Cognitive Psychology and Its Implications, Ch. 5

5Representation of Knowledge

Recall a wedding you attended a while ago. Presumably, you can remember who

married whom, where the wedding was, many of the people who attended, and

some of the things that happened. You would probably be hard pressed, however,

to say exactly what all the participants wore, the exact words that were spoken,

the the way the bride walked down the aisle, and so on—although you probably

registered many of these details. It is not surprising that after time has passed, our

memories lose some of the information of the original experience. What is interesting

is that our loss of information is selective: We tend to remember the gist

(that which is most meaningful or useful) and forget the detail (that which is not

important).

The previous chapter was about our ability to form visual images of the detail of

our experiences. It might seem that it would be ideal if we had the capacity to always

represent information in such detail and remember the detail. However, the histories

of the few individuals who have such detailed memories suggest that this might be a

curse rather than a blessing. Luria (1987) describes the story of a Russian journalist

who lived in the first half of the 20th century, who had very vivid imagery and the

ability to remember a great many perceptual details of his experience. He had problems

with many aspects of ordinary life, including reading:

As he put it: “Other people think as they read, but I see it all.” As soon as he began

a phrase, images would appear; as he read further, still more images were evoked,

and so on. . . . If a passage were read to him quickly, one image would collide with

another in his mind; images would begin to crowd in upon one another and would

become contorted. (p. 112)

Such problems caused him great difficulties in many aspects of life, including keeping

his job. Parker, Cahill, and McGaugh (2006) describe a current case of an individual

with highly detailed memory.1 She is able to remember many details from years ago in

her life but had difficulty in school and seems to perform poorly on tasks of abstract

reasoning such as processing analogies. There are many situations when we need to

rise above the details of our experience and get to their true meaning and significance.

115

1 She has written her own biography, The Woman Who Can’t Forget (Price, 2008).

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116 | Representation of Knowledge

In this chapter we will address the following questions:

• What happens to our memories for gist versus detail with the passage of time? • How do we represent the gist of an experience? • Are there representations of knowledge that are not tied to specific perceptual

modalities? • What is the nature of our representation of categorical knowledge and how

does this affect the way we perceive the world?

•Knowledge and Regions of the Brain

Figure 5.1 shows some of the brain regions involved in the abstraction of

knowledge. Some prefrontal regions are associated with extracting meaningful

information from pictures and sentences. The left prefrontal region is more

involved in the processing of verbal material and the

right prefrontal region is more involved in the processing

of visual material (Gabrielli, 2001). Part of the

processing is to represent events in terms of general

categories such as bride or wedding. This categorical

information is represented in posterior regions of the

temporal, parietal, and occipital cortices. As we will see,

there is evidence that different posterior regions represent

different types of concepts.

We will review neuroscience data on the localization

of processing and information in the brain, but much

of the most striking evidence comes from behavioral

studies that examine what people remember or forget

after an event.

Prefrontal regions of the brain are associated with meaningful processing of

events, whereas posterior regions are associated with representing concepts.

•Memory for Meaningful Interpretations of Events

Memory for Verbal Information

An experiment by Wanner (1968) illustrates circumstances in which people do

and do not remember information about exact wording.Wanner asked participants

to come into the laboratory and listen to tape-recorded instructions. For

one group of participants, the warned group, the tape began this way:

The materials for this test, including the instructions, have been recorded on

tape. Listen very carefully to the instructions because you will be tested on

your ability to recall particular sentences which [sic] occur in the instructions.

Prefrontal regions

that process pictures

and sentences

Posterior regions

that represent

concepts

Brain Structures

FIGURE 5.1 Cortical regions

involved in the processing of

meaning and the representation

of concepts.

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Memory for Meaningful Interpretations of Events | 117

The participants in the second group received no such warning and so had no

idea that they would be responsible for the verbatim instructions. After this

point, the instructions were the same for both groups. At a later point in the

instructions, one of four possible critical sentences was presented:

1. When you score your results, do nothing to correct your answers

but mark carefully those answers which are wrong.

2. When you score your results, do nothing to correct your answers

but carefully mark those answers which are wrong.

3. When you score your results, do nothing to your correct answers

but mark carefully those answers which are wrong.

4. When you score your results, do nothing to your correct answers

but carefully mark those answers which are wrong.

Immediately after one of these sentences was presented, all participants (warned

or not) heard the following conclusion to the instructions:

To begin the test, please turn to page 2 of the answer booklet and judge which

of the sentences printed there occurred in the instructions you just heard.

On page 2, they found the critical sentence they had just heard plus a similar

alternative. Suppose they had heard sentence 1. They might have to choose

between sentences 1 and 2 or between sentences 1 and 3. Both pairs differ only

in the ordering of two words. However, the difference between 1 and 2 does not

contribute critically to the meaning of the sentences; the difference is just stylistic.

On the other hand, sentences 1 and 3 clearly do differ in meaning. Thus, by

looking at participants’ ability to discriminate between different pairs of sentences,

Wanner was able to measure their ability to remember the meaning

versus the style of the sentence and to determine how this ability was affected

by whether or not they were warned.

The relevant data are presented in Figure 5.2. The percentage of correct

identifications of sentences heard is displayed as a function of whether participants

had been warned. The percentages are plotted separately for participants

who were asked to discriminate a meaningful difference in wording and for

those who were asked to discriminate a stylistic difference. If participants were

just guessing, they would have scored 50% correct by chance; thus, we would

not expect any values below 50%.

The implications of Wanner’s experiment are clear.

First, memory is better for changes in wording that result

in changes of meaning than for changes in wording

that result just in changes of style. The superiority of

memory for meaning indicates that people normally

extract the meaning from a linguistic message and do

not remember its exact wording. Moreover, memory for

meaning is equally good whether people are warned or

not. (The slight advantage for unwarned participants

does not approach statistical significance.) Thus, participants

retained the meaning of a message as a normal

Memory for meaning

Memory for style

Unwarned Warned

Correct (%)

100

FIGURE 5.2 Results from

Wanner’s experiment to

determine circumstances in

which people do and do not

remember information about

exact wording. The ability

of participants to remember

a wording difference that

affected meaning versus one

that affected only style is

plotted as a function of whether

or not the participants were

warned that they would be

tested on their ability to recall

particular sentences. (After Wanner,

1968. Adapted by permission of the author.)

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118 | Representation of Knowledge

part of their comprehension process. They did not have to be cued to remember

the sentence.

The second implication of these results is that the warning did have an effect

on memory for the stylistic change. The unwarned participants remembered the

stylistic change at about the level of chance, whereas the warned participants

remembered it almost 80% of the time. This result indicates that we do not

normally retain much information about exact wording, but we can do so when

we are cued to pay attention to such information. Even with a warning, however,

memory for stylistic information is poorer than memory for meaning.

After processing a linguistic message, people usually remember just its meaning

and not its exact wording.

Memory for Visual Information

On many occasions, our memory capacity seems much greater for visual information

in a picture or a scene than for verbal information (whether that verbal

information is presented auditorially by speech or visually by text). Shepard

(1967) reported an experiment in which he had participants study a set of

magazine pictures, one picture at a time. Then they were presented with pairs

of pictures consisting of one they had studied and one they had not. The task

was to recognize which picture of each pair had been studied. This task was

contrasted with a verbal condition in which participants studied sentences and

were similarly tested on their ability to recognize those sentences when they

were presented in pairs containing one new and one studied sentence. Participants

made errors 11.8% of the time in the sentence condition but only 1.5% of

the time in the picture condition. In other words, recognition memory was

fairly high in the sentence condition but was virtually perfect in the picture

condition. There have been a number of experiments like Shepard’s, which

involved 600 pictures. Perhaps the most impressive demonstration of visual

memory is the experiment by Standing (1973), who showed that participants

had only a 17% error rate after studying 10,000 pictures.

Although people can show very good memory for pictures under some circumstances,

what they seem to be remembering is some interpretation of the

picture rather than the exact picture itself. That is, it proves useful to distinguish

between the meaning of a picture and the physical picture, just as it is

important to distinguish between the meaning of a sentence and the physical

sentence. A number of experiments point to the utility of this distinction with

respect to picture memory and to the fact that we tend to remember an interpretation

of the picture, not the physical picture.

For instance, consider an experiment by Mandler and Ritchey (1977). They

asked participants to study pictures of scenes, such as the classroom scenes in

Figure 5.3. After studying eight such pictures for 10 s each, participants were

tested for their recognition memory. They were presented with a series of pictures

and instructed to identify which pictures they had studied. The series included

the exact pictures they had studied as well as distracter pictures. A distracter such

as the one shown in Figure 5.3b was called a token distracter. It differed from

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Memory for Meaningful Interpretations of Events | 119

the target only in the pattern of the teacher’s clothes, a visual detail relatively

unimportant to most interpretations of the picture. The distracter shown in

Figure 5.3c, by contrast, involves a type change—from a world map to an art

picture used by the teacher. This visual detail is relatively more important to

most interpretations of the picture because it indicates the subject being taught.

