Brain and Language 89 (2004) 108–114

www.elsevier.com/locate/b&l

Category size effects in semantic and letter fluencyin Alzheimer�s patientsq

Michael Diaz,a,* Kevin Sailor,a Doris Cheung,a and Gail Kuslanskyb

a Department of Psychology, Lehman College, CUNY, USAb Department of Neurology, Albert Einstein College of Medicine, USA

Accepted 29 July 2003

Abstract

Many studies have found that patients with Alzheimer�s disease (AD) perform significantly worse than normal controls on verbal

fluency tasks. Moreover, some studies have found that AD patients� deficits compared to controls are more severe for semantic

fluency (e.g., vegetables) than for letter fluency (e.g. words that begin with F). These studies, however, have not taken category size

into account. A comparison of AD patients and age-matched controls on three semantic and three letter categories revealed that

both the size and type of a category significantly predicted AD patients� deficits on verbal fluency tasks. These results suggest that the

verbal fluency of AD patients will be most attenuated on large semantic categories.

� 2003 Elsevier Inc. All rights reserved.

1. Introduction

A widely held view in the literature on the cognitive

deficits that are associated with Alzheimer�s disease(AD) is that performance in tasks that are dependent on

semantic memory is frequently more impaired than

performance in tasks that do not rely as heavily on se-

mantic memory (Nebes, 1989). One example of this view

is the claim that category fluency is relatively more im-

paired with the development of AD than is letter flu-

ency. In category fluency tasks, participants are given a

common taxonomic category (e.g., animal, fruit, orvegetable) and asked to generate as many members of

the category as possible in a limited period of time. This

task is believed to be a relatively direct measure of the

structure and processing of semantic categories because

performance is directly dependent on the accessibility of

category members. In letter fluency tasks, participants

qThis research was supported in part by a NIA Grant, AGO3940,

to Einstein Medical College, and a NIH Grant GM08225 to Lehman

College.* Corresponding author. Present address: Psychology Department,

University of Illinois, 603 East Daniel Street, Champaign, IL 61820,

USA.

E-mail addresses: mikediaz@uiuc.edu (M. Diaz), ksailor@

lehman.cuny.edu (K. Sailor).

0093-934X/$ - see front matter � 2003 Elsevier Inc. All rights reserved.

doi:10.1016/S0093-934X(03)00307-9

are asked to generate as many words as possible that

begin with a designated letter (e.g., F, A, or S). This task

is believed to emphasize the phonemic characteristics of

words rather than the meaning of words. This conclu-sion is partly supported by the observation that the

order of words in the protocols frequently reflects the

phonemic overlap between consecutive words (Troyer,

Moscovitch, & Winocur, 1997).

A rather large number of studies have compared

category and letter fluency in AD samples and many of

these studies have found that the difference between AD

patients and normal old controls is greater for categoryfluency scores than letter fluency scores (Barr & Brandt,

1996; Crossley, D�Arcy, & Rawson, 1997; Monsch et al.,

1994). Unfortunately, this finding may not be as general

as many researchers believe it to be. First, most of these

studies have used highly overlapping materials. In an

extensive literature search, we identified a set of 22

studies in which semantic and letter fluency were com-

pared for AD patients and normal controls. To assessletter fluency, these studies relied heavily on three letter

categories (F, A, and S) that are part of the Controlled

Oral Word Association Test (Benton & Hamsher, 1976).

Seventeen of these studies used only these three letter

categories. In contrast, just 3 of the 22 studies used letter

categories that did not include one of these letters.

1 According to the model described by Eq. (1), the approach to

asymptote, k, is equivalent to r=S where r is the sampling rate and S is

the size of the search set (McGill, 1962). If one assumes that

asymptotic recall is equivalent to the size of the search set (e.g., the

number of items in a category) then substituting r=N for lambda in Eq.

(1) and taking the derivative with respect to r gives t � e� rt=N . This

means that the effect of changes in r should increase with category size.

M. Diaz et al. / Brain and Language 89 (2004) 108–114 109

For semantic fluency, most of the studies appear to relyon the categories animals, fruits, and vegetables. Twelve

relied entirely on one or more of these categories. Nine

studies relied in part on one or more of these categories.

