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thesis entitled

THE EFFECTS OF SOMATIZATION ON MEMORY
PERFORMANCE IN OLDER ADULTS

presented by

SAW-MYO TUN

has been accepted towards fulfillment
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MA degree in PSYCHOLOGY

 

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THE EFFECTS OF SOMATIZATION ON MEMORY PERFORMANCE IN
OLDER ADULTS
By

Saw-Myo Tun

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTERS OF ARTS
Department of Psychology

2003

ABSTRACT

THE EFFECTS OF SOMATIZATION ON MEMORY PERFORMANCE IN
OLDER ADULTS

By

Saw-Myo Tun

The aim of the investigation was to explore the advisability of considering
somatization of depression as a masked depression that warrants treatment similar
to depression in studies on memory. Three demographic variables, age, education,
and gender, were included for comparative purposes. A sample of 227 community
dwelling older adults with the age range of 54 to 87 years old (M = 70.21) was
recruited. To assess depression, Beck Depression Inventory (BDI) and Geriatric
Depression Scale (GDS) were used. Memory measures were California Verbal
Learning Test (CVLT), Logical Memory, Benton Visual Retention Test (BVRT),
and Spatial Span. The results indicated that the total and affective scores on the
BDI were negatively correlated with performance on the Spatial Span (r = -. 15,
p<.05, r = -.16, p<.05, respectively). However, a high level of somatization did
not predict performance on the memory measures. Findings regarding age,

education, and gender were presented.

TABLE OF CONTENTS

LIST OF TABLES ......................................................................... iv
INTRODUCTION ............................................................................ 1
Depression and Somatization of Depression in Older Adults ................ 2
Depression and Memory Performance ........................................... 7
Somatization and Memory Performance ......................................... 9
Effects of Age, Level of Education, and Gender on Memory
Performance ........................................................................ 10
Hypotheses .......................................................................... 12
METHOD .................................................................................... 15
Participants ......................................................................... 15
Measures ............................................................................ 15
Statistical Methods ................................................................. 19
Procedure ........................................................................... 19
RESULTS ................................................................................... 21
Analysis of Level of Depression in the Sample ................................ 21
Hypotheses .......................................................................... 20
Post hoc analyses ................................................................... 30
DISCUSSION ............................................................................... 29
Hypotheses ......................................................................... 34
Summary of the findings .......................................................... 45
Limitations of the Study ........................................................... 47
Suggestions for Future Research ................................................ 47
LIST OF REFERENCES ................................................................. 51

iii

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

LIST OF TABLES

Groups Means and Standard Deviations for the BDI and the
GDS ........................................................................ 22

MAN OVA findings on the Relationship between Depressive
Scores and the Memory Measures .................................... 23

MANOVA findings on the Relationship between Somatic
Symptoms of Depression and the Memory Measures ............. 24

MANOVA findings on the Relationship between Demographic
Variables and the Memory Measures ................................ 26

Regression Analyses of the Depression, Somatic Depression, and
Demographic Variables on the Memory Measures ................. 29

T-test Analyses on Depressed and Non—Depressed Groups ....... 3]

Correlations between Affective Symptoms of Depression and the
Memory Measures ...................................................... 32

T-tests Analyses on the Affectively Depressed and Somatically
Depressed Groups ...................................................... 33

iv

Introduction

Although numerous studies have confirmed the clinical tales of age-
related memory decline in older adults, age alone has been insufficient in
explaining wide variations in the level of decline typically exhibited in elderly
populations. For example, a few studies have pointed to a marked decrease in
episodic memory abilities with increasing age (N aveh-Benjamin, 2000; Smith,
1996), whereas age appears to have been of little or no consequence for a sample
of very old adults (Hassing, Wahlin, & Backman, 1998). To account for such
differences in the extent of decline, a number of factors, such as level of education
and lifestyle variables, have been studied. Of these factors, depression in its
various forms, has received much attention as a possible significant contributor to
disparate findings in memory performance (e.g. La Rue, Sawn, & Carmelli, 1995;
McBride & Abeles, 2000).

Notably, however, one manifestation of depression, called somatization,
has been given little research attention to determine its potential impact on
memory performance. Somatization has been suggested to be a form of
depression in which individuals express their distress through somatic complaints
rather than through psychological complaints. The lack of research in the area is
pertinent given the following two suggestions by previous reports: 1) older adults
are more likely to somatize their depression, decreasing the chances of it being
correctly diagnosed and treated (Kirmayer, 1993; Muller-Spahn & Hock, 1994;

Small, 1991) and 2) unrecognized depression may be a causal factor in

deterioration of memory performance, as in the case with pseudodementia (Kim
& Rovner, 1994; Small, 1991).

The purpose of the present study is to explore the validity of considering
somatization of depression as a form of masked depression that deserves
treatment similar to depression in the studies of memory performance. To do so, a
review of the literature will be presented assessing the relevance of recognizing
the effects of depression as well as masked depression on memory performance in
older adults. In evaluating the appropriateness of equating somatization with
depression in its effects on memory performance, three unrelated variables are
employed to serve as a frame of reference for the degree of variance explained.
Thus, the effects of the three variables, age, level of education and gender, on

memory performance will also be reviewed.
Depression and somatization of depression in older adults

Depression is a debilitating disorder with pervasive influences across
several domains of an individual’s life, including cognitive functioning. In the
general population, the lifetime prevalence of depression is estimated to be
between 5.8%-9.7% (Katon, 1987). However, in the elderly population, the
clinical picture of depression becomes more complex. In an epidemiological study
by Blazer and colleagues (1987) on depression in community residents over the

age of 60, 19% endorsed mild depression, while 4 % had symptomatic depression,

and only 0.8% had major depression. Of interest are the relatively high rates of
mild depression as well as symptomatic depression.

Despite the relative high rates of mild and symptomatic depression found
in the study, recent research warns that prevalence rates are likely to be an
underestimation of the actual rates due to a high percentage of undetected
psychological disorders in the geriatric population. In one such study, it is
estimated that only about 24% to 67% of the psychiatric disorders are recognized
in the primary care setting (Kirmayer et 31., 1993). In particular, the likelihood of
geriatric depression being undiagnosed is estimated to be as high as 40% (Small,
1991)

Research has shown that this under-recognition of depression in elderly
populations can be attributed to a number of factors. A study by Rapp and Davis
(1989) showed that frequently, physicians were unfamiliar with diagnostic criteria
for depression, and would rarely screen for depression. Moreover, in primary care
settings, older adults may present with overwhelming physical and cognitive
complaints, which often shifts the focus away from the assessment of
psychological distress (Small, 1991).

In addition to the above-mentioned factors, the high rate of somatiztion
contributes significantly to under-detection of the disorder in elderly populations.
Studies have supported the notion that geriatric populations present depressive
symptoms in a way as to render identification using the normal criteria for
depression diagnosis inappropriate (Muller-Spahn & Hock, 1994). In particular,

older adults have a much higher tendency than younger adults to report somatic

symptoms of psychological distress while denying the involvement of
psychological causes (Kirmayer, 1993; Muller-Spahn & Hock, 1994; Small,
1991). Typically, somatic complaints seen in such cases without clear medical
bases are chronic pain syndromes, sleep disturbances, fatigue, palpitations,
gastrointestinal-related problems, and sexual dysfunction (Akiskal et al., 1982; De
Wester, 1996; Lesse, 1983; Razali & Hasanah, 1999). Also, the somatic pattern of
presentation is distinguishable by the fact that unlike typical depressed patients,
there is a distinct lack of dysphoria or guilt (Weiss, Nagel, & Aronson, 1986),
compounding the difficulty of diagnosis. Taken together, the propensity of the
patient to focus exclusively on the physical symptoms rather than the
psychological symptoms is termed somatization.

It should be noted, however, that not all cases of somatization could be
accounted for by depression (Lipowski, 1988). Additional factors such as
personality traits, socio-cultural influences, and psychological distress other than
depression (e. g. anxiety) may contribute to the tendency to somatize (Kellner,
1990; Lipowski, 1988). Nevertheless, there is a strong consensus in the field that
depression is liable for significant cases of somatization (Collins & Abeles, 1996;
F isch, 1987; Lesse, 1983; Lipowski, 1988). In fact, somatization is often known
as a form of masked depression or depressive equivalent (Kellner, 1990).

Progress in our understanding of somatization as a form of depression in
geriatric populations, however, has not been effortless. The formulation of
somatization as a form of depression has been met with some degree of

skepticism (Kellner, 1990). Yet, despite the lingering debate over its

conceptualization, only a handful of studies have been conducted to address the
issue. In one such study, REM latencies in individuals with probable masked
depression were compared to those diagnosed with major depression (Akiskal et
al., 1982). The findings demonstrated the REM latencies of those with probable
masked depression to be more closely matched with those of individuals with
primary depression (51.1 min i 15) than those of individuals with secondary
depression (75.9 min i 24.7) or the control group (91.1 min i 19). In a separate
study by Akiskal and colleagues (1997), individuals with masked depression were
also found to respond positively to treatment with antidepressants. Moreover, a
report from Leventhal and coworkers (1996) suggested that negative mood state
was a good predictor of future somatic complaints, further suggesting the
correlation between somatic complaints and depression.

