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THESIS
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This is to certify that the
thesis entitled
THE ROLE OF EXECUTIVE FUNCTIONFNG AS A MEDIATOR

OF AGE-RELATED DIFFERENCES IN FREE RECALL
MEMORY PERFORMANCE

presented by
Tara L. Victor

has been accepted towards fulfillment
of the requirements for

MA. degree in Clinica_l Psychology

m LC. Aha

Major professor

 

 

Date (I K 06/2000
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THE ROLE OF EXECUTIVE F UNCTIONING AS A MEDIATOR OF AGE-
RELATED DIFFERENCES IN FREE RECALL MEMORY PERFORMANCE

By

Tara L. Victor

A THESIS

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

MASTER OF THE ARTS

Department of Psychology

2000

ABSTRACT

THE ROLE OF EXECUTIVE FUNCTIONING AS A MEDIATOR OF AGE-
RELATED DIFFERENCES IN FREE RECALL MEMORY PERFORMANCE

By

Tara L. Victor

This thesis investigated the hypothesis that age-related free-recall memory decline
is due to declines in executive function. The relative contributions of processing speed
and working memory were also considered. It was hypothesized that executive ftmction,
processing speed and working memory would all mediate the relationship between age
and free-recall memory performance. In addition, exploratory analyses concerning the
relative contributions of different aspects of executive function (i.e. attention, response
inhibition and set-shifting) to the relationship between age and memory were conducted.
All variables were measured using standard neuropsychological tests with a sample of
normal healthy elderly individuals. Hierarchical multiple regressions indicated that age
(54-87) contributed only 8.8% to the variance in memory performance. Both executive
function and processing speed when entered alone partially mediated the relationship
between age and memory accounting for 52.3% and 73.9% of the variance, respectively.
When entered together, they fully mediated the relationship accounting for 84.1% of the
age-related variance. Working memory was not found to be related to free-recall
memory performance. The difference aspects of executive function assessed in this study
contributed similar amounts to the age-related variance. These results are discussed in
light of their practical, theoretical and methodological implications and in the context of

suggestions for future research.

ACKNOWLEDGMENTS

I would like to thank my advisor, Norman Abeles, Ph.D., for his valuable contributions
and commitment throughout this endeavor. It was his guidance and determination that
made this possible.

I would also like to thank the other members of my committee, Ray Frankman, Ph.D. and
Michael Lambert, Ph.D., for their generous support, constructive feedback and
conceptual assistance.

I would also like to thank Josh for his time and statistical wizardry as well as his ongoing
support.

A special thanks to Terry for his friendship and support and to Allison for her kindness
and generosity.

To my family, thank you for the love and encouragement you have given me from the
very beginning. Your support means the world to me.
Finally, this work was only made possible through funding provided by the Pearl Aldrich

Endowment in Gerontology as well as a Student Award Program grant from the Blue
Cross and Blue Shield of Michigan Foundation.

iii

TABLE OF CONTENTS

List of Tables ................................................................................................................... vii
Introduction ...................................................................................................................... 1
Age-Related Memory Decline ............................................................................. 2
Potential Mediating Variables .............................................................................. 4
Processing Speed ..................................................................................... 4
Working Memory ..................................................................................... 6
Executive Function .................................................................................. 8
Executive function and the frontal lobes of the brain .................. 9

Age—related decline in executive function ................................... 12

Age-related frontal lobe degeneration ......................................... 14

Similarity between age-related memory deficits and those

associated with frontal lobe injury ............................................... 15

Summary and a Few Caveats ............................................................................... 17
Hypotheses ........................................................................................................... 20
Method ......................................... 21
Participants ........................................................................................................... 21
Procedure ............................................................................................................. 21
Measures .............................................................................................................. 22
Screening Instruments .............................................................................. 22

Beck Depression Inventory (BDI) ............................................... 22

Geriatric Depression Scale (GDS) ............................................... 23

Mini Mental Status Exam (MMSE) ............................................. 24

iv

Processing Speed (PS) ... .......................................................................... 25

Working Memory (WM) .......................................................................... 25

Executive Function (EF) .......................................................................... 26

The Stroop Color and Word Test (Stroop) .................................. 26

Wisconsin Card Sorting Test (WCST) ........................................ 27

Trailmaking Test (TMT) .............................................................. 29

Free-recall Memory Performance ............................................................ 29

Results .............................................................................................................................. 32
Descriptive Statistics ............................................................................................ 32
Composite Scores ................................................................................................. 33

Tests for Linearity ................................................................................................ 33
Hierarchical Regression Analyses ....................................................................... 34
Discussion ........................................................................................................................ 38
Methodological Considerations ........................................................................... 43
Measurement Issues ................................................................................. 43

The Sample .................................................... ' .......................................... 45

Correlational Research ............................................................................. 46

Future Research ................................................................................................... 47
Summary and Conclusions .................................................................................. 48
Appendix .......................................................................................................................... 50

Table 1: Means and standard deviations of subjects’ age, education, estimated
VIQ and scores on the BDI, ODS and MMSE .................................................... 51

Table 2: Means, standard deviations and correlations among all study variables
and age ................................................................................................................. 52

Table 3: Correlations between the three free-recall scores as assessed by the
CVLT ................................................................................................................... 53

Table 4: Hierarchical multiple regression analyses predicting free-recall memory
performance from age, executive function, processing speed, attention and set-

shifting ................................................................................................................. 54

List of References ............................................................................................................ 55

vi

Table 1

Table 2

Table 3

Table 4

LIST OF TABLES

Means and standard deviations of subjects’ age, education,
estimated VIQ and scores on the BDI, ODS and MMSE ....... 51

Means, standard deviations and correlations among all study
variables and age ...................................................... 52

Correlations between the three free-recall scores as assessed by the
CVLT .................................................................. 53

Hierarchical multiple regression analyses predicting free-recall
memory performance from age, executive function, processing
speed, attention and set-shifting .................................... 54

vii

INTRODUCTION

The elderly are the fastest growing segment of the US. population, yet the effects
of aging on this groups’ cognitive function are not well-known or sufficiently understood.
This is coupled with the fact that this age group is vulnerable to increasing disabilities,
many of which are the result of the aging central nervous system (CNS) (Woodruff-Pak,
1997). Thus, as the number of older adults continues to increase, so does the importance
of understanding the cognitive aging process. Being able to identify those cognitive
elements that seem to characterize the mental status of elderly populations is very
important to our understanding of this process and has major implications for the lives
and psychological well-beings of these individuals.

One particular area of cognition that declines with age is memory (Craik,
Anderson, Kerr, & Li, 1995). Much research has focused on age-related memory decline;
however, there are many questions still left unanswered. The purpose of this study is to
elucidate the mechanisms through which age exerts its effects on memory performance.
A focus is placed on the mediating role of executive functioning processes, or the class of
cognitive abilities thought to encompass the wide range of mental processes involved in
problem solving. Examples of such processes include those involved in planning,
strategic and abstract thinking, self-monitoring, shifting tasks and behavioral inhibition.
More generally, executive abilities are those functions that control and integrate other
cognitive actions, including memory (Lezak, 1995; Shimmamura, 1995).

However, any theoretical account of age-related memory decline must also take

into consideration those processing resources that have consistently been found to

mediate the age-memory relationship. More specifically, it will also be necessary to
consider the influences of processing speed, the speed at which an individuals processes
stimuli perceived, and working memory, the ability to simultaneously store and
manipulate information (Baddeley, 1986, 1990; Lezak, 1995). Thus, a brief review of the
research surrounding the roles of these theoretical constructs in the age-memory
relationship is given below, followed by a more focused examination of evidence
suggestive of executive function playing a significant role in age-related memory decline.
However, it will first be necessary to review briefly the phenomena of age-related
memory decline.
Age-Related Memory Decline

Some aspects of memory have consistently been found to decline with age. More
specifically, age-related deficits have been found in at least some aspects of the three
types of memory systems outlined by Shimamura (1990) including the implicit;
prospective, and declarative memory systems. Within this classification system, implicit
memory is defined as those memory functions that can be expressed unconsciously as in
the acquisition of skills, classical conditioning and in priming where the individual will
recall, as indicated by their performance, that they were involved in a previous experience
even though they cannot recall the particular experience consciously. Prospective
memory abilities are defined as those “processes and strategies by which one remembers
to perform future actions” (p. 48). Relevant to the work of the present research, however,
is the decline of functioning in the declarative memory system. Thus, the review of

relevant findings will be limited to age differences in declarative memory functioning.

The declarative memory system is described by Shimamura (1990) as “the
memory that is studied most often in psychological experiments, and it can be
demonstrated on standard tests of memory (e. g. recall, recognition)” (p. 40). Unlike the
implicit memory system, the declarative memory system involves “conscious or explicit
memory of facts and episodes” (p. 40). This system is further described by Shimamura
(I990) as important in “the establishment of memory representations during cognitive
activities, such as elaboration, organization, rehearsal, and mediation” (p. 40). In other
words, there are aspects of the declarative memory system that may likely be controlled
or governed by those processes thought to characterize the construct of executive
functioning.

The declarative system is thought to include both verbal and nonverbal types of
memory (Shimamura, 1990). Verbal declarative memory, in particular, has been found to
decrease as a function of age. Specifically, in a cross-sectional study of older individuals
ages 60 to 69 and 70 to 85, age-related deficits in this type of memory were found to be
significant (Golski, Zonderman, Malamut, & Resnick, 1998). Similarly, a study by
Paolo, Troester, & Ryan (1997) found a decline in performance with age on a measure of
verbal learning and memory, the California Verbal Learning Test (CVLT).

In terms of specific types of declarative memory tasks, age-related deficits have
been found for free recall as well as recognition types of memory performance. In fact,
age-related declarative memory deficits are particularly robust for free-recall memory
performance (Burke & Light, 1981; Moscovitch & Winocur, 1995). Findings of
decreased recognition memory performance with age, however, are described as only

“reliable”; the relative strength of these associations is much smaller than for free recall

(Moscovitch & Winocur, 1995; Parkin & Walter, 1991; Rabinowitz, 1984). In sum, it is
clear that an examination of the literature reveals the finding that different aspects of
memory decline during the aging process. Unclear, however, are the mechanisms through
which the aging process exerts its effects on these memory functions. Researchers have
developed different hypotheses as to the nature of variables potentially mediating the age-
memory relationship. The question that is asked is this: Can age-related differences in
memory be attributed to variables that intervene between age and memory and if so, to
what extent? Or, stated in a statistical sense, to what degree do these potential mediating
variables eliminate or attenuate the age-related variance in memory performance?
Potential Mediating Variables

Craik & Byrd (1982) have suggested that the decline of memory with age is a
function of older adults becoming limited in their capacities for processing information.
Operating under this assumption, many researchers have sought to identify the specific
areas of processing potentially mediating this decline. The majority of this research has
operationalized this cognitive processing variable through measures of processing speed
and working memory. More recently, however, the potential mediating role of central
executive functioning has also been entertained. The research surrounding the potential
mediating roles of each of these three areas of cognition in the age-memory relationship is
discussed below with attention focused on the research concerning the role of executive
functioning.

Processing Speed
One area of functioning that has been given considerable attention in the literature

with respect to age-related cognitive decline is that of processing speed, or how quickly

one can think and process information. Specifically, Salthouse (1980, 1985, 1996a) has
argued that memory performance may be negatively affected as one ages because of a
decline in the speed at which the central nervous system (CNS) processes information.

Indeed, there have been consistent reports of a decrease in processing speed with
age (Fisk & Warr, 1996; Salthouse, 1985; Spreen & Strauss, 1998). In addition,
processing speed has been found to mediate a significant amount of the age-related
variance in cognitive ability, generally (Park, Smith, Lautenschlager, Earles,eFrieske,
Zwahr, & Gaines 1996), and in the relationship between age and more specific types of
cognitive abilities such as reasoning and integration (Salthouse, 1993), working memory
(Salthouse & Babcock, 1991; Park et al., 1996), fluency and knowledge (Lindenberger,
Mayr, & Kliegl, 1993), and decision accuracy and decision time (Salthouse, 1994). In
addition, processing speed has been found to mediate the relationship between age and
paired-associated (requiring recall after receiving a cue) and free-recall (requiring active,
conscious search processes) measures of memory (Bryan & Luszcz, 1996; Lindenberger
et al., 1993; Nettelbeck & Rabbitt, 1992; Salthouse, 1993), as well as other measures of
memory performance (Hultsch, Hertzog, & Dixon, 1990; Luszcz, 1992; Salthouse,
Kausler, & Saults, 1988).

