executive functions across the adult life span: age...

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A cumulative dissertation Doctorate in Psychology (Dr. rer. nat.)

Submitted to the University of Bremen, Faculty 11

By

Dorota Buczyłowska

Bremen, February 2017

Colloquium (oral defense) on 8 June 2017

1. Supervisor: PD Dr. Monika Daseking 2. Supervisor: Prof. Dr. Franz Petermann 1. Reviewer: PD Dr. Monika Daseking 2. Reviewer: Prof. Dr. Canan Basar-Eroglu

Contents I

CCoonntteennttss

Contents ................................................................................................................................... I

List of Tables ........................................................................................................................ III

List of Figures ....................................................................................................................... IV

List of Abbreviations ............................................................................................................. V

Abstract ................................................................................................................................. VI

Zusammenfassung ............................................................................................................... VII

Introduction .......................................................................................... 1

Theoretical foundation ........................................................................ 4

1. Executive functions ................................................................................. 4

1.1 Current understanding .......................................................................................... 4

1.2 History of the concept .......................................................................................... 5

1.3 Seminal theories and models ................................................................................ 6

1.4 Developmental trajectories and aging .................................................................. 9

2. Intelligence ............................................................................................. 12

2.1 History of intellectual assessment ...................................................................... 12

2.2 Evolution of the concept .................................................................................... 13

3. Relationships between executive functions and intelligence ............ 16

3.1 Executive functions and general intelligence ..................................................... 16

3.2 Executive functions and fluid versus crystallized intelligence .......................... 18

3.3 The influence of age ........................................................................................... 19

4. The current research ............................................................................ 22

4.1 Age-related differences in executive functions .................................................. 22

4.2 Age-related relationships between executive functions and intelligence ........... 25

Empirical research ............................................................................. 29

5. Methods .................................................................................................. 29

5.1 Sample characteristics and recruitment procedure ............................................. 29

5.2 Assessment tools ................................................................................................ 31

5.3 Data management and statistical analysis .......................................................... 37

6. Results and discussion .......................................................................... 39

6.1 Age-related differences in executive functions .................................................. 39

6.2 Age-related relationships between executive functions and intelligence ........... 43

7. Implications for theory and practice .................................................. 59

7.1 Implications for neuropsychological assessment ............................................... 60

7.2 Implications for theoretical framework .............................................................. 66

7.3 Limitations and directions for future research ................................................... 67

References ........................................................................................... 71

Appendices .......................................................................................... 90

Appendix A: Publication 1 ................................................................................................... 90

Appendix B: Publication 2 ................................................................................................... 91

Appendix C: Publication 3 ................................................................................................... 92

Appendix D: Statement of the candidate’s contribution to each publication ....................... 93

Appendix E: Declaration of Originality ............................................................................... 95

List of Tables III

LLiisstt ooff TTaabblleess Table 1. Demographic composition of the sample of the study on age-related differences in

executive functions .................................................................................................... 30

Table 2. Demographic composition of the sample of the study on age-related relationships

between executive functions and intelligence ........................................................... 31

Table 3. Descriptive statistics for the NAB Executive Functions Module subtests based on

raw scores .................................................................................................................. 40

Table 4. Descriptive statistics for the NAB Executive Functions Module and WAIS-IV ...... 44

Table 5. Intercorrelations between the NAB Executive Functions Module subtests in the age

groups 18-59, 60-88, and 18-88 ................................................................................ 47

Table 6. Correlations of the NAB Executive Functions Module with the WAIS-IV in the age

groups 18-59, 60-88, and 18-88 ................................................................................ 47

Table 7. Multiple regression analyses for the WAIS-IV ......................................................... 50

Table 8. Effect sizes for differences in the correlations between the NAB Executive Functions

Module and WAIS-IV between the age groups 18-59 and 60-88 ............................. 52

Table 9. Variance explained by the best-fitting models of the WAIS-IV ............................... 54

Table D1. The candidate’s contribution to the current research studies……………………..93

List of Figures IV

LLiisstt ooff FFiigguurreess Figure 1. WAIS-IV subtests CHC classification. .................................................................... 36

Figure 2. Mean performance in the NAB Executive Module subtests across ten age groups. 41

Figure 3. Dispersion in the NAB Executive Module subtests across ten age groups ............. 42

Figure 4. Correlationship between the NAB Executive Functions Module subtests and the

WAIS-IV. ................................................................................................................. 45

Figure 5. Scatterplot with linear regression line depicting the standard scores of 18- to 59-

year olds on the WAIS-IV full scale IQ (FSIQ) as a function of NAB executive

functions index (EFI). .............................................................................................. 48

Figure 6. Scatterplot with linear regression line depicting the standard scores of 60 to 88-year

olds on the WAIS-IV full scale IQ (FSIQ) as a function of NAB executive

functions index (EFI). .............................................................................................. 48


year olds on the WAIS-IV Verbal Comprehension Index (VCI) as a function of

NAB Executive Functions Index (EFI). .................................................................. 51


year olds on the WAIS-IV Verbal Comprehension Index (VCI) as a function of

NAB Executive Functions Index (EFI). .................................................................. 51

List of Abbreviations V

LLiisstt ooff AAbbbbrreevviiaattiioonnss CHC Cattell-Horn-Carroll

CTMT Comprehensive Trail Making Test

CV coefficient of variation

D-KEFS Delis-Kaplan Executive Function System

EFI Executive Functions Index

EFs executive functions

FSIQ Full Scale IQ

g general intelligence

GAI General Ability Index

Gc crystallized intelligence

Gf fluid intelligence

MANOVA multivariate analysis of variance

MCST Modified Card Sorting Test

NAB Neuropsychological Assessment Battery

PASAT Paced Auditory Serial Addition Task

PFC prefrontal cortex

PRI Perceptual Reasoning Index

PSI Processing Speed Index

SAS supervisory attentional system

TVCF Test of Verbal Conceptualization and Fluency

VIF variance inflation factor

WAIS-III Wechsler Adult Intelligence Scale – Third Edition

WAIS-IV Wechsler Adult Intelligence Scale – Fourth Edition

WAIS-R Wechsler Adult Intelligence Scale – Revised

VCI Verbal Comprehension Index

WCST Wisconsin Card Sorting Test

WM working memory

WMI Working Memory Index

Abstract VI

AAbbssttrraacctt

Background: Executive Functions (EFs) are considered the most complex human cognitive

capacities. Despite the crucial importance of this cognitive domain for overall mental func-

tioning, no consensus on the definition, terminology, and classification of EFs has been

reached so far. Investigating age-related differences in EFs and the relationship between EFs

and intelligence may help better understand the nature of the construct.

Aims: The current thesis is aimed at exploring differences in executive performance between

healthy adults; additionally, the aim is to examine the relationship between EFs and intelli-

gence through the comparison of younger and older adults.

Methods: The Executive Functions Module of the Neuropsychological Assessment Battery

(NAB), as a measure of EFs, and the Wechsler Adult Intelligence Scale – Fourth Edition

(WAIS-IV), as a measure of intelligence, were implemented. Data of 485 NAB norming sam-

ple participants aged 18-99 were analyzed to examine age-related differences in EFs. Data of

126 NAB norming sample participants aged 18-88, who additionally completed the WAIS-IV,

were used to investigate the relationship between EFs and intelligence.

Results: Overall, decreases in the mean scores and increases in the dispersion of performance

on the NAB Executive Functions Module subtests with advancing age were observed. EF

tasks associated with fluid intelligence (i.e., Mazes, Planning, and Categories) exhibited the

greatest decrease in mean scores and the highest increase in dispersion; in contrast, EF tasks

associated with crystallized intelligence (i.e., Letter Fluency, Word Generation, and Judg-

ment) showed the lowest decrease in mean scores and the lowest increase in dispersion. Addi-

tionally, substantial age-independent and age-related relationships between the NAB Execu-

tive Functions Module and the WAIS-IV were demonstrated. The Categories and Word Gen-

eration subtests correlated substantially with most of the WAIS-IV indices, and were most

frequently included in the WAIS-IV prediction models. Age-related differences with regard to

the relationship between EFs and intelligence were associated with higher scores in older

adults; in particular, the NAB Judgment subtest correlated more strongly with several WAIS-

IV scores, and the NAB Executive Functions Index correlated more strongly with the WAIS-

IV Verbal Comprehension Index.

Conclusion: Ability-related deterioration trends in EFs, the multifactorial nature of EF meas-

ures, and age-related relationship patterns between EFs and intelligence must especially be

considered within neuropsychological assessments. Substantial relationships between EFs and

intelligence should be better reflected within the theoretical framework of cognitive abilities.

Zusammenfassung VII

ZZuussaammmmeennffaassssuunngg Hintergrund: Exekutive Funktionen (EF) werden als die komplexesten kognitiven Fähigkei-

ten betrachtet. Obwohl eine besondere Rolle für die allgemeine Kognition diesem kognitiven

Bereich zugesprochen wird, gibt es bisher keinen Konsens hinsichtlich der Definition, Termi-

nologie und Klassifikation. Die Erforschung der altersabhängigen Unterschiede in den EF und

des Zusammenhangs zwischen den EF und Intelligenz kann zum besseren Verständnis des

Konstrukts beitragen.

Fragestellung: Die vorliegende Arbeit hat zum Ziel, gesunde Erwachsene im Hinblick auf

exekutive Leistungen zu verglichen. Des Weiteren wird der Zusammenhang zwischen den EF

und Intelligenz untersucht. Hierbei wird der Frage nachgegangen, ob sich dieser Zusammen-

hang zwischen jüngeren und älteren Erwachsenen unterscheidet.

Methodik: Das Modul Exekutive Funktionen der Neuropsychological Assessment Battery

(NAB) diente als Maß für EF, während die Wechsler Adult Intelligence Scale – Fourth Editi-

on (WAIS-IV) zur Messung der Intelligenz eingesetzt wurde. Aus der NAB-Normstichprobe

stammende Daten von 485 Testpersonen im Alter von 18 bis 99 Jahren wurden analysiert, um

altersabhängige Unterschiede in den EF zu testen. NAB-Normdaten von 126 Testpersonen im

Alter von 18 bis 88 Jahren, die zusätzlich eine WAIS-IV-Testung absolviert hatten, wurden

verwendet, um den Zusammenhang zwischen den EF und Intelligenz zu untersuchen.

Ergebnisse: Im Allgemeinen zeigten sich mit zunehmendem Alter eine Abnahme des Mittel-

werts und Zunahme der Dispersion hinsichtlich der Leistungen in den Untertests des NAB

Moduls Exekutive Funktionen. Exekutive Aufgaben, die mit der fluiden Intelligenz assoziiert

werden (d.h., Labyrinthe, Planen und Kategorien), zeigten die größte Abnahme des Mittel-

werts und die größte Zunahme der Dispersion; während exekutive Aufgaben, die mit der kris-

tallinen Intelligenz assoziiert werden (d.h., Wortflüssigkeit, Wörter bilden und Urteilen),

zeigten die geringste Abnahme des Mittelwerts und die geringste Zunahme der Dispersion.

Des Weiteren wurden deutliche allgemeingültige und altersabhängige Zusammenhänge zwi-

schen dem NAB Modul Exekutive Funktionen und der WAIS-IV demonstriert. Im Allgemei-

nen korrelieren die NAB Untertests Kategorien und Wörter bilden hoch mit den meisten

WAIS-IV-Indizes und waren am häufigsten als Prädiktoren in den WAIS-IV Vorhersagemo-

dellen enthalten. Altersabhängige Unterschiede im Zusammenhang zwischen den EF und In-

telligenz waren verbunden mit höheren Werten bei den älteren Teilnehmern. Dabei korrelierte

insbesondere der NAB Untertest Urteilen stärker mit mehreren WAIS-IV-Werten und der

NAB Index Exekutive Funktionen korrelierte stärker mit dem WAIS-IV Index Sprachver-

ständnis.

Zusammenfassung VIII

Fazit: Fähigkeitsgebundene Trends der Leistungsabnahme in den EF, die multifaktorielle

Beschaffenheit der Messinstrumente und altersabhängige Zusammenhangsmuster zwischen

den EF und Intelligenz müssen insbesondere im Rahmen der neuropsychologischen Diagnos-

tik in Betracht gezogen werden. Deutliche Zusammenhänge zwischen den EF und Intelligenz

sollten besser in den theoretischen Modellen zu kognitiven Fähigkeiten berücksichtigt wer-

den.

Introduction 1

IInnttrroodduuccttiioonn Executive functions (EFs) are often classified as the highest level of human cognitive capaci-

ties. They are required for the coordination and regulation of cognition, emotion, and behav-

ior; and thus, substantial for intact functioning of every human being (Lezak, 1982; Strauss,

Sherman, & Spreen, 2006). Nevertheless, despite much interest in exploring and crucial im-

portance of this cognitive domain, many research questions are still to be answered.

The existing literature shows much inconsistency in terms of the definition, terminolo-

gy and classification of EFs (Strauss et al., 2006). Thus, working towards a widely accepted

theoretical framework is of high priority. Among many issues to be investigated, exploring

the influence of age on executive functioning may help achieve this. In fact, findings on age-

related changes in EFs across the adult life span are inconclusive (Jurado & Rosselli, 2007;

Mejia, Pineda, Alvarez, & Ardila, 1998; Wecker, Kramer, Wisniewski, Delis, & Kaplan,

2000). Particularly, ability-related deterioration trends and interindividual variability in per-

formance are the issues that require further investigations. Additionally, due to the central role

of EFs for the coordination of the subordinated cognitive processes, exploring the relation-

ships between EFs and other cognitive domains appears essential. Especially the relationship

between EFs and intelligence should be investigated more thoroughly (Lamar, Zonderman, &

Resnick, 2002). Despite existing evidence on substantial associations between EFs and intel-

ligence, the relationships between the individual components of the two constructs are not

well studied. Furthermore, age-related differences in the relationship between the two con-

structs require more extensive investigations.

A better understanding of the nature and solid theoretical foundations of the construct

of EFs are essential for clinical neuropsychology. In particular, improvements in the field of

neuropsychological diagnostics can be achieved by offering assessment tools suitable for de-

tecting cognitive dysfunctions and providing therapy guidelines.

The aim of the current doctoral thesis is to investigate the influence of age on EFs by

examining differences in regard to the mean and dispersion in EF performance between sev-

eral healthy adult age groups across a large age range. Additionally, the thesis is aimed at ex-

ploring the general relationship as well as more specific relationships between EFs and intel-

ligence. Furthermore, the influence of age on the association between EFs and intelligence is

examined.

Introduction 2

FFiieelldd ooff rreesseeaarrcchh The current doctoral thesis is based on two research studies accomplished between 2013 and

2016 at the Center of Clinical Psychology and Rehabilitation, University of Bremen. Data

used in the thesis were collected within the German standardization of the Neuropsychologi-

cal Assessment Battery (NAB). In the initial stage of the project, the German adaptation of the

NAB was created. After successful examination with a small sample, a country-wide norming

was launched. Data used in the first study were collected on four NAB norming sites in Ger-

many including Bremen, Gera, Heidelberg, and Köln, and originated from 485 participants.

Data used in the second study were collected in Bremen and originated from 126 participants.

For the second study, two assessment tools, the NAB and the Wechsler Adult Intelligence

Scale – Fourth Edition (WAIS-IV) were administered. Data collected within the two research

studies resulted in three empirical publications, which have been published in peer-reviewed

scientific journals.

SSttuuddyy II Publication 1 (see Appendix A): Buczylowska, D., & Petermann, F. (2016). Age-related differences and heterogeneity in exec-

utive functions: Analysis of NAB Executive Functions Module scores. Archives of Clinical

Neuropsychology, 31, 254-262. doi:10.1093/arclin/acw005

SSttuuddyy IIII Publication 2 (see Appendix B): Buczylowska, D., & Petermann, F. (2016). Age-related commonalities and differences in the

relationship between executive functions and intelligence: Analysis of the NAB executive

functions module and WAIS-IV scores. Applied Neuropsychology: Adult, 1-16. Advance

online publication. doi:10.1080/23279095.2016.1211528

Publication 3 (see Appendix C):

Buczylowska, D., Daseking, M., & Petermann, F. (2016). Age-related differences in the pre-

dictive ability of executive functions for intelligence. Zeitschrift für Neuropsychologie, 27,

159-171. doi:10.1024/1016-264X/a000179

Introduction 3

Besides the research conducted within the dissertation, additional research activities

were undertaken. This includes activities associated with the German NAB standardization

project; in particular, participation in the German adaptation and the coordination of the NAB

norming. One additional article and one oral presentation at a scientific conference are listed

below.