In contrast, a single study used semantic categories that

did not include one or more of these categories. This

problem has been further compounded by the fact that

data are usually aggregated across each type of category

(e.g., letter or semantic) prior to statistical analysis. Forexample, 17 of the studies reported a single composite

score for the letter categories F, A, and S and just one

study reported multiple measures based on single cate-

gories for both letter and semantic fluency. The reason

that this narrow choice of material and restricted anal-

yses are problematic is that category size is known to

strongly affect fluency (Wixted & Rohrer, 1994) and

these practices have prevented a systematic analysis ofthe influence of category size on fluency.

Letter and category fluency are actually two instances

of the more general free recall task. In free recall tasks,

participants are required to recall from memory the

members of some specified set of items. These items may

be retrieved from semantic memory as is the case in

category fluency or they may reflect retrieval from epi-

sodic memory as is the case when participants retrievethe members of a recently studied list of items. There is a

long history of examining latency in free recall tasks and

a number of general findings have emerged (see Wixted

& Rohrer, 1994 for a comprehensive review). First, the

number of items retrieved in any given period of time is

positively correlated with the size of the set of items that

is being searched. Second, cumulative output as a

function of time is well described by the followingexponential function:

F ðtÞ ¼ Nðl� e�ktÞ; ð1Þwhere F ðtÞ is the cumulative number of items retrieved

by time t, N is the asymptotic recall, and k is the rate of

approach to the asymptote.

This relationship between latency and cumulative

recall is frequently interpreted as indicating that freerecall is accomplished by sampling with replacement

from a finite set of items. According to the preceding

equation, fluency is a function of both the size of the

search set and the rate at which memory is sampled.

This analysis, therefore, suggests that changes in per-

formance that occur with AD can be interpreted either

in terms of changes in the size of the search set or

changes in the rate of search (Rohrer, Wixted, Salmon,& Butters, 1995). If reduced output is caused by a re-

duction in search set, many models of semantic memory

suggest that this change in set size should be a function

of category size. For example, network models assume

that exemplars are organized by connections between

these exemplars (e.g., cow, dog, and trout) and a su-

perordinate term (e.g., animal) (cf., Collins & Loftus,

1975). Thus, the size of a category is determined by thenumber of connections between a superordinate term

and a set of potential exemplars. If items are lost from a

category because AD reduces the strength of these

connections to the point that the connection cannot be

established then it seems reasonable to assume that the

size of this loss ought to be directly proportional to the

number of connections in a category. Thus, changes in

search set size should be proportional to category size.An alternative to the preceding structure-loss hy-

pothesis is the view that a slowing of retrieval processes

reduces verbal fluency. This hypothesis is an outgrowth

of the hypothesis that many age related deficits stem

from a general slowing of all cognitive operations (Ce-

rella, 1985; Salthouse, 1985, 1996). The retrieval-slowing

hypothesis claims that many of the semantic memory

deficits that are associated with AD occur because accessto semantic knowledge is limited by the speed of re-

trieval processes (Nebes & Brady, 1992). Thus, AD pa-

tients produce fewer items in fluency tasks because

slowing of retrieval increases the time required to pro-

duce each item.

According to the preceding equation relating cumu-

lative recall to latency, the proportion of asymptotic

recall reached at time t is a function of k. If k slows withAD then the proportion of asymptotic recall would be

smaller for any given time than it would be for normal

controls. This difference in the proportion of asymptotic

recall should have a larger effect for large categories

because it is weighted by a larger asymptotic value.

Unfortunately, this analysis is complicated by the fact

that the approach to asymptote, k, decreases with the

size of the search set (Wixted & Rohrer, 1994). Withinthe sequential sampling framework this decrease is at-

tributable to the fact that k is the search rate divided by

the search set size. If it is assumed that the search set is

equivalent to asymptotic recall then changes in search

rate should have a greater influence as the search set size

increases.1 Thus, if AD patients produce fewer items

because they search memory more slowly, then

this change will produce greater difference in largercategories.

Although previous researchers have noted that cate-

gory size may affect the difficulty of semantic categories

(Azuma et al., 1997), the preceding analysis of latency in

free recall tasks indicates that category size should have

a pervasive influence on both letter and semantic fluency

tasks. More importantly, this analysis implies that the

110 M. Diaz et al. / Brain and Language 89 (2004) 108–114

effect of category size is not dependent on the particularcharacteristics or internal structure of semantic catego-

ries but that it is an outcome of the retrieval process that

should produce systematic interactions between cate-

gory size and dementia.