Despite the suggestive trends seen in these studies in support of masked
depression, fiirther research is critical to the development of a more complete
conceptualization of this elusive syndrome. At present, a number of etiological
models, as summarized by F isch (1987), have been proposed. In one cognitive
formulation, a masking of depression is attributed to patient’s inability to
verbalize psychological distress. A socio-cultural model, on the other hand,
implied that expression of emotions is dictated by cultural norms, and thus, an
individual’s manner of conveying distress is rooted in his or her culture and
values. Using this model, it is probable that older adults who may have instilled
the personal acculturation of the early 20‘h century, would reject psychological

symptoms of depression as an admission of weakness. Unlike the socio-cultural

model, a familial model of masked depression entailed that familial influences are
the major determinants for shaping one’s responses to emotional stimuli.
Therefore, it is believed that in some families, expression of physiological
problems is more acceptable than problems of emotional nature. So far, there is
some evidence to support a familial model of somatization. A study by Terre and
Ghiselli (1997) reported that some aspects of family life increase the likelihood of
somatic complaints in youths, which make them more susceptible to somatization
later in life.

Although the available etiological models provide some clues into the
psyche of the masked depressed, they still failed to explicitly account for a
particularly high percentage of masked depression seen in geriatric populations. It
is estimated that between 7.9% and 28.6% of older adults fit the criteria for
masked depression (Barret et al., 1988; Collins & Abeles, 1996; Lesse, 1983).
Such a high rate of masked depression is of concern given the possibility of
severe consequences resulting from a delay in treatment. It has been suggested
that unrecognized depression in geriatric populations could ultimately be
responsible for a decreased quality of life, social isolation, increased risk of
suicide, and physical illnesses (Muller-Spahn & Hock, 1994; Small, 1991). In
fact, one research finding estimated the decreased functioning due to depression
to be as debilitating as, or worse than, those of chronic illnesses such as diabetes
(Schonfeld et al., 1997; Wells et al., 1989). Furthermore, the economic impact of
masked depression can be quite substantial due to a particularly high percentage

of medical facility usage by the somaticizers (Kellner, 1990; Lipowski, 1988;

Smith et al., 1986). Thus, further investigation to obtain a better understanding of

masked depression in elderly populations is warranted.

Depression and memory performance

In the last two decades, research has begun to accumulate on the role
depression plays in memory decline. Several studies have confirmed depression
as an influential factor on memory performance, albeit with the extent of
influence varying according to the study (e.g. Collins & Abeles, 1996; La Rue,
Sawn, & Carmelli, 1995; McBride & Abeles, 2000; Rohling & Scognin, 1993;
Salzrnan & Gutfreund, 1986). One line of research in support of the proposed
relationship between memory and depression comes from findings with regards to
pseudodementia. Pseudodementia refers to a medical phenomenon in which
individuals, experiencing a significant decline in memory abilities due to
depression, are mistakenly given the diagnosis of dementia. It is estimated that
10% to 15% of dementia cases are actually cases of depression (Small, 1991).
Unlike true cases of dementia, individuals with pseudodementia return to normal
cognitive functioning after the underlying cause of depression has been resolved
(Kim & Rovner, 1994). Therefore, the reversible nature of dementia, seen after
treatment for depression, is taken as an indication of a link between memory
functioning and depression.

However, the research has not been unidirectional in findings. For

instance, a few studies have suggested that there is little to no obvious relationship

between depression and memory (Bieliauskas, 1993; Luszcz, 1992). Several
factors may be responsible for the apparent inconsistencies in results. Firstly, it
has been noted that memory deficits are more likely to be seen in severely
depressed than in mildly depressed individuals (Rohling & Scogin, 1993).
Second, research has indicated that the effects of depression on cognitive abilities
are unevenly distributed across the age ranges. Stoudemire and colleagues (1989)
reported that depressed older adults are more likely to be adversely affected in the
cognitive domains than younger adults. Therefore, the age of the group studied
will have an influence on the outcome of the study. Third, in the depressed state,
some types of memory processing are more likely to be affected than others
(Christensen et al., 1999; Gainotti & Marra, 1994; McBride & Abeles, 2000;
Rohling & Scogin, 1993). For example, as reviewed by Salzrnan and Gutfreund
(1986), some of the memory processes that are negatively impacted by depression
are recall of new information, increased error of omission, use of less effective
strategies for coding and recall, and decreased attention. However, it is unclear
whether the relationship seen between depression and memory can be taken as a
true association or as an artifact of decline in concentration commonly observed
in depression. Bearing these findings in mind, severity of depression, age, and
type of memory process studied should be considered, in research looking at

memory deficits and depression.

Somatization and memory performance

Research on memory complaints indicates that there is no necessary
correlation between memory complaints and memory performance (Collins &
Abeles, 1996; Larrabee & Levin, 1986). Somatic complaints, on the other hand,
have received too little attention in research to clearly delineate its relationship
with memory functioning. If one were to suppose that somatization is a form of
masked depression, a natural conclusion would be that memory performance of
individuals with somatic symptoms of depression would be similar to
performance of individuals with depression. However, in studying the possible
effects of somatization on memory performance, certain issues should be
addressed. Firstly, it has been suggested that in older adults, an increase in
somatic complaints may signal less severe depression (La Rue, 1992). Similarly, a
lack of dysphoric mood associated with somatic depression may suggest that the
residual effects of the disorder would be less pronounced than in the case of fully
symptomatic depression. At this juncture, it should be acknowledged that the
decline in memory performance due to somatization might be less severe than that
of fully symptomatic major depression. Nevertheless, if the hypothesis that
somatization is a form of masked depression is valid, then it would follow that the
effects of somatization on memory performance will more closely correlate with

the effects of depression than the effects of the other variables studied.

Effects of age, level of education, and gender on memory performance

The goal of the present study is to assess the extent to which depression
and somatization of depression contribute to the variance associated with memory
decline in the elderly. In analyzing these two variables, an inclusion of unrelated
variables that have been shown to influence memory may serve as a frame of
reference. Therefore, three unrelated variables that are commonly tested in
memory research are included in the study.

The first variable to be considered is age. The association of aging and
memory decline has been reported by numerous studies (e. g. Christensen et al.,
1997; Luszcz, Bryan & Kent, 1997; Titov & Knight, 1997). A meta-analysis
conducted by Verhaeghen and coworkers (1993) reported age as a significant
predictor of memory performance in the elderly population. The study estimated
the memory functioning of older adults to be in the range of 3rd to 38th percentile
of the general population.

As with the effects of depression on memory, aging appears to impact
memory processing selectively. Although the findings are somewhat inconsistent
on the exact domains affected, some studies have identified a decline in
performance on working memory tasks (Salthouse, 1991), cued-recall tasks (Park
et al., 1990), free-recall tasks (Smith, 1979), and categorization of list tasks
(Verhaeghen, Marcoen, & Goossens, 1993). To explain the decline in memory
performance, processing speed (Luszcz, Bryan, & Kent, 1997; Salthouse, 1996;

Titov & Knight, 1997), a combination of speed and working memory (Park eta1.,

10

1996), associative deficits (Naveh-Benjamin, 2000), and changes in prefrontal
cortical system (Trott et al., 1999) have all been suggested as the culprits. So far,
there is some support for each of these theories, suggesting the possibility that
multiple pathways may serve to exert the influence of aging on memory.

A second variable to consider in the study is the effect of education on
memory abilities. A number of studies have suggested that high level of education
may serve as a protective factor against the potential decline in memory
performance (Grober et al., 1998). In one meta-analysis, older adults with lower
level of education were found to show greater age differences on some tasks
(Verhaeghen, Marcoen, & Goossens, 1993). On the other hand, a higher level of
education was found to be correlated with a decreased variability in cognitive
performance (Christensen et al., 1999). Moreover, in a study involving a sample
of college faculty, higher education and continual intellectual stimulation lessened
the age associated decline in cognitive functioning (Compton, Bachman, &
Logan, 1997). It has been proposed that extraneous variables, such as type of
occupation and life style differences associated with a higher level of education,
may play a role in the apparent protective effect of education on memory (Avolio
& Waldman, 1994; Christensen et al. 1996). Regardless of how education serves
to prolong the health of memory functioning, it should be mentioned that there is
some suggestion to the effect that the predictive value of education on memory
performance lessens in adult over the age of 70 (Hassing, Wahlin, & Backman,

1998).

ll

The third variable to be included in the study is gender. Some findings
have suggested that in old age, men are at an increased risk for experiencing a
decline in some domains of cognitive functioning, such as episodic memory
(Herlitz, Nilsson, Backman, 1997). One possible explanation for the findings
comes from research on sex differences in brain aging. It has been found that
aging in men is associated with more notable decrease in brain volume, which
may partially account for the sex differences in cognitive decline (Gur et al.

2002).

Depression, somatization, age, level of education, and gender

In light of the preceding discussion, the goal of the present study is to
assess whether variance explained by somatization of depression on memory tasks
is more similar to that of depression or to that of age, education or gender. Two
memory domains of interest in the current study are verbal and visual memory.
Since previous findings on these domains have been highly discrepant, two
measures of each memory domain were used to test for a possible differential
effect between the measures.

To test for verbal memory, California Verbal Learning Test (CVLT) and
Logical Memory were used. These two particular verbal measures were chosen
because they assess a wide range of different aspects of verbal memory such as
immediate recall, delay recall and encoding strategies. Visual memory was

measured by Benton Visual Retention Test (BVRT) and Spatial Span. The

12

rationale behind the inclusion of these two visual measures is that there have been
some suggestions in the literature for the level of task demand influencing the
effects of depression on memory (Jorm, 1986; Weingartner, 1986). In the present
case, Spatial Span is believed to require a higher level of attention and
concentration than the BVRT. Hence, if the task demand view is accurate, we
may observe a difference in the degree of effect between the BVRT and the
Spatial Span.

It is hypothesized that:

A significant negative relationship will be found between depression and
memory performance. Operationally, this will be tested by assessing the
relationship between the depressive scores of those who endorse both affective
and somatic symptoms on the BDI and GDS, and their performances on four
memory measures: CVLT, Logical Memory, BVRT, and Spatial Span.