Buczylowska, D., Bornschlegl, M., Daseking, M., Jäncke, L., & Petermann, F. (2013). Zur

deutschen Adaptation der Neuropsychological Assessment Battery (NAB). [German adaption

of the Neuropsychological Assessment Battery (NAB)]. Zeitschrift für Neuropsychologie, 24,

217-227. doi:10.1024/1016-264X/a000108

Buczylowska, D., & Petermann, F. (2016, September 7-9). Age-related differences in execu-

tive functions. Oral presentation at the 2nd Neurological Disorders Summit (NDS-2016), Bal-

timore, MD, USA.

OOrrggaanniizzaattiioonn ooff tthhee tthheessiiss The current thesis is structured in two major parts. In the first part, the theoretical foundation

and in the second part, the empirical research of the thesis, are presented. The first part is

composed of four chapters, starting with an introduction into the concept of EFs from present

and historical perspective, followed by the description of the main developmental steps of EFs

including childhood, adolescence, adulthood, and age-related decline. In the second chapter,

the concept of intelligence, including its evolution and the current theoretical framework, is

presented. Additionally, the present state of research on the relationship between EFs and in-

telligence is discussed. The first part ends with an introduction into the current research by

providing the rationale for and presenting the major research questions pursued within the

current doctoral thesis. The second part is composed of three chapters, and starts with a chap-

ter on methodological issues describing sample characteristics, assessment tools and statistical

methods used. A subsequent chapter provides an overview and discussion of the results de-

rived from the current research. The final chapter summarizes the main findings and presents

potential implications for theory and practice. Limitations of the current research and direc-

tions for future research are finally outlined in the closing section of the thesis.

1. Executive functions 4

TThheeoorreettiiccaall ffoouunnddaattiioonn

11.. EExxeeccuuttiivvee ffuunnccttiioonnss Executive functions (EFs) are thought to represent the most complex mental domain (Strauss

et al., 2006). Yet there is no consensus on the definition of EFs (Jurado & Rosselli, 2007;

Miyake, Emerson, & Friedman, 2000; Reynolds & Horton, 2008; Salthouse, 2005; Strauss et

al., 2006). The term is rather a collective name for the metacognitive capacities responsible

for coordinating basic cognitive processes, such as attention, language, memory, and percep-

tion (Alvarez & Emory, 2006; Elliott, 2003; Salthouse, Atkinson, & Berish, 2003; Wecker et

al., 2000). Nevertheless, it is widely accepted that executive dysfunctions may affect all as-

pects of everyday life, and that intact executive functioning is essential for independent living

(Lezak, Howieson, Bigler, & Tranel, 2012; Strauss et al., 2006). Hence, growing interest in

investigating EFs in recent years reflects the (actual) importance of this cognitive domain.

In the present chapter, the theoretical framework of EFs is presented including the

current understanding, history, and theoretical foundations of the concept, followed by a brief

overview on the main developmental stages of EFs.

11..11 CCuurrrreenntt uunnddeerrssttaannddiinngg

Although there is a lack of a widely accepted definition, many of the attempts to define EFs

are not mutually incompatible since they just emphasize different aspects of the construct

(Obonsawin et al., 2002). In general, EFs are believed to represent abilities that are crucial for

goal-oriented behavior (Alvarez & Emory, 2006; Welsh, Pennington, & Groisser, 1991),

adapting to changing environments (V. Anderson, Jacobs, & Anderson, 2008; De Luca et al.,

2003; Jurado & Rosselli, 2007), and coping with novel tasks (Duncan, Burgess, & Emslie,

1995; Passingham, 1993). When dealing with complex circumstances or unfamiliar contexts,

well-learned behaviors are less useful. Instead of previously established routines, new strate-

gies must be implemented; essentially, EFs are those processes necessary for creating new

approaches to unknown situations (Strauss et al., 2006). Consequently, EFs “consist of those

capacities that enable a person to engage successfully in independent, purposive, self-directed,

and self-serving behavior” (Lezak et al., 2012, p. 37). Therefore, executive functioning is

thought to be crucial for all aspects of everyday life (Strauss et al., 2006).

EFs are frequently defined as higher mental abilities. This seems plausible in the light

of the complexity of tasks they are responsible for. However, it is not clear what are the key


cognitive operations involved in executive functioning and how they are related to other cog-

nitive processes. The terms frequently used to describe the components of executive function-

ing include problem-solving; mental or cognitive flexibility; planning, modifying, and com-

pleting complex tasks; inhibition, switching, updating or working memory (WM), sustained

and selective attention (Alvarez & Emory, 2006; Arffa, 2007; Baddeley & DellaSala, 1996;

Burgess, Evans, Emslie, & Wilson, 1998; Elliott, 2003; Maricle & Avirett, 2012; Miyake,

Friedman, et al., 2000; Rabbitt, 1997; van der Sluis, de Jong, & van der Leij, 2007). Further-

more, these complex cognitive operations depend upon multiple sub-processes. Thus, the key

role of EFs is to control and coordinate the operation of all these multiple processes to ac-

complish a particular goal (Funahashi, 2001). “Coordination, control and goal-orientation are,

therefore, at the heart of the concept of executive function” (Elliott, 2003; p. 50).

11..22 HHiissttoorryy ooff tthhee ccoonncceepptt

Despite the lack of agreement on the exact definition, attempts have been made already in the

past to define the core aspects of executive functioning. These definitions are partially com-

patible with the current understanding of the concept. Lezak (1982) proposed a definition,

which adequately reflects the significance of EFs for daily functioning: “capacities for formu-

lating goals, planning, and carrying out plans effectively – the executive functions - are essen-

tial for independent, creative, and socially constructive behavior” (p. 281). Much earlier,

Luria (1966) described impairments caused by the lesions of the frontal lobes generally as

intellectual disturbances. He also pointed out that, in the light of evidence showing that a le-

sion of the frontal lobes leads to a disturbance of intelligent behavior as a whole, and simulta-

neously leaves the more elementary processes unchanged, it must be concluded that the func-

tions of the frontal lobes are different from the functions of the other parts of the brain; more-

over, the functions of the frontal lobes are responsible for the coordination, monitoring, and

planning of behavior (Luria, 1976).

Executive functioning as a concept has existed since the late 19th and early 20th centu-

ries; however, the term EFs emerged later. For example, Luria was using the term frontal syn-

drome, when describing intellectual disturbances related to brain lesions (Luria, 1966). Based

on the first findings in frontal pathology obtained from adult patients with large acquired

frontal lobes injuries, EFs have primarily been associated with the frontal lobe functioning

(Ardila, 2008; De Luca & Leventer, 2008; Stuss & Alexander, 2000). As a result, the term

frontal lobe syndrome came into use and has later been used synonymously with executive

dysfunction (Ardila, 2008). Likewise, both terms executive and frontal have been used since


then interchangeably (Elliott, 2003; Stuss & Alexander, 2000). However, further clinical in-

vestigations have reported executive dysfunctions being related to lesions of other brain areas

than frontal lobes (Baddeley & DellaSala, 1996; Stuss, 2011). Moreover, functional imaging

studies indicated posterior, cortical, and sub-cortical brain regions being involved in executive

functioning (Mesulam, 1998; Roberts, Robbins, & Weiskrantz, 2002). At present, it is thus

widely accepted that EFs involve an interaction of dynamic networks, with the prefrontal cor-

tex (PFC) playing a key role (Elliott, 2003; Jurado & Rosselli, 2007; Salthouse et al., 2003).

Consequently, executive dysfunctions may result from lesions of the overall frontal system

rather than exclusively from lesions of the frontal lobes (Royall et al., 2002).

11..33 SSeemmiinnaall tthheeoorriieess aanndd mmooddeellss

Since the concept of executive functioning first emerged, numerous theories and models have

been proposed. Some of them have exerted substantial influence on the evolution of the con-

cept of executive functioning. Thus, two traditional and influential theories are hereafter pre-

sented. Additionally, due to considerable research progress in recent years, the current per-

spective is outlined as well.

MMuullttiippllee--ccoommppoonneenntt mmooddeell ooff wwoorrkkiinngg mmeemmoorryy Baddeley and Hitch (1974) proposed a three-component model of WM composed of the cen-

tral executive and two subsidiary slave systems, the phonological loop and the visuospatial

sketchpad. The authors postulated that the central executive possesses itself no storage capaci-

ty; instead, it controls the two slave systems being responsible for the temporary storage sys-

tem. The phonological loop holds speech-based information using temporary storage and an

articulatory rehearsal system; whereas the visuospatial sketchpad is capable of holding visual,

spatial, and kinesthetic information. Both components of the temporary storage system are

able to hold information for a few seconds (Baddeley, 2000). The central executive executes

its control over the storage system by using sub-processes, such as selective attention and the

ability to focus and switch attention. Another important function of the central executive is to

access and manipulate information in the long-term memory (Baddeley & DellaSala, 1996).

Later on, Baddeley (2000) extended the original model by adding a fourth component - the

episodic buffer. This additional temporary store is responsible for holding and utilizing com-

plex information by integrating information from the phonological loop and visuospatial

sketchpad.


SSuuppeerrvviissoorryy aatttteennttiioonnaall ssyysstteemm The supervisory attentional system (SAS) proposed by Norman and Shallice (1986) is a mod-

el centered on attention and action as its core elements. The SAS is a mechanism located in

the PFC, which is responsible for controlling behavior (Shallice, 2002). This mechanism op-

erates on schemas of action, which are triggered according to situational context. The basic

assumption is the distinction between automatic and controlled processing involved in the

selection and control of action. This refers to the way how certain tasks can be executed. The

automatic processes are involved in actions that can be executed without awareness of their

performance, which is the case in simple or well learned routine tasks; in contrast, the con-

trolled processes are required for the conscious control and modification of performance,

which is crucial in non-routine tasks specifically. The two complementary processes are regu-

lated by contention scheduling, a mechanism aimed at avoiding conflicts in performing rou-

tine tasks. In novel or complex tasks, however, the SAS is needed for the regulation of action,

and this can be accomplished by providing some extra activation and inhibition to schemas.

CCoolldd vveerrssuuss hhoott eexxeeccuuttiivvee ffuunnccttiioonnss Generally, the emphasis in research and theory has been on the more cognitive aspects of ex-

ecutive functioning (Happaney, Zelazo, & Stuss, 2004). In recent years, however, growing

interest on investigating the emotional aspects of EFs has emerged. Several authors proposed

a distinction between cognitive or “cold” EFs, associated with the dorsolateral area of the

PFC, and emotional/motivational or “hot” EFs, which are linked to ventromedial (i.e., orbito-

frontal) cortices (Ardila, 2008; Fuster, 2001; Grohman & Fals-Stewart, 2004; Happaney et al.,

2004; Kerr & Zelazo, 2004). Cold components of executive functioning comprise problem

solving, planning, strategy development and implementation, and WM; these abilities can

typically be well assessed by EF tests; whereas hot EFs are responsible for the control of emo-

tional and instinctual behaviors, and are involved in the coordination of cognition and emo-

tion (Ardila, 2008). The integrative role of hot EFs for affective and non-affective information

seems plausible, given the strong connections between the ventromedial PFC and the amyg-

dala as well as other areas of the limbic system (Happaney et al., 2004). The ability to fulfill

limbic impulses in accordance with social norms, which is associated with the inhibitory con-

trol of behavior (P. Miller & Wang, 2006) or affective decision-making (Happaney et al.,

2004), is considered the key competence of the hot executive functioning. Clinical observa-

tions support the distinction between cold and hot EFs, as lesions of the underlying cortical

structures cause different dysfunctions – predominantly, a lack of cognitive control within the


dorsolateral syndrome and a lack of emotional/motivational control within the orbitofrontal

and medial frontal syndrome (Ardila, 2008).

UUnniittaarryy aanndd nnoonn--uunniittaarryy nnaattuurree ooff eexxeeccuuttiivvee ffuunnccttiioonnss Despite of extensive research, none of the existing theories on executive functioning has re-

ceived sufficient support from findings (Maricle & Avirett, 2012); however, the existing

models can be assigned to two currently dominating approaches in the literature. Some con-

sider EFs as a unitary and hierarchical system, whereas the other perceives them as a set of

distinct but associated cognitive processes (Jurado & Rosselli, 2007; Maricle & Avirett,

2012). Due to existing evidence for both unitary and non-unitary nature of EFs, this issue re-

mains controversially debated since Teuber (1972) first raised the question (Miyake,

Emerson, et al., 2000). Executive functioning from the unity-perspective is regarded as a uni-

tary construct with meta-cognitive character, and, thus, responsible for coordinating other,

subordinate, basic cognitive processes (see Baddeley & DellaSala, 1996; Blair, 2006; de

Frias, Dixon, & Strauss, 2006; Friedman et al., 2006; R. J. Sternberg, 2003). Proponents of

the unity-theory postulate the existence of a unifying, core factor underlying executive func-

tioning, for example general (g) and fluid intelligence (Gf) (Duncan, Emslie, Williams,

Johnson, & Freer, 1996; Obonsawin et al., 2002; Rabbitt & Lowe, 2000), WM (Kane &

Engle, 2002) or inhibition (Barkley, 1997). The central executive (Baddeley & Hitch, 1974)

and SAS (Norman & Shallice, 1986) may be considered as unitary models of executive func-

tioning (Miyake, Emerson, et al., 2000); likewise, Luria’s theory of cognitive functioning and

Cattel-Horn-Carroll (CHC; McGrew, 2009) theory (Maricle & Avirett, 2012) are established

within this domain. Yet, proponents of the non-unity theory cannot reach an agreement as to

what cognitive abilities is executive functioning composed of (Maricle & Avirett, 2012).

From the non-unity perspective, EFs are considered as a collection of distinct but

moderately intercorrelated higher-order processes (see Elliott, 2003; Funahashi, 2001;

Salthouse, 2005; Stuss & Alexander, 2000). Indeed, factor-analytic studies support the non-

unity perspective, as the intercorrelation between different executive tasks is frequently low

(Jurado & Rosselli, 2007; Miyake, Friedman, et al., 2000; Obonsawin et al., 2002). Further-

more, studies with frontal lobe patients show inconsistent performance on different EFs tests,

suggesting the existence of multiple separable control processes (Godefroy, Cabaret, Petit-

Chenal, Pruvo, & Rousseaux, 1999). The diversity of behavioral disturbances encountered in

patients with executive dysfunctions may be considered as another piece of evidence

(Drechsler, 2007).


Nevertheless, there is one perspective shared by the proponents of both approaches,

namely that of the meta-cognitive role of EFs, which is indispensable for controlling and co-

ordinating cognition (see Friedman et al., 2006; Funahashi, 2001; Salthouse, 2005; Stuss &

Alexander, 2000). Furthermore, as evidence exists for both theories (Jurado & Rosselli,

2007), an intermediate position seems plausible. For example, Miyake, Friedman, et al.

(2000) suggest the existence of both unitary and non-unitary elements of executive function-

ing. They propose three, clearly separable functions (i.e., updating, shifting, and inhibition) as

basic executive processes. Based on their correlative studies conducted with the three execu-

tive components, they conclude that EFs are separable but moderately correlated constructs.

11..44 DDeevveellooppmmeennttaall ttrraajjeeccttoorriieess aanndd aaggiinngg

Crucial stages in the development of EFs take place from early childhood through adoles-

cence until early adulthood (De Luca et al., 2003; Reynolds & Horton, 2008; Romine &

Reynolds, 2005). Nevertheless, developmental changes in EFs occur over the entire human

life span (De Luca & Leventer, 2008). In the present section, the current state of research on

the development of executive functioning throughout the human life span along with the in-

fluence of aging is presented.

CChhiillddhhoooodd aanndd aaddoolleesscceennccee The emergence of EFs closely allies with the maturation of the PFC, which already begins in

utero and includes building up all the connections both within the frontal lobes and to other

brain areas (De Luca & Leventer, 2008). As a result, executive skills are present in an imma-

ture state in early childhood and develop protracted through adolescence into early adulthood

(Casey, Giedd, & Thomas, 2000; Steinberg, 2005).