It is clear that the research suggests processing speed seems to be a relevant
mechanism through which age may exert its effects on memory performance. More
recent research (Salthouse, 1996a) has also provided more information about the role of
processing speed in the decline of memory with age. Specifically, it seems that the speed
factor is a more general one as opposed to being linked to specific types of memory

functions. Thus, this particular area of cognition is extremely important to research

concerning memory decline with age. However, processing speed does not account for
all the age-related variance found in memory performance. So this leads to another
question: Are there other theoretical constructs involved in this relationship and what
roles do they play? As cited above, Park et a1. (1996) found perceptual speed to mediate
the age-memory relationship, but this mediation occurred, in part, through working
memory. Indeed, working memory has also been investigated in terms of its potential
mediating role in the relationship between age and memory.
Wtflting Memory

Working memory is defined as the ability to store and manipulate information
simultaneously (Baddeley, 1986, 1990; Lezak, 1995). Older adults have been found to
perform significantly worse on measures of working memory as compared to younger
adults (Baddeley, 1986, 1990; Chamess, 1987, 15; Fisk & Warr, 1996; Light &
Anderson, 1985; Salthouse, 1988; Salthouse & Babcock, 1991)., In addition, working
memory has also been found to mediate the relationships between age and a number of
different cognitive abilities including the relationship between age and memory
(Campbell & Chamess, 1990; F005, 1989; Salthouse, 1992; .1993; Salthouse & Babcock,
1991; Van der Linden, Bredart, & Beerten, 1994). Thus, some researchers have
postulated a two-factor resource model of variable mediation in the age-memory
relationship that includes both perceptual speed and working memory (Park et al., 1996;
Mayr & Kliegl, 1993; Nettelbeck & Rabbitt, 1992; Bryan & Luszcz, 1996). However, it
seems that working memory may only be included in the mediational process for certain
types of memory function. Specifically, Park et a1. (1996) found that working memory

mediated part of the relationship between speed and memory performance for free and

cued recall tasks (recall in the context of no additional information and recall afler being
provided with a cue for remembering, respectively), but not for those requiring spatial
memory abilities (recall of the spatial layout or sequencing of visually presented
information); in other words working memory was implicated in those types of memory
tasks thought to require the use of more resources. Thus, in order to examine this role of
working memory in the age-memory relationship the present study will use free recall
performance as its measure of memory function.

Not only does there seem to be a resource contingency associated with working
memory and its mediational influences, but there also is some discrepancy in the literature
as to whether working memory and processing speed mediate the relationship together or
if working memory actually serves as a secondary mediator between processing speed
and memory performance. For instance, Mayr & Kliegl (1993) and Nettelbeck & Rabbitt
(1992) found these two theoretical constructs to work as mediators together to account for
the age-related variance in memory performance. However, Park et a1. (1996) found
speed to be the sole mediator, accounting for all of the age-related variance in memory
performance with working memory serving as secondary mediator through which speed,
in part, exerted its effect on memory. This latter finding was also supported in a study by
Salthouse & Babcock (1991) in which the potential influence of various working memory
components on age-related deficits was investigated. Their findings suggested that the
relationship between age and working memory was mediated by perceptual speed as
results showed that the age-related variance in working memory performance was

significantly reduced when perceptual speed was statistically controlled.

Based on the literature examined thus far, it seems that both perceptual speed and
working memory play very important roles in the decline of memory with age and should
therefore be considered in any theoretical account of this phenomenon. It also seems that
the influence of working memory may be one of secondary importance, but influencing
memory performance directly, nonetheless.

Since processing speed and working memory are constructs that have been
differentiated in the literature in terms of their mediating roles in the relationship between
age and memory, the present study will control for these variables when examining the
unique mediating influences of other aspects of executive functioning. Even though
working memory has been considered to be a component of executive functioning, it has
also already been consistently shown in previous research to mediate the age-memory
relationship as described above; thus, its importance is known. What is not known,
however, is the extent to which other aspects of executive functioning mediate the
relationship between age and memory. Indeed, this particular theoretical construct as a
whole has more recently been considered in terms of its potential to influence the age-
memory relationship. A brief review of this research is provided below.

Executive Function

Executive function includes a wide range of cognitive abilities involved in
problem solving such as planning, strategic and abstract thinking, self-monitoring,
shifting tasks and behavioral inhibition. Those functions thought to characterize the
central executive include those aspects of cognitive activity that control and integrate
other cognitive actions, including memory (Lezak, 1995; Shimamura, 1995). These

conscious functions work to develop strategies for success and orient towards goal-

directed behavior (Lezak, 1995). Researchers have proposed that age-related declines in
these functions create the disruption in memory function experienced by many
individuals in the normal elderly population (Dempster, 1992; Moscovitch & Winocur,
1992; Parkin, 1996; Troyer, Graves & Cullum, 1994; Woodruff-Pak, 1997). There is
considerable evidence to support this hypothesis. Some of this research, however, must
be interpreted under the assumption that executive functioning processes are anatomically
localized in the frontal lobes of the brain, and indeed, there is considerable evidence to
support this assumption (Lezak, 1995; Moscovitch & Winocur, 1992; Stuss & Benson,
1986). Thus, before examining the evidence suggesting that executive functioning serves
as a mediator in the relationship between age and memory, it is first necessary to discuss
this construct’s association to the frontal lobes of the brain.
Executive function and the frontal lobes of the braig

The relationship between executive functioning and the frontal lobes ofthe brain
has been supported in a number of studies demonstrating that executive firnctioning
measures are sensitive to frontal lobe damage. Specifically, the Wisconsin Card Sorting
Test (WCST; Heaton, 1981), a standard measure of executive functioning thought to
assess hypothesis formation and set-shitting (Moscovitch & Winocur, 1995), has been
shown to be sensitive to dysfunction in the frontal lobes’ neurological connections with
other areas of the brain (Amett, Rao, Bemardin, Grafrnan, Yetkin, & Lobeck, 1994) and
has been associated with the activation of the frontal lobes using PET technology
(Berman, Ostrem, Randolph et al., 1995). In addition, the WCST has been described as a
measure of concept formation processes and inhibition of inappropriate responses,

operations thought to be controlled by the frontal lobes of the brain (Amett et al., 1994).

Poor performance on another standard test of executive function, the Stroop Color Word
Test (ST; Golden, 1978), thought to measure the ability to ignore competing but
irrelevant information, has also been associated with focal frontal lobe damage (Van der
Linden, Bruyer, Roland, & Schils, 1993; Perret, 1974; Holst & Vilkki, 1988). The
Trailrnaking Test (TMT; Reitan, 195 8) is another test of executive function that has been
described as a measure of frontal lobe functioning (Reitan, 195 8; Picton, Stuss, &
Marshall, 1986). Finally, the tests of verbal fluency have also been thought of as
measures of central executive abilities and thus a reflection of fi'ontal functioning.
Specific research associating verbal fluency performance with this particular area of the
brain includes those studies reported by Moscovitch & Winocur (1995) in which deficits
in the letter fluency task have been found in individuals with left orbitofrontal lesions.
Verbal fluency test performance was also found by Crowe (1992) to decline when
prefrontal pathology is present.

Thus, there is considerable evidence linking executive functioning to the integrity
of the frontal lobes of the brain. It is noteworthy to mention, however, that dysfunction in
executive processes can also occur after injury to other areas of the brain (Lezak, 1995).
More recently, researchers have questioned whether those functions forming what is
collectively referred to as the construct of executive function are actually localized in the
frontal lobes of the brain (Parkin, 1998). These researchers argue that such cognitive
functions may also be linked to the integrity of other neurological areas outside of the
frontal system. Some have even called into question the usefulness and empirical
accuracy of the executive functioning concept itself (e. g. Parkin, 1998). They argue that

executive functioning processes are not specifically linked to frontal regions of the brain

10

and, in fact, the use of the term ‘executive functioning’ as a general, all-encompassing
construct for many underlying cognitive abilities is misled and empirically unjustified.
These researchers argue that the literature actually points to the localization of various
cognitive abilities collectively known as executive functions in many different areas of
the brain depending on the specific ability in question. These arguments certainly have
some merit. However, the relationship between executive functioning and the frontal
lobes of the brain is supported by much empirical research. The compelling arguments
are those that attempt to make clear distinctions between the individual components of
the construct. In order to address this issue in the present study, the correlations between
the various measures of executive functioning will be examined. If the measures are not
highly correlated with one another, suggesting the presence of independent influences of
the different aspects of executive functioning, then the extent to which each aspect of
executive functioning (as measured by the various tests used in this study) eliminates the
variance in the age-memory relationship will be examined separately. In this way,
different aspects of executive functioning, as measured by different neuropsychological
tests, will be investigated individually. If, however, the measures of executive
functioning do highly correlate with one another, suggesting the presence of one general
construct, then the measures will be combined to form an overall composite score of the
construct as a whole.

Working within this theoretical framework of localization of function in the
frontal lobes of the brain, three lines of evidence implicating a potential mediating role of
executive functioning in the relationship between age and memory can be identified.

First. there is an accumulation of evidence suggesting that executive functioning, as

measured by a number of standard neuropsychological tests assessing this area of
cognition, decreases with age. The second line of research linking this
neuropsychological construct to age-related memory decline includes evidence suggesting
that there is actual neurodegeneration of the frontal lobes of the brain during the aging
process (Albert & Kaplan, 1980; Shimamura, 1990; West, 1996; Woodruff-Pak, 1997).
The third line of research concerns the striking similarities found between memory
deficits associated with aging and those deficits that result from frontal lobe injury.
These three lines of research surrounding age-related declines in executive functioning
are discussed below.
Age-related decline in executive function

The first of the three lines of research in support of the executive fimctioning
hypothesis is the evidence suggesting that performance on standard measures of executive
functioning declines with age. Based on their research findings, Albert & Kaplan (1980)
developed what is known as the Frontal System Hypothesis, a hypothesis formed from
their conclusion that older adults “do not develop adequate techniques for selectively
attending to material unless a task is constructed in such as way that this necessity is
minimized” (p.416). Their research found that when confronted with a task requiring the
use of systematic and organized strategies for success, the elderly individual displays
inadequacies in performance; in other words, inadequacies in executive functioning.

There are many other studies indicating that older adults perform significantly
worse than younger adults on tests of executive functioning (Haaland, Vranes, Goodwin,
& Gary, 1987; Mittenberg, Seidenberg, O’Leary, & DiGuilio, 1989; Whelihan & Lesher,

1985). Mittenberg et al. (1989) analyzed the performance of elderly individuals on a

series of neuropsychological tests and determined, using factor analytic techniques, that
functions associated with the frontal lobes of the brain were the primary components of
observed cognitive impairment. Similarly, on one measure of frontal lobe functioning,
the Self-Ordered Pointing Task (SOPT), older adults have consistently been found to
perform worse as they enter the sixth decade of their lives (Diagneault & Braun, 1993;
Shimamura & Jurica, 1994). In addition, poorer performance (slower performance and
increased errors) on the Stroop Test with advanced age have been consistently
demonstrated (Boone, Miller, Lesser, Hill, & Elia, 1990; Houx, J olles, & Vreeling, 1993;
Spreen & Strauss, 1998) with age effects most prominent on the color-word interference
trial (Cohn, Dustman, & Bradford, 1984; Diagneault, Braun, & Whitaker, 1992; Macleod,
1991). Whelihan & Lesher (1985) found the older group of subjects in their study to have
lower scores on six different tests of executive functioning including the Stroop Test.

Age differences on the Trail Making Test (Kennedy, 1981) have also been found.

Further, there is also evidence to suggest a decline in performance with age on the
Wisconsin Card Sorting Test (WCST) (Leach, Warner, Hotz-Sud, Kaplan, & Freedman,
1991) that is most notable for the increase in perseverative errors in older groups (Craik et
al., 1990) and it is these types of errors that are the most strongly associated with frontal
dysfunction (Heaton, 1981). Boone, Ghaffarian, Lesser, Hill-Gutierrez, & Berrnan (1993)
conducted a study instituting a cross-sectional design to determine age-related differences
on the WCST and found that older subjects performed worse on two different measures
of this test of executive functioning as compared to younger adults. Other studies have
found similar age-related performance deficits on the WC ST (Mejia. Pineda, Alvarez,

Ardila, 1998). Finally, executive functioning proved to be an age-related phenomenon

l3

in a study conducted by Parkin & Walter (1992) as older adults were found to perform
significantly worse than younger adults on all measures executive functioning used
including the WCST.