All eight pictures shown to participants contained possible token changes and

type changes. In each case, the type change involved a more important alteration

to the picture’s meaning than did the token change. There was no systematic

difference in the amount of physical change involved in a type change versus a

token change. Participants were able to recognize the original pictures 77% percent

of the time and to reject the token distracters only 60% of the time, but

they rejected the type distracters 94% of the time.

The conclusion in this study is very similar to that in the Wanner (1968)

experiment reviewed earlier. Wanner found that participants were much more

sensitive to meaning-significant changes in a sentence; Mandler and Ritchey

(1977) found that participants were more sensitive to meaning-significant

(a)

(b) (c)

FIGURE 5.3 Pictures similar to those used by Mandler and Ritchey in their experiment to

demonstrate that people distinguish between the meaning of a picture and the physical

picture itself. Participants studied the target picture (a). Later they were tested with a series

of pictures that included the target (a) along with token distracters such as (b) and type

Psychological Association.)

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changes in a picture. It may be that people have

better memory for the meanings of pictures than

for the meanings of sentences, but they have poor

memory for the physical details of both.

Bower, Karlin, and Dueck (1975) reported an

amusing demonstration of the fact that people’s

good memory for pictures is tied to their interpretation

of those pictures. Figure 5.4 illustrates some

of the material they used. These investigators had

participants study such drawings, called droodles,

with or without an explanation of their meaning.

Then they were given a memory test in which

they had to redraw the pictures. Participants who

had been given an explanation when studying

the pictures showed better recall (70% correctly

reconstructed) than those who were not given an explanation (51% correctly

reconstructed). Thus, memory for the drawings depended critically on participants’

ability to place a meaningful interpretation on the pictures.

When people see a picture, they tend to remember a meaningful interpretation

of it.

Retention of Detail versus Meaning

There is evidence that people initially encode many of the perceptual details of a

sentence or a picture but tend to forget this information quickly. Once the perceptual

information is forgotten, people retain memory for their interpretation of the

picture. Memory for the orientation of a picture is one of the visual details that

appear to decay rapidly, as demonstrated in an experiment by Gernsbacher

(1985). Participants were shown pictures such as the ones illustrated in Figure 5.5.

After studying one of these pictures, the participants were asked to judge which

of the pair they had seen. After 10 s, participants made their judgments with

79% accuracy, showing considerable retention of information about left–right

120 | Representation of Knowledge

(a) (b)

FIGURE 5.4 Recalling

“droodles.” (a) A midget playing

a trombone in a telephone

booth. (b) An early bird who

caught a very strong worm.

(From Bower, Karlin, & Dueck, 1975.

Reprinted by permission of the publisher.

FIGURE 5.5 Example picture

from an experiment by

Gernsbacher, displayed in

one orientation (left) and the

reverse (right). The experiment

showed that memory for the

orientation of a picture is a

visual detail that appears to

decay rapidly. (From Gernsbacher,

1985. Original illustration from Mercer

and Mariana Meyer, One Frog Too Many.

Reprinted by permission of the publisher,

Dial Books for Young Readers, New York.)

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Memory for Meaningful Interpretations of Events | 121

orientation. After 10 min, however, their accuracy in judgment had fallen to 57%

(50% percent would reflect chance guessing). On the other hand, their memory

for what the picture was about remained high over that period of time.

When I was a graduate student, I performed an experiment (Anderson,

1974b) that made the same point in the verbal domain. Participants listened to a

story that contained various critical sentences that would be tested; for instance:

The missionary shot the painter.

Later, participants were presented with one of the following sentences and

asked whether it followed logically from the story they had heard. They were

also asked to judge which sentence they had actually heard.

1. The missionary shot the painter.

2. The painter was shot by the missionary.

3. The painter shot the missionary.

4. The missionary was shot by the painter.

The first two sentences require a positive response to the logical judgment, and

the last two require a negative response. Participants were tested either immediately

after hearing the sentence or after a delay of about 2 min. The delay had

little effect on the accuracy of their logical judgments (e.g., 1 versus 3 above)—

98% were correct immediately and 96% were correct after a delay. However,

when they were asked to judge which sentence they had heard (e.g., 1 versus 2

above), the delay had a dramatic effect. Participants were 99% correct immediately

after hearing the sentence but only 56% correct after a delay.

Memory for detail is available initially but is forgotten rapidly, whereas

memory for meaning is retained.

Implications of Good Memory for Meaning

We have seen that people have relatively good memory for meaningful interpretations

of information. So when faced with material to remember, it will

help if they can give it some meaningful interpretation. Unfortunately, many

people are unaware of this fact, and their memory performance suffers as a

consequence. I can still remember the traumatic experience I had in my first

paired-associates experiment. It happened in a sophomore class in experimental

psychology. For reasons I have long since forgotten, we had designed a

class experiment that involved learning 16 pairs, such as DAX-GIB. Our task

was to recall the second half of the pair when prompted with the first half. I was

determined to outperform the other members of my class. My personal theory

of memory at that time, which I intended to apply, was that if you try hard

and focus intensely, you can remember anything well. In the impending experimental

situation, this meant that during the learning period I should say (as

loud as was seemly) the paired associates over and over again, as fast as I could.

I believed that this method would burn the paired associates into my mind

forever. To my chagrin, I wound up with the worst score in the class.

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My theory of “loud and fast” was directly opposed to the true means of

improving memory. I was trying to memorize a meaningless verbal pair. But

the material discussed in this chapter so far suggests that we have the best

memory for meaningful information. I should have been trying to convert my

memory task into something more meaningful. For instance, DAX is like dad

and GIB is the first part of gibberish. So I might have created an image of my

father speaking some gibberish to me. This would have been a simple mnemonic

(memory-assisting) technique and would have worked quite well as a means of

associating the two elements.

We do not often need to learn pairs of nonsense syllables outside the laboratory.

In many situations, however,we do have to associate various combinations of

terms that do not have much inherent meaning.We have to remember shopping

lists, names for faces, telephone numbers, rote facts in a college class, vocabulary

items in a foreign language, and so on. In all cases, we can improve memory if we

associate the items to be remembered with a meaningful interpretation.

122 | Representation of Knowledge

effectiveness of this technique (for a review, read Kroll &

DeGroot, 2005). The research shows that, like many

things, one needs to take a nuanced approach in evaluating

the effectiveness of the keyword technique. There is

no doubt that it results in more rapid

vocabulary learning in many situations,

but there are potential costs. One might

imagine that having to go through the

intermediate keyword slows down

speed of translation, and the keyword

method has been shown to result in

slower retrieval times compared to

retrieval of items that are directly

associated without an intermediate.

Moreover, one might wonder about the

implications of having to go through

the intermediate for long-term retention,

and again it has been shown to

result in poorer long-term retention.

Finally, there is evidence that suggests

that although the method may help in passing the immediate

vocabulary test in a class and hurt in a delayed test

that we have not studied for, its ultimate impact on

achieving real language mastery is minimal. Chapter 12 will

discuss issues involved in foreign language mastery.

Implications

Mnemonic techniques for remembering vocabulary items

One domain where we seem to have to learn arbitrary

associations is foreign language vocabulary. For instance,

consider trying to learn that the Italian formaggio

(pronounced “for modge jo”) means cheese. There is a

memorization technique, called the keyword

method, for learning vocabulary

items, which some students are taught

and others discover on their own. The

first step is to convert the foreign word

to some sound-alike in one’s native

language. For example, one might convert

formaggio into “for much dough.”

The second step is to create a meaningful

connection between the sound-alike

and the meaning. For example, we might

imagine expensive cheese being sold for

much money or “for much dough.” Or

consider the Italian carciofi (pronounced

“car-choh-fee”), which means artichoke.

We might transform “car-choh-fee” into

“car trophy” and imagine a winning car at an auto show

with a trophy shaped like an artichoke. The intermediate

(e.g., for much dough or car trophy) is called the keyword,

although in both of these examples they are really

key phrases. There has been extensive research on the

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Propositional Representations | 123

It is easier to commit arbitrary associations to memory if they are converted

into something more meaningful.

