Semantic representation in the mental lexicon

Hacettepe University Faculty of Letters

Department of English Linguistics

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Semantic Representation in the Mental Lexicon: Does Category Size Affect

Categorization Time?

It takes longer to categorize object names into larger categories than into smaller

categories, since they attributed this effect to category size.. This paper investigates how a word

meaning is represented in respect of the mental lexicon of a speaker and analyses whether

category size affects categorization time. An experiment was conducted to disentangle a

possible explanation to the question above. In the experiment, the smaller and larger categories

were nested, as dog is a nested subset of animal. Instances were presented to 16 participants

(8 of which experiment, and the rest is control group) and the results were measured. There

was no evidence that larger categories, in and of themselves, required longer categorization

times than smaller categories. In the very beginning, there will be a brief explanation by

outlining what is mental lexicon and semantic representation, then, theories and researches on

this issue reviewing perspectives of different researches mostly based on the inquiries by T.K.

Landauer & J.L. Freedman, A.M. Collins & M.R. Quillian, A.J. Wilkins, B. Schaeffer & R.

Wallace and C. Conrad. will be introduced and discussed. At the end, a brief discussion about

the investigation of semantic representation in the mental lexicon will be presented.

To begin with, the mental lexicon can be defined as a mental dictionary, which includes

information respecting a word's meaning, pronunciation, syntactic characteristics and so forth.

The mental lexicon is a structure, which is applied in linguistics and psycholinguistics

concerning each individual speaker’s lexical representations. The mental lexicon diverges from

the lexicon itself because it is not just a general collection of words. The mental lexicon works

on how those words are activated, stored, processed, and retrieved in mind by each speaker.

"The fact that a speaker can mentally find the word that he/she wants in less than 200

milliseconds, and in certain cases, even before it is heard, is proof that the mental lexicon is

ordered in such a way as to facilitate access and retrieval." (Faber & Usón, 1999) An individual’s



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mental lexicon alters and extends during the process of learning new words and naturally, it is

always developing. It is also very important to take into account that the environment of an

individual affects his or her mental lexicon, how do words store and what kind of difference

environment can cause on a speaker’s mental lexicon. For instance:

"Even with hard yakka, you've got Buckley's of understanding this dinkum English

sentence, unless you're an Aussie.’’ (Taft, 1991)

While native English might have difficulties to understand the sentence above, an

Australian will not. ‘’[…] The words 'yakka,' 'Buckley's' and 'dinkum' are in the vocabulary of

most Australians, that is, they are stored as entries in the mental lexicon, and therefore an

Australian has access to the meanings of these words and can consequently comprehend the

sentence. […]’’ (Taft, 1991) Consequently, it is safe to say that the process of speaking or

hearing activates speech continuum which links the words that has seen or heard to actual

words in the mental lexicon, then it stimulates the perception of words belong to each speaker’s

sense of understanding.

Semantic representation is an abstract language that meanings can be exemplified.

Language provides us to share experiences, needs, thoughts, desires, and so on. Therefore,

words and their meanings should map out objects, actions or properties into our mental

representation of the world. Moreover, word meanings need to be grounded in our conceptual

knowledge. ‘’[…]Experiments on the structure of conceptual knowledge use words as stimuli but

the findings are discussed in terms of concepts, under the tacit assumption that the use of

words in a given task should produce comparable results to nonlinguistic stimuli (for example,

pictures, or artificial categories). In other words, it is often assumed that the conceptual system

is entirely responsible for categorizing entities in the world (physical, mental), whereas the

http://grammar.about.com/od/mo/g/meaningterm.htm



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assignment of a name to a conceptual referent (and its retrieval) is a transparent and

straightforward matter.’’ (Vigliocco &Vinson, 2005) Apparently, word meanings and concepts

have to be firmly related; furthermore, when a speaker once activates semantic representations,

he or she also activates conceptual information.

