Handbook of psychology volume 7 educational psychology

Cognitive System: Architecture of the Cognitive System

An Information Processing Model

Figure 3.1 presents a model of the human information pro-

cessing system, consisting of three memory stores (repre-

sented as labeled boxes), five basic cognitive processes

(represented as labeled arrows), and two channels of knowl-

edge representation (represented as the top and bottom rows).

The three memory stores are sensory memory, where sensory

input is stored briefly in its original form; working memory,

where a limited number of elements of the presented material

are stored and manipulated within one’s conscious aware-

ness; and long-term memory, where large amounts of knowl-

edge are stored for long periods of time. The five cognitive

processes presented in Figure 3.1 are selecting images, se-

lecting words, organizing images, organizing words, and in-

tegrating. The two channels are the auditory-verbal channel

(in the top row of Figure 3.1), in which material enters the

cognitive system through the ears and eventually is repre-

sented in verbal code, and the visual/pictorial channel (in the

bottom row of Figure 3.1), in which material enters the cog-

nitive system through the eyes and eventually is represented

in pictorial code.

On the left side of the top row, spoken words enter the

cognitive system through the ears, resulting in a short-lasting

acoustic sensation in auditory sensory memory. If the learner

pays attention, parts of the sensation are transferred to verbal

working memory for further processing. The arrow from

acoustic sensation in auditory sensory memory to sound base

in verbal working memory represents the cognitive process

of selecting sounds, and the resulting representation in verbal

working memory is a collection of sounds that can be called

a sound base. If the learner generates visual representations

based on the sounds (e.g., imagining a dog when the word

“dog” is spoken), this process is represented by the arrow

from sound base to image base. The arrow from sound base

to verbal model in verbal working memory represents the

cognitive process of organizing sounds, and the resulting rep-

resentation in verbal working memory is a coherent structure

that can be called a verbal model.

On the left side of the bottom row, printed words and pic-

tures enter the cognitive system through the eyes, resulting

52

Memory and Information Processes

in a short-lasting visual sensation in visual sensory memory.

If the learner pays attention, parts of the sensation are trans-

ferred to visual working memory for further processing. The

arrow from visual sensation in visual sensory memory to

image base in visual working memory represents the cogni-

tive process of selecting images, and the resulting representa-

tion in visual working memory is a collection of images that

can be called an image base. If the learner generates verbal

representations based on the images (e.g., mentally saying

“dog” when a picture of a dog is processed or the printed let-

ters for “dog” are read silently), this process is represented by

the arrow from image base to sound base. The arrow from

image base to pictorial model in visual working memory rep-

resents the cognitive process of organizing images, and the

resulting representation in visual working memory is a co-

herent structure that can be called a pictorial model.

The final cognitive process—integrating—is represented

by arrows connecting pictorial model from visual working

memory, verbal model from verbal working memory, and

prior knowledge from long-term memory. The result is an in-

tegrated representation based on visual and verbal representa-

tions of the presented material as well as relevant prior

knowledge. Overall, the construction of knowledge requires

that the learner select relevant images and sounds from the

presented material, organize them into coherent pictorial and

verbal representations, and integrate the pictorial and verbal

representations with each other and with prior knowledge.

Three Assumptions Underlying the Model

The information processing model presented in Figure 3.1 is

based on three assumptions from the cognitive science of

learning: the dual channel assumption, the limited capacity

assumption, and the active learning assumption (Mayer,

2001). The dual channel assumption is that humans possess

separate information processing channels for visual-pictorial

material and auditory-verbal material (Baddeley, 1998;

Paivio, 1986). For example, printed words and pictorial mate-

rial (e.g., illustrations, graphics, animation, and video) are

processed as visual images (at least initially) in the visual-

pictorial channel whereas spoken words are processed as

sounds (at least initially) in the auditory-verbal channel.

