Cognitive aspects of lexical availability (2006)

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Cognitive aspects of lexicalavailabilityNatividad Hernández-Muñoz a , Cristina Izura b &Andrew W. Ellis ca University of Salamanca , Salamanca, Spainb University of Wales Swansea , Swansea, UKc University of York , York, UKPublished online: 17 Feb 2007.

To cite this article: Natividad Hernández-Muñoz , Cristina Izura & Andrew W.Ellis (2006) Cognitive aspects of lexical availability, European Journal of CognitivePsychology, 18:5, 730-755, DOI: 10.1080/09541440500339119

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Cognitive aspects of lexical availability

Natividad Hernandez-Munoz

University of Salamanca, Salamanca, Spain

Cristina Izura

University of Wales Swansea, Swansea, UK

Andrew W. Ellis

University of York, York, UK

Lexical availability measures the ease with which a word can be generated as amember of a given category. It has been developed by linguistic studies aimed,among other things, at devising a rational basis for selecting words for inclusion indictionaries. The measure accounts for the number of people who generated a givenword as a member of a designated semantic category and the position in which theyproduce the word. We present an analysis of lexical availability from a cognitiveperspective. Data were analysed for Spanish speakers generating words from fivesemantic categories*clothes, furniture, body parts, animals, and intelligence. Sixproperties of words were investigated as potential predictors of lexical availability.Predictors were concept familiarity, typicality, imageability, age of acquisition, wordfrequency, and word length. Categories differed on these variables, and regressionanalysis found concept familiarity, typicality, and age of acquisition to besignificant predictors of lexical availability. The cognitive basis of these findingsand the practical consequences of selecting words on the basis of lexical availabilityare considered.

Languages often contain many more words than are used in everyday

discourse. They also contain many more words than need to be contained

within concise dictionaries. Some principled methods need to be found if

suitable words are to be selected for inclusion in concise dictionaries,

teaching materials for children, and courses for second language learners.

Correspondence should be addressed to Cristina Izura, Department of Psychology, University

of Wales Swansea, Singleton Park, Swansea SA2 8PP, UK. E-mail: [email protected]

We thank Alberto Carcedo Gonzalez for his help and Professors Julio Borrego Nieto, Jose

Antonio Bartol, Rosario Llorente Pinto, Emilio Prieto de los Mozes, and Javier de Santiago

Guervos of the University of Salamanca for their invaluable assistance with this study which forms

part of the project El lexico disponsible del hablante hispano: aportacion de datos y replanteamiento

teorico and was supported by the Grant Ministerio de Ciencia y Tecnologıa (BFF2001-1005).

EUROPEAN JOURNAL OF COGNITIVE PSYCHOLOGY

2006, 18 (5), 730�755

# 2006 Psychology Press Ltd

http://www.psypress.com/ecp DOI: 10.1080/09541440500339119

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One approach that has been adopted is to select words on the basis of

frequency of use in the language. Thorndike’s (1921) compilation of the10,000 words occurring most commonly in written text was a pioneer in this

area and was followed by similar studies in other languages such as Spanish

(Buchanan, 1927) and French (Aristazabal, 1938). Frequency counts

continue to be used as the basis for dictionary creation (e.g., Sinclair,

1987) but they have their problems. Counts based on adult written language

tend to overrepresent words from some areas of discourse (e.g., politics and

finance) while underrepresenting the words of everyday spoken language

(e.g., clothing, food, household objects). Even when attempts have beenmade to include spoken language samples (Baayen, Piepenbrock, & van

Rijn, 1993), it has proved difficult to record and incorporate ordinary,

mundane domestic language. Educational programmes for teaching second

language vocabulary face a similar problem. High frequency words tend to

be considered the suitable vocabulary to teach at initial stages of second

language learning. However, functional language proficiency requires

mastery of a considerably larger number of words and there are no other

criteria applicable to the creation of word lists for use in advanced courses ofsecond language acquisition (Groot, 2000).

An alternative approach to vocabulary selection has been based on the

concept of lexical availability (Carcedo, 1998; Dimitrijevic, 1969; Mackey,

1971; Samper-Padilla, Bellon, & Samper-Hernandez, 2003). This approach

involves asking language users from specific communities to generate words

from different domains or categories. Spanish language studies have mostly

employed a set of 16 semantic categories developed by Michea (1953) and

Gougenheim, Michea, Rivence, and Sauvageot (1964) to capture thevocabulary of everyday life (e.g., Echeverrıa, Herrera, Moreno, & Pradenas,

1987; Lopez Morales, 1996; Mena Osorio, 1986). The 16 categories are body

parts, clothes, parts of the house, furniture, food and drink, objects found on

the table at dinner time, the kitchen and its utensils, the school, heating and

lighting, the city, the countryside, transport, gardening and countryside jobs,

animals, games and hobbies, and professions. Participants are presented with

the name of a category and are usually asked to write down as many words

from that category as they can within a given time period. Techniques havebeen developed to derive an availability measure for different words based

on the number of participants who generate a word and the position it

occupies in their lists (Echeverrıa et al., 1987; Lopez Morales, 1999; Mena

Osorio, 1986). At the time of writing, a major Panhispanic project is under

way aimed at producing a dictionary founded on lexical availability that will

cover most of the Spanish-speaking world (Alba, 1995; Galloso, 2003; Lopez

Morales, 1996; Mateo, 1998).

The task of generating words from predefined semantic categories hasbeen termed ‘‘semantic fluency’’ or ‘‘category instance generation’’. It has

COGNITIVE ASPECTS OF LEXICAL AVAILABILITY 731

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been used extensively in neuropsychological studies of word retrieval in

patients with various forms of brain damage such as aphasia and dementia(e.g., Moulin et al., 2002; Troster, Salmon, McCullogh, & Butters, 1989).

Within cognitive psychology there have been many studies in which

participants are required to judge whether or not stimulus words come

from designated semantic categories (category instance verification), but few

studies involving word generation. Building on a study by Loftus and

Suppes (1972), Catling and Johnson (2005) presented category labels

and initial letters to participants who were asked to generate appropriate

words as quickly as possible (e.g., FRUIT�/O0/ ‘‘orange’’). They foundproduction speed to be predicted by age of acquisition and not by word

frequency.