At first, the research of T.K. Landauer & J.L. Freedman is based on addressing the

general problem of how information is retrieved from long-term memory through classification of

the input in mental lexicon. Two experiments were applied to analyze whether categorization or

category recognition affects retrieval of memory or not. ‘’ The experiments reported here dealt

directly with this issue by studying highly familiar preexisting verbal categories, the content of

which was not enumerated during the experiment, but depended entirely on information brought

to the experiment by the S as part of his long-term memory store.’’ (Landauer & Freedman, 292)

In experiment I, a single nest of five categories such as ‘word’, ‘noun’, ‘living thing’, ‘animal’ and

‘dog’ was constructed. The results of first experiment depended on the particular choice of

categories that had been used. With the aim of comparing whether the results would generalize

to other nested sets, a second experiment was designed with 16 new sets of paired nested

categories plus a different method of presentation and response.

It is safe to say that the categorization depends on individual’s familiarity towards items

presented in experiments according to his or her own semantic perception. ‘’ It would be

tempting to explain the faster identification times for smaller categories as a result of greater

association strength between small categories, or simply in terms of a possible greater

familiarity of the names themselves.’’ (Landauer & Freedman, 294) Although the category-size

effect was especially strong for negative identifications than positive ones, it can be said that

there should be pre-existing associative strength between given words (e.g., a word and a dog)

and the response ‘not a dog’ than ‘not an animal’. ‘’][…] Landauer and Freedman's results for

negative instances apparently do not depend on category size but instead on those instances



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where the larger nested category is semantically confusable with the correct category and the

smaller nested category is not.’’ (Collins & Quillian, 437)

Secondly, it is questioned in A.M. Collins and M.R. Quillian text that whether category size

affects categorization time or not. Similarly, two experiments were applied, in both of the

experiments the smaller and larger categories were nested, as dog is a nested subset of animal.

The categories were specified in advance of when the instances had been presented. ‘’ In a

true/false reaction time (RT) experiment we found that people take less time to confirm a

sentence like "A collie is a dog" than to confirm a sentence like "A collie is an animal" (Collins &

Quillian, 1969).’’ (Collins & Quillian, 432) It was predicted that this difference might occur due to

the inference from subject’s knowledge that collies are dogs and dogs are animals. For negative

instances, if something is not an animal, logically it is not a dog. On the other hand, if something

is not a dog, it does not follow logically it is not an animal (e.g., sparrow is an animal, not a dog).

A volunteer subject proved the question of why the category dog comes out faster. ‘’ After

encountering instances such as camel, otter, rabbit, kangaroo, etc., in the animal list, she was

surprised when she encountered lizard. Apparently, she had a subclass of animals (roughly

"mammals") in her mind and been deciding whether each instance was a member of that

subclass.’’ (Collins&Quillian, 434) The same phenomenon can be easily happen with the list of

birds. Nevertheless, there is no highly stereotyped subclass of dogs. The general pattern of

results can be explained in terms of semantic structure of long-term memory. ‘’ If rejecting an

instance as an animal takes longer when the instance is a plant of some kind, than when it is a

nonliving thing, then semantic relatedness must be a critical factor.’’(436) Certain concepts such

as living and non-living, dogs and cats are closely related semantically, thus they are highly

confusable.



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Thirdly, following two researches by Landauer & Freedman and Collins & Quillian, A.J.

Wilkins’s search comes up with two main issues. The first issue is whether category size affects

categorization time when the categories concerned are not nested. The second is whether the

categorization time of negative instances is affected by the proximity of category and instance

in terms of set. Likewise, two experiments were made to study on these issues. In Experiment

I, regarding to first issue, the nested categories were used in conjunction with the object names,

which were positive instances of more than one category per nest. ‘’ Collins and Quillian (1969),

for example, compared sentences such as "A cedar is a tree" with sentences such as "An elm is

a plant." Meyer (1970) compared "universal affirmatives" such as "All thrones are chairs" with

others such as "All thrones are furniture". Both studies found that sentences of the latter type

took longer to process and their findings were attributed to memory structure.’’ (Wilkins, 385)