•Propositional Representations

We have shown that in many situations people do not remember the exact

physical details of what they have seen or heard but rather the “meaning” of

what they have encountered. In an attempt to become more precise about what

is meant by “meaning,” cognitive psychologists developed what is called a

propositional representation. The concept of a proposition, borrowed from

logic and linguistics, is central to such analyses. A proposition is the smallest

unit of knowledge that can stand as a separate assertion, that is, the smallest

unit one can meaningfully judge as true or false. Propositional analysis applies

most clearly to linguistic information, and I will develop the topic here in terms

of such information.

Consider the following sentence:

Lincoln, who was president of the United States during a bitter war, freed the slaves.

The information conveyed in this sentence can be communicated by the following

simpler sentences:

A. Lincoln was president of the United States during a war.

B. The war was bitter.

C. Lincoln freed the slaves.

If any of these simple sentences were false, the complex sentence also would

be false. These sentences correspond closely to the propositions that underlie

the meaning of the complex sentence. Each simple sentence expresses a primitive

unit of meaning. One condition that our meaning representations must satisfy is

that each separate unit composing them must correspond to a unit of meaning.

However, the theory of propositional representation does not claim that a

person remembers the simple sentences such as the three just presented. Past

research indicates that people do not remember the exact wording of such

simple sentences any more than they remember the exact wording of the

original complex sentence. For instance, in another study I did as a graduate

student (Anderson, 1972), I showed that participants demonstrated poor ability

to remember whether they had heard sentence C or the sentence:

The slaves were freed by Lincoln.

Thus, it seems that we represent information in memory in a way that preserves

the meaning of the primitive assertions but does not preserve any information

about specific wording. A number of propositional notations represent information

in this abstract way. One, used by Kintsch (1974), represents each

proposition as a list containing a relation followed by an ordered list of

arguments. The relations organize the arguments and typically correspond

to the verbs (in this case, free), adjectives (bitter), and other relational terms

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(president of ). The arguments refer to particular times, places, people, or objects,

and typically correspond to the nouns (Lincoln, war, slaves). The relations

assert connections among the entities these nouns refer to. Kintsch represents

each proposition by a parenthesized list consisting of a relation plus arguments.

As an example, sentences A through C would be represented by these lists:

a. (president-of: Lincoln, United States, war)

b. (bitter: war)

c. (free: Lincoln, slaves)

Note that each relation takes a different number of arguments: president of takes

three, free takes two, and bitter takes one. Whether a person heard the original

complex sentence or heard

The slaves were freed by Lincoln, the president during a bitter war,

the meaning of the message would be represented by lists a through c.

Bransford and Franks (1971) provided an interesting demonstration of the

psychological reality of propositional units. In this experiment, participants

studied 12 sentences, including the following:

The ants ate the sweet jelly, which was on the table.

The rock rolled down the mountain and crushed the tiny hut.

The ants in the kitchen ate the jelly.

The rock rolled down the mountain and crushed the hut beside the woods.

The ants in the kitchen ate the jelly, which was on the table.

The tiny hut was beside the woods.

The jelly was sweet.

All these sentences are composed from two sets of four propositions. One set of

four propositions can be represented as

1. (eat: ants, jelly, past)

2. (sweet: jelly)

3. (on: jelly, table, past)

4. (in: ants, kitchen, past)

The other set of four propositions can be represented as

1. (roll down: rock, mountain, past)

2. (crush: rock, hut, past)

3. (beside: hut, woods, past)

4. (tiny: hut)

Bransford and Franks looked at participants’ recognition memory for the following

three kinds of sentences:

1. Old: The ants in the kitchen ate the jelly.

2. New: The ants ate the sweet jelly.

3. Noncase: The ants ate the jelly beside the woods.

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Propositional Representations | 125

The first kind of sentence was actually studied, the second was not but is a combination

of propositions that were studied, and the third consists of words that

were studied but cannot be composed from the propositions studied. Bransford

and Franks found that participants had almost no ability to discriminate between

the first two kinds of sentences and were equally likely to say that they

had actually heard either. On the other hand, participants were quite confident

that they had not heard the third, noncase, sentence.

The experiment shows that although people remember the propositions they

encounter, they are quite insensitive to the actual combination of propositions.

Indeed, the participants in this experiment were most likely to say that they

heard a sentence consisting of all four propositions, such as

The ants in the kitchen ate the sweet jelly, which was on the table,

even though they had not in fact studied this sentence.

According to propositional analyses people remember a complex sentence as a

set of abstract meaning units that represent the simple assertions in the sentence.

Propositional Networks

In the cognitive psychology literature, one sometimes finds propositions represented

in a network form. Figure 5.6 illustrates the structure of a propositional

network that encodes the sentence, “Lincoln, who was president of

(a) (b) (c)

War

United States

President-of

Time Agent

Lincoln

Object

Relation

Lincoln

Freed

Object

Slaves

Agent

Relation

War

Bitter

Subject

Relation

(d)

War

United States

President-of

Time Agent

Lincoln

Object

Relation

Freed

Object

Slaves

Agent

Relation

Bitter

Subject

Relation

FIGURE 5.6 Network

representations for the

proposition underlying the

statement: “Lincoln, who was

president of the United States

during a bitter war, freed

the slaves.”

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the United States during a bitter war, freed the slaves.” In this propositional

network, each proposition is represented by an ellipse, which is connected by

labeled arrows to its relation and arguments. The propositions, the relations,

and the arguments are called the nodes of the network, and the arrows are

called the links because they connect the nodes. For instance, the ellipse in

Figure 5.6a represents proposition (a) from the earlier Kintsch analysis. This

ellipse is connected to the relation president-of by a link labeled relation (to

indicate that it is pointing to the relation node), to Lincoln by an agent link,

to United States by an object link, and to war by a time link. The three network

structures shown in Figures 5.6a, 5.6b, and 5.6c represent the individual

propositions (a) through (c) from the earlier Kintsch analysis. Note that these

three networks contain the same nodes, for example, Figures 5.6a and 5.6b

both contain war. This overlap indicates that these networks are really interconnected

parts of a larger network, which is illustrated in Figure 5.6d. This

last network represents all the meaningful information in the original complex

sentence on page 147.

The spatial location of elements in a network is irrelevant to its interpretation.

A network can be thought of as a tangle of marbles connected by strings.

The marbles represent the nodes, the strings the links between nodes. The network

represented on a 2-D page is that tangle of marbles laid out in a certain

way. We try to lay out the network in a way that facilitates an understanding

of it, but any layout is possible. All that matters is which elements are connected

to which others, not where the components lie.

A number of experiments suggest that it is helpful to think of the nodes

in such networks as ideas and to think of the links between the nodes as

associations between the ideas. Consider an experiment by Weisberg (1969)

that used a constrained association task. In this experiment, participants studied

and committed to memory such sentences as “Children who are slow eat bread

that is cold.” The propositional network representation of this sentence is illustrated

in Figure 5.7. After learning a sentence, participants were administered

free-association tasks in which they were given a word from the sentence and

asked to respond with the first word from the sentence that came to mind.

Participants cued with slow almost always free-associated children and almost

126 | Representation of Knowledge

Relative Subject

Slow Children Bread Cold

Agent Object

Time Relation

Past Eat

Subject Relation

FIGURE 5.7 A propositional network representation of the sentence: “Children who are slow

eat bread that is cold.” Weisberg (1969) used such sentences in an experiment to show that

the proximity of words in a propositional network has more effect on memory than their

physical proximity in the sentence.

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Propositional Representations | 127

never bread, although bread is closer to slow in the sentence than to children.

However, the illustration shows that slow and children are nearer each other in

the network (two links) than slow and bread (four links). Similarly, participants

cued with bread almost always recalled cold rather than slow, although in

the sentence bread and slow are closer than bread and cold. Again, bread and

cold are closer to each other in the network (three links) than are bread and slow

(five links). (A similar point was made in an experiment by R. A. Ratcliff &

McKoon, 1978.)

Propositional information can be represented in networks that display how

concepts relate.

Amodal versus Perceptual Symbol Systems

The propositional representations that we have just considered are examples of

what Barsalou (1999) called an amodal symbol system. By this he meant that

the elements within the system are inherently nonperceptual. The original stimulus

might be a picture or a sentence, but the representation is abstracted away

from the verbal or visual modality. Given this abstraction, one would predict

that participants in experiments would be unable to remember the exact words

they heard or the exact picture they saw.