The aim of the present study was to investigate which cognitive factors

(semantic, lexical, or both) influence the availability of words when

generated in response to category labels. Hernandez-Munoz (2002) collected

lexical availability data from young Spanish people for the 16 categories

mentioned above plus an additional category, intelligence, chosen to act as a

source of more abstract vocabulary. Due to the exploratory nature of thisfirst study, 5 out of the 16 original categories were chosen. As a consequence,

four ratings for 500 instead of 1600 words were collected. Animals, body

parts, clothes, and furniture were selected corresponding to two living and

two nonliving categories and because they were also used commonly in past

studies (Catling & Johnson, 2005; Chertkow, Bub, & Caplan, 1992; Loftus &

Suppes, 1972; Warrington & McCarthy, 1987). Intelligence was selected as

an abstract category likely to elicit words of a less concrete nature. The 100

items from each category with the highest availability scores were chosen,excluding compound words and phrases. Six properties of words were

employed as potential predictors of lexical availability. These were taken

from studies of normal and impaired word retrieval, including studies of

object naming and from studies of category instance verification, which can

be thought of as being to some extent the reverse of category instance

generation.

The first factor was the familiarity of a concept or thing, which can be

defined as the frequency with which people come into contact with, or thinkabout the concept or thing. Concept familiarity was included by Snodgrass

and Vanderwart (1980) among their measures of object qualities. Cuetos,

Ellis, and Alvarez (1999) reported an effect of concept familiarity on Spanish

object naming speed, with more familiar objects being named more rapidly

than less familiar ones. Ellis and Morrison (1998) reported effects of

familiarity in some of their analyses of object naming speed in English.

Neuropsychological studies have indicated that naming accuracy may be

influenced by familiarity in patients with aphasia (Cuetos, Aguado, Izura, &Ellis, 2002) and semantic dementia (Lambon Ralph, Graham, Ellis, &

732 HERNANDEZ-MUNOZ, IZURA, ELLIS

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Hodges, 1998). The familiarity of words and their meanings has also been

held to affect the speed of deciding that words belong in particular semanticcategories (e.g., Larochelle & Pineau, 1994; Malt & Smith, 1982). Familiarity

effects have been assigned to the activation processes of semantic repre-

sentations (Hirsh & Funnell, 1995).

Typicality is a measure of how close a concept lies to the centre of a

particular category. For example, dog and horse are very typical animals

while bat and whale are atypical. It has long been recognised that the

typicality of items with respect to their category is an important determinant

of category verification speed (McFarland, Duncan, & Kellas, 1978; Rosch& Mervis, 1975; Smith, Shoben, & Rips, 1974; Southgate & Meints, 2000).

Jolicœur, Gluck, and Kosslyn (1984) reported that typical items tended to be

named more often using a basic level term (e.g., bird) while atypical items

tended to be named using their subordinate name (e.g., penguin). Barsalou

(1983) found typicality effects for ad hoc categories created for the purposes

of the experiment that were comparable to those of natural categories that

participants might be expected to be previously familiar with. Holmes and

Ellis (in press) found an effect of typicality on object naming speed, withobjects judged typical of their categories being named faster than objects

judged to be atypical.

The imageability of a word is a rating of the ease with which the word can

evoke a mental image of the concept represented. It is considered to be a

semantic variable and it is closely related to concreteness so that words

representing concrete objects tend to be given high imageability ratings while

words representing abstract concepts tend to be given low imageability

ratings. Imageability effects have been most extensively explored in memorytasks where high imageability words are easier to recall than low imageability

words (Coltheart & Winograd, 1986; Mulligan, 1998; Williams, Healy, &

Ellis, 1999) but neuropsychological studies have suggested that the ability of

some brain-injured patients to access words from the lexicon may be affected

by their imageability (e.g., Franklin, Howard, & Patterson, 1995). Paivio,

Clark, Digdon, and Bons (1989) reported a close relationship between the

speed of naming an object picture and the speed with which participants

reported forming mental images to words while de Groot (1992) reported aneffect of imageability on the speed with which bilingual people can translate

words from one language into another.

Word frequency is a measure of how often a word occurs in samples of

spoken and written language. We have noted above that it has been used in

the past as a basis for selecting words for inclusion in concise dictionaries

and in vocabulary lists of second language courses. Oldfield and Wingfield

(1965) proposed that the frequency of object names affected the speed with

which those names can be accessed and produced. Evidence has beensomewhat equivocal (Barry, Hirsh, Johnston, & Williams, 2001; Bonin,


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Chalard, Meot, & Fayol, 2002), but several recent studies have reported

independent effects of word frequency on object naming (Barry, Morrison,

& Ellis, 1997; Ellis & Morrison, 1998), including object naming in Spanish

(Cuetos et al., 1999). Cognitive models of word retrieval have generally

proposed that word frequency affects the process of accessing spoken word-

forms. For example, in Levelt’s influential model of speech production, word

frequency is held to affect the process of accessing phonological representa-

tions of spoken words (‘‘lexemes’’*see Levelt, Roelofs, & Meyer, 1999).

The age of acquisition1 (AoA) of a word is defined as the age at which a

word is typically learned. Age of acquisition has been reported to affect

word recognition and production speed in a variety of lexical processing

tasks including spoken object naming, lexical decision, and word naming or

reading aloud (Carroll & White, 1973; Ellis & Morrison, 1998; Izura & Ellis,

2002; Juhasz, 2005; Monaghan & Ellis, 2002; Morrison & Ellis, 1995, 2000).

In all of these tasks, early learned words have been reported to be processed

faster than later learned words. Of particular interest, given that participants

in lexical availability studies usually produce written lists of items from

categories, are the reports by Bonin, Fayol, and Chalard (2001) and Bonin

et al. (2002) of an effect of age of acquisition on the speed of initiating

1 The suitability of the AoA measure has been a constant matter of concern. Zevin and

Seidenberg (2002, 2004) have recently argued that the high correlations between age of acquisition

and so many other factors (word frequency, familiarity, imageability, word length, concreteness,

and number of neighbours) causes great difficulty when investigating the unique/singular influence

of AoA in lexical tasks. They proposed a new operationalisation of AoA related to the frequency

with which words are experienced through different ages. In their view, early acquired words are

those whose frequency of trajectory through life starts very high during infancy to steadily decrease

through the years. The opposite, words of low frequency in childhood that increase their frequency

through time, constitutes late acquired words. This AoA measure, called frequency of trajectory,

has been found to influence object naming and lexical decision times (Bonin, Barry, Meot, &