Eventually, printed words and pictures may be represented in

the verbal channel even if they were presented visually, and

spoken words may be represented in the visual channel if they

elicit images in the learner. However, the way that verbal and

pictorial material is represented in working memory is differ-

ent, so there is a verbal code and a pictorial code. An impor-

tant aspect of controlling the flow of visual and verbal

information is for learners to build connections between cor-

responding visual and verbal representations of the same

material—an accomplishment that Paivio (1986) calls build-

ing referential connections.

For example, Mayer (2001) reported research in which

students learned about how a scientific system works (e.g., a

bicycle tire pump, a car’s braking system, or the process of

lightning formation) and then took a transfer test that mea-

sured their depth of understanding. Students performed better

on the transfer test when they listened to an explanation and

viewed a corresponding animation than when they only lis-

tened to the explanation. This multimedia effect is consistent

with the idea that people process visual and verbal material in

separate channels. 

The limited capacity assumption concerns constraints on

the amount of material that can be processed at one time in

working memory (Baddeley, 1998; Sweller, 1999). Thus, only

a few images can be held and organized into a coherent visual

model at one time, and only a few words can be held and orga-

nized into a coherent verbal model at one time. An important

aspect of the limited capacity assumption is that the learner’s

cognitive system easily can become overloaded, such as by

presenting a great amount of information simultaneously.

For example, Mayer (2001) reported research in which stu-

dents learned about how lightning storms develop by receiv-

ing a narrated animation and then took transfer tests. When

the presentation contained extraneous words (e.g., interesting

facts about people being struck by lightning), pictures (e.g.,

interesting video clips of lightning storms), and sounds (e.g.,

background music), students performed more poorly on sub-

sequent transfer tests than when extraneous material was ex-

cluded. This coherence effect is consistent with the idea that

the extra material overloaded the learners’ working memo-

ries, thus making it more difficult to construct a mental repre-

sentation of the cause-and-effect system.

The active learning assumption is that meaningful learning

(or understanding) occurs when learners engage in appropri-

ate cognitive processing during learning—including selecting

relevant information, organizing the material into a coherent

representation, and integrating incoming visual and verbal

material with each other and with prior knowledge (Mayer,

1996b, 1999). The balanced and coordinated activation of

these kinds of processes leads to the construction of a mean-

ingful learning outcome that can be stored in long-term

memory for future use. In short, meaningful learning is a gen-

erative process in which the learner must actively engage in

cognitive processing rather than passively receive informa-

tion for storage (Wittrock, 1990).

For example, signaling (Loman & Mayer, 1983; Lorch,

1989; Meyer, 1975) is a technique intended to improve stu-

dents’ understanding of prose in which the key material is

Information Processing and Instruction

53

highlighted (thus fostering the process of selecting) and the

organizational structure is highlighted (thus fostering the

process of organizing). For example, Mautone and Mayer

(2001) presented a narrated animation on how airplanes

achieve lift and then asked students to solve some transfer

problems that required applying what they had learned. Some

students received a signaled version that included a short out-

line stating the main three steps, headings keyed to the three

steps, and connecting words such as “because of this” or

“first . . . second . . . third.” The signals were part of the nar-

ration and added no new content information. Other students

received a nonsignaled version. On the transfer test, there

was a signaling effect in which the students in the signaled

group performed better than students in the nonsignaled

group. Thus, techniques intended to prime active cognitive

processing (e.g., selecting and organizing relevant material)

resulted in better understanding. 

INFORMATION PROCESSING AND INSTRUCTION

In this section I examine three examples of how the informa-

tion processing approach can be applied to instructional is-

sues in three subject matter domains: reading, writing, and

mathematics. In each domain the driving question concerns

the cognitive processes or knowledge that a student needs to

perform competently as an authentic academic task such as

comprehending a passage, creating an essay, or solving an

arithmetic word problem. I focus on these three domains be-

cause they represent exemplary educational tasks that have

been studied extensively in research. 