It is important to note, however, that there has been some inconsistency in the
literature for the demonstration of age-related decline executive functioning. For
example, Haaland et al. (1987) did find a decline in the number of completed categories
and an increase in total errors on the WCST for those individuals older than 80 years of
age; however, these researchers did not find an increase in perseverative errors or
significant failures to maintain set in this older group of individuals. In addition, no
impairments on this test were found for those older adults between the ages of 64 to 80.
Similarly, Benton, Eslinger, & Damasio (1981) found that adults over 80 years were
impaired on a test of verbal fluency, but did not find such impairment in the younger
comparison group used. Thus, there are some studies that do not indicate a decline in
executive functioning with age, but it is very clear that the majority of them do indicate
such age-related impairment.

Age-related frontal lobe degeneration

The second line of research linking this neuropsychological construct to age-
related memory decline includes evidence suggesting that the frontal lobes of the brain
are affected by age (Albert & Kaplan, 1980; Shimamura, 1990; West, 1996; Woodruff-
Pak, 1997). There are also a number of neuropathological and in vivo neuroimaging
studies that suggest the prefrontal cortex is a brain region strongly affected by the aging
process (Coleman & Flood, 1987; Mamo, Meric, Luft, & Seylaz, 1983; Raz, Torres,

Spencer, & Acker, 1993; Tachibana, Meyer, Okayasu, & Kandula, 1984). Other studies

linking the normal aging process to deficits in frontal function include studies of brain
anatomy (Squire, 1987; Haug et al., 1983). Specifically, Squire (1987) conducted a
review of the studies of neuronal loss during the aging process and one of his conclusions
was that the “losses from the prefrontal cortex may be sizeable, even in terms of a daily
estimate” (p.119). Similarly, Haug and his colleagues (1983) identified significant
neuronal loss in the prefrontal cortex with age. In addition, behavioral data also indicates
a decline in fiontal lobe functioning with age consistent with the neuroanatomical
evidence (Albert & Kaplan, 1980; Whelihan & Lesher, 1985; Haaland, Vranes, Goodwin,
& Gary, 1987). Many researchers have actually suggested that deficits of frontal lobe
function are the first deficits found to arise in the normal aging process (Albert & Kaplan,
1980; Diagneault, Braun, & Whitaker, 1992).

Combined with the abovementioned research is a third line of evidence in support
of the executive functioning hypothesis. This research concerns the similarity found
between memory deficits associated with aging and those deficits that result from frontal
lobe injury. Again, the rationale behind this supporting evidence relies on the
relationship between executive function and the frontal lobes of the brain.

Similarity between age-related memory deficitfimd those associated with frontal lobe
m

The third line of research lending support to the idea that executive functioning
may play a mediating role in the relationship between memory and age is the evidence
suggesting there is a remarkable similarity between the memory deficits found in normal
older adults and deficits in those individuals with frontal lobe lesions. Recent reviews of

the literature in this area point to these similarities in deficits of free recall performance,

15

sequencing of responses, and memory for spatio-temporal context (Luszcz & Bryan,
1999; Moscovitch & Winocur, 1995; Petrides & Milner, 1982; Squire, 1987). Indeed,
many studies have reported correlations between standard neuropsychological tests of
frontal dysfunction and measures of certain types of memory including temporal order
(Milner, Petrides, & Smith, 1985), source (Craik et al., 1990; Shimamura, Janowsky, &
Squire, 1991; Schacter, Harbluk, & McLachlan, 1984) and episodic (Parkin & Walter,
1991; Troyer, Graves, & Cullum, 1994) memory. Other studies also indicate potential
dependence of memory functions such as the release from proactive interference (the
ability to free up learning processes for continuing exposures to new information) (Lezak,
1995; Squire, 1982) and normal free recall relative to recognition (Hanley, Davies,
Downes, & Mayes, 1994; Parkin, Yeomans, & Bindschaedler, 1994) on the integrity of
frontal lobe function. Still, other studies linking frontal function with memory have
found that a greater number of perseverative errors on the WCST is related to poorer fact
recall and poorer contextual memory (Spencer & Raz, 1994).

Explanations for the relationship between frontal function and memory
performance have revolved around the idea that inadequate frontal function interferes
with those processes necessary for successful acquisition and retrieval of information. In
terms of acquisition, theorists have postulated that older adults do not utilize mnemonic
strategies effectively to organize and elaborate information at encoding which impairs
their memory performance relative to younger adult groups (dellaRocchetta, 1986; Craik
& Lockhart, 1972; Poon, 1985). Thus, executive function may work to initiate and
implement the use of mnemonic strategies in this way (Moscovitch & Winocur, 1992). In

terms of retrieval, it has been suggested is that older adults have difficulty using strategic

and conscious processes for successfully accessing information needed to be recalled
(Moscovitch, 1989; Moscovitch & Winocur, 1992). An alternative explanation lies in the
difficulty older adults may have in overcoming the effects of interference in their
cognitive processes (Stuss, Kaplan, Benson, Weir, Chiuli, & Sarazin, 1982).

Summary and a Few Caveats

It is clear that there are many studies implicating frontal dysfunction in age-related
memory decline (Parkin & Lawrence, 1994; Hanninen, Hallikainen, Koivisto, Partanen,
Laasko, Riekkinen, Soininen, 1997) and these findings are consistent with the hypothesis
that executive functioning abilities may mediate the relationship between age and some
types of memory tasks. However, there are few studies that take processing speed and
working memory into consideration when examining the role of executive functioning in
age-related memory performance. For example, Troyer et al., (1994) found the executive
functioning variable to mediate 36% of the age-related variance in episodic memory
performance in a sample of healthy older adults, but this study did not include processing
speed and working memory in their account of the age-memory relationship. Thus, the
results are inconclusive. The present study will examine the mediating effects of
executive functioning along with those of processing speed and working memory on
verbal free recall memory performance.

The rationale for focusing specifically on free recall performance is based on the
results of the study by Parkin & Walter (1992) which indicated that an increase in non-
contexually—based responses in a recognition memory task were related to poorer
performance on the WCST. This suggests that contextually-based recognition memory

performance decreases as frontal lobe functioning, as measured by tests of executive

l7

function, declines in older adults. This provides evidence for the role of executive
functioning in recognition memory as it declines with age. It also suggests that, since free
recall performance is thought to rely heavily on memory for context (Parkin & Walter,
1991), scores on this type of task would be even more strongly related to declines in
frontal lobe functioning. Indeed, Moscovitch & Winocur (1995) argue that the more
resource-dependent retrieval processes characterizing free recall memory performance are
mediated by the frontal lobes and hippocampal system of the brain whereas the retrieval
processes associated with recognition performance are mediated by the hippocampal
system only. The former claim of partial mediation by the frontal lobes in recall in
supported by the findings of many different studies (Moscovitch, 1989; Moscovitch &
Umilta, 1990; Moscovitch & Umilta, 1991). A study by Wheeler, Stuss, & Tulving
(1995) also supports this claim. These authors performed a meta-analysis of the effect of
frontal lobe damage on episodic memory performance. Results indicated marked deficits
in free recall performance. It was noted by Moscovitch & Winocur ( 1995) that
individuals with frontal lobe damage seemed to show impairment when the memory task
called for organizational or strategic abilities for successful completion, such as in a task
involving the recall of stories or the recall of categorized lists of words; both of these
tasks require the use of organization and strategy. In addition, they note that when the
structure is provided by the experimenter, the memory deficits seen in these frontal lobe
impaired individuals is very much reduced or even eliminated. A study by Parkin &
Lawrence (1994) found a correlation between differences in performance of recall and
recognition and performance on the WCST in older adults. This makes intuitive sense

because although free recall is thought to primarily involve the declarative memory

18

system, as mentioned above, successful completion of this type of memory task also
involves the ability to organize, manipulate, and retrieve information (Shimamura, 1990).
To the extent that a memory task requires the implementation of strategies for successful
remembering, executive functioning should be an essential cognitive mediator. Thus,
free recall is a memory task more likely to depend on elements of executive functioning
than other types of memory tasks such as cued recall or recognition memory tasks, for
example. Both of these use cues to aid recall and place less demands on cognition. In
other words, a free-recall performance measure of memory will be sufficient for
demonstrating the role of executive functioning in the age-memory relationship if, indeed,
such a mediational effect does exist.

Postulates concerning the nature of the mediating role of executive function in this
relationship certainly include the mediating effect of working memory. However, for the
purposes of this paper, the constructs of executive ftmctioning and working memory will
be considered separately. There are many other components of executive function, such
as attentional processes, mental flexibility in set-shifling, response inhibition, and verbal
fluency that might also mediate the relationship between age and memory. The
contributions of working memory and these other aspects of executive function are
difficult to separate out, but the present study will attempt to examine the independent
contributions of these constructs. Furthermore, there exists theoretical literature to
support the separation of these constructs (Moscovitch & Winocur, 1992; Moscovitch &
Umilta, 1990) and factor analyses have revealed loadings on separate factors for measures
of working memory and executive functioning (Bryan, 1998).

In summary, the literature suggests that the aging process exerts its effects on

19

memory performance, at least in part, through processing speed, working memory and
executive functioning. This study will not only attempt to verify the existence of these
mediating relationships, but will also attempt to make clear the extent to which executive
functioning mediates the relationship between age and memory performance in
comparison with the mediating effects of processing speed and working memory.
Hypotheses
Based on the research reviewed above, the specific hypotheses of the present
study include the expectation of the following results:

(1) Confirmation of a decline in performance with age for all measures used including
those assessing the variables of memory, executive function, processing speed, and
working memory.

(2) Processing speed, working memory, and executive functioning will all mediate the
relationship between age and memory performance. In other words, the relationship
between age and memory will be reduced once processing speed, working memory
and executive function are controlled.

(3) If the multiple measures used for executive functioning are not highly correlated with
one another it may be that the different measures of executive functioning make
different contributions to the age-related variance in memory performance. The
extent to which each aspect of executive functioning (as measured by the various tests
used in this study) eliminates the variance in this relationship will be examined in this
case; however, hypotheses concerning this are exploratory in nature without strong a

priori predictions.

20

METHOD
Participants
A total of 241 adults participated in this study ranging in age from 54-87 years (M

= 68.97, SD = 7.8). All participants were home-dwelling community elderly individuals
recruited through local newspaper advertisements and talks given to local community
groups. Participant variables taken into consideration include number of years of
education, estimated VIQ (determined by the number of errors on the American version
of the National Adult Reading Test [AMNART; Gober & Sliwinski, 1991] and years of
education), and self-reported levels of depression (as measured by the Beck Depression
Inventory [BDI; Beck & Beck, 1972] and the Geriatric Depression Scale [GDS; Brink et
al., 1982]). Those individuals with significant levels of depression (scores of 30 or higher
on the Beck Depression Inventory and/or 20 of higher on the Geriatric Depression Scale)
were excluded from the sample. In addition, the Mini-Mental State Examination
(MMSE; F olstein, F olstein, & McHugh, 1975) was used as a screening measure for
cognitive functioning; only subjects scoring above the standard cutoff of 24 points were
included in the final sample. This was to ensure that the sample examined was
representative of the normal aging population. Given the exclusionary criteria used this
study, the actual sample size used in the data analyses was 210. There were 118 females.
Finally, each participant was offered the opportunity to participate in memory and
attention training workshops as a result of participating in the study.

Procedure

Participants were individually administered a battery of tests aimed at assessing

their mood and cognitive abilities in a session of 90-120 minutes. For the purposes of

21

this study, however, only results from measures of executive functioning, processing
speed, working memory, and verbal memory were analyzed and reported. All tests were
of the paper-and-pencil type and the ordering of tests were randomly varied across
participants. Other relevant background information was also gathered at the time of
testing.
Measures

The measures used in this study include the following:
Screening Instruments

Beck Depression Inventom (BDI; Beck& Beck, 1972): The BDI is a self-report
measure consisting of 21 multiple-choice statements concerned with a particular
depressive symptom. The subject is asked to rate their experience on a scale of graded
severity. The total score for this test is determined by adding the highest number circled
for each of the 21 items; the maximum score is 63. Higher scores indicate higher levels
of depression. The authors of this test identified the following cutoff scores: 0-9 =
normal; 10-15 = mild depression; 16-19 = mild/moderate depression; 20-29 =
moderate/severe depression; 30+ = severe depression (Spreen & Strauss, 1998).

There is an extensive amount of psychometric data for the BDI. The authors of
this test reported a test-retest reliability estimate above .90 (Beck, 1970). Other research
has revealed a Spearman-Brown reliability estimate of .93 and an interitem internal
consistency estimate of .86 (Reynolds & Gould, 1981). Other researchers have reported a
coefficient alpha for this measure of .88 (Steer et al., 1989). In addition, concurrent
validity estimates between the EDI and the MMPI Depression Scale (Reynolds & Gould,

1981), the Hamilton Rating Scale (Brown, Schulberg & Madonia, 1995) and clinical

22

 

ratings of depression (Schaefer, Brown & Watson, 1985) have been reported to be .75,
.85 and .66, respectively.