Chalard, 2004; Zevin & Seidenberg, 2004). Frequency of trajectory correlates highly with AoA

only, representing (at least in part) the AoA variable independently of other factors. Despite this,

frequency of trajectory is not free from problems. Firstly, it reduces age of word learning to written

frequency of exposure knowing that other factors (concept familiarity, imageability, word length,

number of neighbours, and spoken word frequency) also contribute when a word is learnt (Bonin et

al., 2004; Zevin & Seidenberg, 2002). Secondly, the way in which early and late acquired words are

understood (having different frequency of trajectories through time) excludes the great majority of

the words in the vocabulary, that is, those words experienced with an equal or similar frequency

over the years. Finally, although the operationalisation of late acquired words as having low to high

frequency of trajectory might be appropriate, most early acquired words do not have high to low

trajectories. Only words related to the fantastic world such as giant, ogre, and fairy have higher

frequencies in a child’s language than in adults. Generally, early learned words retain into

adulthood the frequencies they had in childhood. Nevertheless, and despite its problems, it would

have been interesting to see the predictor power of frequency of trajectory on the category

generation task. Unfortunately, there is not a reliable database providing word frequencies for the

different schooling years of Spanish children. For this reason we were unable to include frequency

of trajectory as another predictor in the analysis.


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responses in a task requiring participants to write the names of pictured

objects. Theoretical accounts of age of acquisition differ in where exactly

within the cognitive system this variable is thought to exert its influence. A

number of authors have proposed that age of acquisition might affect the

ease of accessing spoken word forms (e.g., Brown & Watson, 1987; Gilhooly

& Watson, 1981), and Levelt et al. (1999) have suggested that age of

acquisition, like word frequency, might affect the retrieval of the phonolo-

gical representations of words (lexemes). Other authors, however, have

favoured a semantic locus for age of acquisition effects, with early meanings

being better represented than later acquired meanings (Brysbaert, van

Wijnendaele, & de Deyne, 2000; Lyons, Teer, & Rubenstein, 1978; van Loon-

Vervoorn, 1989; van Loon-Vervoorn, van Ham, & van der Koppen, 1988). A

third account of how age of acquisition could come to affect lexical

processing was offered by Ellis and Lambon Ralph (2000) who proposed

that the age or order of acquisition of words might affect the strength of

connections between different lexical representations (e.g., semantic and

phonological for naming; orthographic and phonological for reading aloud:

see also Monaghan & Ellis, 2002). Izura and Ellis (2002, 2004) found age (or

order) of acquisition effects in second language learners which reflected the

point at which different words were learned in the second language rather

than the age at which the word with equivalent meaning was learned in the

first language. This finding is problematic for the semantic theory of age of

acquisition effects, which would predict that second language words would

inherit the semantic age of acquisition properties of the equivalent first

language word, and is more compatible with a lexical access or mapping

account of age of acquisition. All these three theories would, however,

predict that early learned words would be easier to access than later acquired

words in the category generation task that underlies studies of lexical

availability. We have noted that Catling and Johnson (2005) found that the

frequency with which words occur in child language samples predicted

response times when participants were given category labels and initial

letters, and argued that child language frequency was probably an indirect

measure of age of acquisition.

Finally, it is unclear whether word length exerts an independent effect on

response speed in naming or verification tasks (e.g., Bachoud-Levi, Dupoux,

Cohen, & Mehler, 1998; Marmurek & Rinaldo, 1992) but Cuetos et al.

(1999) found an effect of length on Spanish object naming speed with age

of acquisition, word frequency and other factors statistically controlled.

They suggested that the greater variability in length of Spanish object names

may make length effects easier to detect in the Spanish language than in

English.


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METHOD

Lexical availability data

The lexical availability data analysed in this paper were taken from a study

by Hernandez-Munoz (2002) who collected written category generation data

from 117 native speakers of Spanish (42 male, 75 female) aged 17�18 years.

All the participants were students in private and public schools with mixed

socioeconomic backgrounds living in the Cuenca region of Spain. Partici-

pants were asked to write down as many items they could think of from the

standard 16 categories used in lexical availability studies (body parts, clothes,

parts of the house, furniture, food and drink, objects found on the table at

dinner time, the kitchen and its utensils, the school, heating and lighting, the

city, the countryside, transport, gardening and countryside jobs, animals,

games and hobbies, and professions) plus the category ‘‘intelligence’’, which

was chosen to elicit more abstract words. They were allowed 2 min for each

category.

Responses from five categories (animals, body parts, clothes, furniture,

and intelligence) were selected for analysis. Morphological variants of the

same word were combined (e.g., singular and plural). If a participant wrote

the same word twice for a particular category, the first production was taken

and the second ignored. Table 1 shows the total number of different,

category-appropriate words produced for each category and the mean

number of different, category-appropriate words written by each participant

for each category. Regarding the grammatical characteristics of the words an

overall predominance of nouns was observed (73%), followed by verbs (21%)

and adjectives (6%). The lexical availability of each word was calculated

using the formula of Lopez Chavez and Strassburguer Frıas (1992, 1993,

2000), which computes an availability value based on the positions that the

word occupies in a given list, the number of participants who place the word

at those positions, and the lowest position the word occupies in any of the

lists. The formula assigns a high value to words that were written by a large

TABLE 1Mean number of different category-appropriate words produced and mean numberof category-appropriate words per participant (excluding repetitions) for the five

categories used in the study

Categories

Animals

Body

parts Clothing Furniture Intelligence

Total number of different words

produced

563 195 192 223 612

Mean number of words per participant 27.1 16.6 17.5 22.9 11.4


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number of participants and which appeared early in their lists, and

exponentially lower value to words written by few participants late in theirlists. Moreno, Moreno, and de las Heras (1995) integrated the formula in the

computer program Lexidisp which is being developed as a web-based tool

(www.linguas.net/LEXIDISP/). The formula is:

D(Pj)�Xn

i�1

e

��2:3�

�i�1

n�1

��

�fji

I1

Where D(Pj)�/the lexical availability of the word j for a given category; I�/

the total number of participants who completed the task; i�/the position of

word j in a given list; f�/the number of participants who wrote word j at that

position in their list; n�/the lowest position occupied by word j in any list

produced for the category in question; and e�/the natural number

(2.718181818459045). The resulting values for lexical availability are all

less than 1 and were multiplied by 100 for ease of presentation. Values

ranged from 82.77 (perro [dog] for the category animals) to 0.64 (tiestos

[flower pot] for the category furniture).

