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Running head: IMPLICATIONS OF THE INFORMATION-PROCESSING THEORY IN MEDIA STUDIES 1

Implications of the Information-Processing Theory in Media Studies:

A Literature Review

Jena J. Thompson

Colorado State University

IMPLICATIONS OF THE INFORMATION-PROCESSING THEORY IN MEDIA STUDIES 2

Introduction

Mass communication research, by comparison to other fields of study, is relatively

young. Consequentially, many of the theories used to research concepts—such as media

sociology, mass messaging, and audience effects—have been borrowed from other research

fields. Psychology, in particular, has generated many theories that have been useful for today’s

media studies researchers. Popular theories include social cognitive theory (Bandura, 1986),

schema theory (Bartlett, 1932), and behaviorism (Watson, 1913) to name a few. However one

theory, originating in the field of cognitive psychology, has had a significant impact on how

researchers from many different disciplines understand information processes. Appropriately

named, the information-processing theory (IPT) “…uses mechanistic analogies to describe and

interpret how each of us takes in and makes sense of the flood of information our senses

encounter every moment of each day” (Baran & Davis, 2012, p. 263). This paper will review the

current literature on IPT to summarize its origin, development, current state in various fields,

operationalization, criticisms, and weaknesses. Additionally, this paper will conclude with a

discussion of potential improvements and new applications for IPT in the field of media studies.

Origins

Although psychological research now heavily emphasizes cognitive processing (e.g.,

perception, attention, memory, consciousness, etc.), most research during the 20th century

focused on human behavior (Bruning, Schraw, Norby, & Ronning, 2004, p. 2). Specifically,

psychologists resided in the paradigm of associationism (Dellarosa, 1988). As the inspiration for

behaviorism, associationism prompted the study of links between stimuli and responses: first in

animals, then in humans. John B. Watson originally fostered an interest in behaviorism through

animal experimentation. He then argued that all human behavior is a result of environmental


stimuli (Watson, 1913). Toward the end of the association era, radical behaviorist, B. F. Skinner,

adopted Watson’s perspective on environmental stimuli and temporarily discredited the study of

consciousness in the field of psychology (Baars, 1986). Skinner’s research suggested that human

behavior could be predicted and controlled for educational purposes (Skinner, 1938, 1953, 1968;

Holland & Skinner, 1961). Many psychologists were enthusiastic about Skinner’s results; yet,

others found his behaviorist approach limited in terms of mental processes. In congruence with

Thomas Kuhn’s paradigm theory, the associationism paradigm had hit a crisis and was no longer

able to provide the appropriate perspective for human responses. According to Von Dietze

(2001), “If and when it can no longer adequately deal with new discoveries, then there may be a

move towards paradigm change, involving a total reevaluation of the content, practice,

philosophy and direction of the particular research area” (p. 36). Thus began the cognitive era.

Shift to Cognitive Processing

The paradigm shift to cognition-driven psychology was not prompted by any individual

event; however, the work of several prominent researchers played a significant role in the

transition (Neisser, 1967; Bruner, Goodnow, & Austin, 1956; Ausubel, 1960; Ausubel &

Youssef, 1963; Miller, 1955; Jenkins, 1974; Minsky, 1975; Rumelhart, 1975; and Schank &

Abelson, 1977). Among these cognitive psychologists, George Miller served as one of the key

players for IPT. In his article, “The Magical Number Seven, Plus or Minus Two: Some Limits on

Our Capacity for Processing Information,” Miller suggests that humans are severely limited by

the amount of information that can be held in short-term memory. Miller’s research suggested

that humans can hold no more than seven (plus or minus two) “chunks” (or units) of information.

However, Miller argued that the amount of information could increase in each chunk, so long as

the number of chunks did not exceed the maximum capacity (Miller, 1955). Miller’s research on


information processing prompted several other researchers to delve into the systems involved in

cognitive processing. As a result, several models were crafted to conceptualize human cognition.

According to Bruning et al. (2004), “The models came to be known collectively as information

processing models…and their common features as the modal model” (p. 15).