A significant negative relationship will be found between somatization of
depression and memory performance. Operationally, this will be tested by
studying the relationship between the somatic scores of those who endorse a high
level of somatic symptoms on the BDI, and their performances on the CVLT,
Logical Memory, BVRT, and Spatial Span.

A significant negative relationship will exist between age and memory
performance. Operationally, this will be tested by measuring the relationship
between age and the scores on the CVLT, Logical Memory, BVRT, and Spatial

Span.

13

4.

A significant positive relationship will exist between a higher level of
education and memory performance. Likewise, a positive correlation will exist
between female gender and memory performance. Operationally, this will be
tested by studying the relationship between level of education and gender, and
performances on the CVLT, Logical Memory, BVRT, and Spatial Span.

Somatization of depression and depression will account for a significant level
of variance in memory performance. The variance explained by somatization of
depression will more closely resemble that attributed to depression than that

attributed to age, education, or gender.

14

METHOD

Participants

The participants will be drawn from an ongoing Michigan State University
Psychological Clinic Aging Research Project. Participants are community
dwelling older adult volunteers who were recruited from senior citizen groups,
faculty and staff retiree groups from MSU and other mid Michigan locations.
Advertisements and flyers were used for recruitment. In order to be included in
the study, the individuals have to score 24 or greater on the Mini-Mental State
Exam (Folstein, F olstein, & McHugh, 1975) with no report of significant history
of severe neurological and medical problems. Severely depressed individuals,
however, are included in the study. This sample of 227 participants contained
protocols of older adults ranging from 54 to 87 years old (M = 70.21; SD = 8.78).
Of the 227 participants, 132 were women and 95 were men. The group had a
mean education of 15.28 years (SD = 3.00)
Measures
1. Be@epression Inventory(BDI)

BDI is a self-rating instrument which was developed to assess depression
and its severity. It consists of 21 items, which were designed to measure

symptoms commonly associated with depression such as guilt, feeling of failure,

15

loss of motivation, loss of energy, and somatic complaints. A cutoff score of 10,
as recommended by Beck and colleagues (1979), has been found to reliably detect
the presence of depression (Olin et al., 1992). Using customary BDI cutoff scores,
misclassification rate was found to be approximately 16-17% (Gallagher, Nies &
Thompson, 1982; Gallagher et al., 1983). Furthermore, previous studies have
indicated that BDI has high internal consistency and stability when used with
older adult population, thus making it appropriate for use with this particular
population (Spitzer, Endicott & Robins, 1978). Overall, BDI is deemed to be an
appropriate measure of depression, including for use with the older adult
population.

In addition, it has been suggested by Collins and Abeles (1996) that BDI
loads on two distinct factors: somatic and affective. It was found that items 1
through 14 measured affective components of depression whereas items 15
through 21 correlated with somatic components of depression. Therefore, in the
present study, the first 14 items were used to assess affective depression while the
last 7 items were used as indicators of somatic depression.

2. Geriatric Depression Scale (GDS)

The GDS, consisting of 30 true/false items, was developed to measure
depression specifically in older adult population (Yesavage, Brink & Rose, 1983).
This second measure of depression was chosen to be included in the battery due to
the fact that it is a more pure measure of depression and does not contain somatic
items. Thus, incorporation of GDS may allow for observation of a differential

impact between somatic and affective components of depression. This measure is

16

believed to be a reliable scale with a test-retest reliability of 0.85 (p_ <.01) for 20
participants given the measure twice, one week apart (Yesavage et al., 1983). The
authors also reported that the GDS has a respectable convergent validity with the
lung Self-Rating Scale for Depression (r = 0.84), and with the Hamilton Rating
Scale for Depression (r = 0.83). Moreover, given its high internal consistency of
0.94 and its respectable stability (Yesavage et al., 1983), the GDS seemed to be an
appropriate measure for use with older adult population.

3. California Verbal Learning Test (CVLT)

CVLT (Delis et al., 1987) is an objective measure of verbal memory
functioning, which requires the participant to repeat a list of 16 shopping items
(List A) orally presented by the clinician. After 5 trials of the List A immediate
recall, a second list of shopping items (List B) is presented as an ‘interference’.
Free and category-cued recall of List A are tested immediately after List B recall
and again after a 20-minute delay. Hence, the task tests total recall, short delay
free recall, long delay free recall, and cued recall.

In a study by Cellucci and colleagues (2001), it was reported that total
recall on CVLT has a stability coefficient of 0.64 when the test was re-
administered a year later in a normal older adult population. Also, the study found
that recall performance on CVLT was moderately correlated with subjective
memory abilities. Thus, it appears that CVLT is appropriate for testing memory

functioning in older adult population.

17

4. Lpgical Memory

Logical Memory I and II from the Wechsler Memory Scale (Wechsler,
1987) are frequently used measures of verbal memory in clinical settings
(Tremont et al., 2000). The task requires the participant to recall two short stories
that are presented verbally. The scores on the task are based on the extent to
which the participant is able to recall the exact wording of the prose and they can
range from 1 to 22 points. In a study of its psychometric properties, test-retest
reliability over a period of 4 to 6 weeks was found to have been 0.79 (Bowden &
Bell, 1992). Furthermore, previous research has suggested that Logical Memory is
sensitive to age related changes in memory (Haaland, Price, & Larue, 2003).

5. Benton Vim Retention Test (BVRT)

The Benton Visual Retention Test (Benton, 1983) consists of a set often
stimulus cards with different geometrical figures that are presented visually for a
ten-second interval each. After the presentation of each stimulus card, the
participant is asked to draw the design on the card from memory. A point is given
for each correct response and the participant can score from 0 to 10 points. In a
large psychometric study of the BVRT, it was found that older age is associated
with a decline in BVRT scores whereas higher education is correlated with an
increase in performance (Youngjohn, Larrabee, & Crook, 1994).

6. Spatial Span

Spatial Span is a measure from the Wechsler Memory Scale (Wechsler,

1987) designed to assess visual recall and it contains two parts. In the forward

condition, the examiner taps a series of cubes and the participant is required to tap

18

the series in the exact order as the examiner. In the backward condition, however,
the participant taps in the reverse order as the examiner. The scores on the test can
range from 0 to 32. Previous research suggests that scores on the BVRT can be
successfully differentiated between normal, mild dementia and severe dementia

(Orsini et al., 1989).

Statistical Methods

Hypotheses for the study will be tested using a number of statistical
methods. To test for the relationship between memory performance and each
predictor, multivariate multiple regression methods will be used. Also, multiple
regression methods will be utilized for determining the variance explained by
each predictor. In order to examine the weight of each factor in predicting
memory performance, all variables will be inserted simultaneously. The mutual
dependency of predictors will then be considered by examining the tolerance
scores. Individual contributions made by each predictor will be evaluated by

comparing the coefficients of each predictor.

Procedure

For the larger study, volunteer participants who met selection criteria were
given weekly workshops on memory and relaxation training. Participants were
also administered assessment of mood and memory pre and post training. The

assessment requires between one-and-a-half hours and two hours to complete. In

19

this particular study, only the scores from the pre-workshop testing will be

utilized. Tests will be scored by the investigator.

20

RESULTS

Results from the present study are subdivided into three sections. First, an
analysis of the group’s level of depressive symptomology on the BDI and GDS
will be presented. Second, findings on the study’s hypotheses are presented.
Lastly, post hoc analyses were conducted to further elucidate the roles of affective
and somatic symptoms of depression on memory performance.

Level of depressive symptomology

 

In the literature, it was often noted that the degree of severity of
depression is critical in determining whether depression has an influence on
cognitive abilities. Therefore, it was deemed necessary to first analyze the level of
depression in the group by looking at the means and the standard deviations of the
scores on the two measures of depression, the BDI and the GDS (see Table 1).

According to pervious research, scores of 10t015 out of the possible score
of 63 on the BDI are considered to be in the minimally depressed range, whereas
scores of 16 to 19 indicate mild to moderate depression. Scores above 20 are
categorized as moderate to severely depressed. On the GDS, scores of 10 to 20 is
recognized as mildly depressed whereas scores above 20 are placed in the
severely depressed range. Overall, the present data suggest that the level of

depression for the group as a whole is in the non-depressed range with the means

21

of 6.87 (SJ; = 5.35) on the BDI and 5.86 (E = 4.68) on the GDS. Nevertheless,
examination of the range of scores does indicate that the data include individuals
in the severely depressed range. Therefore, it was judged feasible to conduct data
analyses on the entire group initially and to perform post hoc analyses on the
segment of the group that endorsed depression to delineate the role of depression

on cognitive abilities.

Table 1

Group Means and Standard Deviations for the BDI and the GDS

 

N Minimum Maximum M _S_D
BDI 203 0 27 6.87 5.35
GDS 202 0 23 5.86 4.68

 

Hypotheses:

Hypotheses regarding the relationship between depression and memory
performance

Based on the previous findings, it was predicted that higher depressive
scores on the BDI and GDS would negatively correlate with performances on the
CVLT, Logical Memory, BVRT and Spatial Span. However, as can be seen in
Table 2, this hypothesis was not supported. In this group with minimal depressive

symptoms, it was found that higher overall scores on the BDImm and the GDS did

22

not impact performance on the CVLT, Logical Memory, and Spatial Span. As for
the BVRT, its data collection was initiated at a later point than the other measures,
and thus the number of participants who completed the task is much lower. In
order to retain a higher N for the MANOVA analyses, the scores from the BVRT

were omitted from all the analyses.