Information Processing in Reading a Passage

What are the cognitive processes involved in comprehending

a passage? Mayer (1996b, 1999) analyzed the reading-

comprehension task into four component processes: select-

ing, organizing, integrating, and monitoring.

Selecting involves paying attention to the most relevant

portions of the passage. This involves being able to tell what

is important and what is not (Brown & Smiley, 1977). For ex-

ample, Brown and Smiley (1977) broke stories into idea units

(e.g., single events or simple facts) and asked children to sort

them into four categories ranging from most to least impor-

tant. Third-graders seemed to sort randomly, such that an im-

portant idea unit was no more likely than an unimportant idea

unit to be sorted into the important category. However, college

students were extremely accurate, such that important idea

units were usually classified as important and unimportant

idea units were usually classified as unimportant. Apparently,

as students acquire more experience in reading for compre-

hension, they develop skill in selecting important information. 

Organizing involves taking the relevant pieces of informa-

tion and mentally connecting them into a coherent structure.

For example, some possible structures are to organize the

material as cause-and-effect sequence, classification hierar-

chy, compare-and-contrast matrix, description network, or

simple list (Chambliss & Calfee, 1998; Cook & Mayer, 1988;

Meyer & Poon, 2001). In an exemplary study, Taylor (1980)

asked fourth- and sixth-grade students to read and recall a

short passage. The sixth-graders recalled much more super-

ordinate material than subordinate material, indicating that

they used the higher level structure to help them organize and

remember the lower level material. In contrast, fourth-grade

readers recalled more subordinate material than superordi-

nate material, indicating that they did not make much use of

the higher level structure to help them mentally organize the

passage. Apparently, as students acquire more experience in

reading for comprehension, they develop skill in organizing

the material into a high-level structure.

Integrating involves connecting the incoming knowledge

with existing knowledge from one’s long-term memory. This

involves activating relevant prior knowledge and assimilat-

ing the incoming information to it (Ausubel, 1968). For ex-

ample, Bransford and Johnson (1972) asked college students

to read an abstract passage about a procedure. If students

were told beforehand that the passage was about washing

clothes, they remembered twice as much as when they were

told the topic afterward. Apparently, priming appropriate

prior knowledge before reading a new passage is a powerful

aid to comprehension. 

Monitoring involves a metacognitive process of judg-

ing whether the newly constructed knowledge makes sense.

For example, in comprehension monitoring readers continu-

ally ask themselves whether the passage makes, whether parts

contradict one another, and whether parts contradict their

past experiences (Markman, 1979). In an exemplary study,

Vosniadou, Pearson, and Rogers (1988) asked third and fifth

graders to read stories that had inconsistent statements. When

prompted to point out anything wrong with the passage, the

fifth graders recognized more than twice as many of the in-

consistencies as did third graders. Apparently, students de-

velop skill in comprehension monitoring as they gain more

experience in reading.

There is overwhelming evidence that the cognitive

processes underlying reading comprehension can be taught

(Pressley & Woloshyn, 1995). For example, Cook and Mayer

(1988) taught students how to outline paragraphs from their

chemistry textbooks based on some of the structures just

listed. Thus, the training focused on the organizing process.

54

Memory and Information Processes

Initially, most students organized passages as lists of facts,

but with training they were able to distinguish between pas-

sages that best fit within the structure of a cause-and-effect

sequence, a classification hierarchy, and so forth. When stu-

dents were tested on their comprehension of passages from a

biology textbook, the structure-trained students performed

much better than did students who had not received training.

Research on teaching of organizing strategies offers one use-

ful demonstration of the positive consequences of teaching

specific ways to process information. 

Information Processing in Writing an Essay

What are the cognitive processes involved in writing an

essay, such as “how I spent my summer vacation”? Hayes

and Flower (1980; Hayes, 1996) analyzed the essay-writing

task in three component processes: planning, translating, and

reviewing.