Theory Development

The modal model (see Appendix A) represents the separate memory systems that are

used during information processing: sensory memory, short-term (or working) memory, and

long-term memory. One of the primary assumptions of the model is that each memory system is

specialized and independent of the other systems (Bruning et al., 2004, p. 15).

The invention of the computer significantly influenced the idea of mental processing in

the cognitive era; thus, many psychologists used the computer as a metaphor for human

cognition. Consequentially, early information-processing models (such as the modal model)

function in linear sequences. Several revisions of the model have since been made to

accommodate for the nonlinearity of human processing (e.g., Quillian, 1968; Collins & Quillian,

1969; J. R. Anderson, 1976, 1983a, 1983b, 1993, 1996; J. R. Anderson & Matessa, 1997;

McClelland, McNaughton, & O’Reilly, 1995; McClelland, Rumelhart, & Hinton, 1986;

Rumelhart, & Todd, 1993). Regardless of its inherent linearity, the model’s original structure

serves as a descriptive representation of the systems pertinent to IPT.

Sensory Memory

The first system of the modal model is sensory memory, which utilizes sensory registers

for perception and pattern recognition of stimuli. The model posits that perception must occur

first by directing attention to a stimulus. Although humans can perceive stimuli fairly quickly,


research has indicated that insufficient perception time will result in a failure to encode

information (Fisher, Duffy, Young, & Pollatsek, 1988). Once stimuli have been perceived, one

must determine if any patterns exist. However, meaning will not be assigned to this perceived

pattern until it is moved to the short-term memory system, according to the modal model. Given

the two components of sensory memory, research in this area has primarily focused on how

humans perceive and recognize stimuli and how attention is assigned to the stimuli. Researchers

have studied perception and recognition of stimuli primarily by looking at sensory registers.

Sensory registers.

Humans have sensory registers for all five senses (Bruning et al, 2004, p. 19); however,

researchers are most interested in visual and auditory registers. The visual sensory register

(icon), as indicated by its name, detects visual stimuli and temporarily holds on to it for attention

allocation and pattern recognition. Sperling (1960) conducted research testing participants’

abilities to recall random arrays of letters after varying perception durations (from 15 to 500

milliseconds). Sperling determined that subjects were consistently limited to recalling about four

letters. This led to two crucial hypotheses regarding the nature of the icon: 1) participants only

registered the letters that were recalled, or 2) all letters were registered but were forgotten before

the participant could recall them. By conducting a follow-up study in which Sperling asked

participants to recall specific rows of letters, he was able to accept his second hypothesis. Thus,

Sperling’s research has suggested that the icon can register massive amounts of information, but

only retain a small percentage—seven to nine pieces of information (Bruning et al., 2004, p. 21).

Research on the auditory sensory register (echo) has closely resembled Sperling’s icon

research. Darwin, Turvey, and Crowder (1972) substituted auditory stimuli for visual stimuli and

tested whether participants could retain pieces of information longer in the echo. According to


the study, information capacity remains limited. Yet, the duration for temporary information

storage is greater in the echo than in the icon (3 seconds for echo and 0.5 seconds for icon).

These findings have significant implications for the type of attention allocation necessary to

detect patterns in sensory memory.

Attention.

Consistent with sensory and short-term memory capacities, the amount of stimuli a

person can pay attention to is very limited (Spear & Riccio, 1994). This cognitive restraint is

known as the limited processing capacity. Individual differences determine how much

information a person can attend to; yet, the average individual is limited to one or two tasks at

one time (Bruning et al., 2004, p. 24).

Within this topic, researchers are most interested in how people allocate their attention to

varying stimuli. Although studies disagree on when attention is assigned, a consensus has been

reached that the level of attention will differ based on the cognitive complexity of the task. Tasks

have been categorized as resource-limited tasks or data-limited tasks (Nusbaum, & Schwab,

1986). Individuals have more control over attention allocation in resource-limited tasks. These

tasks can be improved by assigning more attention resources. (E.g., a person can turn off

distracting music to increase attention toward a given task.) Conversely, data-limited tasks

cannot be improved by the individual’s reallocation of attention. These tasks may be highly

complex or may be missing pertinent information. Thus, an individual may fail to recognize a

pattern regardless of the amount of attention assigned to the task.