Geriatric Depression Scale. (GDS; Yesavage, Brink, Rose, Ltun, Huang, Adey &
Leirer, 1983): The GDS consists of 30 yes/no self-administered questions for which the
directionality of scoring changes randomly. This test was developed specifically for use
with older adults and, therefore, does not include items that focus on issues the authors
found to be less relevant to an aging population (e. g. guilt, sexuality and suicide). It does,
however, include somatic items which are thought to be more appropriate for older
populations (Spreen & Strauss, 1998). The subject is required to read each statement and
answer by circling the yes/no response which most accurately reflects their experience.
The total score for this test is calculated by adding the point values assigned to each
response with the following cutoff scores identifying varying levels of depressive
symptomotology: 0-9 = normal; 10-19 = mild depression; 20-30 = moderate/severe
depression (Spreen & Strauss, 1998).

In terms of reliability, the item-total correlations of the GDS range from .32-.83,
the split-half correlation was .94 and the internal consistency was also found to be .94
(Koenig et al., 1988). The GDS correlates with other self-report measures of depression
including the Beck Depression Inventory (Beck & Beck, 1972; r = .73), the MMPI
Depression Scale (Bielauskas & Lamberty, 1992; r = .72), the DMS-based Symptom
Checklist for Major Depressive Disorders (Bielauskas & Lamberty, 1992; r = .77) and the
Hamilton Depression Scale (Hamilton, 1967; r = .83) (Yesavage, Brink, Rose & Adey,
1986). Factor analyses revealed a major factor of dysphoria (unhappiness, dissatisfaction

with life, emptiness. downheartedness, worthlessness and helplessness). In addition, two

23

minor factors were revealed: one of worry, dread and obsessive thought and the other one
of apathy and withdrawal (Parrnelee, Lawton, & Katz, 1989). Criterion validity has been
measured against the Research Diagnostic Criteria and found to be .82 (Yesavage et al.,
1983)

Mini Mental Status Exam. (MMSE; Folstein et al., 1975): The MMSE was
developed as a primary measure of orientation to be used in assessments of cognitive
functioning. However, the items on this test also measure memory ability, visuospatial
functioning, written expression and the ability to follow simple commands. The test
consisted of 30 items; a cutoff score of 24 is recommended with most populations (Lezak,
1995).

Test-retest reliabilities for this test over a 24—hour period were .85-.99. Test-retest
reliability for this test over a 2 year period was .38. Inter-rater reliability has been shown
to be above .65 (Foster et al., 1988). In addition, performance on the MMSE correlates
with both the Verbal (r = .39) and Performance (r = .30) IO scores of the Wechsler Adult
Intelligence Scale-Revised, providing convergent validity for this test (Mitrushina & Satz,
1991). In fact, this test has been shown to correlate not only with measures of general
intelligence, but also with measures of memory, attention and concentration and
executive functions (Axelrod et al., 1992). According to Spreen & Strauss (1998) studies
show that the MMSE has adequate specificity and sensitivity for detecting dementia
especially in cases of moderate to severe forms of impairment. Norms for the MMSE by
age (18-85) and education based on a sample size of more than 18,000 have been

published by Crum et al. (1993).

24

Processing Speed (PS)

One measure of processing speed was used in this study. The Symbol Digit
Modalities Test (SDMT; Smith, 1991) is a timed test comprised of both written and
verbal components. In the written portion of the test the subject is asked to write in
numbers in empty boxes that are placed below that of boxes with various nonsense
syllables. The correct number to be recorded is determined by the subject’s use of a key
presented at the top of the page. The key pairs each of the nine nonsense geometrical
figures with the numbers one through nine. In the oral portion of the test, the correct
numbers are recorded by the examiner. The subject is given 90 seconds for each portion
of the test. The score obtained from this test is the number of correctly “substituted”
numbers in each portion of the test with a maximum score of 110 on each.

The SDMT is described as a test of “visual scanning, tracking, and motoric speed”
(Spreen & Strauss, 1998). As reported by Spreen & Strauss (1998), out of a collection of
tests thought to measure speed of information processing, Ponsford and Kinsella (1992)
found the oral version of the SDMT to be the most sensitive measure of a reduced speed
of processing. Validity evidence for the SDMT includes observed positive correlations
between its score and scores on other tests of processing speed such as the Coding subtest
of the Wechsler Intelligence Scale for Children-Revised (r = .62) and the Digit Symbol
subtest of the WAIS-R (r = .73-.91). Test-retest reliability correlations have been
reported to be .80 (written portion) and .76 (oral portion) (Spreen & Strauss, 1998).
Working Memory (WM)

WM was assessed through use of the WAIS-R (Wechsler, 1981) Backward Digit

Span Subtest (DSpB). This test involves presenting increasingly longer strings of

25

numbers (fiom 2-8) and asking the subjects to repeat numbers in reverse order, thus
requiring a large degree of mental manipulation of stimuli presented. As Lezak (1995)
reports, “The reversed digit span requirement of storing a few data bits briefly while
juggling them around mentally is an effortful activity that calls upon the working
memory, as distinct from the more passive span of apprehension measured by Digits
Forwar ” (p. 367).
Executive Function (EF)

EF was assessed by three different measures including The Stroop Color and

Word Test (ST; Golden, 1978), the Wisconsin Card Sorting Test (WCST; Heaton, 1981),

 

and the Trailrnaking Test (TMT; Reitan, 1958).

The Stroop Color 3nd Word Test. (Stroop; Golden, 1978): The Stroop consists of
one reading and two naming trials. The reading trial requires the naming of words (either
“blue”, “red”, “green”, or “yellow”) printed in black ink in columns of 20 words with five
columns on the page. The first naming trial requires the naming of the color in which a
group of four “x”s is printed. These groups of colored “x”s are also in the same format at
the words in the reading trial. Finally, the second naming trial requires the naming of
color when names of colors are printed in a color that is different from the actual word
itself. This second naming trial is the “interference” condition.

Scores on this test indicate the decrease in color-naming which is called the
“color-word interference effect.” The Stroop effect is the phenomenon that occurs with
increasing errors and decreasing number of items correctly identified in the colored word
list as compared to the first two lists. Performance is assessed by the time required to

complete each naming trial and the difference between the interference condition and the

26

simple naming of color dots; as stated above, this score is called the interference effect
(Lezak, 1995; Spreen & Strauss, 1998).

The Stroop is thought to measure the degree to which participants are able to
ignore competing but irrelevant information. It requires the subject to be able to
consciously disregard the actual word and concentrate on the color in which the word is
printed (Moscovitch & Winocur, 1995). The test has also been described as a measure of
the degree to which individuals are able to shift their attentional resources and response
inhibition (Spreen & Strauss, 1998). Validity evidence for the Stroop as a measure of
frontal lobe function includes observed high correlations with other tests of frontal lobe
functioning including a verbal fluency test (r = .58) and a version of the Tower of London
(r = .65) (Spreen & Strauss, 1998). Reliability for this test is reported to be “satisfactory”
by Lezak (1995). More specific test-retest reliabilities were reported by Spreen & Strauss
(1998) to be .90, .83, and .91 for the three portions of the Stroop.

Wisconsin Card Sorting Test. (WCST; Heaton, 1981): The WCST is a test that
requires the participant to sort up to 128 consecutive cards, on which are printed one to
four symbols printed in one of four colors, under one of four stimulus key cards. The
participant must deduce from the examiner’s feedback (i.e. saying “correct” or “incorrect”
after each response) the correct principle by which to sort the cards and this changes each
time the participant sorts 10 cards correctly. This change is not conveyed to the
participant; it must be deduced from examiner feedback and response strategy must be
modified as a result. The principles by which the cards can be sorted include geometric
form, color, or number of symbols.

The full, 128-card version with six categories was administered in this study.

27

 

Performance was assessed by the total number of perseverative errors. Those errors
identified as perseverative were in accordance with Heaton’s (1981) criteria: 1) a
response in Categories 2-6 that would have been correct in the immediately preceding
category; 2) repetitions of the first incorrect unambiguous response in Stage 1 (before the
first category is completed); and 3) repetitions of any response after three successive
incorrect, unambiguous matches involving that response.

This test is thought to measure “the ability to form abstract concepts, to shift and
maintain set, and utilize feedback”(Spreen & Strauss, 1998). It has also been described as
a measure of hypothesis formation and set-shifting (Moscovitch & Winocur, 1995) and as
a measure of concept formation processes and inhibition of inappropriate responses,
operations thought to be controlled by the frontal lobes of the brain (Amett et al., 1994).
Validity evidence for this test as a measure of frontal lobe functioning includes research
findings that individuals with prefrontal lesions tend to display perseverative responses
on this test (Milner, 1964; Heaton, 1981; Anderson, Jones, Tranel, Tranel, & Damasio,
1990). In addition, neuro-imaging technique research has also providing evidence that
the WCST is a measure of frontal lobe function. Specifically, magnetic resonance
imaging (MRI) studies have found that the volume of the dorsolateral prefrontal cortex is
significantly correlated with the number of perseverative error made on the WCST in
normal young and normal old adults (Raz, Gunning, Head, Briggs, Dupuis, McQuain,
Loken, Thornton, & Ackers, 1995; Raz, Head, Gunning, & Acker, 1996). In addition, a
study using SPECT scan technology found the WCST task to be associated with
activation of the left dorsolateral prefrontal cortex (Rezai, Anderson, Alliger, Cohen,

Swayze, & O’Leary, 1993). Finally, PET findings also indicate that performance on the

28

WCST is associated with frontal lobe functioning (Berman, Ostrem, Randolph, et al.,
1995). Both intrascorer and interscorer reliability estimates are very impressive ranging
from .88 to .96 (Lezak, 1995).

Trailmaking Test. (TMT; Reitan, 1958): The TMT consists of two parts, A and
B. Trails A requires the drawing of a line to connect numbered circles in chronological
order. Trails B requires the drawing of a line to connect numbered and lettered circles in
interchanging chronological and alphabetical order. Thus, the test taker mustdraw a line
from 1 to A to 2 to B, and so on. On both parts of this test, mistakes made by the subject
concerning the order of circles are corrected by the examiner.

The TMT was scored as the time required to complete each trial, and the
difference in time between trials B and A was calculated and used as a measure of
performance on this test. This difference score reflects the time of cognitive processing
(B) subtracted from psychomotor speed (A). In other words, the difference score allows
one to assess the cognitive processes underlying performance by controlling for
psychomotor speed required in performing the task (Hanninen et al., 1997).

The TMT has been described as “an attention task with an interference
component, involving visual scanning skills, set-shifting ability, and complex conceptual
tracking (Hanninen et al., 1997). There is substantive validity evidence suggesting that
this test is also a measure of frontal lobe functioning (Reitan, 1958, 6; Picton et al., 1986).
Reliability of the difference score is reported to be .71 (Lezak, 1995).

Free-reg Memory Performance
Performance in free recall memory performance was assessed by the California

Verbal Learning Test (CVLT; Delis, Kramer, Kaplan, & Ober, 1987). The CVLT is a

29

 

measure of verbal learning and memory consisting of five oral presentations of a list of 16
shopping items (Monday’s list) followed by periods of free-recall. This is followed by
one oral presentation of a second, interference, list of 16 shopping items (Tuesday’s list)
and a period of free-recall. Each list consists of four items that belong to four different
categories. Immediately after the presentation and free-recall of Tuesday’s list, the
subject is asked to recall all of the items from Monday’s list in a period of free-recall as
well as in a period of cued-category recall (Short Delay). This same procedure is
undertaken 20 minutes later (Long Delay). Finally, a recognition trial is completed
involving the oral presentation of 44 shopping items with the subject’s objective being
that of identifying those items on Monday’s list. There are no time limits on any of the
memory tasks undertaken with this test.

In the free recall portion of the CVLT participants receive a list of 16 different
words. Within this list existed four categories of words including fruits, clothing, tools
and spices/herbs. The words are presented verbally one second per word. After all words
are read by the experimenter, the participants are asked to recall the words they just heard
in any order. Recall occurs after each list is presented. The correlations between the three
obtained scores of free-recall performance on this test (the average free-recall of the first
five trials, the short-delay free-recall and the long-delay free-recall) were positive and
significant. Therefore, a composite score that combined these three scores was used as an
overall indicator of memory performance. This composite was calculated by converting
each of these scores to z-scores and summing them resulting in a single composite
measure of free-recall memory performance.