This issue is caused by specificity in language use, which is a classification of words according

to their supersets. The results of the first experiment were inconsistent with the results of Collins

& Quillian in terms of category size, which was presented before, as there was no relation. One

possible reason of this result is that: ‘’ […] in Collins and Quillian's experiment, Ss' idiosyncratic

interpretation of the category names may have invalidated the assumed differences in the sizes

of the categories.’’ (385) Recalling from their experiment, it was found that volunteer subject

formed a subcategory (e.g., Animal as Mammal).

Experiment II investigates the second issue that whether the categorization time of

negative instances is affected by the proximity of category and instance in terms of set. ‘’ […]

the omission of positive instances of the superset category from the selection of negative

instances of the subset category decreased the mean latency of negative instances of the

subset category.’’ (Wilkins, 382) Obviously, deciding whether a non-animal word like ‘Burial’ is

not a dog is easier than animal word like ‘Horse’. It is found in the second experiment that: ‘’ […]



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negative instances take longer to process if they are related to the category in terms of set

supports the argument that Landauer and Freedman's (1968) selection of negative instances

may have decreased the mean latency of negative instances of the smaller of the nested

categories, and so contributed to the effect which they attributed to category size.’’ (Wilkins,

385)

Fourth of all, the research of B. Schaeffer and R. Wallace is demonstrating the effects of

semantic similarity on the judgment of word meanings. Two experiments were created based on

the questions of whether semantic similarity facilitates the judgment of meaning equivalence

and if semantic similarity hinders the judgment of meaning difference. As it has studied in the

previous searches, here in this review it is claimed that semantically similar words can be more

easily judged equivalent in meaning than semantically dissimilar words, but less easily judged

different. For instance, ‘wren’ and ‘fox’ are equivalent in terms of an animal, but the difference is

only one denotes a mammal. The cues of semantic similarity provide common associations. In

Experiment I, pair of words was shown to Ss, and asked subjects to judge if they had the same

meaning or not. The results have shown that two mammals or two flowers can be easily judged

than a mammal and a flower, likewise two metals and two fabrics can be easily judged than a

metal and a fabric. Apparently, semantic similarity facilitates judgements of meaning

equivalence.

In Experiment II, the Ss were presented two category labels. (e.g., Mammal & Bird or Bird

& Fruit) Labels were followed by a word like ‘wren’, and asked Ss to judge where the word

belonged. The main question here is whether the Ss can decide easily with semantically

dissimilar labels (e.g., Bird & Fruit) or not. The results have shown that semantically similar

labels are harder to differentiate than the dissimilar ones. Eventually, as can be seen, semantic

similarity facilitates the judgment of meaning difference. ‘’ […] two similar words can be judged



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equivalent in fewer steps than can two dissimilar ones; e.g., tulip and pansy would be judged

‘flower-flower’ and ‘same’ while iris and camel would be judged ‘flower-mammal’, ‘living-living’

and ‘same’.’’ (Schaeffer & Wallace, 346) Furthermore, when it comes to commonly associated

labels in Experiment II, it is harder to judge when wren appears with the label mammal-bird than

the label bird-fruit. The reason why this occurs in judgment process is the common associate

animal interferes with the judgement.

Last of all, the research of C. Conrad reported an evidence to support the theory of

hierarchical model of semantic memory organization. Each word has been stored with a

configuration of pointers to other words in memory, this configuration represents the words

meaning. ‘’ Two general types of words are included in a configuration: superordinates (S) of a

word (e.g., yellow is a property of canary). The model includes an assumption of cognitive

economy of storage.’’ (Conrad, 149) Obviously, those properties do not uniquely define a word

but still those are the properties of superordinate. For instance, ‘flies’ is not stored with the

‘canary’ but rather with ‘bird’. The model below draws a hierarchical pattern of the word storage.