By the time an individual reaches adulthood, he will have learned how to efficiently

attend to varying tasks. Moreover, this individual will have rehearsed certain tasks so frequently

that they become automated. First conceived by Neisser (1967), automaticity refers to the


cognitive processes that an individual can perform with minimal effort. Common examples may

be riding a bike, tying shoes, or driving a car. When simpler cognitive tasks become automated,

individuals can allocate more attention and effort to complex cognitive tasks. For example,

someone who is proficient at reading with a solid vocabulary will be able to focus more on the

suggested inferences in an article. In reverse, those who have not automated the simpler tasks

will not be able to focus on the more complex tasks. These aspects of attention directly relate to

the pattern recognition that is moved to short-term and working memory.

Short-term and Working Memory

As stated earlier, the short-term memory system functions as a meaning-making

apparatus. However, the term short-term memory has been used to vaguely describe a slew of

mental processes causing researchers to narrow in on a few specific subsystems and transition to

the name working memory. The most cited model of working memory was proposed by

Baddeley and Hitch (1974). Their working memory model consists of three components: the

executive control system, the visual-spatial sketch pad, and the articulatory loop.

The model functions as a hierarchy, with the central executive system regulating the two

subsystems, referred to as “slave systems.” The executive control system is responsible for

controlling what information enters the memory system and determining the appropriate

strategies for processing accepted information. The strategies chosen will activate one or both of

the slave systems. The articulatory loop rehearses auditory information to evaluate meaning. The

visual-spatial sketch pad holds on to visual information and conducts the appropriate evaluations

for meaning (e.g., mentally rotating an image). Once meaning has been derived from

information, the executive control system will transfer the information to the long-term memory

system.


One key assumption about the model is that the subsystems have access to independent

attention resources (Baddeley, 1986, 2001). This suggests that the subsystems can function

simultaneously without depleting each other’s attentional resources. Regardless of the separate

resource pools, the subsystems still experience a limitation on cognitive resources. This idea

generated the cognitive load theory, which asserts that varying learning environments will

determine the amount of cognitive resources necessary for information processing (Sweller, van

Merrienboer, & Paas, 1998). According to the theory, cognitive resources may be affected by

internal or external environments. An intrinsic cognitive load is dependent upon the complexity

of the information and can only be lessened by altering an individual’s current schemata for the

information (Sweller, 1994). An extraneous cognitive load is dependent upon the presentation of

the information. The extraneous load can be lessened by “…using adjunct aids, providing

specific learning instructions, or enhancing the organization of the to-be-learned information”

(Bruning et al., 2004, p. 30).

Despite the apparent usefulness of Baddeley’s model in education, other researchers have

found it to be contradictory to the true nature of information processing. Some researchers have

criticized the rigid compartmentalization of the model (Miyake, 2001; Miyake & Shah, 1999). A

thorough reevaluation of the model has led researchers to agree on the following characteristics

of working memory: 1) working memory is not physically separate from other memory

processes, but is actually closely linked to long-term memory; 2) working memory processes

information for several memory systems, not just short-term memory; 3) the efficiency of

working memory is related to an individual’s prior knowledge on the subject; and lastly, 4)

working memory is affected by the current emotional states of the individual (Bruning et al.,


2004, p. 30). Of these findings, the one most pertinent to IPT is the connection between working

memory and long-term memory.

Long-term Memory

Bruning et al. (2004) define long-term memory as “…the permanent repository of the

lifetime of information we have accumulated” (p. 36). Long-term memory is most commonly

organized into three types of knowledge: declarative, procedural, and conditional (J. R.

Anderson, 1983a, 1993). However, before these types of knowledge can be defined, it is

important to understand the difference between the two fundamental types of memory in long-

term memory: implicit and explicit.