As an alternative to such theories, Barsalou proposed a hypothesis called

the perceptual symbol system, which suggests that all information is represented

in terms that are specific to a particular perceptual modality (visual,

auditory, etc.) and basically perceptual. The perceptual symbol hypothesis is

an extension of Paivio’s (1971, 1986) dual-code theory that claimed that,

rather than abstract propositional representations, we represent information

in combined verbal and visual codes. Paivio suggested that when we hear a

sentence, we develop an image of what it describes. If we later remember the

visual image and not the sentence, we will remember what the sentence was

about, but not its exact words. Analogously, when we see a picture, we might

describe to ourselves the significant features of that picture. If we later remember

our description and not the picture, we will not remember details we did

not think important to describe (such as the clothes the teacher was wearing in

Figure 5.3).

The dual-code position does not predict that memory for the wording of a

sentence is necessarily poor. The relative memory for the wording versus memory

for the meaning depends on the relative attention that people give to the

verbal versus the visual representation. There are a number of experiments

showing that when participants pay attention to wording, they show better

memory. For instance, Holmes,Waters, and Rajaram (1998), in a replication of

the Bransford and Franks (1971) study that we just reviewed, asked participants

to count the number of letters in the last word of each sentence. This manipulation,

which increased their attention to the wording of the sentence, resulted

in an increased ability to discriminate sentences they had studied from sentences

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with similar meanings that they had not—although participants still showed

considerable confusion among similar-meaning sentences.

But how can an abstract concept such as honesty be represented in a purely

perceptual cognitive system? One can be very creative in combining perceptual

representations. Consider a pair of sentences from an old unpublished study of

mine.2We had participants study one of the following two sentences:

1. The lieutenant wrote his signature on the check.

2. The lieutenant forged a signature on the check.

Later, we asked them to recognize which sentence they had studied. They could

make such discriminations more successfully than they could distinguish

between pairs such as

1. The lieutenant enraged his superior in the barracks.

2. The lieutenant infuriated a superior in the barracks.

In the first pair of sentences, there is a big difference in meaning; in the second

pair, little difference. However, the difference in wording between the sentences

in the two pairs is equivalent. When I did the study, I thought it showed that

people could remember meaning distinctions that did not have perceptual

differences—the distinction between signing a signature and forging is not in

what the person does but in his or her intentions and the relationship between

those intentions and unseen social contracts. Barsalou (personal communication,

March 12, 2003) suggested that we represent the distinction between the two sentences

by reenacting the history behind each sentence. So even if the actual act

of writing and forging might be the same, the history of what a person said and

did in getting to that point might be different. Barsalou also considers the internal

state of the individual to be relevant. Thus, part of the perceptual features

involved in forging might include the sensations of tension that one has when

one is in a difficult situation.3

Barsalou, Simmons, Barbey, and Wilson (2003) cited evidence that when

people understand a sentence, they actually come up with a perceptual interpretation

of that sentence. For instance, in one study by Stanfield and Zwaan

(2001), participants read a sentence about a nail being pounded either into the

wall or the floor. Then they viewed a picture of a nail oriented either horizontally

or vertically and were asked to affirm whether the object in the picture was

mentioned in the sentence that they just read. If they had read a sentence about

a nail being pounded into the wall, they recognized a horizontally oriented nail

more quickly. When they had read a sentence about a nail being pounded into

the floor, they recognized a vertically oriented nail more quickly. In other words,

they responded faster when the orientation implied by the sentence matched the

orientation of the picture. Thus, their interpretation of the sentence seemed to

128 | Representation of Knowledge

2 It was not published because at the time (1970s) it was considered too obvious a result given studies like

those described earlier in this chapter.

3 Perhaps it is obvious that I do not agree with Barsalou’s perspective. However, it is hard to imagine what

he might consider disconfirming data, because his approach is so flexible.

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contain this perceptual detail. As further evidence of the perceptual representation

of meaning, Barsalou et al. cited neuroscience studies showing that concepts

are represented in brain areas similar to those that process perceptions.

An alternative to amodal representations of meaning is the view that meaning

is represented as a combination of images in different perceptual modalities.

•Embodied Cognition

The perceptual symbol hypothesis of Barsalou is an instance of the growing

emphasis in psychology on understanding the contribution of the environment

and our bodies to shaping our cognition. As Thelen (2000) describes the

viewpoint:

To say that cognition is embodied means that it arises from bodily interactions

with the world and is continually meshed with them. From this point of view,

therefore, cognition depends on the kinds of experiences that come from having

a body with particular perceptual and motor capabilities that are inseparably

linked and that together form the matrix within which reasoning, memory,

emotion, language and all other aspects of mental life are embedded. (p. 5)

The embodied cognition perspective emphasizes the contribution of motor

action and how it connects us to the environment. For instance, Glenberg (2007)

argues that our understanding of language often depends on covertly acting out

what the language describes. He points to an fMRI study by Hauk, Johnsrude, &

Pulvermiller (2004), who recorded brain activation while people listened to verbs

that involved the face, arm, or leg actions (e.g., to lick, pick, or kick). They looked

for activity along the motor cortex in different regions associated with the face,

arm, and leg (see Figure 1.10). Figure 5.8 shows the differential activity in these

Embodied Cognition | 129

Region: Face

MR signal change (arbitrary units)

Arm Leg

0.02

0.04

0.06

0.08

0.1

Leg words

Face words

Arm words

FIGURE 5.8 Brain activation in different model regions as participants listen to different types

of verbs.

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different brain regions. As participants listened to each word, there was greater

activation in the part of the motor cortex that would produce that action.

A theory of how meaning is represented in the human mind must explain

how different perceptual and motor modalities connect with one another. For

instance, part of understanding a word like kick is our ability to relate it to a

picture of a person kicking a ball so that we can describe that picture. As another

example, part of our understanding of someone performing an action is

our ability to relate to our own motor system so that we can mimic the action.

Interestingly, mirror neurons have been found in the motor cortex of monkeys,

which are active when the monkeys perform an action like ripping a paper, or

see the experimenter rip a paper or hear the experimenter rip the paper without

seeing the action (Rizzolatti & Craighero, 2004). Although one cannot typically

do single-cell recordings with humans, brain-imaging studies have found

increased activity in the motor region when people observe actions, particularly

with the intention to mimic the action (Iacoboni et al., 1999).

Figure 5.9 illustrates two conceptions of how mappings might take place

between different representations. One possibility is illustrated in the multimodal

hypothesis, which holds that we have various representations tied to

different perceptual and motor systems and that we have means of directly

converting one representation to another. For instance, the double-headed

arrow going from the visual to the motor would be a system for converting a

visual representation into a motor representation and a system for converting

the representations in the opposite direction. The alternative amodal hypothesis

is that there is an intermediate abstract system, perhaps the propositional representation

that we described earlier, and that we have systems for converting

back and forth between the perceptual and motor representations and this

abstract representation. So to convert a picture into an action, one first converts

the visual representation into an abstract representation of its significance

and then converts that representation into a motor representation. These two

approaches offer alternative explanations for the research we reviewed earlier

that indicated people remember the meaning of what they experience, but not

130 | Representation of Knowledge

Multimodal Hypothesis Amodal Hypothesis

Visual

Other

Verbal

Motor

Visual

Other

Verbal

Motor

Meaning

FIGURE 5.9 Representations of two hypotheses about how information is related between

different perceptual and motor modalities. The multimodal hypothesis holds that there are

mechanisms for translating between each modality. The amodal hypothesis holds that

each modality can be translated back and forth to a central meaning representation.

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Conceptual Knowledge | 131

the details. The amodal hypothesis holds that this information is retained in the

central meaning system. The multimodal hypothesis holds that the person has

converted the information from the modality of the presentation to some other

modality.

The embodied cognition perspective emphasizes that meaning is represented

in the perceptual and motor systems that we use to interact with the world.

•Conceptual Knowledge

Consider the picture in Figure 5.3a.When we look at this picture, we do not see

it as just a collection of specific objects. Rather, we see it as a picture of a teacher

instructing a student on geography. That is, we see the word in terms of categories

like teacher, student, instruction, and geography. As we saw, people tend to

remember this categorical information and not the specific details. For instance,

the participants in the Mandler and Ritchey (1977) experiment forgot what the

teacher wore but remembered the subject she taught.

You cannot help but experience the world in terms of the categories you

know. For instance, if you were licked by a four-legged furry object that weighed

about 50 pounds and had a wagging tail, you would perceive yourself as being

licked by a dog.What does your cognitive system gain by categorizing the object

as a dog? Basically, it gains the ability to predict. Thus, you can have expectations

about what sounds this creature might make and what would happen if you

threw a ball (the dog might chase it and stop licking you). Because of this ability

to predict, categories give us great economy in representation and communication.

For instance, if you tell someone, “I was licked by a dog,” your listener can

predict the number of legs on the creature, its approximate size, and so on.