The predictor variables

For the purpose of analysis, the 100 words from each of the five chosen

categories with the highest lexical availability values were chosen (for the 10

first words produced in each category, see Appendix). Values of the six

predictor variables were obtained for those 500 words. Where the predictorsinvolved ratings (i.e., concept familiarity, typicality, imageability, and age of

acquisition), the 500 words were split into two sets of 250, with each set

containing 50 words from each of the five semantic categories. Ratings were

provided by different groups of 50 participants for each scale. The

participants were all Spanish native speakers. They were all tested at the

end of a lecture in a classroom setting and they were allowed as much time as

needed to complete the scale. None of the scales collected was incomplete.

For each rating, half the participants rated one set of words and half theother. Reliabilities were computed for each scale by correlating the means for

the different words from the first 13 raters with the means from the

remaining 12 raters. All were highly significant.

Concept familiarity ratings were obtained using instructions adapted from

Snodgrass and Vanderwart (1980). Participants used a 7-point scale to

estimate how often they think about or come into contact with each of the

concepts (from 1�/‘‘less than once a month’’ to 7�/‘‘many times a day’’)

evoked by the list of 250 words. The 50 raters, students at the University of


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Salamanca, Spain, had a mean age of 20 years (range 17�53). Reliability was

.92.

Typicality ratings were obtained using a 7-point scale ranging from 1�/‘‘a

very atypical example of the category’’ to 7�/‘‘a very typical example’’. The

instructions were based on those used by Hampton and Gardiner (1983).

Within semantic categories we can encounter elements that seem to be better

examples of a category than others. For example, we will all probably think that

apple shares more characteristics with the category ‘‘fruit’’ than coconut.

In this test you are going to find a series of words under a category label. Please rate

the words based on how good an example of the category each word is. A rating of

‘‘1’’ corresponds to the instance being a poor example of the category. A rating of

‘‘7’’ indicates that you consider the instance to be a very good example of the

category. Remember, rate the words based only on how well they represent the

category and not how much you like them.

The 50 participants that rated typicality had a mean age of 24 years (range

18�36). Reliability was .92.

Imageability ratings were obtained using a 7-point scale ranging from 1�/

‘‘very difficult to arouse a mental image’’ to 7�/‘‘very easy to arouse a

mental image’’ (Morrison, Chappell, & Ellis, 1997). The instructions read as

follows:

Words differ in their capacity to evoke mental images. Thus, while some words are

easy to imagine (e.g., apple) others are not (e.g., fact). The purpose of this test is to

estimate how easy or difficult it is to imagine a list of words. The scale ranges from 1

to 7. Rate a word with 7 if you think the word is very easy to imagine, with 1 if you

think the word is very difficult to imagine, and give an intermediate value for those

words that are neither very easy or very difficult to imagine.

The raters had a mean age of 23 years (range 17�58). Reliability was .91.Word frequency values were taken from Alameda and Cuetos (1995),

which is based on a corpus of written Spanish texts comprising 2 million

words from 606 texts distributed across novels (50%), essays (15%),

newspapers (25%), and scientific publications (10%) published between

1978 and 1993. An average frequency value was calculated for morpholo-

gical variants (e.g., masculine and feminine; singular and plural).

Age of acquisition ratings were obtained from 50 native speakers of

Spanish living in Spain (mean age 23 years; range 18�42) using the

methodology of Ghyselinck, de Moor, and Brysbaert (2000) where

participants estimate how old they were when they first learned different

words. They provide the estimate in years rather than using the 5- or 7-point

scales typical of previous studies which constricts the values of late acquired


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words. Adult estimates of age of acquisition have been shown to have high

validity in that they correlate highly with objective measures (Carroll &White, 1973; Gilhooly & Gilhooly, 1980; Morrison et al., 1997). Reliability

was .88.

Word length was measured as the number of syllables in a word.

RESULTS

Before conducting any analyses on the data, 43 of the 500 words (8.6%) were

removed. These were polysemic words, which can apply to more than one

category (e.g., mono which means monkey [animal] or overalls [clothing]),

foreign words (e.g., slip for an item of clothing), or dialect words. Many of

the raters failed to provide ratings for the foreign and dialect words (which

they presumably did not know) and word frequency values based on writtenSpanish with a single value for polysemic words are unreliable for such

words. Three words were deleted from the category of animals, four from

body parts, eighteen from furniture, fourteen from clothing, and four from

intelligence.

As is common practice in the cognitive psychology literature, word

frequency values were transformed using the formula log(1�/x) to reduce

skew. This also compresses the range of values (0 to 3.57 in the present

study). Table 2 shows the mean, standard deviation, and range for eachvariable and category for the 457 remaining words along with the lexicality

availability values for each category. One-way analyses of variance were used

to compare the scores of the words in each category on each of the variables.

There were significant differences among the categories on all of the

predictor variables. These were analysed further by using post hoc tests

(Tukey’s HSD) to compare the categories pairwise on each of the factors.

Familiarity. Categories differed in the familiarity ratings given to the

items within them, F(4, 452)�/42.57, MSE�/71.23, pB/.001. Post hoc tests

showed that familiarity ratings were significantly higher for words in the

category intelligence than for furniture (M�/0.81, SD�/0.19) and body parts(M�/1.35, SD�/0.18), which were significantly more familiar than clothing

(M�/�/1.39, SD�/0.19; M�/�/0.85, SD�/0.18), which were in turn

significantly more familiar than animals (M�/0.19, SD�/0.26).