The frontal lobes are the last areas of the brain to mature (Casey et al., 2000; Reynolds

& Horton, 2008; Rubia et al., 2000) and also one of the first areas to degenerate (De Luca &

Leventer, 2008). Maturation of the frontal lobes is associated with synaptogenesis, mye-

lination and pruning (Maricle & Avirett, 2012). In particular, the protracted process of mye-

lination plays a vital role in the development of frontal lobes as it enhances the speed of neu-

ral communication (Klingberg, Vaidya, Gabrieli, Moseley, & Hedehus, 1999). The frontal

lobes, partiularly their dorsolateral areas, are the last parts of the brain to complete the process

of myelination, which continues into the third decade of life (Klingberg et al., 1999; Rubia et

al., 2000; Sowell, Thompson, & Toga, 2004). Due to the protracted maturation of the PCF,

EFs are one of the last functions to reach maturity (De Luca & Leventer, 2008). Differences


in the neural maturation of specific areas within frontal lobes ally with the timing of matura-

tion of specific executive abilities (P. Anderson, 2002). According to the hierarchical pattern

of brain development, the maturation occurs progressively from more fundamental to more

complex skills (De Luca & Leventer, 2008); for example, attentional control and WM are

considered crucial to success on all executive tasks and mature earlier, especially as compared

with more complex EFs, such as planning and organization skills (Senn, Espy, & Kaufmann,

2004; Smidts, Jacobs, & Anderson, 2004). At the same time, there is much evidence on a bell-

shaped curve in the acquisition and loss of executive skills, suggesting rather a stepwise de-

velopment than existence of linear trajectories in executive functioning (De Luca et al., 2003;

Kray, Eber, & Lindenberger, 2004; Reynolds & Horton, 2008; Romine & Reynolds, 2005).

WM and inhibition are among the EFs that emerge earlier, with their first signs being

observed between 7 and 8 months of age. Great increases in the development of WM, inhibi-

tion, sustained attention, mental flexibility, and concept formation can be seen in the pre-

school period. Goal-oriented behaviors and planning skills similarly begin to mature during

the preschool years (De Luca & Leventer, 2008); however, both functions are considered to

be dependent upon the level of WM and inhibition skills (Brocki & Bohlin, 2004).

The 6 to 8 years range is thought to be the period of greatest development in EFs. Be-

tween 9 and 12 years of age, more moderate improvements in executive performance are ob-

served. In particular, the ability to shift attention is considered to be complete by 10 years of

age (Chelune & Baer, 1986; Welsh & Pennington, 1988). In the period from adolescence to

the early 20s, many EFs reach adult levels (V. A. Anderson, Anderson, Northam, Jacobs, &

Catroppa, 2001; Korkman, Kemp, & Kirk, 2001; Passler, Isaac, & Hynd, 2009; Welsh et al.,

1991). A meta-analysis conducted by Romine and Reynolds (2005) demonstrated similar age

ranges of significant improvement in executive functioning – medium to large increases in

performance between the ages ranges 5 to 8 years and 8 to 11 years; small to medium increas-

es between 11 to 14 years; and greater variability in performance ranging from none to medi-

um age-related changes in performance in the 14 to 17 years range.

AAdduulltthhoooodd aanndd aaggee--rreellaatteedd ddeecclliinnee Consistent with the protracted brain maturation, the second decade of life is expected to be the

period of peak level in executive functioning (De Luca & Leventer, 2008). De Luca et al.

(2003) support this notion as in their study all assessed executive abilities, including WM,

strategic planning, goal setting, and problem solving, reached superior levels in the 20-29 age

group. Evidence on the white and gray matter development continuing well into the third dec-


ade (see Paus et al., 2001; Sowell et al., 2004), however, questions the timing of mature

adulthood (De Luca & Leventer, 2008). Moreover, since neural degeneration may already

begin in the third decade of life (Yang, Ang, & Strong, 2005), it seems plausible why there is

only a short time of plateau in the level of EFs; indeed, there is some evidence on decline in

executive functioning beginning as early as 30 years of age. For example, De Luca et al.

(2003) reported spatial span to decrease significantly in the 30-49 age group and all other

measured executive skills being diminished in 50-64-year-olds. Additionally, performance on

most executive tasks in the 50-64 age group was com coefficient of variation (CV) coefficient

of variation (CV) parable to that of the 8-10 age group. This implies that EFs are particularly

sensitive to cognitive decline (De Luca et al., 2003). Furthermore, there is a growing body of

evidence on age-related effects on executive functioning; Raz, Gunning-Dixon, Head, Dupuis,

and Acker (1998) reported higher likelihood of perseverations with advancing age. Further-

more, several studies demonstrated that younger participants outperform older participants on

tower tasks (Brennan, Welsh, & Fisher, 1997; Gilhooly, Phillips, Wynn, Logie, & Sala,

1999), task-switching (Kray, Li, & Lindenberger, 2002; Kray & Lindenberger, 2000), and

strategic planning (Levine, Stuss, & Milberg, 1997).

Nevertheless, the impact of age on executive functioning appears to be related to the

type of task. For instance, Reynolds and Horton (2008) assessed in their lifespan study a large

number of participants (1600-2000 pro task) across the age range of 8-89 years. Two assess-

ment tools used in that study covered a wide range of executive skills. Furthermore, each of

the tools is thought to assess different types of EFs. The Test of Verbal Conceptualization and

Fluency (TVCF; Reynolds & Horton, 2006) is considered to assess verbal ability; whereas the

Comprehensive Trail Making Test (CTMT; Reynolds & Horton, 2006) is considered to reflect

the perceptual-motor underpinnings of EFs (Reynolds & Horton, 2008). In fact, the data col-

lected from the two measures showed peak performances at different ages. The skills assessed

by the TVCF peaked later than those assessed by the CTMT - category fluency reached high-

est levels of performance at the 60-69 years group, letter fluency at the 50-59 years group, and

verbal classification at the 40-49 years group. Unlike the language-related abilities, all percep-

tual-motor skills from the CTMT peaked at the 20-25 years group. The authors concluded that

there is a parallel between their findings and the research on crystallized and fluid intelligence

(Reynolds & Horton, 2008).

2. Intelligence 12

22.. IInntteelllliiggeennccee The concept of intelligence and the assessment of intellectual abilities have a long and rich

history (Newton & McGrew, 2010). Although intelligence is perhaps the most researched

topic in psychology with many existing theories, the nature of this elusive construct is still

difficult to define (Wasserman, 2012). The most popular definition of intelligence is arguably

proposed by David Wechsler (1939): “Intelligence is the aggregate or global capacity of the

individual to act purposefully, to think rationally and to deal effectively with his environ-

ment” (p. 3). The wide acceptance of this definition may be due to the widespread use of the

Wechsler intelligence scales; additionally, it reflects the rich theoretical framework from

which Wechsler’s understanding of intelligence derives (Wasserman, 2012).

Hereafter, the milestone events in the history of intellectual assessment are outlined,

followed by an introduction to the most seminal theoretical models of intelligence, presented

both from historical and present perspective.

22..11 HHiissttoorryy ooff iinntteelllleeccttuuaall aasssseessssmmeenntt

The first practical intelligence measure was published in 1905 by Binet and Simon, with the

purpose to distinguish between intellectually retarded and normal school children. Although

first implemented in France, the Binet-Simon Scale led to an overall increase in the use of

intelligence tests and to the implementation of the Stanford-Binet intelligence scales in the

United States. In addition to identifying intellectual disability in school children, early intelli-

gence measures aimed at psychological testing of army recruits. For example, the Army men-

tal tests were created for the use in the U.S. Army during World War I. The Army mental

tests comprised two separate tests. The Army Alpha was a language-based assessment tool,

used for examinees with an adequate mastery of English, whereas the Army Beta was intend-

ed for examinees, who were not able to read and write or who had insufficient mastery of

English (Wasserman, 2012). Thus, with the creation of the Army Alpha and Army Beta, the

differentiation between verbal and non-verbal intelligence testing has been implemented. The

success of the Army mental tests contributed to the widespread use of intelligence testing in

other civilian applications of psychology; after World War I, the Army mental tests were

adapted to the use in schools, college and industry. The Stanford revisions of the Binet-Simon

tests were successful as well, both domestically and internationally (Silverman, 2009;

Wasserman, 2012). However, a substantial improvement and a long-lasting influence on the

assessment of intellectual abilities were achieved by David Wechsler, who developed intelli-

2. Intelligence 13

gence tests for clinical use specifically. Although the first Wechsler intelligence scales were

implemented in the 1950s and 1960s already, numerous subsequent revisions of Wechsler

tests have dominated intellectual assessment in the second half of the 20th century. Currently,

the Wechsler intelligence scales remain the most frequently used measures of intelligence

worldwide (Ardila, 1999; Drozdick, Wahlstrom, Zhu, & Weiss, 2012; Flanagan & Kaufman,

2009), and they are considered a standard part of neuropsychological assessment (Ardila,

1999; O'Donnell, 2009).

22..22 EEvvoolluuttiioonn ooff tthhee ccoonncceepptt

GGeenneerraall vveerrssuuss ssppeecciiffiicc ffaaccttoorrss ooff iinntteelllliiggeennccee The general-factor theory of intelligence by Charles Spearman (1904) is considered the first

seminal model of intellectual abilities (Silverman, 2009). Based on the observation that cogni-

tive variables measured by different tests are positively correlated with one another, Spearman

concluded that all these variables have one fundamental function in common, which he la-

belled general ability factor or g. However, he also observed unique performance variance,

which was specific to individual tests. Consequently, Spearman (1927) revised his general-

factor theory into the two-factor theory and postulated the general factor g, which is shared

across measures, and specific factors s, which are unique to individual measures. While

Spearman focused on g, some other researchers sought to construct more complex models of

intelligence or to demonstrate that intelligence cannot be regarded as an entity (Silverman,

2009). For example, Burt (1949) and Vernon (1951) extended Spearman’s two-factor theory

into more hierarchical models of intelligence. Spearman’s strongest opponent, Louis

Thurstone (1938) proposed in his theory of primary mental abilities seven to nine parallel

factors. Joy Guilford (1956, 1988) went even further by postulating 120 to 180 intelligences.

Nevertheless, for the first few decades of the 20th century, Spearman’s concept of g

dominated intellectual assessment (Kaufman, 2009; Silverman, 2009). In intelligence tests

(e.g., Stanford Binet scales, Wechsler intelligence scales) g has often been operationalized as

a global score with the contribution of all subtest scores. Furthermore, the concept of g plays a

vital role in the contemporary intellectual assessment and is in expert opinion an important

predictor of real world outcomes. By contrast, experts did not reach consensus as to the de-

gree of predictive validity of specific factors (Reeve & Charles, 2008).

2. Intelligence 14

TThhee ccoonncceepptt ooff ccrryyssttaalllliizzeedd aanndd fflluuiidd iinntteelllliiggeennccee Some researchers acknowledge the existence of an overarching general factor, while simulta-

neously questioning the unitary nature of intelligence. The concept of crystallized (Gc) and

fluid intelligence (Gf) is one of the most successful attempts to define a more complex struc-

ture of intelligence. This concept was proposed by Cattell (1943) and extended by Horn and

Cattell (1966, 1967). It is based on a distinction between Gc and Gf – two different types of

intellectual abilities. Gc comprises mainly verbal skills such as vocabulary, general infor-

mation, verbal comprehension, and arithmetic; whereas Gf involves more non-verbal skills

such as perception of relations, concept formation and attainment, reasoning and abstracting.

Gc is thought to be the product of acculturation and education. Furthermore, it usually in-

creases with advancing age. In contrast, Gf is to a larger degree affected by heredity factors,

and more vulnerable to aging as well as brain lesions (Horn & Cattell, 1966, 1967).

The model of Gf and Gc has later been extended by Horn and Cattell (1966) from two

to five ability factors (i.e., visualization, retrieval, and cognitive speed). Moreover, the num-

ber of factors in this model has continuously been growing, lacking agreement among Cattell

and Horn, as well as among other researchers, regarding the ultimate number of factors

(Wasserman, 2012). Nevertheless, the concept of Gf and Gc has later been integrated also into

other models of intelligence, such as the Three-Stratum-Model (Carroll, 1993) and the Cat-

tell-Horn-Carroll (CHC) theory (McGrew, 2009). Furthermore, the concept of Gf and Gc has

proven useful in explaining developmental trajectories of cognitive abilities over the lifespan,

both by Horn and Cattell (1967) and by other researchers (Ardila & Rosselli, 1989; Ryan,

Sattler, & Lopez, 2000).

HHiieerraarrcchhiiccaall mmooddeellss ooff iinntteelllliiggeennccee As noted previously, several researchers have undertaken attempts to define the structure of

intelligence by classifying specific intellectual abilities. Especially, the implementation of

correlation and factor analytical methods provided substantial progress in the intelligence re-

search and helped identify a hierarchy among cognitive abilities.

The first comprehensive systematic organization of research on the structure of intelli-

gence and an empirically based taxonomy of cognitive abilities was achieved by Carroll

(1993) in his Human Cognitive Abilities: A Survey of Factor-Analytic Studies (McGrew,

2009; Schneider & McGrew, 2012). Furthermore, Carroll postulated a three-stratum hierar-

chical model of intelligence with an overall g factor at stratum III, eight broad abilities at stra-

tum II and more than 70 narrow abilities at stratum I. Carroll’s research built on the work of

2. Intelligence 15

numerous researchers, such as Cattell, Horn, Thurstone, and Thorndike (McGrew, 2009);

however, the Cattell-Horn Gf-Gc model had the biggest influence on Carroll’s multiple-

stratum model. There are, indeed, remarkable similarities between broad abilities proposed by

Carroll - Gf, Gc, general memory and learning (Gsm), broad visual perception (Gv), broad

auditory perception (Ga), broad retrieval ability (Gr), broad cognitive speediness (Gs), and

processing speed (RT), and Cattell-Horn ability factors.

The commonalities between the Cattell-Horn Gf-Gc concept and Carroll’s taxonomy

of cognitive abilities led to the integration of both models into the CHC theory of intelligence

(McGrew, 2005, 2009). Horn and Carroll agreed to merge their models into a single theory in

the late 1990s (Kaufman, 2009). Since then, originally grounded on hierarchically organized 9

“broad ability domains” and more than 70 specific narrow abilities, the CHC theory has been

refined and extended by additional constructs, also those regarding the human sensory do-

mains of tactile, kinaesthetic, and olfactory abilities (McGrew, 2009; Newton & McGrew,

2010; Schneider & McGrew, 2012). The CHC taxonomy is still considered a framework that

integrates past and current research and is to be further extended (McGrew, 2009). Currently,

the CHC theory is the most popular hierarchical model of intelligence assessment (Keith &

Reynolds, 2010; Newton & McGrew, 2010); moreover, the CHC theory plays a crucial role in

the current intellectual assessment (Kaufman, 2009; Keith & Reynolds, 2010; McGrew &

Wendling, 2010) and is the foundation of many contemporary intelligence tests (Keith &

Reynolds, 2010).

3. Relationships between EFs and intelligence 16

33.. RReellaattiioonnsshhiippss bbeettwweeeenn eexxeeccuuttiivvee ffuunnccttiioonnss aanndd iinntteelllliiggeennccee

Considerable evidence exists on a strong association between executive functions (EFs) and

intelligence (Crawford, Bryan, Luszcz, Obonsawin, & Stewart, 2010; Davis, Pierson, &

Finch, 2011; Friedman et al., 2006; Obonsawin et al., 2002; Roca et al., 2010; Salthouse &

Davis, 2006). However, it is not well explored exactly how the particular components of the

two constructs are interrelated. In addition, the influence of other cognitive and non-cognitive

factors on the relationship between the two constructs has been discussed (Friedman et al.,

2006; Lamar et al., 2002). Especially, the influence of age has received considerable attention

(de Frias et al., 2006; Lamar et al., 2002; Salthouse et al., 2003; Salthouse & Davis, 2006).

The current state of research on the relationships between different EFs and the particular

components of intelligence, along with the influence of age on those relationships, is provided

hereafter.

33..11 EExxeeccuuttiivvee ffuunnccttiioonnss aanndd ggeenneerraall iinntteelllliiggeennccee

Strong relations between EFs and general intelligence (g) have been demonstrated by several

studies (Crawford et al., 2010; Davis et al., 2011; Duncan et al., 2008; Obonsawin et al.,

2002; Salthouse & Davis, 2006). Obonsawin et al. (2002) administered several common EF

tests and the Wechsler Adult Intelligence Scale - Revised (WAIS-R; Wechsler, 1981) to

healthy adults. Significant correlations between individual EF tests ranged from .20 to .50;

however, when the same correlation matrix was covaried with performance on the WAIS-R,

most correlations decreased and only a few remained significant. It was concluded that g

might account for the shared variance between EF tests. Duncan, Johnson, and Swale (1997)

reported similar results from a study conducted with adult patients with brain lesions; after

partialling out the Cattel’s Culture Fair (Institute for Personality and Ability Testing, 1973)

scores, the correlation between remaining executive and non-executive tests decreased.

Duncan et al. (1997) concluded that these tests have little in common besides their g compo-

nent.

Nevertheless, the correlation pattern between global intelligence scores and individual

measures of executive functioning reveals that different EFs might be variously related to g.