The effects of such categorical perceptions are not always positive—for instance,

they can lead to stereotyping. In one study, Dunning and Sherman

(1997) had participants study sentences like

Elizabeth was not very surprised upon receiving her math SAT score.

Bob was not very surprised upon receiving his math SAT score.

Participants who had heard the first sentences were more likely to falsely believe

they had heard “Elizabeth was not very surprised upon receiving her low math

SAT score,” whereas if they had heard the second sentence, they were more

likely to believe they had heard “Bob was not very surprised upon receiving his

high math SAT score.” Categorizing Elizabeth as a woman, the participants

brought the stereotype of women as poor at math to their interpretation of the

first sentence. Categorizing Bob as male, they brought the opposite stereotype

to their interpretation of the second sentence. This was even true among participants

(both male and female) who were rated as not being sexist in their

attitudes. They could not help but be influenced by their implicit stereotypes.

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Research on categorization has focused both on how we form these categories

in the first place and on how we use them to interpret experiences. It has

also been concerned with notations for representing this categorical knowledge.

In this section, we will consider a number of proposed notations for representing

conceptual knowledge. We will start by describing two early theories. One

concerns semantic networks, which are similar to the propositional networks

we just considered. The other is about what are called schemas. Both theories

have been closely related to certain empirical phenomena that seem central to

conceptual structure.

The categorical organization of our knowledge strongly influences the way

we encode and remember our experiences.

Semantic Networks

Network representations have been used to encode conceptual knowledge as

well as propositional knowledge. Quillian (1966) proposed that people store

information about various categories—such as canaries, robins, fish, and so

on—in a network structure like that shown in Figure 5.10. In this illustration, we

represent a hierarchy of categorical facts, such as that a canary is a bird and a bird

is an animal, by linking nodes for the two categories with isa links. Properties

that are true of the categories are associated with them. Properties that are true

of higher-level categories are also true of lower level categories. Thus, because

animals breathe, it follows that birds and canaries breathe. Figure 5.10 can also

132 | Representation of Knowledge

Level 1

Level 2

Level 3 Canary

Can sing

Is yellow

Bird

Has wings

Can fly

Has feathers

Animal

Has skin

Can move around

Eats

Breathes

Ostrich

Has long

thin legs

Is tall

Can’t fly

Shark

Can bite

Is dangerous

Fish

Has fins

Can swim

Has gills

Salmon

Is pink

Is edible

Swims

upstream

to lay eggs

FIGURE 5.10 A hypothetical memory structure for a three-level hierarchy using the example

canary. Quillian (1966) proposed that people store information about various categories in a

network structure. This illustration represents a hierarchy of categorical facts, such as that a

canary is a bird and a bird is an animal. Properties that are true of each category are associated

with that category. Properties that are true of higher level categories are also true of lower level

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Conceptual Knowledge | 133

represent information about exceptions. For instance, even though most birds

fly, the illustration does represent that ostriches cannot fly.

Collins and Quillian (1969) did an experiment to test the psychological reality

of such networks by having participants judge the truth of assertions about

concepts, such as

1. Canaries can sing.

2. Canaries have feathers.

3. Canaries have skin.

Participants were shown these along with false assertions such as “Apples have

feathers.” They were asked to indicate whether a statement was true or false by

pressing one of two buttons. The time from presentation of the statement to

the button press was measured.

Consider how participants would answer such questions if Figure 5.10 represented

their knowledge of such categories. The information to confirm sentence

1 is directly stored with canary. The information for sentence 2, however,

is not directly stored at the canary node. Instead, the have feathers property is

stored with bird, and sentence 2 can be inferred from the directly stored facts

that A canary is a bird and Birds have feathers. Again, sentence 3 is not directly

stored with canary; rather, the has skin predicate is stored with animal. Thus,

sentence 3 can be inferred from the facts that a canary is a bird and a bird is an

animal and animals have skin. So, all the information required to verify sentence

1 is stored with canary; for sentence 2, participants would need to traverse

one link, from canary to bird, to retrieve the requisite information; for sentence 3,

they would have to traverse two links, from canary to animal.

If our categorical knowledge were structured like Figure 5.10, we would

expect sentence 1 to be verified more quickly than sentence 2, which would be

verified more quickly than sentence 3. This is just what Collins and Quillian

found. Participants required 1310 ms to judge statements like sentence 1, 1380

ms to judge statements like sentence 2, and 1470 ms to judge statements like

sentence 3. Subsequent research on the retrieval of information from memory

has somewhat complicated the conclusions drawn from the initial Collins and

Quillian experiment. How often facts are experienced has been observed to

have strong effects on retrieval time (e.g., C. Conrad, 1972). Some facts, such as

Apples are eaten—for which the predicate could be stored with an intermediate

concept such as food, but that are experienced quite often—are verified as fast

as or faster than facts such as Apples have dark seeds, which must be stored more

directly with the apple concept. It seems that if a fact about a concept is encountered

frequently, it will be stored with that concept, even if it could also

be inferred from a more general concept. The following statements about the

organization of facts in semantic memory and their retrieval times seem to be

valid conclusions from the research:

1. If a fact about a concept is encountered frequently, it will be stored

with that concept even if it could be inferred from a higher order

concept.

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2. The more frequently a fact about a concept is encountered, the more

strongly that fact will be associated with the concept. The more strongly

facts are associated with concepts, the more rapidly they are verified.

3. Inferring facts that are not directly stored with a concept takes a

relatively long time.

Thus, both the strength of the connections between facts and concepts (determined

by frequency of experience) and the distance between them in the

semantic network have effects on retrieval time.

When a property is not stored directly with a concept, people can retrieve it

from a higher order concept.

Schemas

Consider our knowledge of what a house is like. We know many things about

houses, such as

• Houses are a type of building. • Houses have rooms. • Houses can be built of wood, brick, or stone. • Houses serve as human dwellings. • Houses tend to have rectilinear and triangular shapes. • Houses are usually larger than 100 square feet and smaller than

10,000 square feet.

The importance of a category is that it stores predictable information about

specific instances of that category. So when someone mentions a house, for

example, we have a rough idea of the size of the object being referred to.

Semantic networks, which just store properties with concepts, cannot capture

the nature of our general knowledge about a house, such as its typical size

or shape. Researchers in cognitive science (e.g., Rumelhart & Ortony, 1976)

proposed a particular way of representing such knowledge that seemed more

useful than the semantic network representation. Their representational structure

is called a schema. The concept of a schema was first articulated in AI and

computer science. Readers who have experience with modern programming

languages should recognize its similarity to various types of data structures.

The question for the psychologist is what aspects of the schema notion are

appropriate for understanding how people reason about concepts. I will describe

some of the properties associated with schemas and then discuss the psychological

research bearing on these properties.

Schemas represent categorical knowledge according to a slot structure, in

which slots specify values of various attributes that members of a category possess.

So we have the following partial schema representation of a house:

House • Isa: building • Parts: rooms

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Conceptual Knowledge | 135

• Materials: wood, brick, stone • Function: human dwelling • Shape: rectilinear, triangular • Size: 100–10,000 square feet

In this representation, such terms as materials and shape are the attributes or

slots, and such terms as wood, brick, and rectilinear are the values. Each pair of a

slot and a value specifies a typical feature. The fact that houses are usually built

of materials such as wood and brick does not exclude such possibilities as cardboard.

Thus, the values listed above are called default values. For instance, the

fact that we represent that birds can fly as part of our schema for birds does not

prevent us from seeing ostriches as birds.We simply overwrite this default value

in our representation of an ostrich.

A special slot in each schema is its isa slot, which is like the isa link in a

semantic network and points to the superset. Basically, unless contradicted, a

concept inherits the features of its superset. Thus, with the schema for building,

the superset of house, we would store such features as that it has a roof and

walls and that it is found on the ground. This information is not represented

in the schema for house because it can be inferred from building. As illustrated

in Figure 5.10, these isa links can create a structure called a generalization

hierarchy.

Schemas have another type of structure, called a part hierarchy. Parts of

houses, such as walls and rooms, have their own schema definitions. Stored

with schemas for walls and rooms would be the information that they have windows

and ceilings as parts. Thus, using the part hierarchy, we would be able to

infer that houses have windows and ceilings.

Schemas are abstractions from specific instances that can be used to make

inferences about instances of the concepts they represent. If we know something

is a house, we can use the schema to infer that it is probably made of

wood or brick and that it has walls, windows, and ceilings. The inferential

processes for schemas must also be able to deal with exceptions: We can

still understand what a house without a roof is. Finally, it is necessary to

understand the constraints between the slots of a schema. If we hear of a

house that is underground, for example, we can infer that it will not have

windows.

Schemas represent concepts in terms of supersets, parts, and other attributevalue

pairs.