The authors of this test report split-half reliability coefficients ranging from .77-

3O

.86. Analyses on this tests’s normative sample also revealed a coefficient alpha of .74
(Delis, Freeland, Kramer & Kaplan, 1988). The correlations between the CVLT and
other tests purporting to measure the intended construct are said to be modest (Lezak,

1995).

31

RESULTS
Descriptive Statistics

Means and standard deviations of age, years of education, BDI, GDS and MMSE
scores for this sample are displayed in Table 1.

Means, standard deviations and correlations among the study variables and age
appear in Table 2. As predicted in hypothesis #1, age was correlated negatively, but
modestly, with free—recall memory performance (5 = -.30 and p_ < .01 ). Age correlated
with the executive function measures, such that increasing age was associated with poorer
performance (Trailmaking Test (TMT) r = .30 and p < .01; Wisconsin Card Sorting Test
(WCST) r = .30 and p < .01), with the exception of the Stroop Test interference score
which was not associated with age. Therefore, no further analyses were conducted with
this particular test of executive functioning. As a result, the operationalization of
executive function was limited to the TMT and the WCST. Age was also related with the
speed of processing measure, the Symbol Digit Substitution Test (SDMT) (r = -.46 and p
< .01) such that increasing age was associated with slower performance. Increasing age
was also associated with lower working memory performance as measured by the
Backwards Digit Span of the WAIS-R (DSpB) (E = -.21 and p < .01).

Of the executive function measures, the TMT (r = -.20 and p < .01) and the
WCST (r = -.22 and p_ < .01) correlated with free recall, such that higher performance was
associated with better recall. The measure of processing speed, SDMT, correlated most
strongly with recall with faster processing related to better recall (r = .36 and p < .01).

F inally. the measure of working memory was not found to be associated with recall

32

performance to any significant degree and was therefore not included in subsequent
analyses.
Composite Scores

Based on the correlations between various measures, composite scores were
formed to simplify analyses and to avoid overrnodelling the data. First, as displayed in
Table 3, correlations between the three scores of free-recall performance as assessed by
the CVLT were positive and significant. Therefore, a composite score that combined the
average free-recall of the first five trials, short-delay and long-delay free-recall of this
measure was used as an overall indicator of memory performance by converting each of
these scores to z-scores and summing them resulting in a single composite measure of
free-recall memory. This composite score was used in all subsequent analyses. Second,
as can be seen in Table 2, the correlation between the remaining two tests of executive
functioning (TMT and WCST) was positive and significant (r = .23 and p <01) and so
these scores were also converted to z—scores and summed to form a single composite
measure of executive functioning. As seen in Table 2, this composite score
(EXECUTIVE) correlated with age such that increasing age. was associated with poorer
performance (5 = .39 and p < .01). The EXECUTIVE composite also correlated with
free-recall memory such that better performance on the executive tasks was associated
with better performance on the memory task (1 = -.27 and p < .01).

Tests for Linearity

Prior to conducting the regression analyses, the relationship between age and all

cognitive measures was examined to determine whether or not there were any non-linear

components. The cognitive measures were regressed on age. Subsequent addition of a

33

quadratic function of age to the regression models did not produce a significant increase
in R2. Thus, none of the relationships deviated from linearity which simplified all later
analyses.

Hierarchical Regression Analyses

In order to elucidate the mechanisms underlying memory function and age-related
differences in memory, hierarchical multiple regression analyses were used. The
dependent variable for the regressions was the free-recall memory composite score.
Hypothesis #2 involved the prediction that measures of executive function, processing
speed and working memory would account for the age-related variance in free-recall
performance. As stated above, working memory was not related to free-recall. In other
words, the prediction that working memory would mediate the relationship between age
and memory was not supported. Thus, this variable was not included in the regression
analyses.

Hypothesis #3 involved the exploratory analysis of the relative contributions of
the difference aspects of executive function assessed in the study to the relationship
between age and memory. As stated above, the Stroop was not found to be associated
with age and was therefore not included in further analyses. However, performance on
both the TMT and the WCST was found to be related to age and to free-recall memory in
the expected directions and so the relative contributions of the aspects of executive
function measured by these tasks to age-related memory decline were examined. TMT
scores were included as a primary measure of attentional processes and the WCST

number of perseverative errors was included in the analyses as a primary measure of set-

34

shifting; both are important cognitive abilities lying under the rubric of executive
function.

The results of the multiple regression analyses appear in Table 4. Models 1-4
address Hypothesis #2. Model 1 shows the amount of variance in free-recall memory
performance predicted by age alone for this sample. Models 2 and 3 assess the age-
related variance in free-recall memory after controlling for executive function and
processing speed, respectively. Model 4 assesses the age-related variance in free-recall
memory after controlling for both processing speed and executive function together. It
also assesses the relationship between executive functioning and memory once processing
speed has been taken into account. By comparing the relative effects of age in Models 1
with Model 2, one can assess the amount of age-related free-recall variance accounted for
by a decline in executive function. By comparing the relative effects of age in Models 1
with Model 3, one can assess the amount of age-related free-recall variance accounted for
by a decline in processing speed. And finally, by comparing the relative effects of age in
Models 1 with Model 4, one can assess the amount of age-related free-recall variance
accounted for by processing speed and executive function together.

Models 5 and 6 address Hypothesis #3. More specifically, models 5 and 6 assess
the age-related variance in free-recall after controlling for attention and set-shifting,
respectively. The comparison of these two models allows one to examine the relative
contributions of these two aspects of executive function to age-related memory decline.

These results indicate that there was at least a partial reduction in the age-related
variance in memory performance in each case where the potential mediating variables

were controlled. Thus, each of these variables accounted for, or explained, part of that

35

age-related variance. The actual percentage of age-related variance (%ARV) accounted
for by mediating variables also appears in the Table 4. This index is calculated by
computing the difference between the age-related variance in memory before and after
mediating variables had been entered, divided by the amount of age-related variance
before mediators had been entered, multipled by 100.

Model 1 shows that age predicted 8.8% of the variance in recall when entered
alone; this represents 100% of the age-related variance in free recall performance. Model
2 shows that executive function predicted 7.1% of the variance in free recall which
represents 52.3% of the age-related variance (% AVR = (.088 - .042) / .088). However,
after executive function had been entered, age continued to accounted for a significant
part of the variance in free recall (4.2%), and so executive function was only a partial
mediator in the relationship between age and free recall. Model 3 shows that processing
speed contributed 12.6% of the variance in free recall which represents 73.9% of the age-
related variance. However, after processing speed had been entered, age continued to
account for a significant amount of the variance in free recall (2.3%), and so processing
speed was only a partial mediator in the relationship between age and free recall. Model
4 shows that when entered together, executive function and processing speed predicted
14.6% of the variance in free recall and, after these variables had been entered, age
accounted for a nonsignificant 1.4%, which represents 84.1% of the age-related variance.
Thus, these variables when entered together, completely mediated the relationship
between age and free recall. Model 4 also shows that executive function continues to be a
significant predictor of memory performance even when processing speed has been

controlled. Model 5 shows that attention predicted a significant 4% of the variance in

36

free recall which represents 31.8% of the age-related variance. Model 6 shows that set-
shifiing predicted a significant 4.8% of the variance in free recall which represents 35.2%

of the age-related variance.

37

DISCUSSION

Hypothesis #1 of this study involved the prediction that there would be significant
age-related declines in performance on measures of executive functioning, processing
speed, working memory and free-recall memory. This hypothesis was supported. One
exception was the Stroop Color and Word Test (Stroop), one of the executive function
measures. This is surprising as there exists a vast amount of literature indicating a
decline in performance on this test with increasing age (Boone et al., 1990; Cohn et al.,
1984; Diagneault et al., 1992; Houx et al., 1993; Macleod, 1991; Spreen & Strauss, 1991;
Whelihan & Lesher, 1985). Albeit less consistently, however, others have also failed to
find a relationship between Stroop performance and age (Bryan, 1998). This may be am
important finding. It may be that in highly competent older adults there is no significant
decline in Stroop performance, or prior decline in the abilities assessed in this task as a
function of age have already taken place and are therefore not picked up in the context of
a nonlongitudinal design.

Hypothesis #2 predicted that processing speed, working memory, and executive
functioning would all mediate the relationship between age and memory performance.
This hypothesis was only partially supported. More specifically, working memory was
not found to be associated with free-recall memory performance and, as a result, was not
included in subsequent analyses. In light of previous research implicating working
memory as an important mediator in the relationship between age and memory
performance (Bryan & Luszcz, 1996; Campbell & Chamess, 1990; F 005, 1989; Mayr &
Kliegl, 1993; Nettelbeck & Rabbitt. 1992; Park et al., 1996; Salthouse, 1992; 1993;

Salthouse & Babcock, 1991; Van der Linden et al., 1994) and in light of the vast amount

of literature indicating a decline in performance on the Stroop with increasing age (Boone
et al., 1990; Cohn et al., 1984; Diagneault et al., 1992; Houx et al., 1993; Macleod, 1991;
Spreen & Strauss, 1991; Whelihan & Lesher, 1985), the most likely explanation for not
finding the existence of expected relationships with this sample may be related to the
attenuation of correlations in this study due to small variances in performance for the
measures used in this study. In addition, the reliability of the measures used, though
satisfactory, it certainly not perfect. Imperfect reliability most likely also attenuated the
correlations in this study. These methodological limitations are discussed and further
elaborated upon in a later section of this report.

Although support for this hypothesis was not found with regard to the construct of
working memory, both processing speed and executive function were found to account
for some of the age-related variance in free recall. However, after controlling for these
variables, age continued to significantly predict free recall; thus, executive function and
processing speed were only partial mediators of the relationship between age and memory
when entered into the regressions alone.

In addition, the results suggest that processing speed contributes more to age-
related free recall performance (73.9%) than executive function (52.3%). This finding is
not entirely surprising as research has been particularly consistent in demonstrating a
decline in processing speed with age (Cerella, 1990; Fisk & Warr, 1996; Hartley, 1986,
1993; Hultsch et al., 1990; Kail & Salthouse, 1994; Salthouse, 1985, 1992; Schaie, 1989,
1994; Van Gorp, Wilfred, Satz, & Mitrushina, 1990) and in demonstrating the mediating
role of processing speed in the relationship between age and various aspects of cognitive

functioning (Lindenberger et al., 1993; Park et al., 1996; Salthouse, 1993, 1994;

Salthouse & Babcock, 1991). In fact, Salthouse (1980, 1985, 1996) has argued that
memory performance, in particular, is negatively affected by age and that this negative
relationship is due, in part, to a decline in the speed of information processing. Indeed,
processing speed has been found to mediate the relationship between age and paired-
associated (requiring recall after receiving a cue) and free-recall (requiring active,
conscious search processes) measures of memory (Bryan & Luszcz, 1996; Lindenberger
et al., 1993; Nettelbeck & Rabbitt, 1992; Salthouse, 1993), as well as other measures of
memory performance (Hultsch et al., 1990; Luszcz, 1992; Salthouse et al., 1988).
Explanations for this relationship are compelling and include the argument that the rate at
which to-be-remembered items can be rehearsed or repetitively cycled is decreased by a
decline in processing speed with age and this, in turn, accounts for deficits observed in
memory performance (Baddeley, 1986; Salthouse,l980).

The results of the regression analyses also indicate that processing speed and
executive function, together, significantly reduce the relationship between age and
memory, accounting for 84.1% of the age-related variance in free recall. In other words,
age was no longer a significant predictor of free recall performance when both of these
variables were taken into consideration. Thus, the importance of both of these variables
in age-related memory decline is highlighted in this study. This finding lends a large
amount of support to the executive functioning hypothesis. Also leading support to this
hypothesis is the fact that executive function continued to be a significant predictor of
free recall performance when processing speed had been controlled. Thus, one can say
that the role of executive function in memory performance is significant and above and

beyond that of processing speed in this study.

40

Explanations for the role of executive functioning in the relationship between age
and memory performance include the idea that inadequate frontal function interferes with
those processes necessary for successful acquisition and retrieval of information. As
reviewed earlier in this report, in terms of acquisition, theorists have postulated that older
adults do not utilize mnemonic strategies effectively to organize and elaborate
information at encoding which impairs their memory performance relative to younger
adult groups (dellaRocchetta, 1986; Craik & Lockhart, 1972; Poon, 1985). Thus,
executive function may work to initiate and implement the use of mnemonic strategies in
this way (Moscovitch & Winocur, 1992). In terms of retrieval, it has been suggested is
that older adults have difficulty using strategic and conscious processes for successfully
accessing information needed to be recalled (Moscovitch, 1989; Moscovitch & Winocur,
1992). An alternative explanation lies in the difficulty older adults may have in
overcoming the effects of interference in their cognitive processes (Stuss et al.,: 1982).