(Figure 5.8) Landauer and Freedman (1968) and Schaeffer and Wallace (1969), by using

paradigms, have found evidence to support Collins and Quillian’s model of hierarchy.



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Similarly, two experiments were created. In the first study, Ss were presented with

compound sentences of the form ‘’An S is an S and is an S’’ (S S and S) and true-false RTs

are recorded. (Figure A. & Table 1, adapted from Conrad, 150) The results have shown that

Collins and Quillian’s theory of which words having stored with their superordinate rather than

word properties is confirmed. ‘’All of these studies support the notion of a hierarchical system of

categories stored in semantic memory. However, the inclusion of only unique properties within

these hierarchies –cognitive economy- is supported only by the findings of Collins and Quillian

(1969).’’ (Conrad, 150)

The Experiment II seems a rather strong test of the theory of cognitive economy of

storage, which was claimed before by Collins and Quillian (1969). On one hand, there are

reasonable evidences to support the model of semantic memory organization that suggests the

words are organizing hierarchically in memory. On the other hand, their second hypothesis –that

of cognitive economy of storage- has received little or no support.



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Experiment

Methodology

1. Subjects

The study consisted of two subjects groups each including eight subjects. The subjects of

experiment group are native speakers of four languages: Bulgarian, Russian, German, and

English. The control group is consisting of Turkish native speakers. Age, gender, and occupation

was not taken into consideration while gathering subjects together, only educational background

was taken as determining variable so as to be sure that everyone understood the instances

correctly. The ages of the subjects ranged from 21 to 30. Six of the subjects were male and ten of

them were female. Participants were all either university graduate or university students who

have at least Intermediate level of English.

2. Data Collection

In the collection of data, twenty-four names of living things (five dogs, four birds, eight

animals, five plants, three living things) and nine names of non-living things were used. The

animal names were retrieved from the website http://animal.discovery.com/, the plants from

http://plants.usda.gov/java/, the living things from http://www.fi.edu/tfi/units/life/, and non-

living examples from http://www.thmsadaqagroup.org/livingthingless on 25th

of November. The

names used in the study were; for dogs: Labrador, Terrier, Bulldog, Golden Retriever, Pit-bull,

for birds: Eagle, Sparrow, Seagull, Pelican, for animals: Camel, Cat, Lion, Elephant, Cobra,

Alligator, Tortoise, Whale, for plants: Oak, Acacia, Tulip, Daisy, Rose, for living things: Fungus,

Planktons, Bacteria, for non-living things: Table, Shirt, Pencil, Car, Paper, Glass, Magnesium,

Iron, Bill.. Specific attention was paid to reaction time (in milliseconds) of the questions.

http://animal.discovery.com/

http://plants.usda.gov/java/

http://www.fi.edu/tfi/units/life/

http://www.thmsadaqagroup.org/livingthingless



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Subjects were instructed to decide on questions whether true or not such as; ‘Labrador is a dog’

or ‘Pelican is animal’. The subjects’ answers were written down and reaction time for the

questions is considered. Since sounds or images did not have a significant importance, no

auditorial or visual data (pictures of animals) was used during the experiment.

3. Procedure

The names used in the experiment grouped into six sets of questionnaires. The sets were

presented as slides, 71 slides in total including instance pages and break pages (see table 1.), five

seconds of wait for each true-false sentence and two seconds of pauses in between them, reaction

times were calculated by the way of calculator manually. The first set was consisting of three

questions with positive instances, under the category of dog and animal separately. The second

was set was consisting of three questions with negative instances including plant names, under

the category of dog and animal. The third set was consisting of three questions with positive

instances, under the category of bird and animal separately. The fourth set was consisting of

three questions with negative instances including plant names, under the category of bird and

animal separately. The fifth set was consisting of three questions with positive instances

excluding plant names, under the category of animal and living things. The sixth set was

consisting of three questions with negative instances excluding plant names, under the category

of animal and living thing. Those sets of questionnaires were given to subjects and expected to

answer the true-false questions. Reaction time was taken into consideration.