Obonsawin et al. (2002) reported significant correlations between the WAIS-R and EF tests,

ranging from .24 to .63. The lowest correlation was observed between the WAIS-R and the

Modified Card Sorting Test (MCST; H. E. Nelson, 1976), which is a measure of concept for-


mation and cognitive set-shifting. The highest correlation was observed between the WAIS-R

and the Paced Auditory Serial Addition Task (PASAT; Gronwall & Wrightson, 1974), which

is a measure of attention and working memory (WM). In addition, a principal components

analysis conducted with all EF tests yielded two factors that accounted for 52.8 % of the total

variance. Scores on the PASAT and verbal fluency tests loaded most highly on the first factor.

Perseverative errors from the MCST were the only measure that loaded highly on factor two.

Furthermore, in addition to strong correlations with the WAIS-R Full Scale IQ (FSIQ; .73),

factor one exhibited equally strong correlations with the verbal and performance scale (.66).

In contrast, the score for the second factor was significantly correlated with performance on

the WAIS-R FSIQ (-.26) and verbal scale (-.31), whereas it was not correlated with perfor-

mance on the performance scale.

Additionally, Davis et al. (2011) demonstrated a generally strong, but variable, rela-

tionship between g and EFs by conducting a canonical correlation analysis between the

Wechsler Adult Intelligence Scale – Third Edition (WAIS-III; Wechsler, 1997) and the Delis-

Kaplan Executive Function System (D-KEFS; Delis, Kaplan, & Kramer, 2001). The canoni-

cal correlation analysis is aimed at finding linear combinations within sets of variables that

maximize the correlation between them. The procedure allows for the calculation of correla-

tion coefficients between different sets of variables and shows which variables contribute the

most to each linear combination. The canonical variate represents the combination of varia-

bles based on their contribution to the canonical correlation. As a result, the correlation be-

tween an individual variable and the canonical variate informs about how strongly this varia-

ble is related to the correlation between the two sets of variables (Davis et al., 2011). The ca-

nonical correlation analysis between broad measures of intelligence represented by the WAIS-

III FSIQ, Performance IQ, and Verbal IQ, and EF measures represented by all subtests of the

D-KEFS, demonstrated that 54% of the variance of one variable set was accounted for by

another. Furthermore, all WAIS-III variables were strongly related to their canonical variate,

with the FSIQ (.99) being the strongest contributor followed by the Verbal IQ (.89) and Per-

formance IQ (.81). Among the D-KEFS variables, the Word Context Test, which involves

verbal ability, deductive reasoning, hypothesis testing, and mental flexibility, had the highest

correlation (.58) with the canonical variate. The correlations of the Sorting Test (.49), the

Proverb Test (.42), Design Fluency Test Switching (.39), and the Trail Making Test (.32) met

the criterion of being greater than .32 to be considered as an essential contributor to the ca-

nonical correlation. In contrast, the correlations of the Color-Word Interference Test and the

Tower Test with their canonical variate were too small to make a significant contribution to


the canonical correlation. It can be concluded that those D-KEFS tasks that assess inhibition,

set-shifting, and planning, have only modest associations with the WAIS-III, whereas those

D-KEFS tasks that assess switching, mental flexibility, and verbal ability, had moderate to

strong associations with the WAIS-III.

33..22 EExxeeccuuttiivvee ffuunnccttiioonnss aanndd fflluuiidd vveerrssuuss ccrryyssttaalllliizzeedd iinntteelllliiggeennccee

As demonstrated by studies by Davis et al. (2011) and Obonsawin et al. (2002), diversity

among EFs in respect to their association with intelligence must be considered; moreover,

different aspects of intelligence must also be taken into account when exploring the relation-

ship between both constructs.

Fluid intelligence (Gf) has been reported to be particularly strongly associated with

EFs (de Frias et al., 2006; Duncan et al., 1997; Egger et al., 2011; Salthouse et al., 2003;

Salthouse & Davis, 2006; van Aken, Kessels, Wingbermuhle, van der Veld, & Egger, 2016).

The overlap between Gf and EFs has also been demonstrated by functional imaging studies,

which showed that the two functions share the same neural substrates (Barbey et al., 2012;

Roca et al., 2010). Several authors consider Gf as the core aspect of both executive and intel-

lectual functioning. (Crawford et al., 2010; Decker, Hill, & Dean, 2007; Duncan et al., 2008;

Egger et al., 2011; Obonsawin et al., 2002; Roca et al., 2012; Salthouse & Davis, 2006).

Duncan et al. (1996) associate executive control with g, but they also suggest that g can be

better assessed by Gf, than by crystallized intelligence (Gc), measures. Furthermore, seeing

that frontal lesions are frequently associated with deficits in Gf, they concluded that Gf re-

flects the “functions of the frontal lobe” (Duncan et al., 1995).

A particularly strong relationship between Gf and WM has been observed (Friedman

et al., 2006; Miyake, Emerson, et al., 2000; Salthouse & Pink, 2008). Conversely, research

studies show a rather modest relationship of Gf with verbal fluency, inhibition, and shifting

(Ardila, Pineda, & Rosselli, 2000; Friedman et al., 2006; Miyake, Friedman, et al., 2000;

Rabbitt & Lowe, 2000; Salthouse et al., 2003; Salthouse & Davis, 2006). To illustrate,

Friedman et al. (2006) investigated the relationship between three basic components of execu-

tive functioning and intelligence in healthy young adults. Intelligence measures shared 41% to

48% of their variance with updating, but only 2% to 14% of their variance with inhibiting and

shifting. When intercorrelations between EFs were considered, the relationship of intelligence

with inhibition and shifting were no longer significant. At the same time, the relationship be-

tween intelligence and updating remained undiminished.


Evidence also exists on a considerable association between Gc and EFs (Ardila et al.,

2000; Obonsawin et al., 2002; Salthouse & Davis, 2006). For example, moderate to strong

relationships of Gc with verbal fluency, concept formation, inhibition, cognitive set-shifting,

and WM have been reported (Ardila et al., 2000; Obonsawin et al., 2002; Salthouse & Davis,

2006).

Nevertheless, EFs are considered more strongly related to Gf than to Gc (de Frias et

al., 2006; Duncan et al., 1995; van Aken et al., 2016). Yet the relationship between EFs and

intelligence has been investigated more frequently in respect to Gf than to Gc (Friedman et

al., 2006; Molnar, 2013). Furthermore, several studies demonstrated that some EFs may be

equally or more strongly related to Gc than to Gf. For example, in the study by Friedman et

al. (2006), updating, inhibiting, and shifting were equally related to Gf and Gc. Additionally,

Obonsawin et al. (2002) found that WM and verbal fluency were equally related to Gf and

Gc; moreover, perseverative errors from the MCST were related to Gc, whereas they were not

related to Gf. Ardila et al. (2000) also demonstrated that perseverative errors from the Wis-

consin Card Sorting Test (WCST; Heaton, 1981) were moderately related to Gc, whereas they

were not related to Gf. These findings suggest that skills involved in WCST and MCST, such

as concept formation, inhibition, and cognitive set-shifting, might be more strongly associated

with Gc than with Gf.

33..33 TThhee iinnfflluueennccee ooff aaggee

When exploring the relationship between EFs and intelligence, there is frequently a substan-

tial portion of variance that remains unexplained (Obonsawin et al., 2002). This suggests the

contribution of other cognitive and non-cognitive factors on the relationship between the two

constructs (Friedman et al., 2006; Lamar et al., 2002). The level of intellectual performance

has been demonstrated to be related to executive performance (Arffa, 2007; Diaz-Asper,

Schretlen, & Pearlson, 2004). Additionally, the influence of several non-cognitive factors on

the relationship among cognitive variables has been postulated; in particular, the influence of

gender (de Frias et al., 2006; Salthouse, 2004), education (Salthouse, 2004), health (Davis et

al., 2011; Lamar et al., 2002; Salthouse, 2004), and age especially (de Frias et al., 2006;

Lamar et al., 2002; Salthouse et al., 2003; Salthouse & Davis, 2006).

Age correlates with many cognitive variables approximately between -.20 to -.60

(Verhaeghen & Salthouse, 1997); moreover, age contributes more to interindividual variabil-

ity in cognitive performance than other individual difference characteristics (Salthouse,

2010b).


Although the impact of age on both EFs (e.g., Christensen et al., 1999; De Luca &

Leventer, 2008; Reynolds & Horton, 2008) and intelligence (e.g., Ardila, 2007; Ryan et al.,

2000; Wisdom, Mignogna, & Collins, 2012) has been frequently investigated, there is a lack

of research on the relationship between the two constructs across different ages; however,

some evidence shows that this relationship may be variable according to age. For example, in

a study by Salthouse and Davis (2006), children, students, and normal adults differed in re-

spect to the relationship structure among several cognitive measures, including EFs tasks. To

illustrate, the number of categories in the WCST had moderate to strong associations with Gf

in each sample. Additionally, this variable was strongly related to Gc in the children sample,

and moderately related to WM in the adult sample. Such variability in the relationship pattern

may be connected to changes in both intellectual and executive skills, which have frequently

been showed to occur with increasing age (Ardila, 2007; Daseking & Petermann, 2013; De

Luca et al., 2003; Wisdom et al., 2012). Furthermore, according to the frontal hypothesis of

aging, age-related decline in many cognitive abilities is associated with the deterioration of

the frontal lobes (Salthouse et al., 2003). This is important since the frontal lobes are the first

areas of the brain to degenerate (De Luca & Leventer, 2008). Such age-related deterioration

of the frontal lobes may have an influence on relationships among cognitive abilities, in par-

ticular those including executive functioning and intelligence (de Frias et al., 2006; Rabbitt &

Lowe, 2000).

DDiiffffeerreennttiiaattiioonn--ddeeddiiffffeerreennttiiaattiioonn--hhyyppootthheessiiss Some evidence exists on the impact of age on the relationship structure among different EF

tasks. Using a sample of young college students, Miyake, Emerson, et al. (2000) found a

three-factor model of EFs best fitting the data. In contrast, examining middle-aged and older

adults, de Frias et al. (2006) found a one-factor solution best fitting the data. de Frias et al.

(2006) provide an explanation for the contradictory findings derived from both studies by

suggesting that the structure of EFs may change across the lifespan from a multidimensional

construct in young adults to a more unidimensional one in aging adults. Consequently, EFs

may “dedifferentiate” during the adult life course. This assumption is consistent with the dif-

ferentiation-dedifferentiation-hypothesis. The differentiation is considered to occur in the

course of childhood development, when the structure of cognitive abilities changes from a

general ability towards a group of more distinct abilities (Garrett, 1938, 1946). This process

of specialization occurs to a certain point during young adulthood and is followed by dedif-

ferentiation of distinct abilities again to a more unidimensional construct (Balinsky, 1941;


Baltes, Cornelius, Spiro, Nesselroade, & Willis, 1980). This means that, with increasing age,

cognitive abilities tend to become more closely related to one another.

IInnvveessttmmeenntt tthheeoorryy Cattell’s (1963, 1987) investment theory provides an explanation with respect to age-related

differences in the relationship between cognitive processes as well. The investment theory is

based on the concept of Gf and Gc (Horn & Cattell, 1966, 1967). Cattell assumed that Gf and

Gc are not independent from one another. For example, Gf usually influences Gc by deter-

mining the rate at which people learn. To illustrate, people with low Gf’s levels must put

more effort to acquire new knowledge. Hence, Cattell assumed that people are able to en-

hance their Gc by “investing” their Gf-abilities in Gc. This especially occurs in children and

young adults. Nevertheless, the influence of “non-ability” factors, such as the quality of edu-

cation, family resources, motivation, and personality, must be considered as well. The non-

ability factors play a role in the learning process, too. The influence of the non-ability factors

on the learning process, however, may alter across adult life. Thus, the investment patterns

may change as well. Consequently, interindividual variability in cognitive performance may

increase with advancing age.

4. The current research 22

44.. TThhee ccuurrrreenntt rreesseeaarrcchh The existing literature shows much inconsistency in terms of the definition and general under-

standing of both executive functions (EFs) and intelligence. Thus, investigating the two con-

structs appears difficult due to their elusive nature. However, empirical investigations are

necessary to build a solid theoretical framework; specifically, investigating the relationship

between EFs and intelligence may contribute to a better understanding of the nature of the

two constructs. Previous research has shown that there are considerable associations between

EFs and intelligence; however, the associations between the individual components of both

constructs are not well studied. Furthermore, though the influence of age on cognitive abilities

has frequently been demonstrated, age-related differences in EFs and age-related differences

in the relationship between EFs and intelligence require further investigations.

Based on the theoretical framework and current state of research outlined in the previ-

ous chapters, hereafter, the rationale and aims of the empirical research conducted within the

present doctoral thesis are presented.

44..11 AAggee--rreellaatteedd ddiiffffeerreenncceess iinn eexxeeccuuttiivvee ffuunnccttiioonnss

TThheeoorreettiiccaall ccoonnssiiddeerraattiioonnss The influence of age on cognition is a frequently studied issue. Furthermore, it is widely ac-

cepted that cognitive decline occurs with advancing age (Ardila, 2007; Salthouse, 2014).

However, research findings in respect to the age-related decline in EFs are inconclusive

(Jurado & Rosselli, 2007; Mejia et al., 1998; Wecker et al., 2000). This may be due to the

different assessment tools that were used in the studies. The characteristics of age groups ex-

amined must be considered as well. Certain studies have investigated the differences in execu-

tive functioning between limited age ranges (e.g., Boone, Miller, Lesser, Hill, & D'Elia, 1990;

Brennan et al., 1997; Raz et al., 1998), but only a few have used several age ranges across the

lifespan (De Luca et al., 2003; Reynolds & Horton, 2008; Salthouse et al., 2003).

Moreover, when investigating age-related differences in cognition, the mean or other

measures of central tendency are often used. However, using methods that focus on changes

in mean performance individual differences might be neglected (E. A. Nelson & Dannefer,

1992). To illustrate, although the mean performance decreases with age, some individuals

may exhibit substantial changes in cognition, whereas others may change very little

(Christensen et al., 1999). Consequently, investigating the differences between individuals

may provide insight into the diversity of cognitive performance and help understand why


some individuals decline in cognition, whereas others do not. Additionally, investigating

interindividual variability, either within one or several cohorts, may provide information on

whether there are similar or distinct cognitive deterioration patterns for different individuals.

Though some studies have focused on interindividual variability in executive functioning,

particularly in respect to the factors that contribute to successful and unsuccessful aging

(Mejia et al., 1998; Ylikoski et al., 1999), there is a lack of research dealing with age-related

differences in interindividual variability. This issue requires investigations because differ-

ences in the magnitude of variability in performance across the lifespan may provide essential

information for neuropsychological assessment. If there is only little variability in perform-

ance between individuals, even small deviations from the mean score of the standardization

sample could imply an impairment of the assessed function. In contrast, if there is high vari-

ability in the performance of the standardization sample, a relatively great deviation from the

mean score does not necessarily imply any pathological impairment.

Some studies have reported that interindividual variability in cognition increases with

age; moreover, a meaningful ability-related pattern in cognitive deterioration has been identi-

fied. That is, visual-spatial perception, attention, speed, and memory were shown to be more

heterogeneous at older, than at younger, ages; furthermore, these abilities were also associated

with the substantial decreases in mean scores that occurred with advancing age. In contrast,

the studies showed no substantial differences between older and younger participants in the

variability of verbal or number-related skills, and the decreases in mean scores were only

moderate (Ardila, 2007; Christensen et al., 1994; Christensen et al., 1999; Daseking &

Petermann, 2013; Morse, 1993; Ryan et al., 2000; Wisdom et al., 2012). The two identified

clusters of cognitive abilities correspond to the concept of fluid (Gf) and crystallized intelli-

gence (Gc). Moreover, the study by Reynolds and Horton (2008) suggests that the deteriora-

tion pattern of EFs might also fit the concept of Gf and Gc. However, age-related differences

in EFs in respect to mean performance, and particularly in respect to interindividual variabil-

ity, require further investigations.