The preceding analysis of free recall tasks suggests

that the effects of category size should be larger for large

categories and that at least some of the difference be-

tween semantic categories and letter categories could beattributable to differences in category size. In the fol-

lowing experiment, we compared fluency data for par-

ticipants on three commonly used semantic categories

(e.g., animals, fruits, and vegetables) to data for three

commonly used letter categories (e.g., F, A, and S).

Although the range of category sizes in these materials is

somewhat limited, participants typically recall more

animals, than vegetables, and more vegetables thanfruits (Azuma et al., 1997; Bayles et al., 1989). Similarly,

participants typically recall more words that begin with

S than with F and more words that begin with F than

with A (Azuma et al., 1997; Kozora & Cullum, 1995).

Instead of aggregating these data into separate scores

for semantic and letter fluency, we used a hierarchical

linear model (Bryk & Raudenbush, 1992) to analyze the

separate fluency scores for each of the six categories. Inthis analysis, fluency scores are nested within an indi-

vidual and individuals within groups. This analysis

makes it possible to determine the relative contributions

of both category type and category size to differences in

the verbal fluency performance of normal old and AD

patients.

Table 1

Participants� demographic and diagnostic information

Groups Age Blessed Education

Normal elderly 79.13 11.16 11.33

AD patients 78.81 2.23 10.74

Table 2

Mean fluency for the three letter categories and three semantic

categories

Category Groups

Normal elderly AD patients

Letter

F 10.23 (3.69) 8.74 (4.82)

A 8.42 (3.74) 6.32 (3.83)

S 11.98 (3.45) 7.95 (3.99)

Semantic

Animal 13.52 (3.40) 7.45 (2.96)

Fruit 12.08 (2.83) 6.34 (2.54)

Vegetable 10.56 (2.53) 5.50 (2.68)

2. Method

2.1. Participants

Participants were evaluated in the Einstein Aging

Study (EAS) at Albert Einstein College of Medicine. All

participants were 55 years of age or older. Participants

were given a comprehensive battery of neuropsycho-logical, neurologic, psychiatric, and medical exams that

included the Mini-Mental Status Examination (Folstein,

Folstein, & McHugh, 1975). Participants who demon-

strated sufficient cognitive and functional decline ac-

cording to well-established clinical and medical criteria

for this condition (NINCDS-ADRDA; McKhann et al.,

1984 and DSM IIIR; American Psychiatric Association,

1987) were classified as having ‘‘probable’’ Alzheimer�sdisease. Participants were also scored on the Clinical

Dementia Rating [CDR] scale (Morris, 1993).

Thirty-two female and 16 male participants with a

CDR¼ 0 who had fewer than 9 errors on the Blessed

Test of Information, Memory, and Concentration

(Blessed, Tomlinson, & Roth, 1968) were assigned to the

normal controls. Twenty-three female and 15 male

participants with a diagnosis of probable AD and aCDR¼ 1.0 were assigned to the AD group. The mean

age, educational level, and score on the Blessed Test of

Information, Memory, and Concentration for partici-

pants from the two samples are presented in Table 1.

2.2. Materials

Six different categories were used in this experiment.Three of the categories were semantic categories and

three were letter categories. The semantic categories

were animals, fruits, and vegetables. The letter catego-

ries were words that begin with letters F, A, and S

2.3. Procedure

Participants were given verbal fluency tasks as part ofseveral continuous batteries of test that are administered

at the EAS. Only the initial assessment for each partic-

ipant on each category was used to avoid practice ef-

fects. At the beginning of each assessment, participants

were told by the experimenters that they had to generate

as many exemplars of the given category as they could in

a 1-min interval. They were instructed that proper nouns

and words with the same stem but different endings (e.g.,send, sending, and sender) would not be counted. All

responses generated by the participant were written

down in order by the experimenter.

