here is how the network works: The lines between circles
indicate two way excitatory connections. We assume that the nodes within the
circles have a inhibitory effect on one another. When any one node is activated
it, inhibits nodes in its own circle and excites nodes to which it is connected
in other circles. These excited nodes go on to excite other nodes. Excitation
and inhibition reverberates back and forth, some nodes will be activated and
others will be inhibited. When one follows the lines back and forth we can see
that the network stores information. For example Joe is a white male professor
who drives a Subaru and likes brie cheese. It is also evident that Harold and
Frank are both black stockbrokers, but one likes brie and the other likes cheese
whiz (Martindale ,1991).
The network has a number of properties that mimic the way people think.
First, all memory is content addressable. Stimulating the network with the word
“Fred” activates the node that codes this name. Soon, the nodes coding these
properties will be activated automatically. There is no need to search for
information, simply saying the name “Fred” automatically retrieves the
Networks also show default assignments. The default assignment is the
ability to hypothesize. When the network is asked about Claudia, the node of
brie cheese will be at least partially activated. This happens because the brie
node will receive activation from the node coding professors. This occurs
because Claudia is a professor (Martindale, 1991).
Although neural networks tend to become more complex than the example
shown, it demonstrates why we experience mental phenomena. The network theory
explains how we are able to retrieve information and then draw conclusions from
Another view or theory that attempts to explain mental phenomenon is the
dual coding theory. This theory uses verbal and nonverbal representations as the
means by which individuals are able to store and retrieve information. Allan
Paivio (1986) states: “The theory is based on the general view that cognition
consists of the activity of symbolic representational systems that are
specialized for dealing with environmental information in a manner that serves
functional or adaptive behavioral goals. This view implies that representational
systems must incorporate perceptual, affective, and behavioral knowledge. Human
cognition is unique in that it has become specialized for dealing simultaneously
with language and with nonverbal objects and events. Moreover, the language
system is peculiar in that it deals directly with linguistic input and output
(in the form of speech or writing) while at the same time serving a symbolic
function with respect to nonverbal objects, events, and behaviors. Any
representational theory must accommodate this functional duality” (p. 53).
It is important to recognize that the general level of the dual coding
theory divides into two subsystems, verbal and nonverbal. These two subsystems
can be divided into sensorimotor subsystems, such as visual, auditory, haptic,
taste and smell( Paivio, 1986). When dealing with this theory it is important to
remember that there is no top to bottom approach. This means that the activating
mechanism can be either verbal or imaginal. For example the instruction to bring
an image to words maximizes the probability that nonverbal representations will
be activated by subsequent verbal cues (Paivio, 1986).
When looking at verbal and imagery representations it is important to
consider how they differ from one another. The imagery or nonverbal system
consists of a set of interconnected parts specialized for dealing with
environmental information. The imagery system relies upon the nonverbal
representations to provide feedback, these are visual, auditory, haptic, taste,
smell and other nonlinguistic representations. The verbal aspect utilizes words
as codes. Objects, events or ideas can be encoded ( Paivio, 1986). Another
difference is how the two representations are organized. Paivio (1986) found
that “intraunit functional structures differ so that component information in
higher-order nonverbal units are synchronously organized, where as verbal
components are sequentially organized”(p. 59).
This means that imagery systems are able to evoke a number of
representations at one time and are therefore capable of encoding much about a
single complex image at one time. The verbal representation on the other hand
must be made sequentially, only processing information one bit at a time.
With a basic understanding for the inner workings of both the verbal and
nonverbal representations it is important that we view the between- system
relations. Although both systems would seem to be independent of one another, in
that they are capable of being active without the other, it is evident that one
system is capable of activating the other system. This would imply that if one
system is capable of activating the other system they must be interconnected
Although the two representational systems are capable of working
independently they are also able to work together through interconnections. This
interconnection is known as a referential connection. The referential connection
is the ability for one system (either verbal or nonverbal) to evoke the other
and vise versa. Through this connection individuals are capable of describing
and imagining any number of situations.