MMeetthhooddoollooggiiccaall ccoonnssiiddeerraattiioonnss A common way of exploring interindividual variability in cognition is to analyze the disper-

sion of the test scores between different individuals (Ardila, 2007). The standard deviation

(SD) is a frequently used measure of dispersion due to its independence from the unit of

measurement. The comparison of standard deviations between different age groups has been

frequently used in the studies on age-related interindividual variability (Rönnlund & Nilsson,


2006; Salthouse, 2010a; Schaie, 1994). However, the standard deviation should not by ana-

lyzed without taking into account the associated mean, especially when comparing the disper-

sion between different age ranges. The coefficient of variation (CV) [(SD/mean) × 100]

(Bartlett, 1946; Hendricks & Robey, 1936; Pearson, 1896; Yablokov, 1974), also referred to

as the percentage of the mean (Ardila, 2007; Daseking & Petermann, 2013), is a more infor-

mative ratio for dispersion in respect to the heterogeneity of scores because it does not repre-

sent the standard deviation alone but rather the percentage of standard deviation in the mean

(Ardila, 2007; Daseking & Petermann, 2013; Morse, 1993; Wisdom et al., 2012). The relation

of the standard deviation with the mean is meaningful since the mean scores may vary across

age groups and so change the meaning of the associated dispersions. As a result, the CV, as

the percentage of the standard deviation in the mean, allows for comparisons within and be-

tween populations (Lande, 1977). This has already been demonstrated in the analysis of age-

related variability in performance on Wechsler intelligence scales (Ardila, 2007; Daseking &

Petermann, 2013; Matarazzo, 1972; Wisdom et al., 2012).

The calculation of the mean and standard deviation raw scores, as well as the CV for

different age groups, is the first step for establishing the extent of variability in performance

across age. The analysis of age-adjusted standard scores does not permit meaningful compari-

sons between age groups with regard to the differences in performance. Only an observed

decrease or increase in the mean or standard deviation raw scores between different age

groups may indicate age-related differences in cognition. Moreover, the amount of change in

the CV determined by the comparison of the age groups with the highest and lowest mean raw

scores is more informative in respect to the heterogeneity in cognitive decline (Wisdom et al.,

2012). In particular, the comparison between the extent of mean cognitive decline, as meas-

ured by the percentage decrease in the mean, and the change in the variability of scores, as

measured by the percentage increase in the dispersion, may be helpful in understanding age-

related differences in cognition over time.

GGeenneerraall aaiimmss aanndd hhyyppootthheesseess The aim of the study was to explore the deterioration pattern of EFs by examining age-related

differences in respect to the mean and dispersion between healthy adults across a large age

range. It was assumed that mean performance on EF tasks would decrease with advancing

age; whereas the dispersions would increase with age. Additionally, it was hypothesized that

the pattern of deterioration in EFs would depend on the type of task used. That is, perform-

ance on EF tasks associated with Gf would show substantial decreases in the mean scores and


substantial increases in the dispersion from early adulthood. In contrast, performance on EF

tasks related to Gc would demonstrate increases in the mean scores, even in late adulthood,

but only small increases in the dispersion.

44..22 AAggee--rreellaatteedd rreellaattiioonnsshhiippss bbeettwweeeenn eexxeeccuuttiivvee ffuunnccttiioonnss aanndd iinntteelllliiggeennccee

TThheeoorreettiiccaall ccoonnssiiddeerraattiioonnss Different age groups have been examined in respect to the relationship between EFs and intel-

ligence (Ardila et al., 2000; Arffa, 2007; Boone, Ponton, Gorsuch, Gonzalez, & Miller, 1998;

Crawford et al., 2010; Friedman et al., 2006; Salthouse & Davis, 2006; van der Sluis et al.,

2007), yet there is a lack of research on the comparison of different age groups with regard to

the relationship between the two constructs. As previously discussed in chapter three

(Salthouse & Davis, 2006), the pattern of relationships between performance on EFs and oth-

er cognitive abilities might not always be consistent across age. Consequently, there might

also be unique, age-related associations of EFs with intelligence. This issue should be ex-

plored further to better understand the nature of the two constructs. In particular, an adequate

understanding of the associations between executive and nonexecutive processes is necessary

to build a coherent theory of executive functioning (Lamar et al., 2002). Furthermore, investi-

gating both age-related commonalities and differences in the relationship between EFs and

intelligence is essential due to potential implications for neuropsychological practice. To illus-

trate, the extent of the overlap between the individual components of the two constructs may

provide guidelines in respect to the interpretation of assessment outcomes. For example, a

substantial overlap between two different components may indicate that these components

depend upon the same mechanism or are dependent from one another. The age-dependence of

the relationship between two components may reflect developmental trajectories of these

components. In addition, indications regarding the kind of information that can be obtained

within assessment according to age might be comprehensively observed. In practical terms,

information may be derived about how useful it is to implement tasks assessing these compo-

nents in different age groups.

Investigating the mutual predictability of EFs and intelligence may provide infor-

mation regarding the extent to which both constructs contribute to the performance of one

another. In fact, the relationship pattern between EFs and intelligence has often been investi-

gated in respect to the mutual predictability of both constructs. Given the practical implica-

tions for clinical neuropsychology, in particular, the intention to estimate the expected per-


formance on other cognitive abilities (Diaz-Asper et al., 2004), EFs have been more frequent-

ly predicted with intelligence (Arffa, 2007; de Frias et al., 2006; Diaz-Asper et al., 2004;

Rabbitt & Lowe, 2000; Salthouse & Davis, 2006) than vice versa (Brydges, Reid, Fox, &

Anderson, 2012; Friedman et al., 2006; Salthouse et al., 2003). Given that EFs are considered

crucial for cognitive functioning (Ardila, 1999; Maricle & Avirett, 2012) and EF tasks are a

part of the major intelligence tests (D. C. Miller & Maricle, 2012), the predictive ability of

executive performance for intelligence performance should be more accurately examined.

MMeetthhooddoollooggiiccaall ccoonnssiiddeerraattiioonnss When investigating the relationship between EFs and intelligence, several methodological

issues must be taken into consideration. In particular, the nature of statistics used must be

taken into account. Correlation is a commonly used method to assess the relationships be-

tween two variables; furthermore, it is the foundation of multivariate statistical procedures

such as multiple regression, factor analysis, and structural equation modelling. These more

complex statistical methods are also frequently used in studies aimed at investigating the rela-

tionship between cognitive variables. Factors affecting the correlation size should therefore be

considered in particular. The factors frequently discussed in the literature include the charac-

teristics of the sample, the amount of variability in either variable, differences in the shapes of

the distributions, lack of the linearity in the relationship between variables, the presence of

outliers in the dataset, and measurement error (Cohen, 2010; Goodwin & Leech, 2006; Hays,

1994; Tabachnick & Fidell, 2013).

Besides the general statistical characteristics, other more research specific factors may

affect the size of a correlation. First, a differentiation must be made between studies con-

ducted with frontal lobe patients versus healthy participants (Obonsawin et al., 2002). For

example, frontal lobe patients have been reported to be impaired on Gf, but not on Gc, meas-

ures (Duncan et al., 1995). In contrast, no substantial discrepancies between Gf and Gc have

been demonstrated in healthy people (Friedman et al., 2006; Obonsawin et al., 2002); thus,

the relationships between EFs and Gf may be stronger in samples comprised of frontal lobe

patients than those comprised of healthy participants.

Second, the relationship between EFs and intelligence depends on the assessment tools

used in the studies. Frequently, several scores from a single measure are used in studies.

However, single measures usually do not assess all aspects of executive functioning (Pickens,

Ostwald, Murphy-Pace, & Bergstrom, 2010). Therefore, multiple measures that assess various

aspects of executive functioning should be used (Davis et al., 2011; Lamar et al., 2002).


Third, psychometric properties of EF measures may influence the relationship between

the measured constructs. Pickens et al. (2010) report that most reviewed studies on available

EF measures lacked the statement of adequate reliability and validity testing. Moreover, only

one of 18 reviewed studies reported excellent psychometric properties. Therefore, when as-

sessing measures for possible use, the focus of attention should be on reliability and validity

testing procedures.

In the research context especially, ecological validity appears crucial, referring to a

differentiation which must be made between measures based on theoretical models and those

based on clinical practice. Measures developed by cognitive neuropsychologists may lack

ecological validity for patients; conversely, measures validated in a clinical context may be

less valid for healthy participants (Bryan & Luszcz, 2000; Chan, Shum, Toulopoulou, &

Chen, 2008). As an example, the Wisconsin Card Sorting Test (WCST; Heaton, 1981) and the

Modified Card Sorting Test (MCST; H. E. Nelson, 1976), have been shown not to be associ-

ated with different Wechsler Intelligence Scales (Adams, Smigielski, & Jenkins, 1984;

Wechsler, 1981, 1993) to a considerable degree (Ardila et al., 2000; Boone et al., 1998;

Obonsawin et al., 2002; Welsh et al., 1991). However, the two measures have been validated

for patients with frontal lobe lesions; thus, due to ceiling effects, they might be inappropriate

for use in healthy populations (Arffa, Lovell, Podell, & Goldberg, 1998; Bryan & Luszcz,

2000; Obonsawin et al., 1999). Moreover, although useful in clinical contexts, these measures

might be less sensitive to executive dysfunctions encountered in normal aging (Bryan &

Luszcz, 2000).

Construct validity should also be taken into account as it is crucial to evaluate a meas-

ure in relation to the abilities that are to be assessed. However, although most EF tasks require

multiple abilities, exactly which abilities are involved in any particular task is often inaccu-

rately defined (Lamar et al., 2002). The validity of EF tasks is difficult to determine due to the

“impurity” of these tasks. That is, also non-executive abilities, for example, attention, mem-

ory, language, and spatial skills, may be involved in EF tasks (Boone et al., 1998; van der

Sluis et al., 2007). Therefore, it is difficult to distinguish between executive and non-

executive tasks. Additionally, different EF tasks may assess similar executive abilities

(Rabbitt, 1997). Consequently, when investigating the relationship between EFs and intelli-

gence, the multifaceted nature of both EF and intelligence measures must be considered.


GGeenneerraall aaiimmss aanndd hhyyppootthheesseess The aim of the study was to explore the general relationship as well as more specific relation-

ships between EFs and intelligence. Thus, the association between the global scores as well as

between individual components of the two constructs was intended to be examined. Further-

more, the influence of age on that association was aimed to be investigated by the comparison

of younger and older healthy adults. A significant relationship between the two constructs was

assumed. In addition to the mutual relationship between the two constructs, the aim also was

to investigate both the general and age-related predictability of intelligence with EFs. Seeing

that many studies have shown that changes in both intelligence and EFs occur with increasing

age, the association between the two constructs was expected to increase with age. Conse-

quently, also a better predictability of intelligence with EFs in older, rather than in younger,

adults, was assumed. Additionally, the influence of the type of EF task on the relationship

between both constructs was intended to be examined. It was assumed that intelligence per-

formance can be predicted more effectively by complex or multi-domain EF tasks rather than

by well-structured, domain specific EF tasks; however, the predictability was also expected to

vary as a function of age, in particular, for the domain specific EF tasks.

In addition, the study aimed at exploring the relationship between EFs and intelligence

separately for younger and older adults. Here, the following research question was in focus:

How does the relationship between EFs and g, as well as individual components of intelli-

gence, vary according to age? In line with existing evidence (see chapter three), it was ex-

pected that there are some individual components of intelligence, such as Gf and WM, which

are more strongly associated with EFs than others. Yet it was also hypothesized that EFs are

more strongly related to g than to any other individual component of intelligence. When tak-

ing age into account, however, the strength of this relationship would change; that is, EFs

would be as strongly related to some individual components of intelligence, such as Gf and

Gc, as they are to g.

5. Methods 29

EEmmppiirriiccaall rreesseeaarrcchh 55.. MMeetthhooddss In the present chapter, methods used in the current research are presented, including sample

characteristics as well as the description of the assessment tools, data management, and statis-

tical analysis.

55..11 SSaammppllee cchhaarraacctteerriissttiiccss aanndd rreeccrruuiittmmeenntt pprroocceedduurree

The participants of the present research were recruited for the purpose of norming the German

adaptation of the Neuropsychological Assessment Battery (NAB; Petermann, Jäncke, &

Waldmann, 2016b). Data were collected between February 2014 and February 2015. The re-

cruitment of participants occurred via advertisements placed in newspapers, job centres, job

search websites, senior residencies, and assisted living facilities. Potential participants were

excluded if they had a history of known cardiovascular, neurological, or psychiatric condi-

tions. All participants provided written informed consent prior to the administration of both

assessment tools. Each participant received a reimbursement for participation of €30. The data

used in the present research originated from subjects who completed Form 1 of the NAB on

the first occasion. The completion of the NAB took three hours on average. The data used in

the study on age-related differences in the relationship between EFs and intelligence originat-

ed from participants who, in addition to the NAB, were administered the ten core subtests of

the German adaptation (Petermann, 2012a) of the Wechsler Adult Intelligence Scale – Fourth

Edition (WAIS-IV; Wechsler, 2008a). The completion of the WAIS-IV took 90 Minutes on

average. Both test batteries were administered in the standard order. The time interval be-

tween the NAB and WAIS-IV assessments ranged from 1 to 92 days (M = 16.87, SD =

17.99). The assessments with both measures were conducted either by trained psychology

students or graduate psychologists with expertise in neuropsychological diagnostics. The au-

thor of the present doctoral thesis was involved in the recruitment of participants and the ad-

ministration of both assessment tools, too.

SSttuuddyy oonn aaggee--rreellaatteedd ddiiffffeerreenncceess iinn eexxeeccuuttiivvee ffuunnccttiioonnss The sample consisted of 485 normal adults, aged 18-99 years, which were divided into ten

age groups. Table 1 presents the demographic composition of the sample including age, gen-

der, and education characteristics. The participants were recruited from four different sites in

5. Methods 30

Germany, including Bremen representing the north, Gera representing the east, Heidelberg

representing the south, and Köln representing the west of the country.

Table 1. Demographic composition of the sample of the study on age-related differences in executive functions

Age Gender Education/type of school, n (%)

Age group Mean age ± SD Male Female Hauptschule/ Volksschule1

Realschule2 Abitur3 Valid N

Total N

18-29 22.58 ± 3.17 26 29 6 (10.9) 25 (45.5) 24 (43.6) 55 55

30-39 33.73 ± 2.88 26 23 9 (18.4) 18 (36.7) 22 (44.9) 49 49

40-49 46.11 ± 2.22 22 25 1 (2.1) 24 (51.1) 22 (46.8) 47 47

50-59 55.00 ± 2.91 27 31 11 (19.0) 16 (27.6) 31 (53.4) 58 58

60-64 61.96 ± 1.54 22 27 7 (14.6) 23 (47.9) 18 (37.5) 48 49

65-69 66.82 ± 1.59 24 26 12 (24.0) 29 (58.0) 9 (18.0) 50 50

70-74 71.67 ± 1.48 27 27 18 (34.6) 29 (55.8) 5 (9.6) 52 54

75-79 76.65 ± 1.40 23 23 21 (45.7) 16 (34.8) 9 (19.6) 46 46

80-84 81.40 ± 1.40 13 29 20 (47.6) 16 (38.1) 6 (14.3) 42 42

85-99 88.49 ± 3.74 16 19 17 (48.6) 11 (31.4) 7 (20.0) 35 35

Total 58.85 ± 20.00 226 259 122 (25.3) 207 (42.9) 153 (31.7) 482 485

Note. 18-9 years of mandatory school, 210 years of advanced school, 3A-level equivalent after regular 12-13 years of school.

SSttuuddyy oonn aaggee--rreellaatteedd rreellaattiioonnsshhiippss bbeettwweeeenn eexxeeccuuttiivvee ffuunnccttiioonnss aanndd iinntteelllliiggeennccee The sample consisted of 126 normal adults, aged 18-88 years, who were all recruited in Bre-

men. The full sample was divided into two age groups, based on the median age (i.e., 59

years). The sample characteristics in respect to age, gender, and education are presented in

Table 2. The decision to divide the sample at the age of 59 years was for two main reasons.

First, age-related changes in cognitive functioning were taken into account, referring to a sub-

stantial reduction in executive functioning that is thought to occur in the 50-60 age range

(Brennan et al., 1997; De Luca et al., 2003; Raz et al., 1998; Robbins et al., 1998). Crystal-

lized intelligence (Gc) has been reported to decline in the 50-60 age range as well. Moreover,

evidence exists on a substantial decline in fluid intelligence (Gf) after the age of 50 years

(Ardila, 2007; Salthouse, 2010b). Secondly, a negative impact of retirement on cognitive

functioning (Bonsang, Adam, & Perelman, 2012; Rohwedder & Willis, 2010) was considered.

The average retirement age in Germany is approximately 59.5 years. The age of 60 years is

also the earliest eligibility retirement age in Germany, and many other western countries.

Consequently, the age of 59 years was deemed appropriate for separating the older partici-

pants from the younger.