Table 2
MANOVA findings on the Relationship between Depressive Scores and the

Memory Measures

 

 

S_opr_c_e_ Dependent variable df E S_ig
BDlmta. Logical Memory 173 .089 .77
CVLT 1 73 .329 .57
Spatial Span 173 .888 .35
GDS Logical Memory 198 .473 .49
CVLT 1 94 .349 .56
Spatial Span 197 3.00 .09

 

CVLT = California Verbal Learning Test, total items recalled from trials 1-5;
Logical Memory, scores on Stories I and II; Spatial Span, total correct responses;
BDImml = Beck Depression Inventory, total score comprising of both affective and

somatic symptoms of depression; GDS = Geriatric Depression Scale, total score.

23

Hypotheses regarding the impact of somatic symptoms of depression on memory
performance

Given the important role somatic symptoms appears to play in the
manifestation of depression in older adults, it was predicted that somatic
symptoms, as assessed by items 15 through 21 of the BDI, would associate
negatively with performances on memory measures. However, this hypothesis
was not supported in the present study (see Table 3). It appeared that somatic
symptoms of depression as a unique variance have a negligible role in predicting

memory performance in older adults.

Table 3

MANOVA findings on the Relationship between Somatic Scores and the Memory

 

 

Measures

Source Dependent variable gfi' E S_ig

BDlsommic Logical Memory 173 .001 .97
CVLT 1 73 .7 1 3 .40
Spatial Span 173 .389 .53

 

BDImmmic = Somatic factor on the BDI comprising of items 15-21.

24

Hypotheses regardimg the effects of agg on memonl performanfl

Based on previous literature, it was predicted that age would have a
significant negative relationship with memory performance. The findings from the
present study partially supported the hypothesis (see Table 4). It was found that
age adversely affects performance on the Logical Memory, F (1,173) = 7.53, M_S_E
= 227.66, p <.01. That is, older age is associated with lower performance on the
Logical Memory. Additionally, it was found that age has a negative relationship
with scores on the Spatial Span, F (1,173) = 19.154, MS}; = 116.39, p < .01. In
other words, the older the age, the more likely it is that the performance on the
Spatial Span will be poorer. However, as can be seen in Table 4, age does not

appear to have a statistically significant effect on the CVLT performance.

Hypotheses reggdirg the effects of edrfltion and gender on memory
performance

Research has indicated that a higher level of education may serve as a
protective factor against age-associated decline in memory. Therefore, it was
predicted that a higher level of education would be correlated with higher scores
on the three measures of memory. This hypothesis was partially supported as seen
in Table 4. The results indicated that higher educational level has a positive
association with scores on the Logical Memory, F (1,173) = 14.44, ME =
436.68, p < .01. That is, the more education a participant has had, the higher his or
her performance is on the Logical Memory task. In addition, there is a positive

association with performance on the CVLT and a higher level of education, E

25

(1,173) = 5.07, MSE = 6.70, p <.05. The present study showed no statistically
significant relationship between education and performances on the Spatial Span.
The study also predicted that being a female would be another protective
factor for minimizing the age related decline in memory performance. This
hypothesis was not supported (see Table 4). In fact, the results suggested that
being male has protective effects on the performance on the Spatial Span, F

(1,173) = -.l6, p <.05. That is, being a male is found to have been associated with

higher scores on the Spatial Span.

Table 4

MANOVA findings on the Relationship between Demographic Variables and the

Memory Measures

 

 

Sparpa Dependent gable Q; E _S_ig
Age Logical Memory 173 7.53 .01 *
CVLT 173 .354 .55
Spatial Span 173 19.15 .00* *
Education Logical Memory 173 14.44 .00**
CVLT 173 6.70 .03 *
Spatial Span 173 .964 .33
Gender Logical Memory 173 2.79 .10
CVLT 173 1.17 .28
Spatial Span 173 9.90 .00

 

* p < .05, ** p <01

26

Hypotheses regarding the variances explained by the demographic variables,
depression and somatization

In order to confirm the earlier findings on the roles of depression and
somatization in memory assessment, variances explained by these two variables
are compared to the variances accounted for by the demographic factors. It was
predicted that in comparison to the demographic variables, depression and
somatic symptoms of depression would account for a similar level of variance on
memory performance in older adults. This hypothesis is tested by initially
performing simple regressions on depression, somatization, age, years of
education, and gender with the three measures of memory performance as
dependent variables. In the first sets of regressions, scores on the BDI served as
an independent variable. For this set of regression, there was no statistically
significant parameter. In addition, the set of regressions with somatization as an
independent variable did not produce statistically significant parameter. Thus, it
appears that neither depression nor somatization serve to predict any significant
variances in memory functioning.

As for regressions with regard to the three demographic variables, age
yielded two statistically significant parameters. It was found that scores on the
Logical Memory were significantly predicated by age with age accounting for
11% of the variances in scores (see Table 5). Additionally, scores on the Spatial
Span were significantly predicated by age with age accounting for 12 % of the

variances in scores.

27

In addition to age, years of education yielded two statistically significant
parameters and gender yielded one statistically significant parameter (see Table
5). The results indicated that years of education accounted for 7% of the variances
on Logical Memory performance and 3% of the variances on CVLT performance.
Furthermore, it was found that gender accounted for 3% of the variances on
Spatial Span performance. All other regressions with regard to demographic
factors failed to yield any significant parameter. Given the lack of significant
linear parameters for most variables with the exception of age, an analysis of data

using multiple regression method was omitted.

28

Table 5

Regression Analyses of the Depression, Somatic Depression, and Demographic

Variables on the Memory Measures

 

Standardized Beta

Depression
Logical Memory

CVLT
Spatial Span

Somatic Depression
Logical Memory
CVLT

Spatial Span

Age
Logical Memory

CVLT
Spatial Span

Education
Logical Memory

CVLT
Spatial Span

Mgr.

Logical Memory
CVLT

Spatial Span

.085
-.043
-. 123

-.008
.034
-.068

-.333
-.066
-.334

.256
.178
.108

.055
.038
-.159

1.191
-.591
-1.731

-.106
.466
-.935

-5.191
-.958
-5.215

3.821
2.571
1.563

.806
.550
-2.388

Sig. t

.235
.555
.085

.916
.642
.351

.000“
.339
.000"

.000"
.011*

.120

.421
.583
.018*

.007
.002
.015

.000
.001
.005

.111
.004
.112

.066
.032

.012

.003
.001
.025

 

*p < .05, ** p < .01

29

Post hoc analyses

In order to further explore the relationship between depression and
memory performance, and to examine the differential impact of affective and
somatic symptoms of depression on memory functioning, three post hoc analyses
were conducted. For the first post hoc analysis, initial results regarding the level
of depression in the sample were reconsidered. It was indicated that although the
data contains individuals who fall into the severe range of depression, the group
as a whole did not endorse depression. Thus, it is conceivable that non significant
findings on the measures of memory when testing the association between
depression and memory might have been due to the group’s non-depressed status.
In order to ascertain this, a subgroup of individuals who scored 10 or above on the
BDI was identified. The total number of individuals who fell in the depressed
category was 43. For the depressed group, the mean on the BDI was 15.17 with a
standard deviation of 4.00.

It was expected that due to the presence of depression in this group, their
memory functioning would be more noticeably impacted by depression than the
original group. T-tests were conducted to examine whether performances on
memory measures differed between the depressed group and the non-depressed
group (see Table 6). On Spatial Span, the depressed group performed worse than
the original non-depressed group (t(l97)=-2.78; p<.01; depressed group
mean=15.22). Thus, it does appear that on Spatial Span task, the higher degree of

depressive symptomology affects the level of performance. However, there were

30

no statistically significant differences between the depressed group and the non-

depressed group on the CVLT and the Logical Memory tasks.

Table 6

T-tests Analyses on Depressed and Non-Depressed Groups

 

Measure Group Mgap t S_ig

Logical Memory Depressed 17.96 1 .00 .3 l7
Non-depressed 16.92

C VLT Depressed . 13 .3 70 .71 1
Non-depressed .06

Spatial Span Depressed 14.04 -2.78 .01**

Non-depressed 15.22

 

**p<.01

In the second post hoc analysis, the unique influence of affective
symptoms of depression on performances on CVLT, Logical Memory, and Spatial
Span is considered. It was found that affective scores on the BDI negatively
correlated with performance on the Spatial Span, g (190) = -.16, p < .05. That is,
older adults who endorse a high level of affective symptoms of depression on the
BDI performed worse on the Spatial Span tasks. Affective symptoms of
depression did not have a statistically significant effect on the CVLT, and the

Logical Memory (see Table 7).

31

Table 7
Correlations between Affective Symptoms of Depression and the Memory

Measures

 

CVLT Logical Memory Spatial Span

BDIaffccfivc .1 1 .03 -.16*

 

* E <.05; BDIaffcctivc = BDI items 1'14

In the third segment of post hoc analysis, the aim was to study the effects
of affective and somatic depressive symptoms on memory in individuals who
manifest these symptoms more definitively. Therefore, two groups of individuals
who endorse these symptoms more unequivocally were identified. In the
affectively depressed group, individuals with scores of eight or higher on the
affective items of the BDI and scores of five or lower on the somatic items of the
BDI were selected. The cut-off scores were based on the observation of the scatter
of depressive scores in the sample. Through this method, fifieen individuals who
met the criteria were identified. Of the 15, 7 were men and 8 were women. The
mean age for the group was 69.93 with a standard deviation of 9.40. Mean level
of education for the group was 14.2 years with a standard deviation of 2.83. Thus,
the affectively depressed group did not differ significantly from the original group
in terms of demographic variables.

For the somatically depressed group, individuals were included if they

scored seven or less on the affective items of the BDI and scored six or higher on

32

the somatic items of the BDI. Using this selection method, 12 individuals were
identified, of which 4 were men and 8 were women. The mean age for the group
was 72.12 with a standard deviation of 7.81. The group had a mean educational
level of 15.17 years and a standard deviation was 2.95. Again, the demographic
characteristics of the somatically depressed group did not differ significantly from
the original group.