Typicality. Categories differed significantly on typicality, F(4, 452)�/

9.72, MSE�/16.85, pB/.001. Post hoc tests showed that typicality ratings

were significantly higher for animals (M�/�/0.81, SD�/0.19), body parts

(M�/0.67, SD�/0.20), and intelligence (M�/0.45, SD�/0.20) than forfurniture and clothing. This implies that furniture and clothing are more


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TABLE 2Mean (M), standard deviation (SD) and ranges on concept familiarity, typicality, imageability, word frequency, age of acquisition,

word length, and lexical availability for words in the five categories used in the study

Predictor variables Animals Body parts Clothing Furniture Intelligence

Concept familiarity M 3.18 4.04 3.57 4.58 5.39

SD 1.00 1.40 1.53 1.51 0.97

Range 1.44�6.04 1.68�6.56 1.16�6.68 1.16�6.76 3.08�6.76

Typicality M 4.82 4.68 3.83 4.01 4.56

SD 1.15 1.20 1.60 1.47 1.16

Range 2.48�6.92 2.44�7.00 1.20�6.72 1.56�7.00 2.44�6.64

Imageability M 6.62 6.07 6.66 6.61 4.36

SD 0.37 0.86 0.32 0.45 1.27

Range 5.36�7.00 3.68�7.00 4.67�7.00 4.16�7.00 2.52�6.96

Log word frequency M 1.00 1.40 1.07 1.19 2.02

SD 0.50 0.79 0.62 0.67 0.66

Range 0.00�2.35 0.00�3.10 0.00�2.23 0.00�2.78 0.00�3.57

Age of acquisition M 5.70 7.00 7.24 6.70 7.05

SD 1.39 2.28 2.63 2.25 1.85

Range 3.00�9.16 2.98�10.95 3.44�15.46 3.08�11.58 3.72�11.48

Word length M 2.67 2.66 2.81 3.06 3.19

SD 0.79 0.78 0.80 0.91 1.05

Range 1.00�5.00 1.00�5.00 1.00�5.00 2.00�5.00 1.00�6.00

Lexical availability (*100) M 11.91 12.34 13.00 10.37 4.09

SD 12.28 17.65 17.55 18.16 4.31

Range 2.64�82.77 0.90�71.69 0.80�70.82 0.64�78.81 1.32�23.89

740

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widely dispersed categories in which items tend to stray further from the

‘‘core’’ of the category than is the case for animals, body parts, andintelligence.

Imageability. Categories differed significantly on imageability, F(4,

452)�/155.34, MSE�/90.44, pB/.001. Post hoc tests showed that items in

the categories clothing (M�/�/0.59, SD�/0.11), animals (M�/�/0.55, SD�/

0.11), and furniture (M�/�/0.54, SD�/0.12) were rated as significantly more

imageable than body parts, which in turn were significantly more imageable

than items in the category intelligence (M�/1.70, SD�/0.11).

Word frequency. Categories differed significantly on word frequency,

F(4, 452)�/37.24, MSE�/16.01, pB/.001. Post hoc tests showed that items in

the category intelligence had significantly higher word frequencies than body

parts (M�/0.62, SD�/0.09) and furniture (M�/0.82, SD�/0.10), which were

significantly higher than clothing (M�/0.32, SD�/0.09) and animals (M�/

0.39, SD�/0.09).

Age of acquisition. Categories differed significantly on age of acquisi-

tion, F(4, 452)�/8.03, MSE�/35.61, pB/.001. Post hoc tests showed that

animals (M�/�/1.30, SD�/0.30) were rated as significantly earlier acquired

than all the other categories, which did not differ from each other (p�/.1).

Word length. Categories differed significantly on word length, F(4,

452)�/6.97, MSE�/5.30, pB/.001. Post hoc tests showed that intelligence

and furniture items had significantly longer names than animals (M�/0.52,SD�/0.13) and body parts (M�/0.53, SD�/0.13). Clothing items were

intermediate in length and not significantly different (p�/.1) from any of the

other categories.

Lexical availability. The categories also differed overall on lexical

availability, F(4, 452)�/5.70, MSE�/0.12, pB/.001. Post hoc tests showed

that words from the intelligence category (M�/�/0.47, SD�/0.06) had

significantly lower lexical availabilities than words from the other fourcategories, which did not differ significantly (p�/.1). This relates to the fact

noted above that the words generated for the category intelligence were

much more variable across participants than the words generated for the

other categories. High variability means that individual words will have been

produced by fewer participants than words from the other categories. The

formula for determining lexical availability is such that smaller numbers of

participants writing each word results in smaller values of f in the equation

and therefore lower values of lexical availability. Figure 1 shows thesignificant and nonsignificant differences amongst factors for each category.


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The correlations of the predictor variables with each other and with

lexical availability across the combined categories for the 457 words used in

the analyses is shown in Table 3. With so many items, even low correlations

emerge as significant at the .05 significance level, so only correlations that

are significant at the .01 level are marked. Among the predictors, concept

familiarity correlated quite highly with word frequency (words that occur

more often in samples of written language denote concepts that are rated as

being encountered more often). Age of acquisition also had relatively high

correlations with concept familiarity and word frequency (early learned

words tend to denote more familiar concepts and to occur with higher

frequency in adult language than later learned words). None of the

correlations between the predictors was high enough (above .7), however,

to undermine the use of regression analysis (Miles & Shevlin, 2001).

Typicality had the highest raw correlation with lexical availability, with

words judged as typical for their category being more available than words

Familiarity

Intelligence Furniture Body parts Clothing Animals

Typicality

Animals Body parts Intelligence Furniture Clothing

Imageability

Clothing Animals Furniture Body parts Intelligence

Word frequency

Intelligence Body parts Furniture Clothing Animals

Age of acquisition

Animals Furniture Body parts Intelligence Clothing

Word length

Intelligence Furniture Clothing Animals Body parts

Figure 1. Differences between categories on familiarity, typicality, imageability, word frequency,

age of acquisition, and word length. The categories joined by lines represent no significant

differences.


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judged to be relatively atypical. Age of acquisition had the second highest

correlation with early learned words being more available than later learned

words. The correlations of lexical availability with concept familiarity,

imageability, word frequency, and length were progressively lower but still

significant.

When predictor variables are themselves intercorrelated, their individual

correlations with lexical availability should be interpreted with caution.

Multiple regression techniques can help to distinguish real from apparent

relationships under such circumstances. The particular technique used here

is known as multilevel multiple regression (Miles & Shevlin, 2001). It was

developed to analyse data that comes grouped in clusters like the semantic

categories of the present study. It can be thought of as a two-step analysis. In

the first step, variance in lexical availability due to differences between the

five categories on the predictor variables was extracted. In the second step,

the six predictor variables were entered in a simultaneous analysis to

determine the ability of each predictor to account for differences between

words in their lexical availability after overall differences between the

categories have been accounted for. For the purpose of these analyses,

lexical availabilities were log transformed to reduce skew.

The results of the second step of the multilevel multiple regression

analysis are shown in Table 4. Taken together, the independent variables

were able to predict lexical availability to a significant degree, F(4, 446)�/

55.94, MSE�/6.27, pB/.001, accounting for 56% of the variance in lexical

availability after the extraction of variance due to differences between the

categories. Concept familiarity, typicality, and age of acquisition all made

significant independent contributions to predicting lexical availability.