5. Methods 31

Table 2. Demographic composition of the sample of the study on age-related relationships between executive functions and intelligence

Age Gender Education/type of school, n (%)

Age group Mean age ± SD Male Female Hauptschule/ Volksschule1

Realschule2 Abitur3 Total N

18-59 40.17 ± 12.08 32 33 6 (9.2) 25 (38.5) 34 (52.3) 65

60-88 70.46 ± 7.46 28 33 17 (27.9) 35 (57.4) 9 (14.8) 61

Sample 54.83 ± 18.23 60 66 23 (18.3) 60 (47.6) 43 (34.1) 126

Note. 18-9 years of mandatory school, 210 years of advanced school, 3A-level equivalent after regular 12-13 years of school.

55..22 AAsssseessssmmeenntt ttoooollss

The selection of the assessment tools for the current research was based on criteria such as the

scope of assessment, the cognitive functions measured, theoretical foundation, and psycho-

metric properties. The NAB Executive Functions Module was used as a measure of executive

functioning; whereas the WAIS-IV was used as a measure of intelligence. Hereafter, some

main characteristics of the two assessment tools are presented.

NNAABB EExxeeccuuttiivvee FFuunnccttiioonnss MMoodduullee The NAB is a battery of neuropsychological tests designed for the comprehensive assessment

of cognitive functions in adults with known or suspected disorders of the central nervous sys-

tem (White & Stern, 2003). In addition to an extensive review of literature and existing

neuropsychological measures, the selection of specific neuropsychological functions to be

incorporated into the NAB assessment was guided by the results of the publisher’s 1997 sur-

vey of neuropsychological assessment practices (Stern & White, 2000). Hence, recommenda-

tions from neuropsychology practitioners greatly took influence on the creation of the NAB.

Furthermore, the development of the NAB followed two theoretical foundations: (a) empiri-

cism and (b) cognitivism. To meet the requirements of the empirical approach, NAB tests

were designed to be sensitive and specific to clinical prediction. Additionally, in the sense of

the cognitive approach, the selection of task paradigms and item content was guided by cogni-

tive psychology (e.g., Kellogg, 2002; R. J. Sternberg, 1999) and cognitive neuropsychology

(e.g., Morris, 1997; Rapp, 2001). In all cases, tests were created to provide a broad and repre-

sentative sampling of the five domains being assessed (i.e., Attention, Language, Memory,

Spatial, and Executive Functions). Thus, by providing a screening module and five domain-

specific modules, the NAB offers a comprehensive assessment of five main cognitive do-

mains.

5. Methods 32

The NAB Executive Functions Module was used in the present research to assess var-

ious aspects of executive functioning. As the NAB is based on a single standardization group,

the NAB Executive Functions Module offers a set of conormed tasks, suitable for a broad

assessment of executive functioning. Each participant of the present research was adminis-

tered all six NAB modules. However, in line with the focus of the present research, only data

from the Executive Functions Module were used.

For the German adaptation, the four original subtests of the Executive Functions Mod-

ule were translated into German and, if necessary, adapted to standard conditions in German-

speaking countries (Buczylowska, Bornschlegl, Daseking, Jäncke, & Petermann, 2013). The

Executive Functions Module of the German NAB adaptation was extended by two additional

subtests: Planning (German Planen) and Letter Fluency (German Wortflüssigkeit); however,

only Letter Fluency is included in the Executive Functions Index (EFI). Additionally, the

Judgment subtest was shortened from ten to eight items. A detailed description of the German

NAB Executive Functions Module follows.

Subtests characteristics

Planning (PLA)

Planning is based on the Bogenhausener Planungstest (von Cramon, 1988; von

Cramon, Matthes-von Cramon, & Mai, 1991), an experimental measure designed to

aid diagnostics of complex planning skills in the context of daily living, such as prob-

lem solving, strategy implementation, and mental flexibility. The examinee is to put

five typical daily-living, time-restricted assignments in the correct order within up to

15 minutes. Each item is scored based on the quality of the presented solution from 0-

3 points. The performance score equals the sum of the scores for all items.

Mazes (MAZ)

Mazes is designed to assess planning, impulse control, and psychomotor speed. The

examinee is to complete seven time-restricted, paper-and-pencil mazes of increasing

difficulty. Each item is scored ranging from 0-5 points, depending on performance

speed. The performance score is the sum of the scores taken from all accomplished

items.

Letter Fluency (LRF)

Letter Fluency is newly designed by the authors of the German NAB adaptation and is

based on the scheme of a widely accepted concept of verbal fluency (Lezak et al.,

2012; Strauss et al., 2006). The examinee is to generate as many words as possible,

5. Methods 33

within 120 seconds, beginning with a specified initial letter. The performance score

equals the number of correct words generated.

Judgment (JDG)

Judgment is designed to assess problem solving and decision-making capacity in dai-

ly-living situations. The examinee is to answer questions associated with home safety,

health, and medicine. Each item is scored from 0-3 points, contingent on the quality of

presented response. The performance score equals the total obtained from all items.

Categories (CAT)

Categories is designed for the assessment of concept formation, cognitive response set,

mental flexibility, and generativity. The examinee is to create different two-group cat-

egories based on photographs and verbal information about six people. The task is

composed of two panels, each of 240 seconds. The performance score equals the sum

of scores from correct categories generated within both panels.

Word Generation (WGN)

Word Generation is a time-restricted task designed to assess verbal fluency and

generativity. The examinee is to create three-letter words based on a visually presented

group of eight letters (three vowels and five consonants). The performance score is the

number of correctly created words within 120 seconds.

Psychometric properties

With respect to psychometric characteristics, information regarding reliability and validity

must especially be considered. In the manual of the German NAB adaptation, reliability coef-

ficients for the Executive Functions Module are reported as follows: internal consistency reli-

ability, α = .82; test-retest reliability for younger age ranges (18-69 years old), r = .86, and for

older age ranges (70 -> 85), r = .85 (Petermann, Jäncke, & Waldmann, 2016a). Evidence for

the content, construct, and criterion validity can be obtained from the manual of the original

NAB (White & Stern, 2003). Evidence for its clinical validity based on studies with several

different patient groups is reported for both the original and German NAB (Petermann et al.,

2016a). In addition to clinical utility, research has demonstrated the validity of the NAB for

healthy adults (Brooks, Iverson, & White, 2007, 2009; Yochim, Kane, & Mueller, 2009).

5. Methods 34

WWAAIISS--IIVV The German adaptation (Petermann, 2012a) of the Wechsler Adult Intelligence Scale – Fourth

Edition (WAIS-IV; Wechsler, 2008a) was used in the present research for the assessment of

intelligence. The WAIS-IV offers a comprehensive assessment of intellectual functioning

based on the long-lasting tradition of intelligence tests authored by David Wechsler. The

Wechsler intelligence scales are the most frequently used measures of intelligence worldwide

(Ardila, 1999; Drozdick et al., 2012; Flanagan & Kaufman, 2009) and considered a standard

part of neuropsychological assessment (Ardila, 1999; O'Donnell, 2009). Moreover, research-

ers have often implemented Wechsler tests when exploring the structure of cognition, in par-

ticular, the relationship between EFs and intelligence (Boone et al., 1998; Davis et al., 2011;

Friedman et al., 2006; Lamar et al., 2002).

There are a few main reasons for the widespread use of Wechsler tests that are as fol-

lows. Primarily, Wechsler intelligence scales are thought to possess strong psychometric

properties (Davis et al., 2011); secondly, they are known for their predictive value and clinical

utility (Drozdick et al., 2012). Furthermore, the successive versions of Wechsler scales have

been revised according to the current state of research (Drozdick et al., 2012).

The WAIS-IV (Wechsler, 2008a) is a revision of the Wechsler Adult Intelligence

Scale – Third Edition (WAIS-III; Wechsler, 1997). The structure and the subtests were modi-

fied to correspond with advances in the field of intellectual assessment. That is, the theoretical

background of the WAIS-IV is coherent with the Cattell-Horn-Carroll (CHC) theory (Keith &

Reynolds, 2010). Consequently, the WAIS-IV does not continue the tradition of the Wechsler

intelligence scales being composed of the Verbal and Performance IQs. Instead of dual IQ,

four index scales representing intellectual functioning in four specific cognitive areas were

implemented: Verbal Comprehension Index (VCI), Perceptual Reasoning Index (PRI), Work-

ing Memory Index (WMI), and Processing Speed Index (PSI) (Wechsler, 2008b). These four

indices contain ten core subtests, which are necessary for the calculation of the Full Scale IQ

(FSIQ), and five supplemental subtests, which are designed to provide additional clinical in-

formation. In addition to the FSIQ, the General Ability Index (GAI) can be calculated, an op-

tional score composed of the VCI and PRI without the contribution of WM and processing

speed (Wechsler, 2008b). Figure 1 presents the coherence of the WAIS-IV subtests with the

CHC theory. A description of the index structure follows.

5. Methods 35

Index structure characteristics

Verbal Comprehension Index (VCI)

The VCI is designed to assess verbal intelligence. It contains three core subtests (Simi-

larities, Vocabulary, and Information), as well as one supplemental subtest (Compre-

hension). The VCI is considered a good measure of Gc (Wechsler, 2008b).

Perceptual Reasoning Index (PRI)

The PRI is a measure of nonverbal reasoning and perceptual organization. It is com-

posed of three core subtests (Block Design, Matrix Reasoning, and Visual Puzzles)

and two supplemental subtests (Figure Weights and Picture Completion). The PRI is

considered a good measure of Gf (Wechsler, 2008b).

Working Memory Index (WMI)

The WMI is a measure of WM, attention, and concentration. It is composed of two

core subtests (Digit Span and Arithmetic), as well as one supplemental subtest (Letter-

Number Sequencing).

Processing Speed Index (PSI)

The PCI is designed to assess the speed of mental and graphomotor processing. It in-

cludes two core subtests (Symbol Search and Coding), as well as one supplemental

subtest (Cancellation). Psychometric properties

For the German adaption of the WAIS-IV, the average reliability coefficients are reported as

follows: internal consistency reliability of the composite scores, VCI, α = .97; PRI, α = .94;

WMI, α = .94; PSI, α = .90; FSIQ, α = .98; GAI, α = .97. For the subtests, internal consisten-

cy reliability coefficients ranged between .78 and .94; test-retest reliability of the composite

scores, VCI, r = .89; PRI, r = .91; WMI, r = .81; PSI, r = .87; FSIQ, r = .94; GAI, r = .90

(Petermann, 2012b).

Evidence for the content, construct, and criterion validity is presented in the manual of

both the original and German WAIS-IV (Petermann, 2012b; Wechsler, 2008b). Furthermore,

the WAIS-IV has been clinically validated by numerous studies with several different patient

groups (Petermann, 2012b; Wechsler, 2008b). Moreover, research has proven measurement

invariance for the WAIS-IV between individuals with clinical disorders and healthy adults

(Weiss, Keith, Zhu, & Chen, 2013).

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Stratum III Stratum II Stratum I

5. Methods 36

5. Methods 37

55..33 DDaattaa mmaannaaggeemmeenntt aanndd ssttaattiissttiiccaall aannaallyyssiiss

GGeenneerraall pprroocceedduurree Statistical analysis was performed using Microsoft Office Excel 2007 and SPSS (version 22).

Test scores were calculated in accordance with manual guidelines. In the first stage of data

management, Excel spreadsheets were used for correcting typing errors and detecting missing

values. In the next step, the data were transferred into SPSS for further analysis. Prior to anal-

ysis, the requirements for statistical methods used were checked.

SSttuuddyy oonn aaggee--rreellaatteedd ddiiffffeerreenncceess iinn eexxeeccuuttiivvee ffuunnccttiioonnss The raw scores of the six subtests of the NAB Executive Functions Module were used to

compare 10 age groups (18-29, 30-39, 40-49, 50-59, 60-64, 65-69, 70-74, 75-79, 80-84, and

85-99). For each age group, the mean, standard deviation, and the coefficient of variation

(CV) [(SD/mean) × 100] were calculated. To assess the extent of average decline as well as

interindividual variability in performance for each subtest over time the percentage decrease

in the mean and the percentage increase in the dispersion [(amount of change/highest mean or

associated CV) x 100] were calculated. In the next step, the index variance inflation factor

(VIF) was calculated to check for the multicollinearity among the six Executive Functions

Module subtests. The low values of the VIF (< 2) excluded the risk of multicollinearity

(O’brien, 2007) and so allowed for conducting a multivariate analysis of variance

(MANOVA). A two-way MANOVA with Bonferroni type post hoc comparisons were ap-

plied to all subtests to test the differences in performance with respect to age group and gen-

der.

SSttuuddyy oonn aaggee--rreellaatteedd rreellaattiioonnsshhiippss bbeettwweeeenn eexxeeccuuttiivvee ffuunnccttiioonnss aanndd iinntteelllliiggeennccee Raw scores of all variables derived from the two assessment tools were transformed into age-

adjusted standard scores. The NAB Planning subtest, being a percentile rank score, was ex-

cluded from the analysis with standard scores. The standard scores were used to calculate cor-

relations between the NAB Executive Functions Module and the WAIS-IV. To check for

normality, the data of both age groups (18-59 and 60-88 years) were subjected to the Kolmo-

gorow-Smirnow test. The data in the NAB subtests were normally distributed. Among WAIS-

IV scores, only the WMI showed a skewed distribution in the younger age group, D (65) =

0.15, p = .001; S = -.247, Ss = .297; K = .559, Ks = .586. All variables were checked for ex-

treme scores; no systematic outliers were detected.

5. Methods 38

The Pearson product-moment correlation coefficient was employed to investigate the

relationship between five Executive Functions Module subtests and the EFI with the FSIQ,

the GAI, and the four WAIS-IV indices. Pearson correlation coefficients were calculated for

both age groups, as well as for the full sample, and subjected to a Fisher r-to-z-transformation.

In the next step, a z-test for independent samples was employed to test for significant differ-

ences in correlation coefficients between the two age groups. The significance of the differ-

ences between correlations within the sample, as well as within the two age groups, was

tested using the procedure for comparing correlated correlation coefficients proposed by

Meng, Rosenthal, and Rubin (1992).

To investigate the predictability of the WAIS-IV with the NAB Executive Functions

Module, the stepwise procedure of linear multiple regression was applied. The best prediction

models were established separately for the WAIS-IV FSIQ, VCI, PRI, WMI, and PSI. The

five NAB Executive Functions Module subtests (i.e., Mazes, Letter Fluency, Judgment, Cate-

gories, and Word Generation), age in years, and sex were used as predictors. Consequently,

the same seven predictors were entered into five separate regression analyses, respectively.

All regression analyses were performed separately for the age groups and once again for the

full sample. The procedure of forward selection was used to establish the best-fitting model

for each regression analysis. At each step of the forward selection, only those variables were

included into the regression equation that resulted the largest significant gain in the variance

accounted for; the probability of F to enter was set at p <= .05 and the probability of F to re-

move was set at p >= .10.

6. Results and discussion 39

66.. RReessuullttss aanndd ddiissccuussssiioonn This chapter shows and discusses main results of the two studies conducted within the current

thesis. First, the findings on age-related differences in executive functions (EFs), and second,

the findings on age-related relationships between EFs and intelligence, are presented. As the

two studies conducted are interrelated, the aim was to show these interrelations when discuss-

ing the study results; the findings from the study on age-related differences in EFs, in particu-

lar, were partly employed to explain the influence of age on the relationship between EFs and

intelligence.

66..11 AAggee--rreellaatteedd ddiiffffeerreenncceess iinn eexxeeccuuttiivvee ffuunnccttiioonnss

The main research aim was to clarify whether there is an age-related effect on executive func-

tioning by exploring differences in performance between ten adult age groups in respect to six

NAB Executive Functions Module subtests. Table 3 presents the descriptive statistics of all

six NAB subtests for ten age groups. These data were used to establish differences in mean

performance and differences in variability in performance between age groups.