Planning involves mentally creating ideas for the essay

(i.e., generating), developing an outline structure for the

essay (i.e., organizing), and considering how best to commu-

nicate with the intended audience (i.e., evaluating). For ex-

ample, the learner may remember specific events from his or

her summer vacation, may decide to present them in chrono-

logical order under the theme “too much of a good thing,”

and may decide that the best way to communicate is through

humor.

In a study of the role of planning, Gould (1980) asked peo-

ple to write (or dictate) a routine business letter for a specific

purpose. People spent about one third of their time writing (or

speaking) and two thirds of their time in silence—presumably

as they planned what to write (or say) next. It is interesting to

note that people began writing (or speaking) immediately, in-

dicating that they engaged in no global planning. These re-

sults suggest that writers spend most of their time in local

planning and therefore point to the need for training in global

planning.

Translating involves actually putting words on paper, such

as through writing, typing, or dictating. For example, the

learner may sit at a word processor and begin to type. In a

study of the role of translating, Glynn, Britton, Muth, and

Dogan (1982) asked students to write a first draft and then a

final draft of a persuasive letter. Some students were told

to write a polished first draft paying attention to grammar

and spelling, whereas other students were told to write an

unpolished first draft minimizing attention to grammar and

spelling. Students wrote a higher quality final draft when they

were told to write an unpolished rather than a polished first

draft. Apparently, the process of translating places a heavy

cognitive load on the writers’ working memories, so if they

have to pay attention to low-level aspects of writing (e.g.,

spelling and grammar), they are less able to pay attention to

high-level aspects of writing (e.g., writing a persuasive argu-

ment). These findings suggest the need to minimize cognitive

load when students are translating. 

Reviewing involves detecting and correcting errors in

what has been written. For example, the learner may read

over a sentence and decide it needs to be made more specific.

In a study of the role of reviewing, Bartlett (1982) found that

middle-school students performed poorly on detecting errors

in their own essays but well on detecting errors in their peers’

essays. Less than half of the detected errors were corrected

properly. These results point to the need for training in how to

detect and correct errors. 

Research on writing shows that learners often have diffi-

culty in the planning and reviewing phases of writing, but

these cognitive processes can be taught with success

(Kellogg, 1994; Levy & Ransdell, 1996; Mayer, 1999). For

example, Kellogg (1994) asked college students to write an

essay on the pros and cons of pledging to give all of one’s in-

come over a certain level to poor families in the community.

One group of students was not asked to engage in any

prewriting activity (no-prewriting group), whereas another

group was asked to begin by producing an outline containing

the relevant ideas (outlining group). The outlining group,

therefore, was encouraged to engage in planning processes

such as generating ideas, organizing ideas, and evaluat-

ing whether the message is appropriate for the audience.

When judges were asked to rate the quality of the essays on a

10-point scale, the essays written by the outlining group re-

ceived much higher quality ratings than did those written by

the no-prewriting group. Apparently, students often ignore

the cognitive processes in planning, but when they are en-

couraged to engage in planning processes, their writing is

much improved.

Information Processing in Solving

a Mathematics Problem

What are the cognitive processes involved in solving an arith-

metic word problem, such as, “At ARCO gas sells for $1.13

per gallon. This is 5 cents less per gallon than gas at Chevron.

How much do 5 gallons of gas cost at Chevron?” (Lewis &

Mayer, 1987). Mayer (1992b) analyzed the task in four

component processes: translating, integrating, planning, and

executing.

Translating involves building a mental representation for

each sentence in the problem. For example, for the first sen-

tence the learner may build a mental representation such as

“ARCO

ϭ 1.13”; and for the second sentence the learner may

Conclusion

55

build a mental representation such as “ARCO

ϭ CHEVRON Ϫ

.05.” In an exemplary study, Soloway, Lochhead, and Clement

(1982) asked college students to write equations for state-

ments such as, “There are six times as many students as profes-

sors at this university.” Approximately one third of the students

translated the statement incorrectly, yielding answers such as

“6S

ϭ P.” Students need training in how to represent some of

the sentences in word problems.