Memory can be recalled through a voluntary search for information (explicit memory) or

through involuntary generation of information (implicit memory). Of these two, the

“unintentional, nonconscious form of retention” (Bruning et al., 2004, p. 40) has drawn more

researcher’s attention. L.L. Jacoby and Witherspoon (1982) used a group of amnesiacs and a

control group of subjects with normal functioning memories to test the implications of implicit

and explicit memory systems. Research at the time contended that the main deficiency in

amnesia was the inability to transfer information from short-term to long-term memory (Bruning

et al., 2004, p. 40). However, L.L. Jacoby and Witherspoon’s study suggested that the issue was

not as simple as a transfer inability; instead, it was a misunderstanding of the explicit and

implicit memory systems. Through this study, the two memory systems became viewed as

separate entities involving different cognitive operations. This explains why amnesiacs could

remember certain, but not all, information from long-term memory. Further, this new

understanding has led some researchers to link explicit memory with declarative knowledge and

implicit memory with procedural knowledge (Squire, 1987).


As mentioned previously, long-term memory is organized into three different types of

knowledge. The first, declarative knowledge, is factual knowledge. Some examples would be

that Santa Fe is the capital of New Mexico, Barack Obama is the president of the U.S., and that

Christmas is always on Dec. 25. Given that an individual can encode subjective and objective

“facts,” declarative knowledge is often divided into two subcategories. Semantic memory refers

to the general principles associated with a concept (Squire, 1987). The example of the capital of

New Mexico can be considered declarative knowledge stored in semantic memory. The second

subcategory is episodic memory, which refers to personalized, subjective facts (Tulving, 1983,

1985). Childhood memories would be an ideal example of declarative knowledge stored in

episodic memory. The second most commonly used type of knowledge is procedural. This refers

to knowing how to perform certain tasks, such as driving a car or riding a bike. The last type of

knowledge is conditional knowledge, which is simply a combination of declarative and

procedural knowledge. In other words, conditional knowledge is information on when and why

to use stored information.

Besides the organizational concepts of long-term memory, it is essential to understand the

process by which information is stored and retrieved. These processes relate most to the concept

of encoding (Bruning et al., 2004, p. 65). Information can be encoded through two primary forms

of rehearsal: maintenance (Craik, 1979) and elaborative (Craik & Lockhart, 1986). Maintenance

rehearsal involves repetition of information for immediate use. Thus, this method does not often

solidify information in long-term memory. Elaborative rehearsal, on the other hand, requires a

more analytical process. Those who use this type of rehearsal often relate new ideas to already

understood ideas, and thus, expand upon current schemata. Consequentially, this encoding

process results in better information storage and retrieval. The rehearsal method chosen is


typically dependent upon the level of processing an individual utilizes. A variety of internal and

external factors will determine if a person utilizes shallow or deep encoding processing (Craik &

Lockhart, 1972). In shallow processing, the individual encodes only the most basic

characteristics of a concept (e.g., lemons are yellow). In deep processing, an individual will

encode the actual meaning of a concept (e.g., lemons can be used as sustenance to provide

energy for the body).

Ultimately, the encoding process completes the journey of a stimulus from initial

perception in sensory memory to meaning-making in working memory to schema building in

long-term memory. The following section will provide insight into how IPT has been

incorporated in research from varying fields of study.

Current State of IPT in Varying Fields

Because of the vastness of IPT, many researchers often focus only on certain components

of the theory (such as attention allocation, encoding and retrieval processes, perception, and etc.).

Regardless, a review of the literature has indicated some common areas of interest within IPT—

particularly in media studies and developmental and educational psychology.

Media Studies

Because media messages are often mediated through television, radio, and the Internet,

mass communications researchers have fostered an interest in attention allocation in relation to

technology. Methods of attention allocation operationalization have varied from self-report

(Biocca, David, & West, 1994) to physiological measures (Ravaja, 2004). Moreover, the most

sought after operationalization has been the use of secondary task reaction times (STRTs)

(Alwitt, D. R. Anderson, & Lorch, 1980; Chaffee & Schleuder, 1986; J. A. Deutsch & D.


