FEATURE COMPARISON MODEL

“The part of long-term memory dealing with words, their

symbols, and meanings is semantic memory.”

Semantic memory allows humans to communicate with

language. In semantic memory, the brain stores

information about words, what they look like and

represent, and how they are used in an organized way. It

is unusual for a person to forget the meaning of the word

"dictionary," or to be unable to conjure up a visual image

of a refrigerator when the word is heard or read. Semantic

memory contrasts with episodic memory, where memories

are dependent upon a relationship in time. An example of

an episodic memory is "I played in a piano recital at the

end of my senior year in high school."

Models of semantic memory make assumptions about

both the structures of knowledge and the processes that

operate on these structures. Direct-storage models, such

as Collins and Quillian’s, tend to make elaborate

assumptions about the structuring of knowledge and

explain differences in verification times in terms of

differences in underlying knowledge structures (e.g.,

differences in the number of levels in the hierarchy).

Computational models such as Smith, Shoben, and Rips’,

on the other hand, make minimal assumptions about the

structuring of knowledge; instead, they elaborate on the

processes that operate in semantic memory and explain

differences in verification times in terms of differences in

processing. In examining Smith, Shoben, and Rips’ model,

we will first look at how they assume semantic memory is

structured. Then we will examine the detailed processing

model they proposed for operating on these structures.

Smith, Shoben, and Rips proposed a model called a

feature comparison model of semantic memory. The

assumption behind this model is that the meaning of any

word or concept consists of a set of elements called

features. Features come in two types:

Defining, meaning that the feature must be present in

every example of the concept, and

Characteristics, meaning the feature are usually, but not

necessarily present.

According to Smith et al., concepts are stored in semantic

memory as sets of attributes, called semantic features.

The following sets of features illustrate how the concepts

robin and bird might be represented in someone’s

memory.

ROBIN = {has wings, lays eggs, has feathers, can fly, is

red-breasted, eats worms}

BIRD = {has wings, lays eggs, has feathers, can fly, eats

worms}

The features associated with a given concept vary in the

degree to which they are central to defining the concept.

Those features which are essential to defining the concept

are called defining features; those features which are

often associated with a concept but which are not

essential to its definition are called characteristic features.

Defining features are attributes that are shared by all

members of a category. For example, the features has

wings, lays eggs, and has feathers, are all defining

features of the concept bird because all birds have these

attributes. Characteristic features are attributes that are

shared by many, but not all, members of a category. The

feature can fly, for example, is a characteristic rather than

a defining feature of the concept bird because most, but

not all, birds can fly.

Assuming the semantic memory is organized in terms of

feature list, question arises, how is it knowledge retrieved

and used? According to Smith et al. model verification of

sentences as “robin is a bird” is carried out in two stages.

In the first stage, the feature list (containing both the

defining and the characteristics features) for the two

terms are accessed, and a quick scan and comparison is

performed. If the two lists show a great deal of overlap,

the response ‘true’ is made very quickly, if the overlap is

very small, then the response ‘false’ is made, also very

quickly. If the degree of overlap in the two feature list is

neither extremely high nor extremely two low, then a

second stage of processing occurs. In this stage the, a

comparison is made between the sets of defining features

only. If the list match the person respond ‘true’; if the list

do not match, the person respond ‘false’. This process can

be easily understood through following flowchart-

The feature comparison model can explain many finding

that the hierarchical network model could not. One finding

it explain is the typicality effect: sentences such as “ a

robin is a bird” are verified more quickly then sentences

such as “a turkey is bird” because robin being more

typical examples of birds, are thought to share more

characteristics feature then ‘bird’ then do turkeys. The

feature comparison model also explains fast rejection of

false sentences, such as “a table is a fruit.” In this case

the list of feature for ‘table’ and the list of ‘fruit’

presumably share very few entries.

The feature comparison model also provides an

explanation for a finding known as the category size

effect. This term refers to the fact that if one term is a

subcategory of another term people will generally be

faster to verify the sentence with the smaller category.

That is people are faster to verify the sentence “a collie is

a dog” than to verify “a collie is a animal,” because a set

of dog is a part of the set of animals. The feature

comparison model explain this effect as follows, it

assumes that as category grows larger (for example from

robin, to bird, to animal, to living thing) they also become

more abstract. With increased abstractness, there are

fewer defining features. Thus in first stage of processing

there is less overlap between the feature list of the term

and the feature list of an abstract category.

The model also explains how “hedges” such as “a bat are

sort of like of bird” are processed. Most of us know even

though bat fly and eat insects, they are really mammals.

The feature comparison model explains that the

processing of hedges consist of a comparison of a

characteristic features but not the defining features.

Because bat share some characteristics features with

birds (namely flying and eating insects), we agree they

are “sort of like” birds. We recognize, however that bat

are not really birds, presumably because they don’t share

the same defining features.

Read sentence

Retrieve feature lists of subjects and predicate

nouns.

Compare both lists.

Degree of

similarity?

Respond “true” Compare

defining features only

Respond “false”

Lists match?

Respond “true”

Respond “false”

High Low

Medium

Depiction of Smith et al. Feature Comparison Model