Psychological Reality of Schemas

One property of schemas is that they have default values for certain slots or

attributes. This property provides schemas with a useful inferential mechanism.

If you recognize an object as being a member of a certain category, you can

infer—unless explicitly contradicted—that it has the default values associated

with that concept’s schema. Brewer and Treyens (1981) provided an interesting

Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 135

demonstration of the effects of schemas on memory inferences.

Thirty participants were brought individually to the room

shown in Figure 5.11. Each was told that this room was the office

of the experimenter and was asked to wait there while the experimenter

went to the laboratory to see whether the previous participant

had finished. After 35 s, the experimenter returned and

took the waiting participant to a nearby seminar room. Here, the

participant was asked to write down everything he or she could

remember about the experimental room. What would you be

able to recall?

Brewer and Treyens predicted that their participants’ recall

would be strongly influenced by their schema of what an office

contains. Participants would recall very well items that are part

of that schema; they would recall much less well office items that

are not part of the schema; and they would falsely recall items

that are part of the schema but not in this office. Brewer and Treyens found just

this pattern of results. For instance, 29 of the 30 participants recalled that the

office had a chair, a desk, and walls. Only 8 participants, however, recalled that

it had a bulletin board or a skull. On the other hand, 9 participants recalled

that it had books, which it did not. Thus, we see that a person’s memory for the

properties of a location is strongly influenced by that person’s default assumptions

about what is typically found in the location. A schema is a way of encoding

those default assumptions.

People will infer that an object has the default values for its category, unless

they explicitly notice otherwise.

Degree of Category Membership

One of the important features of schemas is that they allow variation in the

objects that might fit a particular schema. There are constraints on what typically

occupies the various slots of a schema, but few absolute prohibitions. Thus,

if schemas encode our knowledge about various object categories, we ought to

see a shading from less typical to more typical members of the category as the

features of the members better satisfy the schema constraints. There is now considerable

evidence that natural categories such as birds have the kind of structure

that would be expected of a schema.

Rosch did early research documenting such variations in category membership.

In one experiment (Rosch, 1973), she instructed participants to rate the

typicality of various members of a category on a 1 to 7 scale, where 1 meant

very typical and 7 meant very atypical. Participants consistently rated some

members as more typical than others. In the bird category, robin got an average

rating of 1.1, and chicken a rating of 3.8. In reference to sports, football was

thought to be very typical (1.2), whereas weight lifting was not (4.7). Murder

was rated a very typical crime (1.0), whereas vagrancy was not (5.3). Carrot was

a very typical vegetable (1.1); parsley was not (3.8).

136 | Representation of Knowledge

FIGURE 5.11 The “office room”

used in the experiment of

Brewer and Treyens to demonstrate

the effects of schemas

on memory inferences. As they

predicted, their participants’

recall was strongly influenced by

their schema of what an office

contains. (From Brewer & Treyens, 1981.

Reprinted by permission of the publisher.

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Conceptual Knowledge | 137

Rosch (1975) also asked participants to identify the category of pictured

objects. People are faster to judge a picture as an instance of a category when it

presents a typical member of the category. For instance, apples are seen as fruits

more rapidly than are watermelons, and robins are seen as birds more rapidly

than are chickens. Thus, typical members of a category appear to have an

advantage in perceptual recognition as well.

Rosch (1977) demonstrated another way in which some members of a category

are more typical. She had participants compose sentences for category

names. For bird, participants generated sentences such as

I heard a bird twittering outside my window.

Three birds sat on the branch of a tree.

A bird flew down and began eating.

Rosch replaced the category name in these sentences with a typical member

(robin), a less typical member (eagle), or a peripheral member (chicken) and

asked participants to rate the sensibleness of the resulting sentences. Sentences

involving typical members got high ratings, sentences with less typical members

got lower ratings, and sentences with peripheral members got the lowest

ratings. So the evidence shows that when people think of a category member,

they generally think of typical instances of that category.

Failing to have a default or typical value does not disqualify an object from

being a member of the category, however. People should have great difficulty

and should be inconsistent in judging whether items at the periphery of a category

are actually members of that category. McCloskey and

Glucksberg (1978) looked at people’s judgments about what

were or were not members of various categories. They found that

although participants did agree on some items, they disagreed on

many. For instance, whereas all 30 participants agreed that cancer

was a disease and happiness was not, 16 thought stroke was a disease

and 14 did not. Again, all 30 participants agreed that apple

was a fruit and chicken was not, but 16 thought pumpkin was a

fruit and 14 disagreed. Once again, all participants agreed that a

fly was an insect and a dog was not, but 13 participants thought

a leech was and 17 disagreed. Thus, it appears that people do not

always agree among themselves.McCloskey and Glucksberg tested

the same participants a month later and found that many had

changed their minds about the disputed items. For instance, 11

out of 30 reversed themselves on stroke, 8 reversed themselves

on pumpkin, and 3 reversed themselves on leech. Thus, disagreement

about category boundaries does not occur just among

participants—people are very uncertain within themselves exactly

where the boundaries of a category should be drawn.

Figure 5.12 illustrates a set of materials used by Labov (1973).

He was interested in studying which items participants would call

cups and which they would not.Which do you consider to be cups

12 15

11 14

10 13

2 3 4

FIGURE 5.12 The various

cuplike objects used in Labov’s

experiment that studied the

boundaries of the cup category.

(After Labov, 1973, in Bailey & Shuy, 1973.

Adapted by permission of the author.

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and which do you consider bowls? The interesting

point is that these concepts do not appear to have

clear-cut boundaries. In one experiment, Labov

used the series of items 1 through 4 shown in Figure

5.12 and a fifth item, not shown. These items

reflect an increasing ratio of width of the cup to

depth. For the first item, that ratio is 1, whereas for

item 4 it is 1.9. The ratio for the item not shown

was 2.5. Figure 5.13 shows the percentage of participants

who called each of the five objects a cup

and the percentage who called it a bowl. The solid

lines indicate the classifications when participants

were simply presented with pictures of the objects

(the neutral context). As can be seen, the percentages

of cup responses gradually decreased with increasing

width, but there is no clear-cut point where participants stopped using

cup. At the extreme 2.5-width ratio, about 25% percent of the participants still

gave the cup response, whereas another 25% gave bowl. (The remaining 50%

gave other responses.) The dashed lines reflect classifications when participants

were asked to imagine the object filled with mashed potatoes and placed on a

table. In this context, fewer cup responses and more bowl responses were given,

but the data show the same gradual shift from cup to bowl. Thus, it appears that

people’s classification behavior varies continuously not only with the properties

of an object but also with the context in which the object is imagined or presented.

These influences of perceptual features and context on categorization

judgments are very much like the similar influences of these features on perceptual

pattern recognition (see Chapter 2).

Different instances are judged to be members of a category to different

degrees, with the more typical members of a category having an advantage

in processing.

Event Concepts

It is not only objects that have a conceptual structure.We also have concepts of

various kinds of events, such as going to a movie. Schemas have been proposed

as ways of representing such categories. We can encode our knowledge about

stereotypic events according to their parts—for instance, going to a movie involves

going to the theater, buying the ticket, buying refreshments, seeing the

movie, and returning from the theater. Schank and Abelson (1977) proposed

versions of event schemas that they called scripts. They pointed out that many

circumstances involve stereotypic sequences of actions. For instance, Table 5.1

shows their hunch as to what the stereotypic aspects of dining at a restaurant

might be and represents the components of a script for such an occasion.

Bower, Black, and Turner (1979) reported a series of experiments in which

the psychological reality of the script notion was tested. They asked participants

to name what they considered the 20 most important events in an episode,

138 | Representation of Knowledge

1.0 1.2

1.5 1.9 2.5

100

Cup

Bowl

Cup

Neutral context

Food context

Relative width of cup

Response (%)

FIGURE 5.13 Results from

Labov’s experiment demonstrating

that the cup category does

not appear to have clear-cut

boundaries. The percentage of

participants who used the term

cup versus the term bowl to

describe the objects shown in

Figure 5.10 are plotted as a

function of the ratio of cup

width to cup depth. The solid

lines reflect the neutral-context

condition, the dashed lines the

food-context condition. (After

Labov, 1973, in Bailey & Shuy, 1973.

Adapted by permission of the author.

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Conceptual Knowledge | 139

such as going to a restaurant.With 32 participants, they failed to get complete

agreement on what these events were. No particular action was listed as part of

the episode by all participants, although considerable consensus was reported.