Words representing familiar concepts that are typical of their category and

are learned early in life had high availabilities while words representing less

familiar concepts that are less typical of their categories and are later

TABLE 3Correlation among the predictor variables and with lexical availability

Concept

familiarity Typicality Imageability

Word

frequency

Age of

acquisition

Word

length

Concept familiarity 1.00

Typicality �/.25** 1.00

Imageability �/.09 .00 1.00

Word frequency .66** .29** �/.20** 1.00

Age of acquisition �/.47** �/.24** �/.34** �/.44** 1.00

Word length .03 �/.08 �/.18** �/.19** .23** 1.00

Lexical availability .24** .59** .33** .21** �/.43** �/.14**

**p B/.01.


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acquired had lower availabilities. The independent contribution of word

frequency was very close to significance (p�/.051), reflecting a tendency for

higher frequency words to be more available than lower frequency words.

The independent contributions of imageability and word length were not

significant: There was no significant tendency for more available words to

differ from less available words in terms of imageability or length once

differences in familiarity, typicality, etc. had been accounted for.

GENERAL DISCUSSION

We have analysed the words produced by young adult speakers of Spanish

when asked to generate as many items as they could from five semantic

categories (clothes, furniture, body parts, animals, and intelligence) given 2

minutes per category. Participants were able to generate substantially more

words for some categories than others in the time available. Animals and

furniture elicited the greatest number of category-appropriate words per

participant, while intelligence elicited the fewest. At the same time, the

category of intelligence elicited the greatest number of relevant words (n�/

612) across the 117 participants. That is because participants tended to write

relatively few words in response to the category label ‘‘intelligence’’ but the

words produced were very variable from one participant to another. The

categories of body parts and clothing elicited intermediate numbers of words

per participant but the smallest numbers of different words, indicating that

participants tended to write the same words in response to those category

labels. That is, there was much greater homogeneity across participants in

the responses generated in response to those category labels. Categories

differ in their apparent difficulty (as measured by the mean number of words

per participant) and in the variability of words elicited (as measured by the

total number of different words produced).

TABLE 4Results of a multilevel regression analysis of lexical availability for six predictorvariables entered in the second stage of the analysis after removal of variance

attributable to overall differences between the categories

Predictor variable B Std. error Beta t

Concept familiarity 0.007 0.018 0.226 4.23**

Typicality 0.184 0.013 0.504 14.32**

Imageability 0.000 0.024 0.023 0.41

Word frequency 0.006 0.033 0.097 1.96a

Age of acquisition �/0.003 0.011 �/0.115 �/2.43**

Word length 0.002 0.019 0.041 1.18

ap�/.051, **p B/.01.


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Our analyses focused on the six properties of words and their meanings

selected because they had been implicated as potential determinants of ease

of word retrieval in previous cognitive and neuropsychological studies and/

or because they had been implicated as predicting the speed of deciding that

words belong in different semantic categories (category instance verifica-

tion). The six properties were concept familiarity, typicality, imageability, age

of acquisition, word frequency, and word length. The first point to note is

that the words generated for the different categories varied to some extent on

these properties. For example, animals had the earliest age of acquisition

ratings but had low concept familiarity ratings because while animals like

gato (cat) and perro (dog) are highly familiar (i.e., rated as being encountered

or thought about at frequent intervals), the majority of animals generated

have much lower rated concept familiarities (e.g., sapo [toad]; oso [bear]).

Funnell and Sheridan (1992) noted the lower average familiarity of animals

compared with nonliving categories. Animals also had low frequency and

relatively long names but higher imageability and typicality ratings

compared with other categories.

It is clear that Hernandez-Munoz’s (2002) introduction of the category of

intelligence was partially successful in stimulating the production of more

abstract words. Imageability ratings were lower for intelligence words than

for the other categories. But the mean imageability ratings of the intelligence

words was still above the mid-point of the rating scale and, somewhat

surprisingly, the words produced for the category intelligence were more

familiar and of higher frequency than the other categories and denoted more

familiar concepts. Inspection of the intelligence words shows that the

students who generated the words in the intelligence category chose to write

many education-related words such as profesor (teacher), libro (book), and

estudiar (to study) that are very familiar concepts for them and also relatively

high frequency in written language samples.

We analysed the ability of the six word properties to predict the lexical

availability of different words in relation to the five category labels. The

formula for determining the lexical availability of a word takes into account

the number of times that word is generated at different positions in lists so

that words have high lexical availability if they are generated by many people

early in their lists and low availability if they are generated by few people late

in their lists. The six properties of the words used as predictors were jointly

capable of accounting for 56% of the variance in lexical availability. This is

an impressively high proportion given that there are undoubtedly factors

affecting the selection of words and the order in which they are produced

that we have not investigated here (for example, the use of strategies like

grouping subcategories of animals together, e.g., ‘‘pets, farm animals, wild

animals’’, or parts of the body that are physically adjacent).


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Four of the word properties had independent influences on lexical

availability, taking into account the contributions of all the other predictors.The property that correlated most highly with lexical availability and that

emerged as the strongest predictor in the regression analysis was typicality.

Words that are judged to be highly typical exemplars of a chosen category

are generated by more people earlier in their lists than words that belong in

the category but are judged to be less typical exemplars. Randall, Moss,

Rodd, Greer, and Tyler (2004) have argued that there is more mutual support

between typical concepts that share more features with other, related

concepts than atypical items do. This could help explain why typical itemsare more easily accessed in object naming (Holmes & Ellis, in press) and in

the category instance generation task that underlies lexical availability

studies, as well as being faster to verify as members of a category in a

recognition task (McFarland et al., 1978; Rosch & Mervis, 1975; Smith et al.,

1974; Southgate & Meints, 2000).