Hypothesis #3 stated that if the multiple measures used for executive functioning
were not highly correlated with one another their contributions to age-related free-recall
performance would be examined separately and that the nature of these examinations
would be exploratory in nature. As noted above, performance on the Stroop Test was not
included in the mediational analyses since scores on this test were not found to be
associated with age. However, the two remaining tests of executive functioning
(Trailmaking Test and Wisconsin Card Sorting Test) were found to be associated with
age and were included in all analyses. The correlation between scores on these two tests
was found to be positive and significant; however, the correlation was modest.

Consequently, the two aspects of executive functioning that these two tests purport to

41

measure (attention and set-shifting) were treated as separate constructs in models 5 and 6
of the regression analyses. It was thought that this type of analysis (examining each of
the two aspects of executive functioning separately) would provide a more detailed,
precise picture of the role executive function plays as a mediator in the relationship
between age and memory as compared to examining the construct as a single unity
(Parkin, 1998). The results indicated that both attention and set-shifting contributed
similar amounts to free-recall performance and both accounted for similar amounts of the
age-related variance in free-recall performance.

It should be addressed that age contributed only 8.8% to the overall variance in
memory performance in this particular sample of older adults. This finding is surprising
in light of a long history of past research consistently demonstrating a decline of memory
with age (e.g. Burke & Light, 1981; Golski et al., 1998; Moscovitch & Winocur, 1995;
Paolo et al., 1997; Shimamura, 1990). It may be that this sample population is
surprisingly intact and well functioning, and among this group, age may not be a very
important variable with respect to memory functioning. In that case, it would be
important to compare this group to other less intact groups of older individuals to
ascertain what protective factors may involved in age-related cognitive decline. In
addition, however, this could also be an underestimate of the variance in memory
attributable to age in which case it is necessary to raise questions related to the
measurement of intended constructs and characteristics of the sample, each of which is

discussed in the following section.

42

Methodological Considerations

In assessing the quality of research in this area, it becomes quite clear that
difficulty arises in interpretation of the findings because of certain methodological
limitations. Below is a discussion of the major limitations identified in the present
investigation.

Measurement Issues

First, the use of different measures, or operational definitions, of the intended
constructs across individual studies has created difficulty in interpretation. Operationally
defining the constructs of interest poses a particularly troublesome limitation in this area
of research. What is the most accurate way of assessing constructs such as executive
function, processing speed and working memory? The answer to this question is different
for different people working within this area of research and this makes comparison
between studies and interpreting data very difficult. This questions also raises issues of
construct validity and highlights the need to use multiple measures when attempting to
assess constructs of the sort examined in this study.

Indeed, the use multiple measures of constructs within any particular study is ideal
as it works to minimize the variance specific to particular procedures or from the stimulus
materials
used to assess these constructs. In other words, no single test is likely to be a pure or
completely accurate estimate of the intended construct because of the influence of task-
specific factors even if the task seems to meet the criteria outlined in the definitions and
current understanding of these constructs. More specifically, the variance within any

given measure can be postulated to involve: (1) variance associated with the theoretical

43

construct it purports to measure; (2) variance associated with specific aspects of the
measure itself (e. g. stimulus materials, procedure, etc); (3) variance associated with
random error variance or unsystematic error; and (4) variance associated with theoretical
constructs one is not trying to measure, but that the test is picking up nonetheless. Thus,
there are three out of four sources of variance contributing to the score obtained for an
individual on some measure that interfere with the accurate assessment of her
performance. The Wisconsin Card Sorting Test (WCST), for example, has been
criticized for its lack of specificity as an indicator of frontal lobe functioning (Anderson,
Damasio, Jones, & Tranel, 1991) and for the lack of reliability data available (Spreen &
Strauss, 1998).

Imperfect measurement is a limitation that extends beyond the use of the WCST.
It is plausible and likely that tasks such as the Stroop Color and Word Test (Stroop)
require working memory processing demands to some extent even though this test was
not used as a primary measure of this construct in the present study; instead, the
backwards form of the WAIS-R Digit Span subtest was used as the measure of working
memory. This is but one example of the overlap of functions assessed by each of the tests
used in this study and the difficulty in interpretation that can arise when attempting to
accurately operationalize these constructs.

In light of this information, the use of multiple measures of any given construct
becomes very important. It would then be possible to combine scores on the related
measures to form composite scores that can be assumed to be more accurate
representations of the constructs of interest. This is a procedure endorsed by Salthouse &

Babcock (1991). This study used this approach with regard to free-recall memory.

44

However, it would have been ideal to include multiple measures of all constructs
examined in this study. The use of factor analytic techniques to provide further construct
validity evidence for chosen measures would also have been ideal.
The Sample

The second major limitation in this study is concerned with its sample which
consisted of normal, older adults. Exclusions from the sample were made when subjects
indicated clinical levels of depression and/or exhibited impaired performance on a
dementia screener. This was to ensure that the final sample would consist only of those
individuals experiencing the normal aging process. However, this was a fairly well-
functioning sample and may therefore have encompassed selection biases not controlled
for in this study. For instance, the mean years of education in this sample was 15.85.
This is a relatively high mean level of education. Samples such as this are
underrepresentative of socioeconomic, educational and other types of relevant'variation
in the general population. However, there is clearly interest in understanding how well
educated, highly functioning older adults are performing as a group as they age. This is
certainly an understudied population. It is simply important for the reader to note this
particular aspect of the sample used in this study and to take it into consideration when
attempts are made to generalize the findings in this study to a larger population of older
individuals. Nevertheless, as mentioned earlier, this lack of variation in the sample likely
contributed to an attenuation of the correlations in this study because of a restriction of
range in performance.

This discussion highlights the fact that a younger comparison group was not

included in this study. Including younger groups for comparison purposes would have

45

increased the range of variation in performance. The comparison of extreme age groups
as opposed to the exploring questions of interest in a continuous age range sample would
be more likely to pick up on and emphasize age-related variance in cognitive
performance. In addition, the inclusion of a younger group would have made it possible
to examine other interesting research questions such as: Does executive functioning relate
to the types of memory assessed in this study in normal young subjects, or is the age-
related impairment in functioning necessary before the relation appears? On the other
hand, a younger group could introduce cohort or generation biases into the study. Cross-
sectional studies have the potential to lead to an overestimation of age-related cognitive
changes due to cohort or generational factors and the obvious solution, longitudinal
investigations, have a tendency to underestimate cognitive decline due to selective
attrition of subject samples over time (Boone et al., 1990)

Correlational Research

It is the view of the author that a third limitation in thisresearch lies in the nature
of its design. More specifically, this study is correlational in nature. Therefore, causal
inferences cannot be made concerning the decline of memory with age. This point is very
important to keep in mind, certainly when making interpretations from the data, but also
in outlining the methodological issues surrounding this area of research.

Related to the issue of causality is the fact that it is very difficult, if not
impossible, to sort out all of the possible contributing variables surrounding or co-
occurring with the decline of memory with age. These other, uncontrolled variables serve
to impede any attempt to isolate the contributions of particular variables of interest.

Furthermore. correlational data indicates associations between the variables, but offers no

46

explanation as to why these relationships exist. It is necessary to estimate significant
relationships, but it is also necessary to postulate the mechanisms through which the
relationships exist. This kind of analysis can be partially accomplished through the use of
hierarchical regression as was done in the present study; however, a more sophisticated
and informative approach for elucidating the mechanisms through which the relationships
exist may lie in the use of statistical techniques such as structural equation modeling. The
utility of this particular type of data analysis in this area of research is provided below
along with other recommendations for future research.
Future Research

As briefly mentioned above, there are some noteworthy comments to be made
concerning issues related to data analysis and measurement of constructs for future
research in this area. More specifically, a developing trend in this area of research has
been the use of statistical procedures such as structural equation modeling. There are
many strengths associated with using this particular approach. First, it permits the
simultaneous evaluation of competing theoretical views within a single analytical
paradigm. Second, it provides a more easily understandable account of the relative
contributions of different mechanisms to the behavior of interest, in this case the decline
of memory with age. Third, it allows you to control for measurement error in your study.
Finally, the use of structural equation modeling allows you to examine a theoretical
construct at different levels of analysis. For example, it makes it easy to look at the
contributions of the variables of interest on recall and recognition separately. However, it
also allows for analysis of a more general memory construct combining all memory tasks.

Another issue related to data analysis and measurement worth noting is the

47

importance of using factor analytic techniques in this area of research. Future research
should strive to achieve the most accurate measure of the constructs of interest. The use
of confirmatory factor analytic techniques allows one to verify that the measures used in a
particular study reflect the purported underlying constructs.

In addition, future research would likely benefit from the inclusion of a younger
comparison group to increase the variation in performance assessed and, in turn, highlight
age-related differences in cognition, including differences in other types of memory not
accounted for in this study. As stated in a previous section of this report, it is likely that
executive function processes, in particular, will play different roles with regards to
recognition or cued memory tasks as compared to free-recall tasks. The potential
mediating roles these variables play in other types of memory performance (i.e.
prospective memory) is also important and warrants further investigation. In addition,
this study found the two aspects of executive functioning assessed to account for similar
amounts of the age-related variance in memory performance; however, the roles of other
aspects of executive functioning are still unknown and should thus be examined in future
research in this area. Similarly, the roles of other relevant cognitive variables as potential
mediators in this relationship should be examined. Together, processing speed and
executive function accounted for approximately 84% of the age-related variance in
memory performance. Clearly, there is variance left unexplained. Investigation of other
potentially relevant factors is certainly warranted.

Summary and Conclusions
To summarize, it appears that executive function and processing speed only

partially mediate age differences in free-recall when considered alone. However, when

48

considered together, these variables completely mediate the relationship. These findings
highlight the importance of both processing speed and executive function in the
relationship between age and memory. It was also found that executive function
continued to be a significant predictor of age-related variance in memory performance
once processing speed was controlled. This lends a large amount of support to the
executive function hypothesis of cognitive aging and, again, highlights this construct’s
importance in the relationship between age and memory. This study also found that two
specific aspects of executive function, attention and set-shitting, made similar
contributions to the relationship between age and memory performance; however, the
relative contributions of other aspects of executive function are still unknown. It is
advised that future research continue with attempts to further elucidate the mechanisms

through which these relationships exist.

49

APPENDIX

50

 

APPENDIX

Table 1

Means and standard deviations of subjects’ age, education, estimated VIQ and scores on

 

 

 

 

 

 

 

 

the BDI, GDS and MMSE.

Participant Variable N Mean (SD)
Age (years) 210 68.97 (7.8)
Years of Education 210 15.85 (3.02)
(years)

Estimated VIQ“ 210 117.74 (14.0)
BDI total score 210 6.32 (4.8)
GDS total score 210 5.86 (5.0)
MMSE total score 210 28.39 (1.5)

 

 

 

 

Note. The equation used to compute the estimated VIQ = 118.2 - .89 (AMNART errors)
+ .64 (Years of Education); (Grober & Sliwinski, 1991 ). ‘

51

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52

Table 3

Correlations between the three free-recall scores as assessed by the CVLT.

 

 

 

 

 

Score Five Trial Average Short-Delay Long-Delay
Average Five 1.00

Trials

Short-Delay .83** 1.00

Long-Delay .84** .88** 1.00

 

 

 

 

 

Note. ** p value <01.

53

Table 4
Hierarchical multiple regression analyses predicting free-recall memory performance

from age, executive function, processing speed, attention and set-shifting.

 

 

 

 

 

 

 

 

Model Step Predictors Beta R2 R2 %ARV
Change

1 1 Age -.296 .088 .088“ 100.0
2 1 Executive Function -.267 .071 -----

2 Age -.225 .113 .042M 52.3
3 1 Processing Speed .355 .126 -----

2 Age -.172 .149 .023* 73.9
4 1 Processing Speed .355 .126 -----

2 Executive Function -.156 .146 .020*

3 Age -.140 .161 .014 84.1
5 1 Attention -. 199 .040 -----

2 Age -.259 .100 .060“ 31.8
6 1 Set-Shifting -.218 .048 -----

2 Age -.250 .104 .057" 35.2

 

 

 

 

 

 

 

 

Note. * p < .05, ** p< .01
ARV = age-related variance accounted for
Sample sizes ranged from 199 to 206.