DDiiffffeerreenncceess iinn mmeeaann ppeerrffoorrmmaannccee Analysis of variance showed a highly significant main effect of age for all subtests. The high-

est effect sizes were for Mazes, F (9, 465) = 65.34, p < .001, η² = 0.53, and Categories, F (9,

465) = 27.25, p < .001, η² = 0.33; followed by Planning, F (9, 465) = 12.73, p < .001, η² =

0.19, Judgment, F (9, 465) = 10.23, p < .001, η² = 0.16, Letter Fluency, F (9, 465) = 5.16, p <

.001, η² = 0.09, and Word Generation, F (9, 465) = 3.71, p < .001, η² = 0.07. Furthermore,

there was a significant main effect of Gender for Mazes, F (1, 465) = 16.54, p < .001, η² =

0.03, as men outperformed women, and Letter Fluency, F (1, 465) = 17.59, p < .001, η² =

0.04, where women outperformed men, albeit the effect sizes were small. There was no sig-

nificant age group-by-gender interaction across all six subtests. Consequently, the present

research demonstrated differences in performance in individual EF tasks according to age

group. As depicted by Figure 2, there was a general decrease in performance with advancing

age for all NAB Executive Functions Module subtests. However, differences due to the type

of task appear meaningful because the percentage decrease in the mean performance across

the entire age range differed depending on subtest. Moreover, performance on individual

NAB subtests peaked and decline at different ages. The highest and lowest test scores were

generally observed in the youngest and oldest age groups, respectively. However, perform-


ance on Letter Fluency and Word Generation reached high and low points in the 40-49 and

80-84 age groups, respectively. Hence, the 85-99 age group achieved higher scores in Letter

Fluency and Word Generation than the 80-84 age group. Although, analysis of variance

showed no significant differences in performance between these two age groups. Further-

more, although there was a particular age group in each subtest that produced the highest

score, analysis of variance showed no significant differences in performance between the age

groups with the highest score and other age groups with superior performance. However, the

age range of superior performance differed according to subtest. For Mazes, there was no dif-

ference in mean performance only between the 18-29 and 30-39 age groups. The age range of

superior performance for Planning and Categories was 18-59, whereas for Letter Fluency,

Judgment, and Word Generation, it was 18-74 years.

Table 3. Descriptive statistics for the NAB Executive Functions Module subtests based on raw scores

Age group 18-29 30-39 40-49 50-59 60-64 65-69 70-74 75-79 80-84 85-99

PLA Mean 9.1 8.4 8.1 7.5 6.9 6.1 6.2 4.9 4.6 3.8 SD 2.0 2.8 3.1 3.4 3.7 3.6 3.6 3.3 3.3 3.3 CV 22.1 33.3 38.9 45.5 53.1 58.9 58.6 66.2 71.9 86.0

MAZ Mean 21.0 19.3 16.6 14.3 12.8 9.5 8.9 7.2 4.7 4.1 SD 4.7 5.1 5.1 5.6 5.4 5.6 5.3 5.1 3.4 3.3 CV 22.3 26.4 30.5 39.2 41.9 58.9 59.1 70.9 72.2 79.7

LRF Mean 16.1 15.8 18.4 18.0 16.9 15.7 15.7 14.1 12.6 12.8 SD 5.8 5.3 6.4 6.1 6.2 5.7 5.4 5.5 4.7 5.2 CV 36.3 33.2 35.0 33.9 36.9 36.5 34.2 39.1 37.5 40.9

JDG Mean 12.9 12.8 12.6 12.8 12.1 11.9 11.9 10.8 10.9 10.3 SD 1.9 1.6 1.7 1.9 2.1 1.9 2.0 2.1 1.9 2.2 CV 14.8 12.7 13.5 14.4 17.1 16.3 16.9 19.8 17.8 21.2

CAT Mean 27.4 25.2 24.0 22.5 18.4 16.1 15.4 11.5 9.5 9.4 SD 10.8 8.1 8.8 8.8 8.2 8.5 8.2 7.5 6.2 6.2 CV 39.6 32.2 36.7 39.0 44.3 52.8 53.5 65.4 64.5 65.5

WGN Mean 7.6 7.4 8.1 7.6 7.9 6.8 6.7 5.9 5.6 6.3 SD 3.0 2.9 3.4 2.2 2.9 2.9 2.6 2.8 2.7 3.2 CV 39.8 39.0 42.1 29.2 37.3 42.3 38.9 47.7 47.4 49.7

Note. CV = (SD/mean) x 100; PLA = Planning, MAZ = Mazes, LRF = Letter Fluency, JDG = Judgment, CAT = Categories, WGN = Word Generation; modified from Buczylowska and Petermann (2016b).


Hence, the first significant difference in performance between the age group with the highest

score and any following age group with a lower score may be used to determine age-related

onset of decline. Moreover, there is an association between the age-related onset of decline

and the percentage decrease in the mean. Thus, Planning, Categories, and Mazes, the subtests

with the earliest onset of decline, showed the largest age-related decline, with a decrease in

the mean ranging from 58% to 81%; whereas Judgment, Word Generation, and Letter Flu-

ency, the subtests with the latest onset of decline, showed a moderate decrease in the mean

ranging from 21% to 32%.

Figure 2. Mean performance in the NAB Executive Module subtests across ten age groups.

DDiiffffeerreenncceess iinn vvaarriiaabbiilliittyy The coefficient of variation (CV) was used as a ratio of dispersion and the percentage increase

in the CV across age was interpreted as the magnitude of change in the variability of perform-

ance. As depicted by Figure 3, there was an increase in dispersion in all subtests, varying

from 7% to 12% for Letter Fluency and Word Generation, 43% to 65% for Judgment and

Categories, and 258% to 289% for Mazes and Planning. Interestingly, there was an associa-

tion between age-related average decline and age-related variability. That is, the subtests with

the greatest decrease in the mean also showed the greatest increase in dispersion and the sub-

tests with the lowest decrease in the mean showed the lowest increase in dispersion. Letter

Fluency, Word Generation, and Judgment were those subtests that showed the lowest increase

21%

31%

32%

58%

66%

81%

0

5

10

15

20

25

30

18-29 30-39 40-49 50-59 60-64 65-69 70-74 75-79 80-84 85-99

Judgement

Word Generation

Letter Fluency

Planning

Categories

Mazes

Age group

Raw

sco

re


in dispersion and the lowest decrease in the mean, as well as the latest onset of decline. The

three subtests require verbal skills as well as general knowledge and are associated with crys-

tallized intelligence (Gc). The highest increase in dispersion, the greatest decrease in the mean

and the earliest onset of decline were observed in Planning, Mazes, and Categories. The three

subtests represent visual and speed-related tasks, which also involve fluid intelligence (Gf).

Figure 3. Dispersion in the NAB Executive Module subtests across ten age groups

These findings are not surprising as they are consistent with prior research on developmental

trajectories of intelligence (Ardila, 2007; Daseking & Petermann, 2013; Morse, 1993; Ryan et

al., 2000; Wisdom et al., 2012). That is, the pattern of development of EFs is consistent with

the concept of Gf and Gc. Hence, performance on those EF tasks that require visual-spatial

perception, attention, and speed may show greater decrease in the mean and greater increase

in interindividual variability. In contrast, performance on those EF tasks that require verbal

skills and general knowledge may show smaller decrease in the mean and smaller increase in

interindividual variability. However, the Categories and Planning subtests require special at-

tention. These two tasks involve similar skills such as mental flexibility, strategy implementa-

tion, and problem solving (Stern & White, 2003; von Cramon et al., 1991). Both subtests also

seem to follow the same developmental trajectory with the highest score at 18-29 years and

onset of decline at > 60 years of age, with similar extent of deterioration. However, the two

subtests differ considerably in the magnitude of variability, with the dispersion in Planning

being two-fold higher than that of Categories. The difference in variability may be due to the

7%

12%

43%

65%

258% 289%

0

10

20

30

40

50

60

70

80

90

100

18-29 30-39 40-49 50-59 60-64 65-69 70-74 75-79 80-84 85-99

Letter Fluency

Word Generation

Judgement

Categories

Mazes

Planning

Raw

sco

re

Age group


multifactorial nature of both subtests. While they involve similar abilities, they might differ in

their demand for attentional capacity. Planning, as a paper-pencil-test, might reflect a substan-

tial attentional component with the greater variability observed as age advances.

66..22 AAggee--rreellaatteedd rreellaattiioonnsshhiippss bbeettwweeeenn eexxeeccuuttiivvee ffuunnccttiioonnss aanndd iinntteelllliiggeennccee

The main research aim was to clarify whether there is an age-related effect on the relationship

between executive functioning and intelligence by exploring commonalities and differences in

that relationship between younger and older adults. In addition to correlationship, the predic-

tive ability of EFs for intelligence was examined. Table 4 presents an overview of perform-

ance on the NAB Executive Functions Module and WAIS-IV for each age group, respec-

tively. Further analyses were based on those scores.

First, the correlationship between the NAB Executive Functions Module and the

WAIS-IV in the full sample is presented. Second, an overview on intercorrelations between

the NAB subtests is given. Third, the results on commonalities and differences between the

two age groups in respect to the relationship between the NAB Executive Functions Module

and the WAIS-IV are presented and discussed. Fourth, commonalities and differences in the

correlationship between the Executive Functions Index (EFI) and individual WAIS-IV index

scores within the two age groups and the sample are presented. Fifth, the findings are dis-

cussed from statistical and theoretical perspective.

CCoorrrreellaattiioonnsshhiipp wwiitthhiinn tthhee ssaammppllee Figure 4 provides a general overview on the correlationship between the NAB Executive

Functions Module subtests and the WAIS-IV. Here, only correlations derived from the full

sample with values > .30 are exhibited. First, attention should be directed to the relationship

between the subtests of the two measures. In most cases, three of the five NAB EF subtests

were at least moderately correlated with the WAIS-IV subtest. This suggests notable relation-

ships between the two measures on the individual task level. The correlation pattern is not

surprising as it fulfils the expectations based on the underlying abilities. For example, Letter

Fluency correlated with all three Verbal Comprehension Index (VCI) subtests and Mazes cor-

related with two Perceptual Reasoning Index (PRI) and two Processing Speed Index (PSI)

subtests, respectively. Most interestingly, however, Word Generation and Categories were the

subtests that most frequently reached the value > .30 in the correlations with the WAIS-IV


subtests. Furthermore, both subtests reached the value > .30 in the correlations with all four

WAIS-IV indices and were substantially correlated with the Full Scale IQ (FSIQ). Conse-

quently, the potential meaning of the two subtests is discussed in the following sections.

Table 4. Descriptive statistics for the NAB Executive Functions Module and WAIS-IV

Age group 18-59 60-88 18-88

NAB M SD M SD M SD

Mazes 49.92 10.54 50.70 9.19 50.30 9.87

Letter Fluency 53.09 10.38 49.67 9.62 51.44 10.13

Judgment 49.75 8.92 51.59 8.89 50.64 8.92

Categories 51.28 9.03 50.93 8.71 51.11 8.84

Word Generation 51.15 10.82 50.70 10.58 50.94 10.66

EFI 102.51 13.19 101.70 15.21 102.12 14.15

WAIS-IV M SD M SD M SD

VCI 103.66 10.32 100.64 11.60 102.20 11.02

PRI 100.11 12.88 99.33 12.67 99.73 12.73

WMI 98.69 13.79 103.43 14.65 100.98 14.35

PSI 101.60 11.98 100.80 12.67 101.21 12.28

FSIQ 101.35 11.48 100.97 13.21 101.17 12.30

GAI 101.92 10.85 99.93 12.16 100.96 11.50

Note. EFI = Executive Functions Index, VCI = Verbal Comprehension Index, PRI = Perceptual Reasoning In-dex, WMI = Working Memory Index, PSI = Processing Speed Index, FSIQ = Full Scale IQ, GAI = General Ability Index; standard score scale for the NAB subtests = 50 ± 10, standard score scale for the EFI and WAIS-IV scores = 100 ± 15.

IInntteerrccoorrrreellaattiioonnss wwiitthhiinn tthhee NNAABB EExxeeccuuttiivvee FFuunnccttiioonnss MMoodduullee ssuubbtteessttss In order to better understand the nature of the individual EF tasks used, the correlationship

between these tasks should be considered.

Table 5 demonstrates that intercorrelations among the individual NAB Executive Functions

Module subtests were low to moderate. The variance shared by the EF subtests ranged, in the

18-59 age group from 0.04% to 16%, and in the 60-88 age group from 0.5% to 20%. It may

be concluded that NAB EF tasks are not redundant, as they involve somewhat different abili-

ties. Moreover, in addition to executive skills, non-executive skills such as attention, memory,

speed, and language, may contribute to performance on the NAB EF tasks. As expected, EF

tasks that involve similar abilities were more strongly intercorrelated than EF tasks that in-

volve different abilities. To illustrate, Categories, Word Generation, and Letter Fluency were

moderately intercorrelated in both age groups; whereas the correlation between Word Genera-

tion and Judgment was low. Nevertheless, differences between the two age groups with re-

gard to intercorrelations among the NAB EF subtests were evident as well.


WM

I

FSIQ

PRI

VC

I

CA

T

WG

N

LRF

.56

.36

.35

VC

IN

SI

CA

T

WG

N

LRF

.48

.45

.42

WG

N

CA

T

MA

Z

.45

.42

.35

LRF

CA

T

WG

N

.43

.41

.40

WG

N

MA

Z

CA

T

.43

.42

.35

MA

Z

CA

T

WG

N

.39

.33

.39

CA

T

WG

N

LRF

.44

.39

.33

LRF

CA

T

WG

N

.45

.44

.40

CA

T

LRF

WG

N

.57

.45

.45

CA

T

WG

N

.41

.32

MA

Z

WG

N

.47

.43

WG

N

CA

T

WG

N

LRF

.58

.57

.41

CA

T

WG

N

.45

.40

CA

T

WG

N

.36

.35

.39

Figu

re 4

. Cor

rela

tions

hip

betw

een

the

NA

B E

xecu

tive

Func

tions

Mod

ule

subt

ests

and

the

WA

IS-I

V.

Not

e. p

< .0

1, b

ased

on

two-

taile

d pr

obab

ility

; N =

126

; FSI

Q =

Ful

l Sca

le IQ

, VC

I = V

erba

l Com

preh

ensi

on In

dex,

PR

I = P

erce

ptua

l Rea

soni

ng In

dex,

WM

I = W

orki

ng

Mem

ory

Inde

x, P

SI =

Pro

cess

ing

Spee

d In

dex,

SI =

Sim

ilarit

ies,

VC

= V

ocab

ular

y, IN

= In

form

atio

n, B

D =

Blo

ck D

esig

n, M

R =

Mat

rix R

easo

ning

, VP

= V

isua

l Puz

zles

, D

S =

Dig

it Sp

an, A

R =

Arit

hmet

ic, C

D =

Cod

ing,

SS

= Sy

mbo

l Sea

rch,

MA

Z =

Maz

es, L

RF

= Le

tter F

luen

cy, C

AT

= Ca

tego

ries,

WG

N =

Wor

d G

ener

atio

n.

VP

MR

BD

D

S

AR

SS

CD

PSI


CCoommmmoonnaalliittiieess bbeettwweeeenn yyoouunnggeerr aanndd oollddeerr aadduullttss In the further step of the analysis, the focus of attention was on commonalities between

younger and older participants in the relationship of EFs with intelligence. Table 6 provides

an overview on the correlationship between the NAB Executive Functions Module and the

WAIS-IV in the two age groups and the sample. Not surprisingly, the composite score EFI

with the FSIQ and WAIS-IV indices produced highest correlations. There were no significant

age-related differences in the correlations between the EFI and FSIQ (see Figure 5 and Figure

6 for bivariate scatterplots for the EFI and FSIQ for both age groups, respectively). In addi-

tion, the NAB subtests were substantially correlated with the FSIQ, with only Judgment fail-

ing to reach significance in younger participants. In most cases, there were no significant age-

related differences in the correlations between the NAB subtests and WAIS-IV indices.

Moreover, the correlation pattern between the NAB subtests and WAIS-IV indices was con-

sistent for both age groups in the following cases: Categories and Word Generation correlated

substantially with all WAIS-IV indices, with only one correlation failing to reach signifi-

cance; Letter Fluency was substantially correlated with the VCI and Working Memory Index

(WMI), but was not significantly correlated with the PRI. In contrast, Mazes correlated sub-

stantially with the PSI, but not with the VCI.