Table 5.2 lists the events named. The items in roman type were listed by at least

25% of the participants; the italicized items were named by at least 48%; and

the boldfaced items were given by at least 73%. Using 73% as a criterion, we

find that the stereotypic sequence was sit down, look at menu, order meal, eat

food, pay bill, and leave.

Bower et al. (1979) went on to show a number of the effects that such action

scripts have on memory for stories. They had participants study stories that included

some but not all of the typical events from a script. Participants were

then asked to recall the stories (in one experiment) or to recognize whether

TABLE 5.1

The Schema for Going

to a Restaurant

Scene I: Entering

Customer enters restaurant

Customer looks for table

Customer decides where to sit

Customer goes to table

Customer sits down

Scene 2: Ordering

Customer picks up menu

Customer looks at menu

Customer decides on food

Customer signals waitress

Waitress comes to table

Customer orders food

Waitress goes to cook

Waitress gives food order to cook

Cook prepares food

Scene 3: Eating

Cook gives food to waitress

Waitress brings food to customer

Customer eats food

Scene 4: Exiting

Waitress writes bill

Waitress goes over to customer

Waitress gives bill to customer

Customer gives tip to waitress

Customer goes to cashier

Customer gives money to cashier

Customer leaves restaurant

From Schank & Abelson (1977). Reprinted

by Erlbaum.

TABLE 5.2

Empirical Script Norms at Three Agreement

Levels

Open doora

Enterb

Give reservation name

Wait to be seated

Go to table

Sit downc

Order drinks

Put napkins on lap

Look at menu

Discuss menu

Order meal

Talk

Drink water

Eat salad or soup

Meal arrives

Eat food

Finish meal

Order dessert

Eat dessert

Ask for bill

Bill arrives

Pay bill

Leave tip

Get coats

Leave

aRoman type indicates items listed by at least 25% of the participants.

bItalic type indicates items listed by at least 48% of the participants.

cBoldface type indicates items listed by at least 73% of the participants.

After Bower, Black, & Turner (1979). Adapted by permission of the

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various statements came from the story (in another experiment). When recalling

these stories, participants tended to report statements that were parts of the

script but that had not been presented as parts of the stories. Similarly, in the

recognition test, participants thought they had studied script items that had not

actually been in the stories. However, participants showed a greater tendency to

recall actual items from the stories or to recognize actual items than to misrecognize

foils that were not in the stories, despite the distortion in the direction of

the general schema.

In another experiment, these investigators read to participants stories composed

of 12 prototypical actions in an episode; 8 of the actions occurred in

their standard temporal position, but 4 were rearranged. Thus, in the restaurant

story, the bill might be paid at the beginning and the menu read at the end. In

recalling these stories, participants showed a strong tendency to put the events

back into their normal order. In fact, about half of the statements were put

back. This experiment serves as another demonstration of the powerful effect

of general schemas on memory for stories.

These experiments indicate that new events are encoded with respect to general

schemas and that subsequent recall is influenced by the schemas. I have talked

about these effects as if participants were misrecalling the stories. However, it is

not clear that these results should be classified as acts of misrecall. Normally, if a

certain standard event, such as paying a check, is omitted in a story, we are supposed

to assume it occurred. Similarly, if the storyteller says the check was paid at

the beginning of the restaurant episode, we have some reason to doubt the storyteller.

Scripts or schemas exist because they encode the predominant sequence of

events in a particular kind of situation. Thus, they can serve as valuable bases for

predicting missing information and for correcting errors in information.

Scripts are event schemas that people use to reason about prototypical events.

Abstraction versus Exemplar Theories

We have already described semantic networks and schemas as two ways of representing

conceptual knowledge. It is fair to say that although each has merits,

the field of cognitive psychology has concluded that both are inadequate. We

already noted that semantic networks do not capture the graded character of

categorical knowledge such that different instances are better or worse members

of a category. Schemas can do this, but it has never been clear in detail how

to relate them to behavior. The field is currently struggling between two alternative

ways of theorizing about conceptual knowledge. One type of theory

holds that we have actually abstracted general properties from the instances we

have studied; the other type holds that we actually store only specific instances,

with the more general inferences emerging from these instances. We will call

these the abstraction theories and the exemplar theories. The debate between

these two perspectives has been with us for centuries—for instance, in the

debate between the British philosophers John Locke and George Berkeley.

Locke claimed that he had an abstract idea of a triangle that was neither

oblique or right-angled, neither equilateral, isosceles, or scalene, but all of these

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Conceptual Knowledge | 141

at once, while Berkeley claimed it was simply impossible for himself to have an

idea of a triangle that was not the idea of some specific triangle.

The schema theory we have considered is an abstraction theory, but others

of this type have been more successful. One alternative assumes that people

store a single prototype of what an instance of the category is like and judge

specific instances in terms of their similarity to that prototype (e.g., Reed,

1972). Other models assume that participants store a representation that also

encodes some idea of the allowable variation around the prototype (e.g., Hayes-

Roth & Hayes-Roth, 1977; Anderson, 1991).

Exemplar theories could not be more different. They hold that we store no

central concept, but only specific instances. When it comes time to judge how

typical a specific object is of birds in general, we compare it to specific birds

and make some sort of judgment of average difference. Exemplar theories

include those of Medin and Schaffer (1978) and Nosofsky (1986).

Given that abstraction and exemplar theories differ so greatly in what they

propose the mind does, it is surprising that they generate such similar predictions

over a wide range of experiments. For instance, both types predict better

processing of central members of a category. Abstraction theories predict this

because central instances are more similar to the abstract representation of the

concept. Exemplar theories predict this because central instances will be more

similar, on average, to other instances of a category.

There appear to be subtle differences between the predictions of the two

types of theories, however. Exemplar theories predict that people should be

influenced by studying specific instances similar to a test instance and that such

influences should go beyond any effect of some representation of the central

tendency. Thus, although we may think that dogs in general bark, we may have

experienced a peculiar-looking dog that did not, and we would then tend to

expect that another similar-looking dog would also not bark. Such effects of specific

instances can be found in some experiments (e.g., Medin & Schaffer, 1978;

Nosofsky, 1991). On the other hand, some research has shown that people will

infer tendencies that are not in the specific instances (Elio & Anderson, 1981).

For example, if one has encountered many dogs that chase balls and many dogs

that bark at the postman, one might consider a dog that both chases balls and

barks at the postman to be particularly typical. However, we may never have

observed any specific dog both chasing balls and barking at the postman.

Much of the past research on categorization has been an effort to determine

whether abstraction theories or exemplar theories are correct. The recent trend,

however, has been a recognition that people may sometimes use abstractions

and other times use instances to represent categories (Anderson & Betz, 2001;

Ashby, Alfonso-Reese, Turken, & Waldron, 1998; Erickson & Kruschke, 1998;

Gobet, Richman, Staszewski, & Simon, 1997; Palmeri & Johansen, 1999; Smith &

Minda, 1998). Perhaps the clearest evidence for this expanded view comes from

neuroimaging studies showing that different participants use different brain

regions to categorize instances. For example, Smith, Patalano, and Jonides

(1998) had participants learn to classify a set of 10 animals like those shown in

Figure 5.14. One group was encouraged to use rules such as “An animal is from

Venus if at least three of the following are true: antennae ears, curly tail, hoofed

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feet, beak, and long neck. Otherwise it is from Saturn.” Participants in a second

group were encouraged simply to memorize the categories for the 10 animals.

Smith et al. found very different patterns of brain activation as participants

classified the stimuli. Regions in the prefrontal cortex tended to be activated in the

abstract-rule participants, whereas regions in the occipital visual areas and the

cerebellum were activated in the participants who memorized instances. Smith

and Grossman (in press) review evidence that this second exemplar system also involves

brain regions supporting memory such as the hippocampus (see Figure 1.7).

There may be multiple different ways of representing concepts as abstractions.

Although the Smith et al. study identified an abstract system that involves

explicit reasoning by means of rules, there is also evidence for abstract systems

that involve unconscious pattern recognition—for instance, our ability to distinguish

dogs from cats, without being able to articulate any of the features that

separate the two species. Ashby and Maddox (2005) argue that this system depends

on the basal ganglia (see Figure 1.8). As they review, damage to the basal

ganglia (as happens with Parkinson’s and Huntington’s disease) results in

deficits in learning such categories. The basal ganglia region has been found to

be activated in a number of studies of implicit category learning.

Categories can be represented either by abstracting their central tendencies

or by storing many specific instances of categories.

