Theories Of Knowledge And Psychological Applications Essay, 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,

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. 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

information.

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

that information.

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

(Paivio, 1986).

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

descriptions” (p.63).

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

knowledge theories.

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

connection necessary.

References

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.

Belmont,CA: Brooks/Cole.

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.