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Table 1. Instances as slides with 5 mins duration and 2 mins break.

4. Limitation

As this study is a small-scaled one, the number of languages and the speakers of those

languages were limited in number. Five different languages, four subjects formed the experiment

group and four native Turkish subjects formed the control group. The only limitation was the

English level of the participants. Minimum required level was Intermediate. The names used in

the experiment were thirty-two and thirty-six questions asked in total.



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Data Analysis

In this part, the data gathered from eight subjects, four of which are native Turkish

speakers and four of which are the speakers of different languages, on 36 instances and 35 blank

screens have been presented in slides. In the second table, general representation of all sentences

and the sets presented to subjects is demonstrated. In the third table, the reaction time of the Ss’

during the decision time of the true-false questions is demonstrated. In the following tables, each

set of the experiment with the RT of the Ss’ will be demonstrated regarding each subject’s RT by

the way of graphics separately, at last, a total number of RT will be given as a table.

instance number

I. SET Dog vs. Animal + II. SET Dog vs Animal - III. SET Bird vs. Animal +

1 A bulldog is a dog A daisy has paws An eagle can fly

2 A terrier has a tail A rose has a tail A lion has a mane

3 A camel is an animal An orchid is a dog A seagull is carnivorous

4 A cat has paws A labrador is a cat A pelican has a big beak

5 A Golden-Retriever is golden Magnesium is a dog An alligator can swim

6 A pittbull is an animal An elephant has a bill A whale lives in the sea

IV. SET Bird vs. Animal - V. SET Animal vs. Living Thing + VI. SET Animal vs. Living Thing

-

1 An oak can fly An elephant has a long trunk Iron has a mane

2 A whale has wings A fungus is a living thing A glass is a living thing

3 A sparrow lives in the sea Bacteria can make humans sick Planktons have tails

4 A tulip can swim A cobra is backboneless A tortoise is a pencil

5 An alligator has fur Planktons are in the sea A paper has a beak

6 A table walks fast A camel has a hunch Whales drive car

Table 2. All instances and sets



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The reaction times of the Ss’ are demonstrated below. RTs are crucial point of this

experiment since this ‘categorization time ’ is being investigated to find out the effects of larger

or smaller nested categories on it.

Set I

instance

numberSubject A Subject B Subject C Subject D Subject E Subject F Subject G Subject H

1 0,731 0,761 0,712 0,845 0,723 0,891 0,742 0,772

2 0,803 0,814 0,88 0,722 0,764 0,842 0,836 0,856

3 1,214 1,296 1,254 1,23 1,36 1,297 1,27 1,3

4 0,82 0,79 0,91 0,876 0,836 0,943 0,886 0,731

5 0,9 0,812 0,764 0,85 0,793 0,881 0,813 0,719

6 1,277 1,31 1,297 1,224 1,234 1,23 1,229 1,294

Set II

instance


1 1,654 1,564 1,671 1,531 1,561 1,572 1,549 1,492

2 1,562 1,492 1,54 1,494 1,482 1,54 1,517 1,635

3 1,532 1,635 1,672 1,635 1,593 1,635 1,639 1,519

4 1,783 1,779 1,819 1,789 1,738 1,724 1,767 1,802

5 1,364 1,357 1,366 1,364 1,347 1,334 1,34 1,388

6 1,397 1,412 1,39 1,393 1,382 1,371 1,35 1,391

Set III

instance


1 0,723 0,785 0,851 0,751 0,759 0,775 0,746 0,744

2 0,863 0,861 0,795 0,742 0,795 0,772 0,726 0,764

3 0,752 0,732 0,791 0,783 0,81 0,744 0,768 0,783

4 0,849 0,861 0,88 0,813 0,8 0,836 0,864 0,871

5 1,295 1,195 1,235 1,32 1,265 1,237 1,279 1,252

6 1,268 1,29 1,274 1,122 1,124 1,251 1,267 1,164



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Table 3. RT of the subjects

As it seems below (table 4.) in the first set, the positive instance of Dog vs. Animals, faster

reaction time was given the sentences such as ‘Bulldog is a dog’ or ‘Cat has paws’. Semantic

relatedness makes the decision easy and fast since in the mental lexicon of Ss’ everyone builds