Natural Categories in the Brain

The studies we have been discussing look at the learning of new laboratorydefined

categories. There has always been some suspicion about how similar

such laboratory-defined categories are to the kinds of natural categories that

we have acquired through experience, such as birds or chairs. Laboratory categories

display the same sort of fuzzy boundaries that natural categories do

and share a number of other attributes. However, natural categories arise over

a much longer time period than a typical laboratory task. Over their long

learning history, people come to develop biases about such natural categories

142 | Representation of Knowledge

FIGURE 5.14 Examples of the drawings of artificial animals used in the PET studies of

Smith, Palatino, and Jonides showing that people sometimes use rule-based abstractions

and sometimes use memory-based instances to represent categories. (After Smith, Palatino, & Jonides,

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Conceptual Knowledge | 143

as living things and artifacts.Much of the research documenting these biases has

been done with primary-school children who are still learning their categories

but have advanced beyond younger children. For instance, if primary-school

children are told that a human has a spleen, they will conclude that dogs have

spleens too (Carey, 1985). Similarly, if they are told that a red apple has pectin

inside, they will assume that green apples also have pectin (Gelman, 1988).

Apparently, children assume that if something is a part of a member of a

biological category, it is an inherent part of all members of the category. On the

other hand, if told that an artifact such as a cup is made of ceramic, they do not

believe that all cups are made of ceramic. The pattern is just the opposite with

respect to actions. For instance, if told that a cup is used for “imbibing” (a term

they do not know), they believe that all cups are used for imbibing. In contrast,

if told that they can “repast” with a particular red apple, they do not necessarily

believe that they can repast with a green apple. Thus, artifacts seem distinguished

by the fact that there are actions appropriate to the whole category of

artifacts. In summary, children come to believe that all things in a biological

category have the same parts (like pectin in apples) but all things in an artifact

category have the same function (like imbibing for cups).

Cognitive neuroscience data suggest that there are different representations

of biological and artifact categories in the brain. Much of this evidence comes

from patients with semantic dementia, who suffer deficits in their categorical

knowledge because of brain damage. Patients with damage to different regions

show different deficits. Patients who have damage to the temporal lobes suffer

deficits in their knowledge about biological categories such as animals, fruits,

and vegetables (Warrington & Shallice, 1984; Saffran & Schwartz, 1994). These

patients are unable to recognize such objects as ducks, and when one was asked

what a duck is, the patient was only able to say “an animal.”However, knowledge

about artifacts such as tools and furniture is relatively unaffected in these

patients. On the other hand, patients with frontoparietal lesions are impaired

in their processing of artifacts but unaffected in their processing of biological

categories.

Table 5.3 compares example descriptions of biological categories and artifact

categories by two patients with temporal lobe damage. These types of

patients are more common than patients with deficits in their knowledge

of artifacts.

It has been suggested (e.g.,Warrington & Shallice, 1984; Farah & McClelland,

1991) that these dissociations occur because biological categories are more associated

with perceptual categories such as shape, whereas artifacts are more

associated with the actions that we perform with them. Farah and McClelland

offer a computer simulation model of this dissociation that learns associations

among words, pictures, visual semantic features, and functional semantic features.

By selectively damaging the visual features in their computer simulation,

they were able to produce a deficit in knowledge of living things; and by selectively

damaging the functional features, they were able to produce a deficit in

knowledge of artifacts. Thus, loss of categorical information in such patients

seems related to loss of the feature information that defines these categories.

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Recent brain-imaging data also seem consistent with this conclusion (see

A. Martin, 2001, for review). In particular, it has been shown that when people

process pictures of artifacts or words denoting artifacts, the same regions of

the brain that have been shown to produce category-specific deficits when damaged

tend to be activated. Processing of both animals and tools activates regions

of the temporal cortex, but the tool regions tend to be located above (superior

to) the animal regions. There is also activation of occipital regions (visual

cortex) when processing animals. In general, the evidence seems to point to a

greater visual involvement in the representation of animals and a greater motor

involvement in the representation of artifacts. There is some debate in the literature

over whether the real distinction is between natural categories and artifacts

or between visual-based and motor-based categories (Caramazza, 2000).

There are differences in the way people think about biological categories and

artifact categories and differences in the brain regions that support these

two types of categories.

•Conclusions

We remember only a tiny fraction of what we experience. Estimates of the

storage capacity (e.g., Treves & Rolls, 1994; Moll & Miikkulainen, 1997) of the

brain differ substantially, but they are all many orders of magnitude less than

144 | Representation of Knowledge

TABLE 5.3

Performance of Two Patients with Impaired Knowledge of Living Things

on Definitions Task

Patient Living Things Artifacts

1 Parrot: Don’t know Tent: Temporary outhouse, living home

Daffodil: Plant Briefcase: Small case used by students

Snail: An insect animal to carry papers

Eel: Not well Compass: Tool for telling direction you

Ostrich: Unusual are going

Torch: Handheld light

Dustbin: Bin for putting rubbish in

2 Duck: An animal Wheelbarrow: Object used by people

Wasp: Bird that flies to take material about

Crocus: Rubbish material Towel: Material used to dry people

Holly: What you drink Pram: Used to carry people, with wheels

Spider: A person looking and a thing to sit on

for things, he was a spider Submarine: Ship that goes underneath

for his nation or country the sea

Psychology: General.

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Key Terms | 145

1. Jill Price, a person with superior autobiographical

memory described at the beginning of the chapter,

can remember what happened on almost any day of

her life (see her interview with Diane Sawyers: http://

abcnews.go.com/Health/story?id=4813052&page=1).

For instance, if you ask her, she can tell you the date

of the last show of any former TV series she watched.

On the other hand, she reported great difficulty in

remembering the dates in history class.Why do you

think this is?

2. Take some sentences at random from this book and try

to develop propositional representations for them.

3. Barsalou (2008) claims little empirical evidence has

been accumulated to support amodal symbol systems.

What research reviewed in this chapter might be

considered evidence for amodal symbol systems?

4. Consider the debate between amodal theories and

multimodal theories and the debate between exemplar

and abstraction theories. In what ways are these

debates similar and in what ways are they different?

Questions for Thought

Key Terms

abstraction theory

amodal hypothesis

amodal symbol system

arguments

default values

dual-code theory

embodied cognition

exemplar theory

isa link

link

mirror neuron

mnemonic technique

multimodal hypothesis

node

perceptual symbol

system

proposition

propositional network

propositional

representation

relations

schema

script

slot

what would be required to store a faithful video recording of our whole life.

This chapter has reviewed the studies of what we retain and what we forget—

for instance, what subject was being taught, but not what the teacher was

wearing (Figure 5.3), or that we were in an office, but not what was in the

office (Figure 5.11). The chapter also reviewed three perspectives on the basis

for this selective memory.

1. The multimodal hypothesis (Figure 5.9a) that we select aspects of our

experience to remember and often convert from one medium to another.

For instance, we may describe a room (visual) as an “office” (verbal).

This hypothesis holds that we maintain the perceptual-motor aspects

of our experience but only the significant aspects.

2. The amodal hypothesis (Figure 5.9b) that we convert our experience

into some abstract representation that just encodes what is important.

For instance, the chapter discussed how propositional networks

(e.g., Figure 5.7) captured the connections among the concepts in

our understanding of a sentence.

3. The schema hypothesis that we remember our experiences in terms of

the categories that they seem to exemplify. These categories can either

be formed from a bundle of specific experiences or an abstraction like

a category.

These hypotheses need not be mutually exclusive, and cognitive scientists are

actively engaged in trying to understand how to coordinate these explanations.

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146

Human Memory: Encoding

and Storage

Past chapters have discussed how we perceive and encode what is in our present.

Now we turn to discussing memory, which is the means by which we can

perceive our past. People who lose the ability to create new memories become

effectively blind to their past. I would recommend the movie Memento as providing

a striking characterization of what it would like to have no memory. The protagonist

of the film, Leonard, has anterograde amnesia, a condition that prevents him from

forming new memories. He can remember his past up to the point of a terrible

crime that left him with amnesia, and he can keep track of what is in the immediate

present, but as soon as his attention is drawn to something else, he forgets what

has just happened. So, for instance, he is constantly meeting people he has met

before, who have often manipulated him, but he does not remember them, nor can

he protect himself from being manipulated further. Although Leonard incorrectly

labels his condition as having no short-term memory, this movie is an accurate

portrayal of anterograde amnesia—the inability to form new long-term memories. It

focuses on the amazing ways Leonard tries to connect the past with the immediate

present.

This chapter and the next can be thought of as being about what worked and

did not work for Leonard. This chapter will answer the following questions: • How do we maintain a short-term or working memory of what just happened?

This is what still worked for Leonard. • How does the information we are currently maintaining in working memory

prime knowledge in our long-term memory? • How do we create permanent memories of our experiences? This is what did

not work any more for Leonard. • What factors influence our success in creating new memories?

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