54

LIST OF REFERENCES

LIST OF REFERENCES

Albert, M.S., & Kaplan, E. (1980). Organic implications of neuropsychological
deficits in the elderly. In L.W. Poon, J .L. F ozard, L.S. Cerrnack. D. Arenberg, & L.W.

Thompson (Eds), New directions in memog and aging: Proceedings of the George A.
[alland Memorial Conference. (pp. 403-432). Hillsdale: Erlbaum.

Anderson, S. W., Damasio, H., Jones, R. D., & Tranel, D. (1991). Wisconsin
Card Sorting Test performance as a measure of frontal lobe damage. Journal of Clinical
and Consulting Psychology. 13, 909-922.

Anderson, S.W., Jones, R.D., Tranel, A.P., Tranel, D., & Damasio, H. (1990). Is
the Wisconsin Card Sorting Test an index of frontal lobe damage? Journal of Clinical
and Experimental Neuropsychology, 12, 80 (Abstract).

Amett, P.A., Rao, S.M., Bemardin, L., Grafman, J., Yetkin, F 2., & Lobeck, L.
(1994). Relationship between frontal lobe lesions and Wisconsin Card Sorting Test
performance in patients with multiple sclerosis. Neurology, 44. 420-425.

Axelrod, B. N., Goldman, R. S. & Henry, R. R. (1992). Sensitivity of the Mini-
Mental State Examination to frontal lobe dysfunction in normal aging. Journal of
Clinical Psychology, 48 68-71.

Baddeley, A. (1986). Working memom Oxford: Clarence Press.
Baddeley, A. (1990). Human memory: Theory and practice. London: Erlbaum.

Beck, A. T. (1970). Depression: Causes and Treatment. Philadelphia: University
of Pennsylvania Press.

Beck, A.T., & Beck, R.W. (1972). Screening depressed patients in family
practice. Postgraduate Medicine, 52, 81-85.

Benton, A.L., Eslinger, P.J., & Damasio, AR. (1981). Normative observations
on neuropsychological test performances in old age. Journal of Clinical
Neuropsvchology, L 33-42.

Berrnan, K. F., Ostrem, J. L., Randolph, C., Gold, J ., Goldberg, T. E., Coppola,
R., Carson, R. E., Herscovitch, P., & Weinberger, D. R. (1995). Physiological activation
of a cortical network during performance of the Wisconsin Card Sorting Test: A positron
emission tomography study. Neuropsychologica, 44, 1027-1046.

56

Bielauskas, L. A. & Lamberty, G. J. (1992). Assessment of depression in elderly
patients. Clinical Neuropsychologisté, 322.

Boone, K. B., Ghaffarian, S., Lesser, I. M., Hill-Gutierrez, E. & Berman, N. G.
(1993). Wisconsin Card Sorting Test Performance in Healthy, Older Adults:
Relationship to Age, Sex, Education, and IQ. Journal of Clinical Psychology, 49, 54-60.

Boone, K. B., Miller, B. L., Lesser, I. M., Hill, B. L., & D’Elia, L. (1990).
Performance on frontal lobe tests in healthy, older individuals. Developmental
Neuropsychology, 6, 215-223.

Brink, T.L., Yesavage, J .A., Lum, O., Heersema, P.H., Adey, M., & Rose, TS.
(1982). Screening tests for geriatric depression. Clinical Gerontologist, 1, 37-43.

Brown, C., Schulberg, H. C., & Madonia, M. J. (1995). Assessing Depression in
Primary Care Practice with the Beck Depression Inventory and the Hamilton Rating Scale
for Depression. Psychological Assessment, L 59-65.

Bryan, J ., & Luszcz, MA. (1996). Speed of information processing as a mediator
between age and free recall performance. Psychology and aging, 11, 3-9.

Burke, D.M. & Light, LL. (1981). Memory and aging: The role of retrieval
processes. Psychological Bulletin, fl 513-516.

Campbell, J. 1., & Chamess, N. (1991). Age-related declines in working-memory
skills: Evidence from a complex calculation task. Develgamental Psychology, 26. 879-
888.

Cerella, J. (1990). Aging and inforrnation-processing rate. In J. E. Birren & K.
W. Shaie (Eds). Handbook of the Psychology of Aging (3rd.ed., pp. 201-221). New
York: Academic.

Chamess, N. (1987). Component processes in bridge bidding and novel problem-
solving tasks. Canadian Journal of Psychology, 41, 223-243.

Cohn, N.B., Dustman, R.E., & Bradford, DC. (1984). Age-related decrements in
Stroop Color Test performance. Journal of Clinical Psychology, 40, 1244-1250.

Coleman, P.D. & Flood, D.G. (1987). Neuron numbers and dendritic extent in
normal aging and Alzheimer’s Disease. Neurobiology of Aging, 8, 521-545.

Craik, F .I.M., Anderson, N.D., Kerr, S.A., & Li, K.Z.H. (1995). Memory
changes in normal ageing. In A.D. Baddeley & B.A. Wilson (Eds). Handbook of
memory disorders (pp.211-241). Chichester, England UK: John Wiley & Sons.

57

Craik, F.I.M., & Byrd, M. (1982). Aging and cognitive deficits: The role of
attentional resources. In F.I.M. Craik, & S. Trehub (Eds), Aging and cogr_ritive
processes. (pp. 191-211). New York: Plenum Press.

Craik, F.I.M & Lockhart, RS. (1972). Levels of processing: A framework for
memory research. Journal of Verbal Leaminmnd Verbal Behavior, 11, 671-684.

Craik, F.I.M., Morris, L.W., Morris, R.G., & Loewen, ER. (1990). Relations
between source amnesia and frontal lobe functioning in older adults. Psychology and

Aging, 5, 148-151.

Crowe, S. F. (1992). Dissociation of two frontal lobe syndromes by a test of
verbal fluency. Journal of Clinical and Experimental Neuropsychology, 14, 327-339.

Crum, R. M., Anthony, J. C., Bassett, S. S. & Folstein, M. F. (1993). Population-
based norms for the Mini-Mental State Examination by age and education level. Journal
of the American Medical Association. 269, 2386-2391.

Delis, D. C., Freeland, J., Kramer, J. H., & Kaplan, E. (1988). Integrating clinical
assessment with cognitive neuroscience: Construct validation of the California Verbal
Learning Test. Journal of Consulting and Clinical Psychology, 56, 123-130.

Delis, D.C., Kramer, J .H., Kaplan, E., & Ober, BA. (1987). Qalifomia Verlfl
Learning Test: Adult Version Manual. San Antonio, TX: The Psychological Corporation.

Dempster, F. N. (1992). The rise and fall of the inhibitory mechanism: Toward a
unified theory of cognitive development and aging. Developmental Review, 12, 45-75.

Diagneault, 8., Braun, C. M. J. (1993). Working memory and the Self-Ordered
Pointing Task: Further evidence of early prefrontal decline in normal aging. Journal of
Clinical and Experimental Neuropsychology, 15, 881-895.

Diagneault, S., Braun, C. M. J ., Witaker, H. A. (1992). Early effects of normal
aging on perseverative and non-perseverative prefrontal measures. Developmental
Neuropsychology, 8, 99-114.

Fisk, J .E. & Warr, P. (1996). Age and working memory: The role of perceptual
speed, the central executive, and the phonological loop. Psychology and Aging, 11, 316-
323.

58

 

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

Foos, P. W. (1989). Adult age differences in working memory. Psychology and
Aging, 4, 269-275.

Foster, J. R., Sclan, S., Welkowitz, J ., Boksay, I. & Seeland, I. (1988).
Psychiatric assessment in medical long-term care facilities: Reliability of commonly used
rating scales. International Journal of Geriatric Psychiatry. 3. 229-233.

Golden, J .C. (1978). Stroop Color and Word Test. Chicago, IL: Stoelting
Company. . . - ,

. - Golski, S., Zonderman,-AB” Malamut, B.L., & Resnick, SM. (1998). Verbal
and figural recognition memory: Task development and age associations. Experimental

Aging Research, 24, 359-385.

Grober, E., & Sliwinski, M. (1991). Development and validation of a model for
estimating premorbid verbal intelligence in the elderly. Journal of Clinical and
Experimental Neuropsychology, 13, 933-949.

Haaland, K.Y., Vranes, L.F., Goodwin, J .S., & Garry, P.J. (1987). Wisconsin
Card Sort Test performance in a healthy elderly population. Journal of Gerontology, 33,
345-346. 5

Hamilton, M. (1967). Development of a rating scale for primary depressive
illness. British Journal of Social and Clinical Psychology, 6, 278-296.

Hanley, J .R., Davies, A.D.M., Downes, J.J. & Mayes, AR. (1994). Impaired
recall of verbal material following rupture and repair of an anterior communicating artery
aneurysm. Cognitive Neuropsychology, 11, 543-578.

Hanninen, T., Hallikainen, M., Koivisto, K., Partanen, K., Laakso, M.P.,
Riekkinen, P.J., & Soininen, H. (1997). Decline of frontal lobe functions in subjects
with age-associated memory impairment. Neurology. 48. 148-153.

Hartley, J. T. (1986). Reader and text variables as determinants of discourse
memory in adulthood. Psychology and Aging, 1, 150-158.

Hartley, J. T. (1993). Aging and prose memory: Tests of the resource-deficit
hypothesis. Psychology and Aging, 8, 538-551.

Haug, H., Barrnwater, U., Eggers, R. Fischer, D., Kuhl, S., & Sass, N.L. (1983).
Anatomical changes in the aging brain: Morphometric analysis of the human

59

prosencephalon. In J. Cervos-Navarro & H.I. Sarkander (Eds). Brain aging:
Neuropathology and neuropharrnacology. (Vol 21). New York: Raven Press.

Heaton, R.K. (1981). Wisconsin Card Sorting Test Manual. Odessa, FL:
Psychological Assessment Resources, Inc.

Holst, P. & Vilkki, J. (1988). Effect of frontomedial lesions on performance on
the Stroop Test and word fluency tasks. Joumalgof Clinicai and ExperimerLal
Neuropsycholog, 10, 79 (Abstract).

Houx, P. J., Jolles, J. Vreeling, F. W. (1993). Stroop interference: Aging effects
assessed with the Stroop Color-Word Test. Experimental Aging Resaarch. 19. 209-224.

Hultsch, D.F., Hertzog, C., & Dixon, RA. (1990). Ability correlates of memory
performance in adulthood and aging. Psychology and agipg, 5, 356-368.

Incisa della Rochetta, A. (1986). Classification and recall of pictures after
unilateral frontal of temporal lobectomy. Cortex, 22, 189-211.

Kail, R. & Salthouse, T. A. (1994). Processing speed as a mental capacity. Acta
Psychologica. 86. 199-225.

Kennedy, K. J. (1981). Age effects on Trail Making Test performance.
Perceptual and Motor Skills. 52. 671-675.

Koenig, H. G., Meador, K. G., Cohen, H. J. & Blazer, D. G. (1988). Self-rated
depression scales and screening for major depression in older hospitalized patients with
medical illnesses. Journal of the American Geriatfirics Society. 3@ 699-796.

Leach, L.R., Warner, C.M., Hotz-Sud, R., Kaplan, E., & Freedman, M. (1991).
The effects of age on Wisconsin Card Sorting variables. Journal of Clinical and
Experimental Neuropsychoogy. 13, 28 (Abstract).

Lezak, MD. (1995). Neuropsychological Assessment. 3rd edition. New York:
Oxford University Press.

Light, L.L. & Anderson, PA. (1985). Working memory capacity, age, and
memory for discourse. Jour_nal of Gerontology, 40, 737-747.

Lindenberger, U., Mayr, U., & Kliegl, R. (1993). Speed and intelligence in old
age. Psychology and Aging,8, 207-220.

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

60

Luszcz, M. A. & Bryan, J. (1999). Toward understanding age-related memory
loss in late adulthood. Gerontology. 45, 2-9.

Macleod, CM. (1991). Half a century of research on the Stroop effect: An
integrative review. Psychological Bulletin, 109, 163-203.

Mamo, H., Meric, P., Lufl, A., & Seylaz, J. (1983). Hyperfrontal pattern of
human cerebral circulation: Variations with age and arteriosclerotic state. Archives of
Neurolagy. 40, 626-632.

Mayr, U., & Kliegl, R. (1993). Sequential and coordinative complexity: Age-
based processing limitations and figural transformations. J oumal of Experimenth
Psychology: Learning. Memory, & Cognition. 19. 1297 - 1320.

Mejia, S., Pineda, D., Alvarez, L.M. & Ardila, A. (1998). Individual differences
in memory and executive function abilities during normal aging. International Journal of
Neuroscience, 95, 271-284.