It was believed that by conducting data analyses on the affectively
depressed and somatically depressed groups in isolation from the original group,
the possible effect of affective and somatic depression on memory performance
can be more clearly delineated. However, results from the t-test analyses did not
yield any statistically significant differences between the affectively depressed
and the somatically depressed groups (see Table 8).

Table 8

T-tests Analyses on the Affectively Depressed and Somatically Depressed Groups

 

Measure Group Mean 1 S_ig
Logical Memory Depressed 1 6.07 -.091 .929

Non-depressed 16.27

CVLT Depressed -.18 -.385 .704
Non-depressed .01 6
Spatial Span Depressed 14.47 -.654 .519

Non-depressed 15.09

 

** p<.01

33

DISCUSSION

The discussion of the findings of the current study considers the outcome
of the hypotheses on depression and memory functioning posited at the outset.
The discussion also includes the findings from the post hoc analyses that pertain
to the impact of depression. Findings regarding the effects of somatization on
memory functioning will also be considered. Furthermore, the discussion
incorporates an assessment of the usefulness of separating affective and somatic
components when thinking about depression. This will be followed by an
evaluation of the results regarding the influence of age, higher education, and
gender on memory functioning. In conclusion, limitations of the study as well as

possible directions for future research are explored.

Findings regarding the relationship between depression and memory performanpp
In previous research, an emphasis has ofien been given to the severity of
depression as playing a crucial role in determining depression’s influence on
cognitive abilities. For example, studies by Rohling and Scogin (1993) and by
Burt and colleagues (1995) have independently asserted that literature showing

deficits in memory performance due to depression mainly utilized severely

34

depressed inpatients whereas research with null findings relied on outpatients or
individuals who had not sought help for depression. On the other hand, Austin
and colleagues (2001) reviewed the available literature and reported inconsistent
findings with regard to the role of severity of depression. The authors noted that
there have been nine studies which found no association between performance
and severity of depression, and that there have been eleven that did find such an
association.

Hence, in the light of these reports, the initial step in the current study was
to observe the sample’s characteristics with regard to severity of depression.
Examination of the data revealed that the group as a whole fell in the non-
depressed range. It was also noted that the sample does contain individuals with
endorsement for moderate to severe level of depressive symptoms. At this
juncture, it was decided that it would be informative to run two separate analyses
of memory performance with one for the group as a whole, and the other for a
subgroup of individuals with symptoms of depression.

First, data were analyzed on the entire group, which as a group showed no
notable sign of depression. In this group, the results suggested that depression, as
measured by the BDI and the GDS, has no association with performance on any
of the memory measures. This finding is supportive of previous reports in the
field (Bieliauskas, 1993; Luszcz, 1992; Rohling & Scogin, 1993). The results,
however, are inconsistent with some other reports in the literature (King etal.,
1991; La Rue, 1989). It should be noted that the studies that reported significant

findings were performed on psychiatric inpatients.

35

In order to clarify the relationship further, additional data analyses were
conducted on a segment of the population that endorses a certain degree of
depressive symptoms. In this group, the results revealed that depression appears
to have a selective impact on memory performance in the elderly population. In
particular, it was found that depression had an adverse effect on older adults’
ability to perform well on Spatial Span task, which required the use of attention as
well as visual memory. This finding is in congruence with a long-standing
assertion in the field (e.g. Miller, 1975) that one of the most likely targets of
depression on cognitive functioning is attention. Furthermore, the results support
previous research that depression can contribute to a decline in visual memory
(Austin et al., 1999; Boone etal., 1995).

However, a more interesting finding of the study is that it did not provide
support for a detrimental impact of depression across all measures of memory. In
the present inquiry, the findings showed no association between depression and
the two measures of verbal memory. This selective nature of decline in memory
performance due to depression is consistent with previous literature (Christensen
et al., 1999; Gainotti & Marra, 1994; Hertel & Hardin, 1990; Ilsey et al., 1995)
which reported varying domains in which depression appears to unfavorably
influence memory performance. Some of the cognitive domains believed to be
affected by depression are episodic memory, verbal recall, word generation,
visuoconstruction, information processing, and processing speed (e. g. Boone et

al., 1995; Palmer et al., 1996).

36

There are several possible explanations for interpreting the variability in
results seen in the current study. One speculation for the findings is that verbal
memory is less susceptible to the effects of depression than visual memory.
Another possible explanation proposed for the variation in cognitive abilities
affected is the level of effort the task requires (Hasher & Zacks, 1979; Jorm,
1986; Weingartner, 1986). It has been suggested that the higher the task demand,
the more likely that the effects of depression on performance will be discemable.

Yet another possible explanation for diverging results seen across the
measures relates to level of depression in the group. Although previous findings
on verbal performance per se in depressed samples have been inconsistent
(Palsson et al., 2000; Roy-Byme et al., 1986), it was expected that immediate
recall aspect of the measures would lead to a decline in performance. A potential
explanation for the null finding of this prediction is the lack of notable depressive
symptoms in the group as a whole. It appears that the presence of the severely
depressed individuals within the group was not significant enough to lend support
for the hypothesis.

Here once again, the controversy over whether the severity of depression
affects the outcome of memory performance comes into play. Although the
depressed group was selected based on an endorsement of some symptoms of
depression, the group mean for depression scores still fell in the mildly depressed
range. In previous research, there are some suggestions that in order to more
clearly distinguish the effects of depression on memory, the study’s sample

should involve inpatients with severe level of depression (Burt et al., 1995;

37

Rohling& Scogin, 1993). According to Rohling and Scogin (1993), psychiatric
hospitalization and psychotropic medications are more significant predictors of
memory performance than depression per se. Therefore, it is questionable whether
the mild degree of depression seen in our depressed sample is considerable
enough to impact memory performance.

However, when placing the current findings in the larger context of
evaluating whether depression affects memory, severity of depression alone does
not seem to fully account for the high degree of inconsistency in results seen in
studies across the field. One possible contributor to the diverging results seen in
the field may lie in the underlying cognitive domains the measures are assessing.
Several studies have hinted that the cognitive tests rarely are pure measures of the
cognitive domains under investigation, making the identification of the affected
domain difficult (Austin, Mitchell, & Goodwin, 2001). Compounding the
difficulty, occasionally, as it is the case with Spatial Span, there is a disagreement
as to what the test is actually measuring. According to Wilde and Strauss (2003),
Spatial Span did not correlate very highly with visual memory or working
memory in their study. It was suggested that a more appropriate use for Spatial
Span would be to assess processing speed.

Moreover, Lichtenberg and colleagues (1995) had pointed out that
inconsistencies in results seen in depression and memory performance research
might be attributable to the measures used in the studies. The authors reported that
some measures are more sensitive to detecting the depression than others. As an

example, they mentioned Benton Visual Retention Test as one of the measures

38

that were less sensitive for differentiating between normal and depressed
individuals. Thus, it is conceivable that a lack of consistency in results between
the measures can be attributed to the underlying constructs the measures were
assessing and their sensitivity to the depressive effects.

Overall, despite the existence of possible explanations for the
inconsistencies in results, the current findings suggest that depression has a
limited impact on cognitive abilities in this healthy, high functioning sample of
older adults. To the extent that depression does impact memory, it affects visual
memory when the severity of depression falls in the mild to moderate range.
Given these findings, therefore, depression appears to play a minor role in

predicting memory performance of high functioning older adults.

Findings on somatization and memogy functioning

Although some researchers have supported the view that somatization
deserved to be treated as a depressive equivalent (De Wester, 1996; Kirmayer &
Robbins, 1996; Stone & Folks, 1992), there has been little inquiry done into
determining whether somatization has a similar impact on one’s functioning as
depression. If one were to favor the line that somatization is just a variation in the
manifestation of depression, it would follow that somatization will have a
comparable impact on any given functioning. Therefore, the current study looked
at the relationship between somatizaiton and depression by comparing their

relative impact on memory performance.

39

Contrary to prediction, however, the results showed that there is no
association between somatization of depression and memory performance in the
present sample. The findings suggested that somatization as a unique variance did
not account for much of the variance in explaining memory performance. This
finding is similar to the finding on depression in that neither total depressive
scores nor somatic scores were significant in predicting memory performance.

To delineate the relationship further, additional analyses were completed
on the affective component of the BDI. The goal of this set of analyses is to
determine if the affective component has more predictive power on memory
performance than somatization scores. The results indicated that there is a
negative relationship between affective scores on the BDI and the performance on
the Spatial Span. This finding is similar to the findings regarding the Spatial Span
performance of the depressed group in Hypothesis 1. Hence, it suggests that
unlike somatic depression, affective depression may have some impact on visual
memory performance, just as mild to moderate depression does.

In order to determine whether the lack of findings regarding somatization
were due to a low level of somatic symptoms endorsed in the sample, subgroups
with high somatic depression and high affective depression were identified. It was
believed that assessing the two ends of the spectrum would allow for a clearer
pattern to emerge. Nevertheless, memory performance was not related to either
somatic depression or affective depression in these select samples.

Given the null findings across the board on the impact of somatization on

memory functioning, it raises the question of how useful it is to incorporate

40

somatic complaints when assessing memory functioning in older adults. The
present results suggest that the presence of somatization will not be a significant
consideration in memory assessment of older adults. To the extent that depression
does impact visual memory performance, it seems that it would be more advisable
to take into account the overall depressive scores or affective scores. One
cautionary note, however, is that there have been some reports of somatization as
a milder form of depression (La Rue, 1992). Therefore, although somatic
complaints do not affect memory performance, one cannot rule out the possibility

that a mild form of depression is present in the patient.

