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,
1995).
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,
1991).
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
units.".
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
activated
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,
1991).

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. ...