Paivio (1986) states that “the interconnections are not assumed to be
one-to-one, but rather one-to-many, in both directions. The assumption
parallels the familiar fact that a thing can be called by many names and a name
has many specific references. This translates into the dual coding assumption
that a given word can evoke any number of images, corresponding to different
exemplars of a referent class (e.g., different tables) or different versions of
a particular class member ( e.g., my dinning room table imaged from different
perspectives). Conversely, a given object (or imaged object) can evoke different
All that we hear, see, touch and smell is encoded into our verbal and
nonverbal knowledge base. It is how we are able to store and retrieve these
representations that make us capable of providing a verbal representation of an
image in our minds, or enables us to imagine a verbal description.
Comparisons and contrasts
To have complete understanding of these two theories is important to
compare and contrast them. It is important because commonalities allow for
similar explanations of mental phenomena.
Both theories do an exceptional job of explaining the processes of the
of the mind. One similarity between neural network theory and dual coding theory
is that they both divide the components of their theory into subsets. The
network theory puts the similar nodes into one set and the dual coding theory
puts the verbal in one set and the imaginal into another set. Both theories
utilize connections between subsets as a way of storing and retrieving knowledge.
While the theories have a number of similarities they also have some
differences. The dual coding theory has two subsets, the verbal and the imaginal.
The neural network theory has numerous amounts of nodes grouped into many
different sets. These sets form webs. There are numerous webs layered one on top
of the other and each is able to access one another. With the infinite number of
webs being able to access one another the network theory has the potential to
become more complicated than the dual coding theory.
Both theories make valid points as to how individuals process and retain
knowledge. While the two theories may differ on the internal representations of
the storage of knowledge, both have similar foundational beliefs: knowledge is
taken in, it is stored, there are connections between the stored groups of
knowledge and there is a retrieval process.
How the theories apply to psychology
Why is it important for a psychologist to know and understand the
theories of knowledge? It is important because the field of psychology studies
the processes of humans (how they act, react, develop, make decisions, cope,
ect.). If a psychologist has a basic understanding of the knowledge theories,
then they will have a better understanding of the thought processes of a client.
Therapies such as relaxation therapy, rational emotive therapy, art
therapy and choice therapy must be able to appeal to the individuals knowledge
constructs. Clients in cognitive therapy tend to posses irrational thoughts. In
order to bring about change in the clients thought processes the therapists must
assist the client to analyze their faulty logic. Through challenging what the
client believes to be true the client is then able to analyze and reconstruct
the knowledge that is stored in the verbal and imaginal compartments of the dual
coding theory as well as the nodal compartments of the network theory.
In observing art therapy it is evident that the understanding of the
knowledge theory would prove useful. Art therapy can be represented in three
ways: it is experienced internally, it is expressed verbally, or constructed and
represented through the media ( Lusebrink, 1990).
Lusebrink (1990) states that “Internal experiences of images and there
external representations influence each other. . .The internal image is based on
sensory, affective, and thought processes. The image is externalized either
through verbal descriptions or through the manipulation of media” (p. 6)
In the above statement we can see a definite connection between art therapy
and the knowledge theories. Through art therapy an individual must be able to
view an image, internalize that image and be able to make the connection to
express how that image expressed their feelings. This is much the same as the
The theories of knowledge are tied directly to psychological therapies.
The knowledge theories explain how a therapy technique is able to connect with a
client’s internal construct and assist in expressing or altering cognition.
While absolute understanding of the knowledge theory may not be essential to an
effective outcome of a therapy, it would assist in the understanding of how the
therapy is able to work.
The theories of knowledge tend to be quite complex. In the terms of a
psychological context it is important to understand the knowledge theories. The
history, the construct, and their similarities all allow the psychologist to
better understand how an individual internalizes the world around them. The
basic understanding of the knowledge theories allows the psychologist to
comprehend how therapeutic techniques effect the clients’ internal constructs
and also how all knowledge, both past and present, plays a role in making those
Arbib, M. (1995). The hand book of brain theories and neural networks.
Cambridge, MA: MIT press.
Lusebrink, V. (1990). Imagery and visual expression in therapy. New
York: Plenum press.
Martindale, C. (1991). Cognitive psychology a neural-network approach.
McClelland, J., & Rumelhart, D. (1988). Explorations in parallel
distributed processing. Cambridge, MA: MIT press.
Paivio, A. (1986). Mental representations a dual coding approach. New
York: Oxford University Press.
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