The other independent predictors of lexical availability were concept

familiarity (words denoting very familiar objects or concepts are more

available than words denoting less familiar objects or concepts), age ofacquisition (objects or concepts whose names are learned early in life are

more available than those whose names are learned later in life), and word

frequency (objects or concepts whose names are encountered and used with

higher frequency are more available than those whose names are encoun-

tered and used with lower frequency). The results imply that when a

participant is given the name of a category, the search for exemplars to

produce starts with the items that are closest to the core of the category; that

is, with the most typical items. Among those, the most available are thoseitems that are most familiar and earliest acquired, with a small additional

bias towards those with higher frequency names. We would suggest that

familiarity effects, like typicality effects, arise within the semantic system

itself, with the strength of the semantic representations of concepts varying

according to how often they are activated (by either verbal or nonverbal

perceptual experience). This would explain the effect of familiarity on object

naming (Cuetos et al., 1999; Ellis & Morrison, 1998) and on category

instance verification (Larochelle & Pineau, 1994; Malt & Smith, 1982) aswell as explaining the better preservation of familiar meanings in neurop-

sychological patients with known semantic impairments (Hirsh & Funnell,

1995; Lambon Ralph et al., 1998). The effect of age of acquisition on lexical

availability could also arise within the semantic system, with early acquired

meanings being more available that later acquired meanings (cf. Brysbaert et

al., 2000; Lyons et al., 1978; van Loon-Vervoorn, 1989; van Loon-Vervoorn

et al., 1988), but it could equally arise at the lexical level, affecting the ease of

accessing spoken word forms (Brown & Watson, 1987; Gilhooly & Watson,1981; Levelt et al., 1999) or in the ease of mapping between the semantic and


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phonological representations of words (Ellis & Lambon Ralph, 2000; Izura

& Ellis, 2002, 2004). The present data do not arbitrate between thesealternatives. They do, however, add another layer of empirical support to the

claim that age of acquisition is an important determinant of the ease with

which words can be accessed and produced.

The independent contribution of word frequency to predicting lexical

availability was only marginally significant. We note that in the area of

object naming, genuine effects of word frequency have been hard to

demonstrate when factors like familiarity and age of acquisition have been

controlled, with some studies finding effects (e.g., Barry et al., 1997; Ellis &Morrison, 1998) and others not (e.g., Barry et al., 2001; Bonin et al., 2002).

Part of the problem here may be the fact alluded to in the introduction that

even when researchers have gone to the effort of obtaining spoken as well as

written language samples (e.g., Baayen et al., 1993), the ordinary language of

the home has not been sampled adequately. Hence the frequencies recorded

for domestic vocabulary may considerably underestimate the actual,

experienced frequencies of those words. This is particularly true of the

present study where the only word frequency count available for Spanish(Alameda & Cuetos, 1995) is based entirely on written language samples.

The poor relationship between written frequencies and ordinary language

was the original motivation to develop a measure such as lexical availability

(Gougenheim et al., 1964; Michea, 1953). Nevertheless, if there is an effect of

word frequency on lexical availability over and above the effect of concept

familiarity it may reflect a role of frequency in influencing ease of access to

spoken word forms for output in the category generation task (cf. Levelt et

al., 1999).Imageability did not emerge as a significant predictor of lexical

availability when the other factors were taken into account. It may simply

be that abstract concepts and words are no harder to activate than more

concrete concepts of comparable typicality, familiarity, word frequency, and

age of acquisition. We note, though, that Schwanenflugel and colleagues

have shown that any processing differences between concrete and abstract

words when encountered in isolation can be removed if the abstract or low

imageability words are provided with a supportive context such as a sentencestem (Schwanenflugel, Harnishfeger, & Stowe, 1988; Schwanenflugel &

Shoben, 1983; Schwanenflugel & Stowe, 1989). In the category instance

generation task the category label and the preceding items in the written list

provide a context for the retrieval of the next word, which may eliminate any

intrinsic differences in availability between concrete and abstract words.

Word length also failed to emerge as a significant predictor of lexical

availability. As noted in the introduction, studies of word naming have

produced inconsistent findings as regards the possible effect of length onnaming speed, although Cuetos et al. (1999) reported an effect for Spanish


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naming. But length may affect articulation speed or some other relatively

peripheral process. It may not be a factor influencing the probability ofaccessing a word in a situation such as the category instance generation task

on which calculations of lexical availability are based.

The results of this study carry some practical lessons for the use of lexical

availability as a means of selecting words for inclusion in dictionaries or

teaching materials. The first point we would note is that lexical availability is

not a fixed property of a word but rather an interaction between a word and

a category label*a measure of how available a word is as an exemplar of a

particular category. Looking at Hernandez-Munoz’s (2002) full data set, wesee that coche (car) appears in three categories but that it has a high

availability in the category ‘‘transport’’, a somewhat lower availability in the

category ‘‘the city’’, and a low availability in the category ‘‘countryside’’.

Likewise perro (dog) has high availability as an animal but a much lower

availability in the categories ‘‘city’’ and ‘‘countryside’’. The apparent

availability or unavailability of a word depends crucially on the categories

chosen, making the selection of appropriate categories vital.

Secondly, the present experiment might also be a source of valuableinformation for those researchers interested in the study of semantic

memory. With the intention of facilitating research in this area, Battig and

Montague (1969) published a set of norms for words in 56 categories. The

norms included information about three different measures of frequency and

the rank or order in which words were produced as members of a category.

Given the extended used of Battig and Montague’s norms (cited in excess of

1600 times since 1969), van Overschelde, Rawson, and Dunlosky (2004)

produced an updated version of Battig and Montague’s norms, adding 14new categories, data from participants with different geographical back-

grounds, and mean reaction times for the first response in a written category

generation task. In Spanish, Soto, Sebastian, Garcıa, and del Amo (1982)

created a set of norms for nine categories. These norms, derived from the

results of a category generation task, consist of the frequency of occurrence

of each word in the first seven locations of the list and rank position. It is

probable that frequency of occurrence is a factor of recognised importance in

the organisation of categories as are typicality and concept familiarity. Thepresent study shows that in addition to frequency, typicality, and familiarity,

AoA is another factor to consider in categorisation studies. It also reveals a

new measure when computing the resulting data from category generation

tasks. Lexical availability is a reliable and useful measure for this purpose

since it encapsulates not only the frequency of word occurrence but also the

order of word production. It is also of importance to mention here that the

major advantage of the category instance generation task, as opposed to the

word association task to collect data, is that it focuses on the properties ofwords generated rather than the properties of the stimulus words. Brysbaert


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et al. (2000) investigated effects of AoA on the speed of making free word

associations. However, like other studies of word association, they only

looked at the characteristics of the stimulus words used to elicit the

associations, failing to appreciate that a complete study of word association

should look at the properties of both stimulus and response words.