Integrating involves building a mental representation of

the entire situation presented in the problem. For example,

the learner may visualize a number line with ARCO at the

1.13 point on the line and Chevron .05 spaces to the right. In

an exemplary study, Paige and Simon (1966) gave students a

problem with an internal inconsistency, such as: “The num-

ber of quarters a man has is seven times the number of dimes

he has. The value of the dimes exceeds the value of the quar-

ters by $2.50. How many of each coin does he have?” Most

students failed to recognize the inconsistency; some con-

structed equations such as Q 

ϭ 7D and D (.10) ϭ 2.50 ϩ

Q(.25), and solved for Q. Students need training in how to in-

tegrate the information into a meaningful representation that

can be called a situation model (Kintsch & Greeno, 1985;

Mayer & Hegarty, 1996).

Planning involves creating a strategy for solving the prob-

lem, such as breaking a problem into parts. For example, the

learner may develop the plan: Add .05 to 1.13, then multiply

the result by 5. Reed (1987) has shown that giving stu-

dents worked examples with commentary can help them

apply appropriate strategies when they receive new problems.

Chi, Bassok, Lewis, Reimann, and Glaser (1989) found

that students who spontaneously produced self-explanations

as they read worked examples in textbooks tended to excel

on subsequent problem-solving tests. Students need prac-

tice in understanding the strategies used to solve example

problems.

Executing involves carrying out a plan, resulting in the pro-

duction of an answer. For example, the learner may compute

.05

ϩ 1.13 ϭ 1.18, 1.18 ϫ 5 ϭ 5.90. An accompanying

process is monitoring, in which the learner evaluates whether

the plan is being successfully applied. Fuson (1992) has iden-

tified four stages in the development of simple addition for

problems (such as 3 

ϩ 5 ϭ ___): counting all, in which the

student counts 1-2-3, and then 4-5-6-7-8; counting on, in

which the student starts with 3 and then counts 4-5-6-7-8;

derived facts, in which the student changes the problem into

4

ϩ 4 and gives 8 as the answer; and known facts, in which the

student simply retrieves 8 as the answer. When the lower-level

skill is automatic—requiring minimal attention—the student

can devote more cognitive resources to understanding the

problem and planning the problem solution.

Together, translating and integrating constitute the phase

of problem understanding, whereas planning and executing

constitute the phase of problem solution. Research shows

that learners have difficulty with problem understanding—

translating and integrating—although instruction emphasizes

problem solution, particularly executing (Mayer, Sims, &

Tajika, 1995). 

An important contribution of the information processing

approach to mathematical cognition is the design of pro-

grams to teach students how to process mathematics prob-

lems. For example, Lewis (1989) taught students how to

represent arithmetic word problems in pictorial form as vari-

ables along a number line. A sentence like “Megan has $420”

is represented by placing “Megan” along a number line along

with “$420.” Then, the sentence, “She saved one fifth as

much as James saved” means that “James” should be placed

on the number line to the right of “Megan,” indicating that

the amount James saved is greater than the amount Megan

saved. By converting the sentences into an integrated number

line, students learn how to engage in the cognitive processes

of translating and integrating. Students who practiced these

processes on a variety of problems for approximately 60 min

performed much better on tests of solving new arithmetic

word problems than did students who spent the same amount

of time working with the problems without explicit training

in converting them into number-line representations. These

findings encourage the idea that students can learn to improve

the way they process mathematics problems.

Future research on the psychology of subject matter

(Mayer, 1999) is likely to provide detailed analyses of the

cognitive processes needed for success on a variety of acade-

mic tasks, to uncover individual differences, and to discover

instructional techniques for fostering the development of

appropriate learning skills.