Deutsch, 1963; Grimes & Meadowcroft, 1995; A. Lang, 1990; P. J. Lang, Bradley & Cuthbert,

1997; Reeves, Thorson, & Schleuder, 1986; Thorson, Reeves, & Schleuder, 1986). An example

STRT study by A. Lang and Basil (1998) instructed participants to pay close attention to a

mediated message, via television, that they would be tested on later. The participants were also

instructed to look for a signal (e.g., a flashing light or tone). The speed with which participants

acknowledge the secondary task (by pushing a button) indicated the STRT. Thus, A. Lang, Park,

Sanders-Jackson, Wilson, and Wang (2007) have found that STRTs measured during TV

viewing can predict the amount of available resources that can be used for other tasks.

While several researchers have focused on how information can be processed through

mediated sources, other researchers have shifted their focus to the general perception of mediated

sources. This field of study is called telepresence or “the perceptual illusion of nonmediation”

(Lombard & Ditton, 1997). Telepresence is commonly studied in virtual reality environments but

has recently been introduced to everyday forms of media entertainment. According to Bracken &

Skalski (2010), telepresence is “the extent to which media users feel ‘in’ a media environment or

‘with’ mediated others…” (p. 3). To determine the level of immersion in a medium, media

psychologists study the level of attention given to the medium in contrast to the level of attention

given to external cues (Hartmann, Klimmt, & Vorderer, 2010).

The final prominent research area in media studies using IPT relates to marketing and

advertising. Many marketing studies have been conducted on information-processing as it relates

to brand evaluations (Johar, Jedidi, & J. Jacoby, 1997), consumer behavior (Bettman, 1970), and

advertising across mediums (Edell & Keller, 1989). These studies generally relied on revised

versions of IPT models to improve current marketing efforts. Other studies focus on storage and

retrieval functions of IPT to measure public understanding of company practices. For example,


Tybout, Calder, and Sternthal (1981) measured how individuals process mass-circulated rumors

of companies and how PR departments can rewire people’s schemata for the companies.

Education and Developmental Psychology

Researchers interested in the underlying issues and solutions for learning disabilities

(LD) have also incorporated components of IPT. According to Swanson (1987), “few, if any,

substantive scientific outcomes have been accomplished in terms of operationalizing, preventing,

or ameliorating learning disabilities” (p. 157). Thus, many researchers in this field began

referring to the IPT to better understand the underlying processes in LD. For example, Palincsar

and Brown (1987) and Pressley, Johnson, and Symons (1987) began to focus on studying

metacognitive knowledge. These researchers hypothesized that teaching LD children about their

own learning processes and abilities would lead to improvement in basic information processing.

Other researchers (Kershner, Henninger, & Cooke, 1984; Swanson, 1986; Swanson & Mullen,

1983) have used IPT to research individual differences in the different hemispheres of the brain.

Swanson (1987) claims that “the wide range of ability group differences suggests that

information processing is characterized by different hemispheric resource combinations, which

may or may not require supplies primarily from one or both cerebral hemispheres” (p. 163).

More recent uses of IPT in LD research have narrowed in on attention allocation.

Finneran, Francis, & Leonard (2009) studied limitations in attentional capacity for children with

specific language impairments. Other studies have linked IPT attention concepts to studies on

ADD and ADHD children and adults (Cosden, Patz, & Smith, 2009; Hazell et al., 1999; Kataria,

Hall, Wong, & Keys, 1992; Weiler, Bernstein, Bellinger, & Waber, 2002).


Operationalization of IPT Concepts

As previously mentioned, the vastness of IPT combined with the complexity of each

individual memory system makes it difficult to detail all operationalized concepts. However, the

most common concepts researched are attention, propositions, schemata, productions, and

scripts. Other than attention, the remaining concepts all serve as important building blocks of

long-term memory. Each of these concepts has added to the predictive power of the IPT. Further,

a detailed explication of each concept will demonstrate IPT’s ability to consistently predict

human information-processing tendencies. The previous section on the current state of IPT has

demonstrated how attention can be operationalized through self-report, physiological responses,

STRTs, and internal and external cues. Therefore, this section will briefly discuss how the other

concepts are operationalized.