Thirdly, whatever the categories, items having high availability will be

those that are most typical of the categories, whose meanings are most

commonly encountered in everyday life and learned earliest in life, and

which are communicated using words that are of higher frequency. The effect

of familiarity that we have identified is probably desirable. If lexical

availability is to be used to constrain the selection of words for dictionaries

and other purposes, then a criterion that is influenced by the familiarity of

concepts should be desirable as it would introduce words referring to

concepts that are frequent for the speaking community. For most purposes, a

bias towards early learned concepts with higher frequency names should also

be a advantageous, however, although we note that materials for use with

adults learning a language late in life often introduce words and concepts to

do with travel, finding accommodation, dealing with money, etc. early in the

training programme, although native speakers tend to acquire them

relatively late (Izura & Ellis, 2002). Language teaching materials aimed at

adults will either need to derive lexical availabilities from adults in response

to typically adult categories such as travel and money, or will need to admit

words for specific purposes whose availability, as determined from the

standard category list, is low.

The general bias towards typical rather than atypical concepts could be

more of a problem for applications of lexical availability. Some familiar

items with high frequency names have relatively low typicalities, at least in

relation to the standard categories. For example, reloj (wristwatch) has low

typicality as an item of clothing and a correspondingly low availability

but is a very familiar object and a word that one would want to see

included in a dictionary or teaching materials. Ventana (window) is also a

low typicality item of furniture with a correspondingly low availability but

a useful word to know. Finally, most of the words analysed in the present

study are nouns because the category labels encourage noun production. If

lexical availability is to be used to select useful words for inclusion in

dictionaries, then alternative categories that elicit words from other

grammatical classes (e.g., words that emphasise actions) will need to be

added.

Original manuscript received September 2004

Revised manuscript received May 2005

First published online 9 March 2006


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APPENDIX

First 10 words calculated to have the highest lexical availability (LA) scores and theirvalues in typicality (Typ), concept familiarity (CFam), age of acquisition (AoA),imageability (Imag), written frequency (WFreq), and number of syllables (Len)

Spanish word (English translation) Typ CFam AoA Imag LA WFreq Len

Animals

Perro (dog) 6.92 6.04 3.88 7 82.771 112 2

Gato (cat) 6.72 5.48 3 6.84 76.056 70 2

Leon (lion) 6.8 3.12 5.28 6.92 37.723 27 2

Caballo (horse) 6.48 4.2 3.64 7 34.723 93 3

Conejo (rabbit) 6.12 4.2 3.54 6.84 30.302 9 3

Vaca (cow) 6.72 4.32 4.52 6.96 27.32 14 2

Gallina (hen) 5.88 4.88 3.36 6.88 25.464 19 3

Tigre (tiger) 6.52 2.44 6.04 6.88 25.222 7 2

Burro (donkey) 5.56 4.28 3.72 6.92 23.995 17 2

Elefante (elephant) 6.32 3.36 4.36 6.92 21.565 9 4

Body parts

Brazo (arm) 7 6.32 2.98 6.92 71.694 96 2

Cabeza (head) 7 6.28 2.98 6.8 71.287 418 3

Mano (hand) 6.92 6.56 3.96 6.96 63 550 2

Pie (foot) 6.6 5.96 4.08 7 61.8 193 1

Ojo (eye) 6.64 6.24 4.08 6.76 60.1 96 2

Pierna (leg) 6.68 5.92 4.64 6.6 58.1 39 2

Dedo (finger) 6.36 6.04 3.04 6.92 53.948 66 2

Nariz (nose) 6.48 5.48 4.16 6.96 50.4 71 2

Oreja (ear) 4 3.48 8.6 5.56 110 3 2

Boca (mouth) 2.8 2.44 8.6 5 93.5 2 4

Furniture

Silla (chair) 6.8 6.72 4.64 7 78.805 58 2

Mesa (table) 6.92 6.76 4.2 7 78.615 228 2

Cama (bed) 6.92 6.76 3.08 6.84 67.877 199 2

Armario (wardrobe) 7 6.36 4 6.72 63.039 38 3

Sillon (armchair) 6.76 5.84 5 7 61.626 31 2

Sofa (sofa) 6.8 6.36 6.24 7 56.795 4 2

Estanterıa (shelves) 6.28 6.24 5.28 6.88 44.794 5 5

Mesita (bedside table) 6.36 5.48 5.04 6.84 42.025 2 3

Escritorio (desk) 5.88 5.8 7.46 6.76 28.341 14 4

Lampara (lamp) 3.8 5.64 3.96 6.76 24.219 22 3

Clothes

Jersey (pullover) 6.56 5.52 5.78 6.84 70.819 10 2

Pantalon (trousers) 6.72 6.68 4.44 6.76 70.674 35 3

Camiseta (T-shirt) 6.64 6.16 3.64 7 64.236 12 4

Camisa (shirt) 6.48 4.8 4.28 6.84 62.484 61 3

Calcetines (socks) 4.92 5.8 3.44 6.92 58.667 12 3

Calzoncillos (pants) 4.64 4.04 3.84 6.88 46.91 5 4

Bragas (pants) 4.44 5.28 3.64 6.88 45.394 9 2


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Appendix (Continued )

Spanish word (English translation) Typ CFam AoA Imag LA WFreq Len

Zapato (shoes) 3.76 6 4.76 6.8 39.132 69 3

Chaqueta (jacket) 6.16 4.92 4.64 6.76 37.792 33 3

Sudadera (sweater) 6.4 5.04 8.83 6.92 37.104 0 4

Intelligence

Cerebro (brain) 6.12 5.4 7.04 5.48 23.892 87 3

Listo (smart) 6.2 5.32 5.2 3.84 22.067 16 2

Sabidurıa (wisdom) 5.76 3.96 8.56 2.92 17.492 26 5

Pensamieto (thought) 5.72 5.56 8.32 3.28 14.611 134 4

Saber (to know) 5.52 5.64 6.36 3 14.086 287 2

Memoria (memory) 4.96 5.32 6.48 3.2 13.008 203 3

Estudiar (to study) 5.4 6.52 5.56 4.68 12.902 45 3

Pensar (to think) 6.24 5.76 7.44 3.08 11.034 218 2

Mente (mind) 6.24 4.68 8.2 2.68 10.509 87 2

Libro (book) 3.92 6.76 4.68 6.96 10.342 199 2


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Cognitive aspects of lexical availability (2006)

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