3. Results

The mean fluency for each category is displayedin Table 2 for each of the two groups. These data

were submitted to three separate analyses. In all three

Table 3

Size estimates based on observed fluency and model estimates

Category Category size estimates

Observed fuencya Model estimates

Letter

F 14.52 10.39

A 13.89 11.73

S 16.88 15.96

Semantic

Animal 17.19 15.62

Fruit 14.06 12.19

Vegetable 14.40 11.77

aNote. Observed fluency was from two published samples— Azuma

et al. (1997), and Bayles et al. (1989).

Table 4

Estimated level 2 coefficients (standard errors) for size estimates based

on observed fluency and model fits

Analysis

Observed fluency Model estimates

Category type ðb1jÞIntercept (c10) 1.83 (0.36)�� 1.69 (0.36)��

Education (c11) )0.34 (0.11)� )0.35 (0.11)�

AD (c12) )3.23 (0.73)�� )3.11 (0.72)��

Category size ðb2jÞIntercept (c20) 0.86 (0.095)�� 0.51 (0.066)��

AD (c21) )0.46 (0.15)� )0.37 (0.10)�

Intercept ðb0jÞIntercept (c00) 10.15 (0.42)�� 10.23 (0.42)��

Education (c01) 0.40 (0.10)� 0.41 (0.10)�

AD (c02) )2.33 (0.71)�� )2.39 (0.70)��

* p < :01.** p < :001.

M. Diaz et al. / Brain and Language 89 (2004) 108–114 111

analyses, a hierarchical linear model was used to predictfluency from two item factors (category type and cate-

gory size) and two subjects factors (education and di-

agnosis of AD). In these analyses, a level one equation

was used to predict the performance of individual par-

ticipants for each category using category size and

category type as predictors. In all of the analyses, the

category type variable was added to the model uncen-

tered. It was coded as a 0 for each of the three lettercategories and as a 1 for each of the three semantic

categories. Size was added to the model centered around

the grand mean. Thus, the level one equation was:

Yij ¼ b0j þ b1jðCategory TypeÞ þ b2jðSizeÞ þ eij; ð2Þ

where Yij is the fluency for category i for subject j, b0j is

the expected or average value for person j for letter

categories of an average size, b1j is the average difference

between letter and semantic categories for person j, and

b2j is the average effect of a unit change in category size,

and eij is the residual.

In all of these analyses, the effects of AD and edu-

cation were modeled in a set of level 2 equations byusing these variables to predict each of the coefficients in

the preceding level one equation. The equation for the

category type coefficient, blj, was

blj ¼ c10 þ c11ðEducationÞ þ c12ðADÞ þ t1; ð3Þ

where education was centered around the grand mean,

AD was coded as 0 for normal controls and 1 for AD

patients and t1 is a random variance component. Thus,

the coefficient c12 is the difference in the slope of b1j

attributable to AD when controlling for education.

Initial model fits revealed that education did not have

a reliable impact on the effects of category size and it

was dropped from the level 2 equation for the categorysize coefficient. Thus, the level two model for category

size was

b2j ¼ c20 þ c21ðADÞ þ t2; ð4Þ

AD was entered uncentered and was coded as in Eq. (3).Therefore, the coefficient c21 is the difference in the slope

of b2j attributable to AD irrespective of possible differ-

ences in levels of education.

Finally, the effects of education and AD on the av-

erage level of performance for letter categories when

category size is equal to its mean was estimated with the

following level 2 equation:

b0j ¼ c00 þ c01ðEducationÞ þ c02ðADÞ þ t0 ð5Þ

in which education was centered around the grand mean

and AD was dummy coded as in the other level 2

equations.

The first two analyses predicted fluency with twoslightly different measures of category size that are

presented in Table 3. In the first analysis, category size

was estimated from the weighted average of elderly

controls� fluency from two published samples (Azuma

et al., 1997; Bayles et al., 1989). In the second analysis,

category size estimates were obtained from a subset of

36 participants in the normal control condition of this

study. For each of these participants, cumulative recall

was available at 15, 30, 45, and 60-s intervals. Asymp-

totic recall was estimated for each of the six categories

by fitting the mean fluency at each interval for eachcategory to Eq. (1) using nonlinear regression in which

the sum of squared residuals was minimized. The first

estimates of category size have the advantage of being

based on independent data but they are indirect mea-

sures of category size because they reflect performance

in a limited time period rather than asymptotic recall. In

addition, these estimates are based on the performance

of a younger (M ¼ 69:8) and more educated (M ¼ 15years of education) sample. Although recall at any point

in time is highly related to asymptotic recall, the second

set of estimates were derived to provide a closer estimate

of category size based on asymptotic recall.