Long-term Memory Concepts

Propositions are most often used as a way to represent declarative knowledge. Bruning et

al. (2004) describe a proposition as “the smallest unit of meaning that can stand as a separate

assertion” (p. 47). Therefore, propositions can be operationalized as the various meanings

deducted from a sentence, paragraph, article, etc. Kintsch’s (1986, 1988) research on declarative

knowledge has found that entire texts can be translated into a list of propositions. Thus,

operationalizing these propositions can show researchers how meaning is organized and

processed in long-term memory.

Schemata (sing., schema) are complex mental frameworks that control encoding, storage

and retrieval of information (Bruning et al., 2004, p. 48). Schemata use different “slots” to store

a range of information, such as knowledge about objects, events, actions, or sequences of events

and actions (Rumelhart, 1981). One example of schema operationalization is measuring the


differences in what people read (“The paratrooper leaped out the door”) versus what is recalled

(“The paratrooper jumped out of the plane”). Bruning et al. (2004) suggest that “because the

contents of memory consist of representations of knowledge, rather than exact copies of it,

encoding will vary according to the schemata activated at the time of encoding” (p. 51). This

explains why individuals may recall the same stimulus differently.

Propositions and schemata represent forms of declarative knowledge; whereas, the last

two concepts represent procedural knowledge. Productions are automatic, implicit if/then rules

that guide behavior. One straightforward method of operationalizing productions was through a

computer program, called READER (Just & Carpenter, 1987). This program used if/then code to

essentially teach a computer how to read. Thus, READER demonstrates how procedural

knowledge may function as a set of productions in long-term memory. The last concept is the

script, which is the schema-equivalent for procedural knowledge. Scripts hold onto procedural

information related to events, and thereby, describe how individuals know how to behave in

certain situations. Schank and Abelson (1977) originally proposed the idea of scripts in an

attempt to explain how individuals automatically engage in certain routines in everyday

scenarios.

These key concepts have elevated the status of the IPT to a highly predictive and

explanatory theory. However, continued research on the various memory systems has led some

researchers to criticize the modal model that originally jump-started the theory.

Criticisms

Perhaps, one of the greatest flaws of IPT is its attempt to cover far too many concepts in

an overly simplistic model. Many researchers have rejected the linearity and rigid

compartmentalization of the modal model in an attempt to focus more on information processes


rather than information storage (Collins & Loftus, 1975; Craik & Lockhart, 1972; Jenkins, 1974;

Ericsson & Kintsch, 1995). Consequentially, three revised versions of the modal model have

come to represent IPT: network models, the ACT model, and connectionist models.

Network Models of Memory

In an attempt to describe the complexity of human information processing, network

models of memory use webs (or networks) consisting of nodes and links (J. R. Anderson, 1983b,

1993, 1996). The nodes represent concepts or schemata and are connected via relational links.

Quillian (1968) and Collins and Quillian (1969) incorporated this structure into one of the

earliest network models, the Teachable Language Comprehender (TLC). One of the primary

assumptions—which directly contradicts the serial processing of the modal model—is the

concept of spreading activation. The idea is that during memory retrieval, activation will spread

from the primary node being accessed to all other related nodes via the relational links (Bruning

et al., 2004, p. 56). Another assumption of the TLC is that nodes are arranged hierarchically (see

Appendix B for an example model). This allows schemata to be organized from general to

specific. Although the TLC’s use of nodes and links serve as a step toward neurologically correct

architecture, the model cannot be deemed comprehensive of cognitive processes.

The ACT Model

Developed by J. R. Anderson (1976, 1983a, 1983b, 1993, 1996), the ACT model (short

for Adaptive Control of Thought) attempts to describe all cognitive processes including initial

encoding, storage, and retrieval, while considering declarative and procedural knowledge. J. R.

Anderson (1996) and J. R. Anderson and Matessa (1997) later proposed the revised model, ACT-

R (short for Adaptive Control of Thought – Rational). In this revision (see Appendix C),


declarative knowledge is represented by schemata; whereas procedural knowledge is represented

by productions. The two types of knowledge are connected, thereby explaining how declarative

knowledge (semantic or episodic) can be evaluated by production rules in procedural knowledge.