Set IV

instance


1 1,325 1,36 1,325 1,36 1,319 1,38 1,382 1,366

2 1,349 1,382 1,317 1,349 1,32 1,36 1,371 1,357

3 1,394 1,371 1,4 1,381 1,362 1,349 1,471 1,364

4 1,4 1,471 1,312 1,38 1,435 1,381 1,412 1,492

5 1,78 1,724 1,705 1,762 1,671 1,6 1,78 1,819

6 1,591 1,513 1,548 1,6 1,493 1,762 1,548 1,532

Set V

instance


1 0,723 0,823 0,791 0,813 0,783 0,861 0,795 0,764

2 1,247 1,195 1,235 1,237 1,252 1,265 1,295 1,297

3 0,816 0,813 0,836 0,849 0,791 0,863 0,764 0,88

4 1,294 1,32 1,265 1,268 1,274 1,29 1,296 1,254

5 1,315 1,382 1,35 1,35 1,371 1,31 1,224 1,297

6 0,861 0,881 0,79 0,91 0,876 0,813 0,793 0,836

Set VI

instance


1 0,9 0,875 0,836 0,849 0,88 0,813 0,8 0,79

2 0,731 0,719 0,795 0,742 0,792 0,772 0,812 0,726

3 1,312 1,232 1,313 1,291 1,4 1,248 1,361 1,293

4 0,82 0,791 0,861 0,772 0,742 0,863 0,764 0,772

5 0,813 0,816 0,731 0,845 0,723 0,772 0,712 0,742

6 0,793 0,801 0,845 0,723 0,742 0,891 0,731 0,799



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up a smaller category, dogs or cats rather than animals, and stores basic features of things at first

before the larger category distinction.

Table 4. Six instances and RTs of the first set

In the second set (table 5.), negative instances of Dog vs. Animals including plant names,

faster RT was given sentences such as ‘Magnesium is a dog’ and ‘Elephant has a bill. As it is

obvious, it is easier to realize false sentences without semantic relatedness since the mental

lexicon stores features regarding to distinctive properties of things.

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

Subject A Subject B Subject C Subject D Subject E Subject F Subject G Subject H

Response Time for Set I

1 2 3 4 5 6



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Table 5. Second set with the negative instances

In the third and fourth set of Bird vs. Animals with positive and negative instances

including plant names, similar results of first and second set can be seen (table 6, 7). On the other

hand, as well as it did not make any significant difference, when instances came to Alligator

some of the Ss surprised. Yet, it did not affect the RT but most probably, Ss created a

subcategory of mammals since all they saw was mammals until the third set. It should also be

stated that, until the fifth category, plant names were used in the negative instances so as to

observe whether Ss connected the dogs, cats, birds and animal names with the plants since they

all belong to Living Things. The important detail here is whether it is an animal or a plant, the

mental lexicon is able to correlate the characteristic of being a living thing as a semantic

similarity.

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

2

Subject A Subject B Subject C Subject D Subject E Subject F Subject G Subject H

Response Time for Set II

1 2 3 4 5 6



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Table 6 and 7. The Third and Fourth set

In the fifth and sixth set of Animals vs. Living Things represent the larger categories

comparing to each other (table 8, 9). The significant incident here is removal of plant names

from negative instances. The impact of removing plant names affected RT became faster. Since

there was nothing left to connect the semantic relatedness in the mental lexicon, to make

decisions whether the sentence true or false became easier.

Table 8.