Findings on effects of age on memog functioning

As hypothesized, older age is associated with a decline in selective
domains of memory functioning. In particular, older age is associated with poorer
performance on the Logical Memory and the Spatial Span. These results are
consistent with reports in the literature (Christensen et a1. 1997; Compton et al.
1997; Lichtenberg et al., 1995; Luszcz, Bryan & Kent, 1997; Orsini et al., 1986;
Titov & Knight, 1997).

Although results obtained for the effects of age on verbal memory differed
between the two measures, there are several possible explanations for the apparent
inconsistency. First, a study conducted by Delis and colleagues (1988) have
shown that correlation between the Logical Memory and the CVLT can vary
significantly based on the type of memory strategy used on the CVLT task. For

instance, it was found that when participants engaged in a more active strategy for

41

recall on the CVLT, performance on the task is comparable to performance on the
Logical Memory. On the contrary, if the participant approached the CVLT task
with a more passive recall strategy, such as recalling only the most recent items,
scores on the CVLT was negatively correlated with scores on the Logical
Memory. Therefore, variance in recall strategies used by the participants in the
present study may have contributed to the lack of consistency in results between
the two measures of verbal memory.

Second, a study done by Tremont and colleagues (2000) has raised the
question of the advisability of using the Logical Memory and the CVLT measures
interchangeably to predict verbal memory. In their study, individuals with
executive dysfunction performed significantly worse on the CVLT when
compared to the control group whereas performance on the Logical Memory did
not significantly differ between the two groups. Thus, the study concluded that the
general practice of using these two measures interchangeably may not be
appropriate in neuropsychological testing.

Yet another possible explanation for differences in results across the
measures relies on the theory of crystallized and fluid intelligence as originally
proposed by Horn and Cattell (1966a, b). According to this theory, fluid
intelligence (Gf), which refers to innate abilities, and crystallized intelligence
(Gc), which includes learned abilities, have differential impact on one’s cognitive
functioning depending on the age. This view has been supported by findings from
a number of studies (Anstey, Luszcz, & Sanchez, 2001; McArdle et al., 2000;

McArdle et al., 2002; Stankov, 2000). The general trend of functioning suggested

42

by these studies is that with an increase in age, both Gf and Ge rise upward at
different rates. However, around the age of 18, Gf begins to descend in its slope
and continues to do so throughout the life span. As for Go, approximately around
20 years of age, the initial upward trend begins to level off and remains relatively
stable at that stage. Therefore, it appears that applying the Gf-Gc theory to the
current study may help explain some of the results.

In the present study, performance on the Spatial Span is dependent upon
the visual-spatial and processing abilities that are generally considered to be
components of fluid intelligence. According to the Horn and Cattell’s theory,
these fluid abilities are in the state of decline in older adults. Thus, the results
indicating a drop off in performance on the Spatial Span with age are in alignment
with the predictions of the Gf-Gc theory.

Explaining the findings on the Logical Memory using the Gf-Gc theory
proves to be more challenging, however. As a measure of verbal memory, Logical
Memory appears to rely on the crystallized abilities. Hence, based on the theory,
one would expect no notable decline in performance on the Logical Memory as
one reaches older adulthood. The results of the study are inconsistent with the
trend found in previous studies. A number of explanations are possible to
elucidate the findings. First, Logical Memory may not be a completely pure
measure of crystallized intelligence. It is quite conceivable that the measure
incorporates abilities such as short term memory. In such case, short term

acquisition and retrieval aspects of memory have been identified by McArdle and

43

colleagues (2002) as components of fluid intelligence. Thus, a decline observed
on the Logical Memory performance may be less surprising.

A second possible explanation for the apparently discrepant findings is
that although fluid abilities are purported to undergo more significant changes in
older adults, there have been suggestions that it does not completely explain the
deterioration of memory functioning associated with age. In a report by McArdle
and colleagues (2000), there are supports for hypotheses on “general memory
loss” and “general slowing”, in addition to the Gf-Gc hypothesis. In other words,
regardless of the type of ability, there appears to be a more general weakening of
the cognitive functioning with advancing age. This particular suggestion is in

support of the finding on Logical Memory.

Findipgs on the effects of education and gender on memory perfornlance

As predicted, higher education appears to serve as a protective factor
against a decline in verbal memory. This finding is in accord with previous
literature (Christensen etal., 1999; Compton, Bachman, & Logan, 1997; Grober
et al. 1998). As for spatial memory, it appears that there is a trend toward spatial
abilities being affected by the level of education. However, the relationship did
not reach the level of statistical significance. This finding is contrary to reports by
Orsini and colleagues (1986) in which a positive relationship between spatial
memory and higher level of education was found. One difference that has been
noted between the study by Orsini and the current study is the measures used. In

Orsini’s study, Corsi’s block-tapping test was used to test spatial memory. Due to

44

the fact that Coris’s blocking-taping test is a predecessor of Spatial Span, the
measures may not be comparable.

As for gender, being a female is negatively correlated with poorer
performance on the Spatial Span. This finding is consistent with findings by
Orsini and colleagues (1986). Furthermore, the framework of Gf-Gc is applicable
to the present results. Previous findings in relation with the Gf-Gc theory have
suggested that there is a gender difference in terms of fluid and crystallized
intelligence (Ackerman et al., 2001). The study suggested that men perform at a
higher level than women on tasks that require fluid abilities. This proposition is in
accord with the results observed in the current study.

On the other hand, gender did not have significant impact on verbal
memory performance. Similar results relating to verbal memory and gender have
been found by Orsini and colleagues (1986). Here again, the findings are in
agreement with the Gf-Gc framework in that research in this area had reported no

notable gender differences on tasks of crystallized abilities.

S_um__mary of the findinga

In reviewing the overall findings of the study, a few notable trends were
found. Firstly, most of the significant results of the study related to the
performance on the Spatial Span. It was observed that the performance on the
Spatial Span was negatively impacted by depression as assessed by the BDI total

in the depressed group, the affective scores on the BDI, age, and being a female.

45

A second notable finding was that protective effects of education on memory
were particular to the verbal memory.

In viewing these findings collectively, the theory concerning fluid and
crystallized intelligence emerged as a strong possible underlying mechanism for
interpreting the results. As noted previously, fluid intelligence has been found to
deteriorate with age (Anstey, Luszcz, & Sanchez, 2001; McArdle et al., 2000;
McArdle et al., 2002; Stankov, 2000). Therefore, the prevailing trend observed in
the study of a decline in Spatial Span performance can perhaps be attributed to the
downward trajectory of fluid intelligence associated with advancing age. It is
speculated that this possibility may have even been an overriding factor across all
the independent variables and that it explains the findings in regard to depression
and affective depression. This suggestion is supported by the fact that age
accounted for the greatest degree of variances in memory performance.

As for the results on verbal memory, it appears that with the exception of
findings on age, verbal memory was found to be well preserved in this highly
educated sample. Once again, Gf-Gc theory emerged as an overarching
framework, applicable to our understanding of the results. Based on the theory,
crystallized intelligence, which underlies verbal memory, is expected to remain
relatively intact even in older age. This prediction up holds our findings that a
decline in verbal memory was not seen for most of the verbal memory measures.
In fact, with a higher level of education, which contributes to crystallized
intelligence, a higher level of performance on verbal memory measures was

noted, again signaling a possible role of the Gf-Gc theory.

46

Limitaticms of the study

The current study has several limitations. First and the most critical
limitation of the study was the population used. In a study examining the
relationship between depression and memory performance, perhaps a population
with a more severe endorsement of depression would have been more appropriate.
However, it was felt that being able to identify the relationship in the general,
non-psychiatric patient population would be informative. Another potential
weakness of the sample population is that it consisted of individuals who were
more highly educated and were of higher SES than the average population.
Therefore, the generalizability of the findings from the study is limited to
individuals with an above-average level of education and SES. Third limitation of
the study was the measures used. It would have been instructive to employ
specific measures that were known to be sensitive measures for distinguishing

between the memory performances of non-depressed and depressed populations.

Saggestions for future resear_cfi

The aim of the current study is to explore the concept of somatization and
its possible impact on the elderly population. Given that somatic complaints are
commonly encountered in the elderly population, null findings between it and
memory performance should not foreclose the discussion of its effects on older
adults. Perhaps what the findings of this study call for is reexamination of our
understanding of somatization. It is conceivable that somatization is an expression

of some other psycholgogical distress such as anxiety (Kellner; 1990; Lipowski,

47

1988). Therefore, an evaluation into how well somatization correlates with
psychological disorders other than depression is warranted.

In studying somatization and its relationship to depression, an important
issue that was encountered in the study was the severity of depression. Thus, if
future research indeed finds somatization to be an expression of masked
depression, it would be apprOpriate to assess the severity of depression associated
with it. In the past, there has been some research to indicate that somatization is a
milder form of depression (La Rue, 1992). However, it is not yet known how mild
the depression is in individuals who endorse somatic complaints. More
clarification on this front is needed.

Although the role of somatization on memory performance was not
confirmed in the present investigation, many questions surrounding the concept of
somatization remain unanswered. For instance, despite the lack of a relationship
between somatization and memory functioning, it is conceivable that, in terms of
daily functioning, somatization and depression have comparable effects. Given
the subtle impact depression has on memory performance (Lichtenberg et al.,
1995), perhaps memory performance was not an ideal candidate for comparative
purposes. Hence, it might be potentially more illuminating to do a similar
investigation on a function that is more clearly defined and observable.

Furthermore, if research is to continue in the area of somatization, better
tools for assessing somatic complaints are much needed (Collins & Abeles, 1996).
SO far, the only scientific method available for measuring somatization is through

the use of BDI Soamtic items as suggested by Collins and Abeles (1996).