This intimate connection has led to ample research on problem-solving and decision making in

association with ACT-R (J. R. Anderson, 1993, 1996). In congruence with network models,

ACT-R also functions by spreading activation. However, the initial activation areas are called

focus units instead of nodes (Bruning et al., 2004, p. 58). Ultimately, this model has expanded

upon the original concepts of the modal model in a more systematic way. Consequentially, it has

been used as a key model in computer science (Hochstein, 2002). This, however, has led

cognitive researchers to adapt more “brain metaphor” models as opposed to “computer

metaphor” models (McClelland et al., 1995; McClelland et al., 1986; and Rumelhart & Todd,

1993).

Connectionist Models

As an attempt to humanize information-processing models, several researchers shifted to

connectionist models. Also known as the parallel distributed processing (PDP) model, this

structural model focuses on the idea that human cognition does not engage in serial processing,

but instead uses parallel processing. This refers to the fact that mental processes are often

simultaneous and do not occur in a linear fashion. Contrary to other models, McClelland (1988)

contends that units (or nodes) are not stored in the PDP model. Instead, the strengths that connect

units are stored and retrieved during concept activation. Thus, environmental stimuli activate an

input unit, which generates spreading activation through connections until an output is reached

(see Appendix D for an example PDP model). Another quality that sets the PDP model apart

from previous IPT models is its similarity to the structure of the brain. Bruning et al. (2004)


contend that “In connectionist models, processing units are roughly analogous to neurons or

assemblies of neurons, and the connections by which units are linked are seen as roughly

analogous to synapses” (p. 61).

This move toward biologically correct graphic representations has greatly enhanced IPT’s

level of sophistication since the modal model. Researchers have used PDP to obtain a more

accurate understanding of the structure of the brain as it relates to sensory, short-term, working,

and long-term memory (McClelland et al., 1995; McClelland & Seidenberg, 2000). However,

despite the continued effort to realistically account for human cognitive processes, IPT remains

limited by its metaphorical state. The following section will discuss the weaknesses of IPT as a

realistic roadmap for human cognition.

IPT Weaknesses

This literature review has discussed the great improvements IPT has made over the years

from the simplistic modal model to the elaborate connectionist model. However, researchers

have failed to accurately link human information processing to the actual structure of the brain.

As mentioned in the previous section, the applicability of IPT is halted by its current state as an

unrealistic metaphor. What follows are several restrictive characteristics of IPT’s most

sophisticated connectionist models.

Biologically Incorrect Features of IPT Models

Reeke and Edelman (1988) conducted a thorough investigation of information processing

models to determine the best method for improving Artificial Intelligence systems. Recognizing

that computers are limited by serial sequencing, Reeke and Edelman (1988) directed their

attention to the PDP model in an attempt to capitalize on the complexities of human cognition.


Although they found some components promising, the inherent failure to mirror biological

processes weakened the model’s credibility.

The first biological discrepancy involves “…the notion of memory as a replica or a

transformation of ‘information’ given in the world…” (pp. 152-153). Consistent with the

critiques of working memory mentioned earlier, human information processing is not

independent of context and affective processes. Connectionist models often ignore the

impression these factors can make during encoding and retrieval processes.

The second biological discrepancy is the “…conception of memory retrieval as the

relaxation of a network to a stable state…” (p. 153). This concept suggests that the brain can

only reach a state of equilibrium once it returns to its original state prior to the initial encoding.

Thus, the information added (encoded) must be removed (retrieved). However, the brain is in a

state of constant flux and can never reach the state of equilibrium connectionist models assume.

The third biological discrepancy is “…the idea of energy minimization through simulated

annealing…” (p. 153). Simplified, Reeke and Edelman note that the type of algorithm

connectionist models use to lessen the necessary cognitive effort for information processing is

far too slow. Realistically, neurons process information at an exponentially greater speed,

debunking the possibility of any currently proposed algorithms.