Milner, B. (1964). Some effects of frontal lobectomy in man. In J .M. Warren &
K. Akert (Eds). The frontal gianular cortexjand behavior. New York: McGraw Hill.

Milner, B., Petrides, M., & Smith, ML. (1985). Frontal lobes and the temporal
organization of memory. Hum Neurobiology,_4_, 137-142.

Mitrushina, M. & Satz, P. (1991). Reliability and validity of the Mini-Mental
State Exam in neurologically intact elderly. Journal of Clinical Psychology, 47, 537-543.

Mittenberg, W., Seidenberg, M., O’Leary, D. S. & DiGuilo, D. V. (1989).
Changes in cerebral functioning associated with normal aging. J oumal of Clinical and
Experimental Neuropsychology, 1 1, 918-932.

Moscovitch, M. (1989). Confabulation and the frontal system: Strategic vs.
associative retrieval in neuropsychological theories of memory. In H.L. Roediger III &
F.I.M. Craik (Eds). yineties of memory and consciousness: Eggs in honor of Endel
Tulving. Hillsdale NJ: Erlbaum.

Moscovitch, M. & Winocur, G. (1992). The neuropsychology of memory and
aging. In F .I.M. Craik, & T.A. Salthouse (Eds), The handbook of aging and cognition.
Hillsdale: Erlbaum.

Moscovitch, M. & Winocur, G. (1995). Frontal lobes, memory, and aging.
Annals of the New York fidemy of Sciences. 769, 119-150.

Moscovitch, M. & Umilta, C. (1990). Modularity and neuropsychology:
Implications for the organization of attention and memory in normal and brain-damaged

61

people. In M. E. Schwartz (Ed.), Modular processes in dementia, Cambridge, MA: MIT
Press/Bradford.

Moscovitch, M. & Umilta, C. (1991). Conscious and nonconscious aspects of
memory: A neuropsychological framework of modules and central systems. In R.G.
Lister & H.J. Weingartner (Eds), Perspectives on cognitive neuroscience. Oxford,
England: Oxford University Press.

Nettelbeck, T. & Rabbitt, P.M.A. (1992). Aging, cognitive performance, and
mental speed. Intelligence. 16, 189-205.

Paolo, A.M., Troster, A.I., & Ryan, J .J . (1997). California Verbal Learning Test:
Normative data for the elderly. Journal of ClinicaL and Experimental Neuropsychology,
fl, 220-234.

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, 621-637.

Parkin, A.J. (1996). Exploration_s in cognitive neuropsychology. Oxford:
Blackwell.

Parkin, A.J. (1998). The central executive does not exist. Journal of the
International Neuropsycholomcai Society. 4. 518-522.

Parkin, A.J. & Lawrence, A. (1994). A dissociation in the relation between
memory tasks and frontal lobe tests in the normal elderly. Neuropsychologia. 32. 1523-
1532.

Parkin, A.J. & Walter, BM. (1991). Aging, short-terrn memory, and frontal
dysfunction. Psychobiology. 19, 175-179.

Parkin, A.J. & Walter, BM. (1992). Recollective experience, normal aging, and
frontal dysfunction. Psychology and Aging, 7, 290-298.

Parkin, A.J., Yeomans, J. & Bindschaedler, C. (1994). Further characterization
of the executive memory impairment following frontal lobe lesions. Brain and Cogm'tion,
E, 23-42.

Parrnelee, P.A., Lawton, M.P. & Katz, LR. (1989). Psychometric properties of
the Geriatric Depression Scale among the institutionalized aged. Psychological
Assessmena 1, 331-338.

Perret. E. (1974). The left frontal love of man and the suppression of habitual
responses in verbal categorical behaviour. Neuropsychologica, 12, 323-330.

62

Petrides, M. & Milner, B. (1982). Deficits on subject-ordered tasks after frontal
and temporal lobe lesions in man. Neuropsychologica. 20, 249-262.

Picton, P.W., Stuss, D.T., & Marshall, KC. (1986). Attention and the brain. In
S.L. Friedman, K.A. Klivington, & R.W. Petersen (Eds), The brain. cognition. and
education. (pp.19-79). New York: Academic Press.

Ponsford, J. & Kinsella, G. (1993). Attentional deficits following closed-head
injury. Journal of Clinical and Experimental Neuropsychology, 14, 822-838.

Poon, L.W. (1985). Differences in human memory with aging: Nature, causes,
and clinical implications. In J .E. Birren & K.W. Schaie (Eds). Igndbook of the
psychology of aging (2nd ed.). New York: Van Nostrand Reinhold.

Rabinowitz, J .C. (1984). Age and recognition failure. Journal of Gerontolggy,
fl, 65-71.

Raz, N., Gunning, F ., Head, D.P., Briggs, S.D., Dupuis, J .H., McQuain, J., Loken,
W.J., Thornton, A.E., & Ackers, J .D. (1995). In search of neuroanatomical substrates of
memory and executive abilities: Aging as a model system. Soc. Neuroscience Abstracts,
21, 195-914.

Raz, N., Head, D.P., Gunning, F., & Ackers, J .D. (1996). Neural correlates of
working memory and strategic flexibility: A double dissociation study. Paper presented
at meeting of the International Neuropsychological Society.

Raz, N., Torres, I.J., Spencer, W.D. & Acker, J .D. (1993). Pathoclysis in aging
human cerebral cortex: Evidence from in vivo MRI morphometry. Psychobiology, 21,
151-60.

Reitan, R. M. (195 8). Validity of the Trail Making Test as an Indicator of
Organic Brain Damage. Perceptual and Motor Skills, 8, 271-276.

Reynolds, W. M. & Gould, J. W. (1981). A Psychometric Investigation of the
Standard and Short form Beck Depression Inventory. Journal of Consulting and CHM
Psychology. 49, 306-307.

Rezai, K., Anderson, N.C., Alliger, R., Cohen, G., Swayze II, V., & O’Leary, D.S.

(1993). The neuropsychology of the prefrontal cortex. Archives of Neurology. 50. 636-
642.

63

Salthouse, T.A. (1980). Age and memory: Strategies for localizing the loss. In
L.W. Poon, J .L. Fozard, L. Cerrnack, D. Arenberg, & L.W. Thompson (Eds), New
directions in memory and aging. (pp. 47-65). Hillsdale: Lawrence Erlbaum.

Salthouse, T.A. (1985). A theory of cognitive aging. Amsterdam: North
Holland.

Salthouse, T.A. (1988). Resource-reduction interpretations of cognitive aging.
Developmeml Review, 8, 238-272.

Salthouse, T.A. (1992). What do adult age differences in the Digit Symbol
Substitution Test reflect? Journal of Gerontology: Psychological Sciences, 47. 121-128.

Salthouse, T.A. (1993). Speed mediation of adult age differences in cognition.
Developmental Psychology, 29, 722-738.

Salthouse, T. A. (1994). The nature of the influence of speed on adult age
differences in cognition. Developmental Psychology, 30, 240-259.

Salthouse, T.A. (1996a). General and specific speed mediation of adult age
differences in memory. Jourmrls of GerontologLSenes B. Psychological Sciences. &
Social Sciences, 51, 30-42. ‘

Salthouse, T. A. (1996b). The processing-speed theory of adult age differences in
cognition. Psychologiaal Review, & 403-428.

Salthouse, T. A. & Babcock, R. L. (1991). Decomposing adult age differences in
working memory. Developmental Psychology. 27. 763-776.

Salthouse, T.A., Kausler, D.H., & Saults, J .S. (1988). Utilization of path-analytic
procedures to investigate the role of processing resources in cognitive aging. Psychology

and Aging, 3, 158-166.

Schacter, D.L., Harbluck, J .L. & Mclachlan, DR. (1984). Retrieval without
recollection: An experimental analysis of source amnesia. Journal of Verbgrl Iflming
and Verbal Behavior, 23, 593-611.

Schaefer, A., Brown, J. & Watson, C. G. (1985). Comparison of the Validities of

the Beck, lung, and MMPI Depression Scales. Journal of Consulting and Clinical
Psychology. 53. 415-418.

64

Schaie, K. W. (1989). Perceptual speed in adulthood: Cross-sectional and
longitudinal studies. Psychology and Aging, 4, 443-453.

Schaie, K. W. (1994). The course of adult intellectual development. American
Psychologist, 49, 304-313.

Shimamura, A. P. (1990). Aging and memory disorders: A neuropsychological
analysis. In M.L. Howe, M.J. Stones, & C.J. Brainerd (Eds), Cognitive and Behavioral

Performance Factors in Agpical Aging. (pp. 37-65). New York: Springer.

Shimamura, AP. (1995). Memory and frontal lobe function. In M.S. Gazzarriga
(Ed.). The cognitive neurosciences (pp.803-813). Cambridge MA: MIT press.

Shimamura, A. P. & Jurica, P. J. (1994). Memory interference effects and aging:
Findings from a test of frontal lobe function. Neuropsychology, 8, 408-412.

Shimamura, A.P., J anowsky, J .S., & Squire, LR. (1991). What is the role of
frontal lobe damage in memory disorders? In H.S. Levin, H.M. Eisenberg, & A. L.
Benton (Eds), Frontal Lobe Function fld Dysfunction. (pp. 173-195) NewYork:
Oxford University Press.

Smith, A. (1991). Symbol Digit Mogdalities Test. Los Angeles: Western
Psychological Services.

Spencer, W.D. & Raz, N. (1994). Memory for facts, source, and context: Can
frontal lobe dysfunction explain age-related differences? Psychology and Aging, 9, 149-
159.

Spreen, O., & Strauss, E. (1998). A compendium of neuropsychological tests:
Administration, norms, and commentary, 2nd edition. NewYork: Oxford University
Press.

Squire, LR. (1982). Comparisons between forms of amnesia: Some deficits are
unique to Korsakoff’s syndrome. J oumal of Experimental Psychology: Learning;
Memory and Cognition, 8 560-571.

Squire, LR. (1987). Memory and braia New York: Oxford University Press.
Stuss, D.T. & Benson, DP. (1986). The frontal lobes. New York: Raven Press.
Stuss, D.T., Kaplan, E.F., Benson, D.F., Weir, W.S., Chiuli, S. & Sarazin, RF.

(1982). Evidence for the involvement of orbitofrontal cortex in memory functions: An
interference effect. Journal of Comparative and Physiological Psychology, 96, 913-925.

65

 

Tachibana, H., Meyer, J .S., Okayasu, H. & Kandula, P. (1984). Changing
topographic patterns of human cerebral blood flow with age measured by xenon CT.
American Journal of Roentgenology. 142, 1027-1034.

Troyer, A. K., Graves, R. E., & Cullum, C. M. (1994). Executive functioning as a
mediator of the relationship between age and episodic memory in healthy aging. A '

and Cogpition, 1, 45-53.

Van der Linden, M., Bredart, S. & Beetren, A. (1994). Age-related differences in
updating working memory. The British Journal of Psychology. 85, 145-52.

Van der Linden, M., Bruyer, R., Roland, J ., & Schils, JP (1993). Proactive
interference in patients with amnesia resulting from anterior communicating artery
aneurysm. Journal of Clinicafirnd Experimental Neuropsychology, 15. 525-536.

Van Gorp, Wilfred, G., Satz, P. & Mitrushina, M. (1990). Neuropsychological
processes associated with normal aging. Developmental Neuropsychologya, 279-290.

Wechsler, D. (1981). WAIS-R: Wechsaer Adult Intelligence Scale - Revised.
New York: Harcourt, Brace, J ovanovich.

West, R.L. (1996). An application of prefrontal cortex function theory to
cognitive aging. Psychological Bulletin. 120. 272-292.

Wheeler, M.A., Stuss, D.T., & Tulving, E. (1995). Frontal lobe damage produces
episodic memory impairment. Journal of the International Neuropsychological Sociegi,
L, 525-536.

 

Whelihan, W. M. & Lescher, E. L. (1985). Neuropsychological changes in frontal
functions with aging. Developmental Neuropsychology, 1. 371-380.

Woodruff-Pak, D. D. (1997). The neuropsycholgy of aging. Oxford: Blackwell.

Yesavage, J. A., Brink, T. L, Rose, T. L. & Adey, M. (1986). The Geriatric
Depression Rating Scale: Comparison with other self-report and psychiatric rating scales.
In L. W. Poon (Ed.). Handbooflf Clinical Memory Assessment of Older Adults.
Washington, D. C.: American Psychological Association.

Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, 0., Huang V., Adey, M. B. &
Leirer, V. O. (1983). Development and validation of a Geriatric Depression Rating Scale:
A preliminary report. Journal of Psychiatric Research, 17, 37-49.

66

 

   

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