The estimated coefficients for each of these two

analyses are presented in Table 4. In support of previous

Table 5

Estimated level 2 coefficients (standard errors) for log transformation

of fluency

Category type ðb1jÞIntercept (c10Þ 0.082 (0.015)��

Education (c11Þ-0.020 (0.006)�

AD (c12Þ )0.140 (0.038)�

Category size ðb2jÞIntercept (c20Þ 0.033 (0.003)��

AD (c21Þ )0.005 (0.007)���

Intercept ðb0jÞIntercept ðc00Þ 1.019 (0.016)��

Education ðc01Þ 0.021 (0.005)��

AD ðc02Þ )0.130 (0.035)��

* p < :01.** p < :001.*** p < :52.

112 M. Diaz et al. / Brain and Language 89 (2004) 108–114

claims that semantic fluency is relatively more impairedthan letter fluency with AD, both analyses indicated that

category type affected fluency even when simultaneously

controlling for category size. To assess the relative dif-

ficulty of letter and semantic categories for the two

groups, the coefficient for the intercept, c10, and the

coefficient for AD, c12, must both be taken into account.

As a result of the dummy coding, the intercept provides

a relatively direct measure of the relative difficulty ofsemantic and letter categories for normal controls. Thus,

the positive values in Table 4 indicate that semantic

fluency was higher than letter fluency for normal con-

trols. For AD patients, the sum of the intercept and the

coefficient for the AD variable indicates the relative

difficulty of letter and semantic fluency. In both analy-

ses, the combined value is clearly negative which indi-

cates that AD participants have more difficulty withsemantic categories than letter categories. Finally, the

coefficient for education is negative in both analyses,

which indicates that semantic fluency is reduced relative

to letter fluency with higher levels of education. Com-

pared to a level 2 model with no predictors for the in-

tercept, category type, or category size, the addition of

AD and education as predictors accounted for 31.4% of

the variance in the slope of category type.According to previous arguments, the size of a cate-

gory influences a participant�s recall for that category. As

expected, fluency increased with category size for normal

controls as indicated by the positive intercept, c20, in bothanalyses. AD patients also displayed higher fluency for

larger categories as evidenced by the fact that the sum of

the intercept, c20, and the AD coefficient, c21, is positivefor both analyses. Of particular interest, however, is thatin both analyses the estimated category size slope, b2j, fornormal controls was more than double the estimated

slope for AD patients. This difference in slope means that

the absolute size of the discrepancy between normal

controls� and AD patients� fluency is dependant on cate-

gory size. Compared to a level 2 model with no predictors

for the intercept, category type, or category size, the ad-

dition of AD as a predictor accounted for 64.5% of thevariance in the slope of category size.

The intercept, b0j, was very similar for both analyses.

Not surprisingly, AD patients were estimated as having

a lower average letter fluency than normal controls as

indicated by the AD coefficient c02. Consistent with

previous findings, higher levels of education were asso-

ciated with increased letter fluency (Auriacombe et al.,

2001; Crossley et al., 1997) but, as noted earlier, thiseffect of education was smaller for semantic categories.

Compared to a level 2 model with no predictors for the

intercept, category type, or category size, the addition of

AD and education as predictors accounted for 24.9% of

the variance in the intercept.

According to the sequential sampling model that was

described in the introduction, absolute size of AD pa-

tients� impairments should be a function of the category

size. To address the question of the proportionality of

AD deficits, the third analysis regressed the same pre-

dictors on the log transforms of the fluencies for each

category. If AD fluency is a constant proportion of

normal control fluency for any given category, then the

difference in log transformed fluencies of AD patients

and normal controls should be the same value for eachof the categories. With the exception of this transfor-

mation to the dependent measure, the treatment of the

predictors was identical to the first analysis in which we

again utilized the norms for normal controls� fluency on

these categories (Azuma et al., 1997; Bayles et al., 1989)

to estimate category size. The results of this analysis are

presented in Table 5. In contrast to the earlier analyses,

the AD variable did not reliably influence the slope ofcategory size, p ¼ :52. This equivalence in slopes for the

transformed data for category size is consistent with an

account in which category fluency of AD participants is

proportional to that of normal controls across catego-

ries of different sizes. With the exception of this change

in the influence of AD on category size, all of the other

level two effects were quite similar between the analysis

of raw fluency and log transformed fluency. Thus, edu-cation and AD influenced both the slope of the category

type effect and the value of the intercept in the level two

model.