The fourth biological discrepancy involves “…the notion of bidirectional and symmetric

single connections…” (p. 153). As research on IPT evolved, the idea of reciprocity between

input and output units became an essential feature of the more sophisticated models. While

research indicates that long-term memory can affect sensory memory, this model does not mirror

the structures of the brain (Neath, 1998). In reality, synapses are monodirectional (Reeke &

Edelman, 1988).


The final weakness in regard to biological discrepancies is “…the idea that learning can

proceed by clamping the output of the system to a desired value while synaptic weights are

adjusted according to some rule…” (p. 153). Basically, this connectionist idea suggests that

external factors can alter the algorithm of the brain’s motor functions. Although ideal, this is not

necessarily true. The brain cannot be completely rewired by simple, conscious effort. Ultimately,

these unrealistic features of IPT’s most sophisticated models suggest that further research is

necessary to truly operationalize these theoretical concepts.

Discussion

IPT’s heavy emphasis on mental processes has made it popular in the fields of education

and developmental psychology. Yet, based on this comprehensive literature review, it is apparent

that IPT has not been adequately utilized in the field of media studies. Ample research has been

conducted on attention allocation and perception; however, researchers have not fully ventured

into the realm of comprehension. A resulting research question is “how can we determine the

level of comprehension an individual gains from media messages?” Additionally, “can IPT be

studied in conjunction with other media theories to further expand upon audience’s levels of

comprehension?”

IPT can provide excellent insight into how different individuals can experience the same

stimuli but encode separate interpretations. As mentioned in the operationalization section, this is

partly dependent upon individuals’ current schemata. Yet, it is also dependent upon the type of

rehearsal (maintenance or elaborative) combined with the depth of information processing

(shallow or deep). Further research focused on an audience’s encoding processes while attending

to public communication campaigns could guide future message production strategies. For

example, a study could investigate the level of processing used during exposure to disaster


preparation campaigns. A researcher could determine if individuals are encoding basic,

procedural information on what’s advised in disaster situations or meaningful, conditional

information that could be used during a crisis. The researcher could operationalize this through

an experiment that measures individuals’ abilities to apply knowledge obtained in the study in a

simulated “disaster” scenario.

While considering the levels of processing an individual will use, the concept of

motivation inevitably comes to mind. It is likely that a person’s level of motivation to learn will

affect the amount of attention allocated to encoding. Since IPT does not go into depth on the idea

of motivation, it would be helpful to conduct a study combining IPT with the elaboration

likelihood model (ELM) (Petty & Cacioppo, 1986). For example, a researcher could investigate

the comprehension gained from a public communication campaign based on the encoding

processes and the prerequisites determined for central and peripheral processing (as defined by

the ELM). Thus, the study would consider motivation combined with cognitive ability to

determine the level of encoding as measured by an individual’s ability to demonstrate

comprehension.

These are just two potential elaborations on IPT research. Given the wealth of

information IPT provides, there are practically innumerable possibilities for operationalizing the

theory in media studies. Yet, as previously mentioned, the theory may be best used in

conjunction with other media studies theories—particularly, the ELM. Thus, it can be

acknowledged that there are very favorable implications for IPT in media studies that researchers

should capitalize on to push the field further into the realm of credible empirical studies.


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Appendix A

The Modal Model

Figure A1. The Modal Model. (Bruning et al., 2004, p. 16).


Appendix B

Example Network Model

Figure B1. A Network Model of Memory. Source: This sample of a network is modeled after those developed by Collins and Quillian (1969) (Adapted from Bruning et al., 2004, p. 56).

Mammals

Horses

Quarter Horses Palominos Arabians

Cows

Holsteins Jerseys

Warm blooded

Have skin

Nurse youngHave manes

Have hooves

Can be riddenAre fast

Are agile

Are gentle


Appendix C

The ACT-R Model

Figure C1. The ACT-R Model. (ACT-R Research Group, n.d.).


Appendix D

Example Connectionist Model

Figure D1. An example of a connectionist model. (Berkeley, 1997).

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