48

Apart from clarifications that are needed in studying somatization, this
investigation highlights the need for further research in depression and its impact
on memory functioning. A review of the literature clearly indicated that the field
is divided on at what level of severity, depression starts to impact memory
performance. This needs further illumination. Moreover, issues surrounding the
measures, which are sensitive enough to detect the effects of depression, need
resolving. Lastly, the findings from the present study were best conceptualized
through the Gf-Gc framework. Therefore, further application of the Gf-Gc theory

in future research on memory performance may be warranted.

49

LIST OF REFERENCES

50

Ackerman, P.L., Bowen, K.R., Beier, M.E., Kanfer, R. (2001). Determinants of
individual differences and gender differences in knowledge. Journal of
Educational Psychology, 93, 797-825.

Akiskal, H.S., Lemmi, H., Yerevanian, 3., King, D., & Belluomini, J. (1982). The
utility of the REM latency test in psychiatric diagnosis: A study of 81
depressed outpatients. Psychiatric Research, 7, 101-110.

Akiskal, H.S., Judd, L.L., Gillin, C., & Lemmi, H. (1997). Subthreshold
depressions: Clinical and polysomnographic validation of dysthymic,
residual, and masked forms. Journal of Aflective Disorders, 45, 53-63.

Antsey, K.J., Luszcz, M.A., & Sanchez, L. (2001). A reevaluation of the common
factor theory of shared variance among age, sensory function, and
cognitive function in older adults. Journal of Gerontology: Psychological

Sciences, 56, 3-11.

Austin, M. R, Mitchell, P., Wilhelm, K., et a1. (1999). Melancholic depression: A
pattern of frontal cognitive impairment. Psychological Medicine, 29, 73-
85.

Austin, M.P., Mitchell, P., Goodwin, GM. (2001). Cognitive deficits in
depression: Possible implications for functional neuropathology. The
British Journal of Psychiatry, 178, 200-206.

Avolio, B.J. & Waldman, DA. (1994). Variations in cognitive, perceptual, and
Psychomotor abilities across the working life span: Examining the effects

of race, sex, experience, education, and occupational type. Psychology and
Aging, 9, 430-442.

Barret, J.E., Oxman, J.A., Gerber, T.E., & Paul, D. (1988). The prevalence of
psychiatric disorders in a primary care practice. Journal of Psychiatric
Research, 45, 1100-1106.

Beck, A.T., Rush, A.J., Shaw, B.F., & Emery, G. (1979). Cognitive therapy of
depression. New York: Guilford Press.

Benton, AL. (1983). Benton Visual Retention Test. San Antonio: Psychological
Corporation.

Bieliauskas, LA. (1993). Cognitive and psychopathological changes in

degenerative disease in the elderly. Journal of Clinical and Experimental
Neuropsychology, 15, 421-438.

51

ll

Blazer, D., Hughes, D.C., & George, L.K. (1987). The epidemiology of
depression in an elderly community population. Gerontologist, 27,281 -
287.

Boone, K.B., Lesser, I.M., Miller, B.L., Wohl, M., Berman, N. Lee, A.B.P., &
Back, C. (1995). Cognitive functioning in older depressed outpatients:
Relationship of presence and severity of depression to neuropsychological
test scores. Neuropsychology, 9, 390-398.

Bowden, S. & Bell, R. (1992). Relative usefulness of the WMS and WMS-R: A
comment on D’Elia et a1. (1989). Journal of Clinical and Experimental
Neuropsychology, 14, 340-346.

Burt, D.B., Zembar, M.J., & Niederehe, G. (1995) Depression and memory
impairment: A meta-analysis of the association, its pattern, and specificity.
Psychological Bulletin, 117, 285-305.

Cellucci, T., Evans, W.J., & Callaruzza, CS. (2001). Stability and correlates of
California Verbal Learning Test for a sample of normal elderly persons.
Psychological Reports, 88(1), 171-174.

Christensen, H., Henderson, A.S., Griffiths, K., & Levings, C. (1997). Does aging
inevitably lead to declines in cognitive performance? A longitiudinal study
of elite academics. Personality and Individual Diflerences, 23(1), 67-78.

Christensen, H., Korten, A.E., Jorm, A. F ., Henderson, A.S., Scott, R. &
Mackinnon, A. J. (1996). Activity levels and cognitive firnctioning in an
elderly community sample. Age and Aging, 25, 72-80.

Christensen, H., Mackinnon, A.J., Korten, A.E., Jorm, A.F., Henderson, A.F.,
Jacomb, P., & Rodgers, B. (1999). An analysis of diversity in the
cognitive performance of elderly community dwellers: Individual

differences in change scores as a function of age. Psychology and Aging,
14(3), 365-379.

Collins, M.W., & Abeles, N. (1996). Subjective memory complaints and
depression in the able elderly. Clinical Gerontologist, 16(4), 29-54.

Compton, D.M., Bachman, L.D., & Logan, IA. (1997). Aging and intellectual
ability in young, middle-aged, and older educated adults: Preliminary
results from a sample of college faculty. Psychological Reports, 81, 79-90.

Delis, D.C., Kramer, J., Kaplan, E., & Ober, B. (1987). The California Verbal
Learning Test. New York: Psychological Corporation.

52

Delis, D.C., Cullum, C.M., Butters, N., & Cairns, P. (1988). Wechsler Memory
Scale-Revised and California Verbal Learning Test: Convergence and
divergence. The Clinical Neuropsychologist, 2, 188-196.

De Wester, J .N. (1996). Recognizing and treating the patient with somatic
manifestations of depression. Journal of Family Practice, 43 (6 Suppl), S3-
SIS.

Fisch, R.Z. (1987). Masked depression: Its interrelations with somatization,
hypochondriasis, and conversion. International Journal of Psychiatry in
Medicine, 1 7, 367-379.

Folstein, M.F., Folstein, S.E., & McHugh, PR. (1975). ‘Mini-Mental State’: A
practical method for grading the cognitive state of patients for clinician.
Journal of Psychiatric Research, 12, 189-198.

Gainotti, G., & Marra, C. (1994). Some aspects of memory disorders clearly
distinguish dementia of the Alzheimer’s type from depressive
pseudodementia. Journal of Clinical and Experimental Neuropsychology,
16, 65-78.

Gallagher, D., Breckenridge, J., Steinmetz, J. & Thompson, L.W. (1983). The
Beck Depression Inventory and Research Diagnostic Criteria: Congruence

in an older population. Journal of Clinical and Consulting Psychologz,
51, 945-946.

Gallagher, D., Nies, G., & Thompson, L.W. (1982). Reliability of the Beck
Depression Inventory with older adults. Journal of Consulting and
Clinical Psychology, 50, 152-153.

Grober, E., Lipton, R.B., Katz, M., & Slimwinski, M. (1998). Demographic
influences on free and cued selective reminding performance in older

persons. Journal of Clinical and Experimental Neuropsychology, 20(2),
221-226.

Gur, R.C., Gunning-Dixon, F.M., Turetsky, B.I., Bilker, W.B., & Gur, RE.
(2002). Brain region and sex differences in age association with brain
volume: A quantitative MRI study of healthy young adults. American
Journal of Geriatric Psychiatry, 10(1), 72-80.

Haaland, K.Y., Price, L., & Larue, A. (2003). What does the WMS-III tell us
about memory changes with normal aging? Journal of the International

Neuropsychological Society, 9 (1), 89-96.

Hasher, L. & Zacks, RT. (1979). Automatic and effortful processes in memory.
Journal of Experimental Psychology: General, 108, 356-3 88.

53

Hassing, L., Wahlin, A., & Backman, L. (1998). Minimal influence of age, 1
education, and gender on episodic memory functioning in very old age: A

population-based study of nonagenarians. Archives of Gerontology and
Geriatrics, 27, 75-87.

Herlitz, A., Nilsson, L, & Backman, L. (1997). Gender differences in episodic
memory. Memory & Cognition, 25(6), 801-811.

Hertel, P. T. & Hardin, TS. (1990). Remembering with and without awareness in
a depressed mood: Evidence of deficits in initiative. Journal of
Experimental Psychology, 119, 45-59.

Horn, J.L., & Cattell, R.B. (1966a). Age differences in primary mental ability
factors. Journal of Gerontology, 21, 210-220.

Horn, J.L., & Cattell, R.B. (1966b). Refinement and test of the theory of fluid and
crystallized intelligence. Journal of Educational Psychology, 57, 253-270.

Ilsley, J .E., Moffoot, A.P.R., & O’Carrol, RE. (1995). An analysis of memory
dysfunction in major depression. Journal of A flective Disorders, 35, 1-9.

Jorm, A. F. (1986). Controlled and automatic information processing in senile
dementia: A review. Psychological Medicine, 16, 77-88.

Katon, W., Ries, R.K., & Kleinman, A. (1984). The prevalence of somatization in
primary care. Comprehensive Psychiatry, 25, 208-215.

Kellner, R. (1990). Somatization: Theories and research. Journal of Nervous and
Mental Disease, 1 79(3), 150-160.

Kim, B, & Rovner, B.W. (1994). Depression in dementia. Psychiatric Annals, 24,
173-177.

King, D.A., Caine, E.D., Conwell, Y., & Cox, C. (1991). The neuropsychology of
depression in the elderly: A comparative study of normal aging and
Alzheimer’s disease. Journal of Neuropsychiatry and Clinical
Neurosciences, 3, 163-168.

Kirmayer, L.J., Robbins, J .M., Dworkind, M., & Yaffe, M.J. (1993). Somatization
and the recognition of depression and anxiety in primary care. American
Journal of Psychiatry, 150, 734-741.