4. General discussion

Consistent with the view that AD patients are moreimpaired in semantic fluency tasks than letter fluency

tasks, AD patients were found to have a negative slope

for category type which indicates that they recalled more

items from letter categories than semantic categories.

Normals, on the other hand, had a positive slope for

category type, which indicates that they recalled more

items from semantic categories than letter categories.

M. Diaz et al. / Brain and Language 89 (2004) 108–114 113

This difference in the sign of the slope was true evenwhen log transformed fluency was the dependent mea-

sure. Thus, AD patients recalled a smaller proportion of

exemplars from semantic categories than from letter

categories compared to controls. This difference between

the category types remained even after category size was

controlled statistically.

Although this finding supports the view in the liter-

ature that AD patients are more impaired on semanticcategories than letter categories there are a number of

reasons to be cautious about concluding that this finding

is true for all semantic and letter categories. First, cat-

egory size was also found to be an important factor in

distinguishing between the fluency for AD patients and

normal controls in this study. Specifically, the category

size slope for normal controls was more than twice the

slope for the AD patients. AD patients will thereforehave a greater deficit for larger categories than they

would for smaller categories and differences in individ-

ual semantic and letter categories may reflect differences

in the sizes of the categories as well as differences in how

dependent they are on semantic organization. Second, as

documented in the introduction, the literature on se-

mantic and letter fluency has used a very limited sample

of letter and semantic categories. This restricted range ofcategories may limit other important variables (e.g.,

category size) that could potentially provide useful in-

sight into AD patients� fluency performance. Although

the current analyses controlled for category size, the

broader distinction between semantic and letter cate-

gories needs to be better studied by using a greater va-

riety of these categories (Clark, 1973).

Finally, there are many other distinctions in additionto category size that can be made among categories.

Categories may vary substantially in terms of their in-

ternal structure (Azuma et al., 1997). For example, some

categories such as musical instruments may have more

easily identifiable and nameable groupings (e.g., brass,

wind, string, and percussion instruments) than other

categories such as pets. As Azuma et al. (1997) noted,

the presence of sub-categories or clusters within a cate-gory may affect retrieval of items from a category.

Similarly, it is possible to distinguish among categories

in terms of how easily their members can be retrieved.

For example, many common taxonomic categories such

as vehicle or fruits are well represented in memory and

their members are highly associated with the category

label. In contrast, goal derived categories (Barsalou,

1985) which are ad hoc categories whose members servesome common purpose or need (e.g., things to take on

vacation) are not well represented in memory even

though they depend on semantic knowledge. Even

within the distinction between semantic and phonemic

characteristics, there are a number of variants or cate-

gory types that have not been explored. For example,

phonemic categories could be defined in terms of num-

ber of syllables (e.g., long vs. short words) or commonvowel sounds (e.g., words that rhyme with shout). These

examples are intended to illustrate the difficulty of

making broad generalizations about the nature of re-

trieval from semantically and phonemically defined

categories given the rather limited number of categories

that have been tested. Thus, it is difficult to know how

important these characteristics might be in comparison

to the global distinction between semantic and phone-mic categories.

Previous research on differences in semantic and

letter fluency has primarily focused on differences in

patient populations such as patients with AD (Bayles

et al., 1989), Parkinson�s disease (Azuma et al., 1997)

or Huntington�s disease (Butters, Granholm, Salmon,

Grant, & Wolfe, 1987) using a fairly limited set of

materials. Although this research has revealed inter-esting differences among these groups and normal

controls, the current research demonstrates the poten-

tial impact of variables such as category size that may

be partially confounded with the distinction between

semantic and letter fluency. Future research needs to

explore some of these distinctions to further elucidate

the connection between neurological status and cogni-

tive performance

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