00,20,40,60,8

11,21,41,6

Subject ASubject BSubject C SubjectD

Subject E Subject F SubjectG

SubjectH

Response Time for Set V

1 2 3 4 5 6



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Table 9.

Discussion

Figure 1. Collins, A. M., & Quillian, M. R. (1970)

Every word is stored in mental lexicon regarding its characteristic features, properties and

when it is needed with subcategories (Figure 1). Human brain is tend to process words as a

dictionary, with a semantic language. The mental lexicon makes people recall, compare, contrast,

or use these words correctly in daily life. This experiment conducted to find out whether a person

is affected by the categorization mechanism of mental lexicon during the decision time.

Obviously, human brain stores words with their subcategories and very distinctive features at



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first, which makes recalling process faster when encountering the subcategory or those features.

Furthermore, when a subcategory is repeated (such as mammal names one after another), mental

lexicon forms a structure as it will continue with that subcategory repeatedly (surprising

Alligator). When it comes to negative instances, semantic relatedness has an important role in

decision part. People are tend to decide faster whether an instance is true or not, if there is no

semantic relatedness for the mental lexicon to connect to. On the other hand, if the sentence is

false and contains semantically related words such as ‘Labrador is a cat’, decision will be

affected and reaction time will get slower. Despite of the fact that normally it is a false sentence

and can be decided easily, it most probably takes time to think while encountering many

instances semantically related. Words can be discovered with their features, then those features

forms categories and these categories grow bigger when adding more words with similar

properties, which is how category size is comprised. When a person retrieving a word from

mental lexicon, the smallest category will show up than bigger one, and when comparing (false

sentence to true one), semantic relatedness will take the control. In this way, decision making

and reaction time will be affected by size of the category and the semantic features also.

Although this experiment is not the only one among the psycholinguistic inquiries, it can be a

crosscheck done by a Turkish native control group providing the evidence for the experiments

of Collins, A. M., & Quillian, M. R. (1970) and Landauer, T. K., & Freedman, J. L. (1968).



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In summary, the results of this study refine our understanding of the connection between

the mental lexicon and semantic representation. Several theories have been proposed as to

how people store and process the meanings of words. All these theories have been tested by

experiments of comparing differences in processing time between words or sentences and

memory storage. Semantic judgment task has been used to find out that whether an individual

can answer easily or rather have difficulties to decide. In general, an individual forms a semantic

representation of a word in his or her mental lexicon, regarding to word’s subcategories by its

similar and dissimilar versions. Those related or associative meanings share a portion of their

semantic representation, whereas unrelated or dissimilar meanings have separate

representations. Consequently, it is safe to say that every sense of a word has a separate

semantic representation in a speaker’s the mental lexicon.



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Works Cited:

COLLINS, A. M., & QUILLIAN, M. R. Retrieval time from semantic memory. Journal of Verbal Learning

and Verbal Behavior, 1969, 8, 240-247.

COLLINS, A. M., & QUILLIAN, M. R. Does category size affect categorization time ? Journal of Verbal

Learning and Verbal Behavior, 1970, 9, 432-438.

CONRAD, C., Cognitive economy in semantic memory. Journal of Experimental Psychology, 1972,

92(2), 149-154.

FABER, P.B., & USÓN R.M., Constructing a Lexicon of English Verbs. Functional Grammar Series [FGS]

23, Walter de Gruyter. 1999

LANDAUER, T. K., & FREEDMAN, J. L. Information retrieval from long-term memory: Category size and

recognition time. Journal of Verbal Learning and Verbal Behavior, 1968, 7, 291-295.

SCHAEFFER, B., & WALLACE, R. Semantic similarity and the comparison of word meanings. Journal of

Experimental Psychology, 1969, 82, 343-346.

TAFT, M., Reading and the Mental Lexicon. Psychology Press, 1991

VIGLIOCCO G. & VINSON D.P., Semantic Representation. The Oxford Handbook of Psycholinguistics,

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Semantic representation in the mental lexicon

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