Larrabee, G.J., & Levin, HS. (1986). Memory self-ratings and objective test

performance in a normal elderly sample. Journal of Clinical and
Experimental Neuropsychology, 8, 275-284.

54

La Rue, A. (1989). Patterns of performance on the F uld Object Memory
Evaluation in elderly inpatients with depression or dementia. Journal of
Clinical and Experimental Neuropsychology, 11, 409-422.

La Rue, A. (1992). Aging and neuropsychological assessment. New York:
Plenum Press.

La Rue, A., Sawn, G.E., & Carmelli, D. (1995). Cognition and depression in a
cohort of aging men: Results from the Western Collaborative Group
Study. Psychology and Aging, 10(1), 30-33.

Lesse, S. (1983). The masked depression syndrome—Results of a seventeen year
clinical study. American Journal of Psychotherapy, 3 7, 456-475.

Leventhal, E.A., Hansel], S., Diefenbach, M., Leventhal, H., & Glass, DC.
(1996). Negative affect and self-report of physical symptoms: Two
longitudinal studies of older adults. Health Psychology, 15, 193-199.

Lichtenberg, P.A., Ross, T., Millis, S.R., & Manning, CA. (1995). The
relationship between depression and cognition in older adults: A cross-

validation study. Journal of Gerontology: Psychological Sciences, 50B,
P25-P32.

Lipowski, Z.J. (1988). Somatization: The concept and its clinical applications.
American Journal of Psychiatry, 145, 1358-1368.

Lusczcz, MA. (1992). Predictors of memory in young-old and old-old adults.
International Journal of Behavioral Development, 15, 147-166.

Lusczcz, M.A., Bryan, J ., & Kent, P. (1997). Predicting episodic memory
performance of very old men and women: Contributions from age,
depression, activity, cognitive ability, and speed. Psychology and Aging,
12(2), 340-351.

McArdle, J.J., Hamagami, F ., Meredith, W., & Bradway, KR (2000). Modeling
the dynamic hypotheses of Gf-Gc theory using longitudinal life-span data.
Learning and Individual Differences, 12, 53-79.

McArdle, J .J ., Ferrer-Caja,E., Hamagami, F., & Woodcock, R.W. (2002).
Comparative longitudinal structural analyses of the growth and decline of
multiple intellectual abilities over the life span. Developmental

Psychology, 38, 1 15-142.

McBride, A.M., & Abeles, N. (2000). Depressive symptoms and cognitive
performance in older adults. Clinical Gerontologist, 21(2), 27-46.

55

(s

 

 

Miller, W. R. (1975). Psychological deficits in depression. Psychological Bulletin,
82, 238-260.

Muller-Spahn, F ., & Hock, C. (1994). Clinical presentation of depression in the
elderly. Gerontology, 40, 10-14.

Naveh-Benj amin, M. (2000). Adult age differences in memory performance:
Tests of an associative deficit hypothesis. Journal of Experimental
Psychologi: Learning, Memory, and Cognition, 26(5), 1170-1187.

Olin, J .T., Schneider, L.S., Eaton, E.M., Zemansky, M.F., & Pollock, V.E. (1992).
The Geriatric Depression Scale and the Beck Depression Inventory as
screening instruments in an Older adult population. Psychological
Assessment, 4, 190-192.

Orsini, A., Trojano, L., Chiacchio, L., & Grossi, D. (1989). Immediate memory
spans in dementia. Perceptual and Motor Skills, 67 (1), 267-272.

Orsini, A., Chiacchio, L., Cinque, M., Cocchiaro, C., Schiappa, 0., & Grossi, D.
(1986). Effects of age, education and sex on two tests of immediate

memory: A study of normal subjects from 20 to 99 years of age.
Perceptual and Motor Skills, 63, 727-732.

Palmer, B.W., Boone, K.B., Lesser, I.M., & Wohl, MA. (1996).
Neuropsychological deficits among older depressed patients with

predominantly psychological or vegetative symptoms. Journal of Affective
Disorders, 41, 17-24.

Palsson, S. Johansson, B., Berg, 8., & Skoog, I. (2000). A population study on the

influence of depression on neuropsychological functioning in 85-year-
olds. Acta Psychiatr Scand, 101 , 185-193.

Park, D.C., Smith, A.D., Lautenschlager, G., Earles, J .L., Frieske, D., Zwahr, M.,
& Gaines, CL. (1996). Mediators of long-term memory performance
across the life span. Psychology and Aging, 11(4). 621-637.

Park, D.C., Smith, A.D., Morrell, R.W., Puglisis, J .T., & Dudley, W.N. (1990).
Effects of contextual integration on recall of pictures by older adults.
Journal of Gerontology: Psychological Sciences, 45 , P52-P57.

Rapp, S.R., & Davis, KM. (1989). Geriatric depression: Physicians’ knowledge,
perceptions, and diagnostic practices. Gerontologist, 29, 94-103.

Razali, S.M., & Hasanah, C1. (1999). Somatization of depression: Prevalence and
characteristics features. International Medical Journal, 6(1), 27-31.

56

Rohling, M.L., & Scogin, F. (1993). Automatic and effortful memory processes in
depressed persons. Journal of Gerontology, 48, 87-95.

Roy-Byrne, P.P., Weingartner, H., Bierer, L.M., et a1. (1986). Effortful and
automatic cognitive processes in depression. Archives of General
Psychiatry, 43, 265-267.

Salthouse, TA. (1991). Mediation of adult age differences in cognition by
reductions in working memory and speed of processing. Psychological
Science, 2, 179-183.

Salthouse, TA. (1996). The processing theory of adult age differences in
cognitions. Psychological Review, 103, 403-428.

Salzman, C., & Gutfreund, M.J. (1986). Clinical techniques and research

strategies for studying depression and memory. In L.W. Poon (Ed.).
Clinical memory assessment of older adults (pp. 257-267). Washington,
DC: American Psychological Association.

Schnofeld, W.H., Cerboncoeur, C.J., F ifer, S.K., Lipschutz, R.C., Lubeck, D.P., &
Buesching, DP. (1997). The functioning and well-being of patients with

unrecognized anxiety disorders and major depressive disorder. Journal of
Affective Disorders, 43, 105-119.

Small, G.W. (1991). Recognition and treatment of depression in the elderly.
Journal of Clinical Psychiatry, 52, 11-22.

Smith, AD. (1979). The interaction between age and list length in free recall.
Journal of Gerontologi, 34, 375-3 80.

Smith, G.R., Monson, R. A. & Ray, DC. (1986). Patients with multiple
unexplained physical symptoms: Their characteristics, functional health
and health care utilization. Archives of Internal Medicine, 146, 69-72.

Smith, AD. (1996). Memory. In J.E. Birren & K.W. Schaie (Eds.) Handbook of
the psychology of aging (4th ed., pp.236-250). San Diego, CA: Academic
Press.

Spitzer, R.L., Endicott, J ., & Robbins, E. (1978). Research Diagnostic Criteria:
Rationale and reliability. Archives of General Psychiatry, 35, 773-782.

Stankov, L. (2000). Structural extensions of a hierarchical view on human
cognitive abilites. Learning and Individual Diflerences, 12, 35-51.

Stone, T. & Folks, D.G. (1992). Somatization in the elderly. Psychiatric
Medicine, 10, 25-32.

57

Stoudemire, A., Hill, C., Gulley, L.R., & Morris, R. (1989). Neuropsychological
and biomedical assessment of depression-dementia syndromes. Journal of
Neuropsychiatry and Clinical Neurosciences, I, 347-361.

Terre, L. & Ghiselli, W. (1997). A developmental perspective on family risk

factors in somatization. Journal of Psychosomatic Research, 42(2), 197-
208.

Titov, N., & Knight, R.G. (1997). Adult age differences in controlled and
automatic memory processing. Psychology and Aging, 12(4), 565-573.

Tremont, G., Halpert, S., Javorsky, D., & Stern, R. (2000). Differential impact of
executive dysfunction on verbal list learning and story recall. The Clinical
Neuropsychologist, 14(3), 295-302.

Trott, C.T., Friedman, D., Ritter, W., Fabiani, M., & Snodgrass, J .G. (1999).
Episodic priming and memory for temporal source: Event-related

potentials reveal age-related differences in prefrontal functioning.
Psychology and Aging, 1 4 (3), 390-413.

Wechsler, D. (1987). Wechsler Memory Scale-Revised. San Antonio, TX: The
Psychological Corporation.

Weingartner, H. (1986). Automatic and effort-demanding cognitive processes in
depression. In L.W. Poon (Ed.), Clinical memory assessment of older
adults. Washington, DC: American Psychological Association.

Weiss, I.K., Nagel, C.L., & Aronson, MK. (1986). Applicability of depression
scales to the old old person. Journal of the American Geriatric Society, 3 4,
215-218.

Wells, K.B., Stewart, A., Hays, R.D., Burnam, M.A., Rogers, W., Daniels, M.,
Berry, 8., Greenfield, S., & Ware, J. (1989). The functioning and well-
being of depressed patients: Results from the Medical Outcomes Study.
Journal of American Medical Association, 262, 914-919.

Verhaeghen, P., Marcoen, A., & Goossens, L. (1993). Facts and fiction about

memory aging: A quantitative integration of research findings. Journal of
Gerontology, 48(4), P157-P17l .

Yesavage, J .A., Brink, R., & Rose, TL. (1983). Development and validation of a
Geriatric Depression Scale. Journal of Psychiatric Research, I 7, 31-49.

Youngjohn, J.R., Larrabee, G.J., & Crook, TH. (1994). New adult age and

education correction norms for the Benton Visual Retention Test. Clinical
Neuropsychologist, 7 (2), 155-160.

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