Tab

le 5

. Int

erco

rrel

atio

ns b

etw

een

the

NA

B E

xecu

tive

Func

tions

Mod

ule

subt

ests

in th

e ag

e gr

oups

18-

591 , 6

0-88

2 , and

18-

88³

MA

Z LR

F JD

G

CA

T W

GN

EF

I

Age

18

-59

60-8

8 18

-88

18-5

9 60

-88

18-8

8 18

-59

60-8

8 18

-88

18-5

9 60

-88

18-8

8 18

-59

60-8

8 18

-88

18-5

9 60

-88

18-8

8

MA

Z -

- -

.10

.36**

.2

0* -.2

9* .2

9* -.0

3 .1

2 .4

2**

.25**

.4

0**

.07

.25**

.5

2**

.61**

.5

6**

LRF

.10

.36**

.2

0* -

- -

-.02

.34**

.1

3 .2

8* .4

5**

.36**

.3

7**

.45**

.4

0**

.64**

.7

7**

.69**

JD

G

-.29*

.29*

-.03

-.02

.34**

.1

3 -

- -

.22

.41**

.3

0**

-.11

.17

.03

.23

.63**

.4

3**

CA

T .1

2 .4

2**

.25**

.2

8* .4

5**

.36**

.2

2 .4

1**

.30**

-

- -

.27*

.42**

.3

4**

.65**

.7

8**

.71**

W

GN

.4

0**

.07

.25**

.3

7**

.45**

.4

0**

-.11

.17

.03

.27*

.42**

.3

4**

- -

- .7

3**

.65**

.6

8**

EFI

.52**

.6

1**

.56**

.6

4**

.77**

.6

9**

.23

.63**

.4

3**

.65**

.7

9**

.71**

.7

3**

.65**

.6

8**

- -

-

Not

e. *

p <

.05;

**p

< .0

1; tw

o-ta

iled

prob

abili

ty; N

1 =

65, N

2 = 6

1, N

³ = 1

26; M

AZ

= M

azes

, LR

F =

Lette

r Flu

ency

, JD

G =

Jud

gmen

t, C

AT

= C

ateg

orie

s, W

GN

=

Wor

d G

ener

atio

n, E

FI =

Exe

cutiv

e Fu

nctio

ns In

dex;

mod

ified

from

Buc

zylo

wsk

a an

d Pe

term

ann

(201

6a).

T

able

6. C

orre

latio

ns o

f the

NA

B E

xecu

tive

Func

tions

Mod

ule

with

the

WA

IS-I

V in

the

age

grou

ps 1

8-59

1 , 60-

882 , a

nd 1

8-88

³

MA

Z LR

F JD

G

CA

T W

GN

EF

I

Age

18

-59

60-8

8 18

-88

18-5

9 60

-88

18-8

8 18

-59

60-8

8 18

-88

18-5

9 60

-88

18-8

8 18

-59

60-8

8 18

-88

18-5

9 60

-88

18-8

8

VC

I .0

8 .1

6 .1

1 .3

5**

.52**

.4

5**

.06

.45**

.2

4**

.48**

.6

6**

.57**

.2

9* .6

2**

.45**

.4

5**

.71**

.5

9**

PRI

.44**

.2

3 .3

5**

.17

.22

.20*

.01

.38**

.1

9* .4

1**

.43**

.4

2**

.44**

.4

6**

.45**

.5

4**

.50**

.5

2**

WM

I .2

9* .1

9 .2

4**

.37**

.5

5**

.42**

.0

9 .4

3**

.27**

.4

2**

.56**

.4

8**

.40**

.5

3**

.45**

.5

7**

.66**

.6

1**

PSI

.46**

.3

7**

.42**

.1

0 .3

2* .2

1* .1

1 .2

7* .1

9* .2

2 .4

8**

.35**

.4

0**

.46**

.4

3**

.48**

.5

6**

.52**

FSIQ

.4

0**

.29*

.35**

.3

2**

.50**

.4

1**

.08

.47**

.2

8**

.51**

.6

5**

.58**

.5

0**

.64**

.5

7**

.66**

.7

5**

.71**

GA

I .3

3**

.21

.27**

.3

0* .4

3**

.37**

.0

5 .4

7**

.25**

.5

4**

.62**

.5

7**

.46**

.6

2**

.54**

.6

0**

.69**

.6

5**

Not

e. *

p <

.05;

**p

< .0

1; tw

o-ta

iled

prob

abili

ty; N

1 =

65, N

2 = 6

1, N

³ = 1

26; M

AZ

= M

azes

, LR

F =

Lette

r Flu

ency

, JD

G =

Judg

men

t, C

AT

= C

ateg

orie

s, W

GN

=

Wor

d G

ener

atio

n, E

FI =

Exe

cutiv

e Fu

nctio

ns I

ndex

; VC

I =

Ver

bal C

ompr

ehen

sion

Inde

x, P

RI =

Per

cept

ual R

easo

ning

Inde

x, W

MI

= W

orki

ng M

emor

y In

dex,

PS

I = P

roce

ssin

g Sp

eed

Inde

x, F

SIQ

= F

ull S

cale

IQ, G

AI =

Gen

eral

Abi

lity

Inde

x; m

odifi

ed fr

om B

uczy

low

ska

and

Pete

rman

n (2

016a

).



Figure 5. Scatterplot with linear regression line depicting the standard scores of 18- to 59-year olds on the WAIS-IV Full Scale IQ (FSIQ) as a function of NAB Executive Functions Index (EFI). Note. Modified from Buczylowska and Petermann (2016a).

Figure 6. Scatterplot with linear regression line depicting the standard scores of 60 to 88-year olds on the WAIS-IV Full Scale IQ (FSIQ) as a function of NAB Executive Functions Index (EFI). Note. Modified from Buczylowska and Petermann (2016a).

The current study also revealed some prediction patterns being similar for both age

groups. These prediction patterns are based on best-fitting models established for each of the

WAIS-IV indices and for the FSIQ, respectively (see Table 7).

The Categories and Word Generation subtests were most frequently included in the

best-fitting prediction models of both age groups as well as of the sample. Consequently, the

two subtests appear to be the best predictors for the entire WAIS-IV. Both subtests are time


restricted and language-based tasks. Categories involves several crucial executive skills –

concept formation, cognitive response set, mental flexibility, and generativity, (Stern &

White, 2003). Word Generation is a measure of verbal fluency and generativity (Stern &

White, 2003); however, due to the nature of the German language, the generation of three-

letter words based on a group of eight letters appears to be a demanding task. Thus, Word

Generation requires more complex skills, such as strategy implementation and mental flexibil-

ity. Consequently, the Categories and Word Generation subtests as well as the WAIS-IV ap-

pear to have common underlying skills. EFs might be the common element. Yet the complex-

ity and multifactorial nature of Categories and Word Generation might also explain why the

two NAB subtests strongly correlate with the WAIS-IV and are in general a good predictor of

intelligence. Furthermore, similar findings were derived from the study by Davis et al. (2011),

which was already discussed in chapter three; the Word Context Test from the D-KEFS,

which is an EF measure involving complex verbal skills, demonstrated there the highest rela-

tionship with the WAIS-III.

In contrast, Letter Fluency and Mazes showed a fairly consistent correlation and pre-

diction pattern for both age groups only with those WAIS-indices that involve similar specific

skills. The Mazes subtest, which involves planning, impulse control, and psychomotor speed,

correlated substantially with the PSI and was included in the PSI best prediction model, but

was neither significantly correlated with the VCI nor included in the VCI best prediction

model. The Letter Fluency subtest, being a time-restricted task with predominant verbal and

working memory (WM) components, correlated substantially with the VCI and WMI, but was

not significantly correlated with the PRI. Letter Fluency was included in the VCI best-fitting

models of both age groups as well. Moreover, the best-fitting model for the FSIQ included in

both age groups the Categories and Word Generation subtests; whereas the Letter Fluency

subtest was included in the FSIQ best-fitting model of neither age group. Letter Fluency was

also the subtest least frequently included in all WAIS-IV best-fitting models. As a result, Let-

ter Fluency may be a less good predictor for general intelligence (g) when compared to the

other NAB subtests. In line with previous findings on a strong relationship of verbal fluency

tasks with Gc (Ardila et al., 2000; Salthouse & Davis, 2006), Letter Fluency appears to be a

good predictor for crystallized abilities. The current findings suggest age-independent, unique

relationships between the NAB Executive Functions Module subtests and the WAIS-IV. Fur-

thermore, they demonstrate that there might be executive and non-executive abilities, which

are involved in these measures regardless of age.


Table 7. Multiple regression analyses for the WAIS-IV

Age 18-59 60-88 18-88

Variable Predictor β p Predictor β p Predictor β p

FSIQ CAT .401 .000 CAT .340 .000 CAT .349 .000 WGN .302 .006 WGN .477 .000 WGN .428 .000 MAZ .235 .026 Age .266 .000 JDG .175 .007

JDG .268 .001 MAZ .140 .033 Sex -.133 .033

VCI CAT .412 .001 CAT .323 .000 CAT .414 .000 LRF .234 .041 WGN .397 .000 WGN .231 .003

Age .360 .000 LRF .206 .009 JDG .204 .009 LRF .181 .031

PRI MAZ .334 .002 WGN .398 .000 WGN .335 .000 Age .316 .002 JDG .320 .004 CAT .260 .001

WGN .251 .021 Sex -.232 .033 MAZ .179 .023 CAT .228 .028 Sex -.162 .032

WMI CAT .338 .004 CAT .326 .005 CAT .286 .000 WGN .311 .008 LRF .288 .014 WGN .267 .001

WGN .262 .022 Age .263 .000 LRF .246 .002

PSI MAZ .438 .000 CAT .229 .080 WGN .344 .000 JDG .263 .019 WGN .348 .004 MAZ .334 .000

WGN .258 .027 MAZ .248 .039 JDG .186 .014 Note. FSIQ = Full Scale IQ, VCI = Verbal Comprehension Index, PRI = Perceptual Reasoning Index, WMI = Working Memory Index, PSI = Processing Speed Index, MAZ = Mazes, WGN = Word Generation, JDG = Judgment, CAT = Categories, LRF = Letter Fluency; modified from Buczylowska, Daseking, and Petermann (2016).

DDiiffffeerreenncceess bbeettwweeeenn yyoouunnggeerr aanndd oollddeerr aadduullttss Statistical analysis revealed differences between age groups in regard to the correlationship

between EFs and intelligence, too. In all significant differences between correlations, the

older age group had the higher score. That is, the participants aged 60-88 years showed a

stronger relationship between the NAB Executive Functions Module and the WAIS-IV, than

those aged 18-59 years. In particular, the NAB Judgment subtest correlated more strongly

with the VCI, z = 2.29, p = .011; PRI, z = 2.15, p = .016; WMI, z = 2.01, p = .022; FSIQ, z =

2.38, p = .008; and, General Ability Index (GAI), z = 2.53, p = .006. Additionally, there was a

stronger correlation between the EFI and VCI, z = 2.22, p =.013 (see Figure 7 and Figure 8 for

bivariate scatterplots for both age groups, respectively). All significant differences between

correlations were supported by medium effect sizes (Cohen, 1988) as appears in Table 8.


Figure 7. Scatterplot with linear regression line depicting the standard scores of 18- to 59-year olds on the WAIS-IV Verbal Comprehension Index (VCI) as a function of NAB Executive Functions Index (EFI).

Figure 8. Scatterplot with linear regression line depicting the standard scores of 60- to 88-year olds on the WAIS-IV Verbal Comprehension Index (VCI) as a function of NAB Executive Functions Index (EFI).

The current research also suggests that there might be age-related differences in the

predictability of intelligence with EFs. Especially, the contrast between prediction models of

the younger and older age group, and the entire sample, demonstrates the significance of age

in the WAIS-IV predictability. As there was no WAIS-IV index with the same best-fitting

model for the two age groups, the prediction models of the entire sample seem to be less accu-

rate.


Among the NAB subtests, Mazes and Judgment demonstrated the strongest age-

related predictive validity for the WAIS-IV. The Mazes subtest was included in the FSIQ,

PRI, and PSI best-fitting models of the younger age group and in only one best-fitting model

(PSI) of the older age group. In contrast, the Judgment subtest was included in FSIQ, VCI,

PRI best-fitting models of the older age group and in only one best-fitting model (PSI) of the

younger age group. The Mazes subtest is a visual task with strong perceptual-motor underpin-

nings, short maturation path, and rapid onset of decline (Buczylowska & Petermann, 2016b).

Consequently, differences between individuals in the Mazes performance start increase al-

ready in early adulthood. This might explain why Mazes is a good predictor of intelligence in

younger age ranges.

Table 8. Effect sizes for differences in the correlations between the NAB Executive Functions Module and WAIS-IV between the age groups 18-59 and 60-88

Mazes Letter Fluency

Judgment Categories Word Generation EFI

VCI .08 .22 .42* .27 .43* .40*

PRI .24 .05 .39* .02 .03 .05

WMI .10 .23 .37* .19 .16 .15

PSI .12 .23 .17 .30 .07 .11

FSIQ .13 .21 .43* .22 .21 .18

GAI .13 .15 .46* .12 .23 .15

Note. *p < .05; two-tailed-probability; interpretation guidelines for effect sizes (Cohen, 1988): .10 = small, .30 = medium, .50 = large; EFI = Executive Functions Index, VCI = Verbal Comprehension Index, PRI = Perceptual Reasoning Index, WMI = Working Memory Index, PSI = Processing Speed Index, FSIQ = Full Scale IQ, GAI = General Ability Index.

Judgment is a daily living test, assessing the knowledge of key aspects related to home

safety, health, and medical issues, as well as problem solving and decisional capacity (Stern

& White, 2003). Generally, judgment is defined as the capacity to make decisions after con-

sidering available information (Figueirêdo Vale Capucho & Dozzi Brucki, 2011; Rabin,

Borgos, & Saykin, 2008). In addition to EFs (Duke & Kaszniak, 2000; Rabin et al., 2008),

judgmental capacity requires several other cognitive skills such as language, memory, atten-

tion, and reasoning (Allaire & Marsiske, 1999). Furthermore, judgment is considered an es-

sential part of dementia diagnostics (Duke & Kaszniak, 2000; Rabin et al., 2007). The diag-

nostic utility of the NAB Judgment subtest has been confirmed as well – particularly with

patients with traumatic brain injuries (Zgaljardic, Yancy, Temple, Watford, & Miller, 2011)


and Alzheimer’s disease (Gavett et al., 2012). Macdougall and Mansbach (2013), assessing

participants with Mild Cognitive Impairment and dementia, as well as normal adults over 60

years of age, demonstrated good predictive validity of the NAB Judgment subtest. In that

study, Judgment highly correlated with measures of executive and general cognitive function-

ing, as well as with instrumental activities of daily living. Moreover, Judgment predicted a

significant proportion of variance in activities of daily living, over and above the variance

predicted by the executive and general cognitive functioning measures. The current study

supports the findings on the superior predictive validity of the NAB Judgment subtest for

older populations illustrated in the previous research (Macdougall & Mansbach, 2013). More-

over, the current study demonstrates that the Judgment subtest might be a sensitive measure,

not only for patients, but also for healthy older adults.

The influence of age on the WAIS-IV predictability was examined by the comparison

between the entire sample and the two age groups; comparatively, age along with sex and the

NAB subtests was used as a predictor for the WAIS-IV scores. Indeed, the results suggest that

age is a significant predictor within the age groups, too. In 60- to 88-year olds, age was in-

cluded in the best fitting-model of the FSIQ and VCI, and in 18- to 59-year olds, in the best

fitting-model of the PRI. It appears that age can better predict WAIS-IV performance in older

participants, especially because age was included in the FSIQ best-fitting model of the older

age group. These findings are consistent with the previous research that demonstrated acceler-

ated changes in cognition at older age ranges (Ardila, 2007; Wisdom et al., 2012). Moreover,

the older age group demonstrated the highest total variance explained for the FSIQ and the

most of the WAIS-IV indices (see Table 9 for R² in % for each criterion variable). In the PRI

only, the total variance explained was higher in the younger age group, as well as in the entire

sample, when compared to the older age group. The total variance accounted for in older par-

ticipants, 72% for the FSIQ and 75% for the VCI, suggests that at older ages the two WAIS-

IV scores may be accurately predicted with the NAB Executive Functions Module subtests

along with age. By contrast, the highest total variance accounted for in younger participants

was 46% for the PRI and 45% for the FSIQ. Apparently, besides to EFs, other predictors

should be taken into account for younger age ranges.

The contribution of sex to WAIS-IV prediction should be considered as well. The

analysis revealed that sex may significantly predict the FSIQ in the entire sample, with male

sex being related to better performance. However, sex was not included in the FSIQ best-

fitting models of any age group. Sex also was a significant predictor for the PRI in the entire


sample, as well as in the older age group, with male sex being related to better performance.

These findings are not surprising as previous research has already demonstrated the advantage

of males in visual-spatial abilities (Keith, Reynolds, Roberts, Winter, & Austin, 2011). Never-

theless, the previous and current research indicates that potential sex differences in intelli-

gence might differ according to age. Thus, this issue should be investigated further in future

studies. Table 9. Variance explained by the best-fitting models of the WAIS-IV

Age 18-59 60-88 18-88

Variable R R² se R R² se R R² se

FSIQ .672 .451 8,709 .845 .715 7,305 .747 .559 8,338

VCI 528 .279 8,904 .868 .753 6,026 .656 .431 8,415

PRI .680 .462 9,760 .603 .364 10,365 .589 .347 10,462

WMI .517 .267 11,989 .694 .481 10,825 .651 .424 11,073

PSI .579 .336 10,004 .602 .362 10,383 .568 .322 10,230

Note. FSIQ = Full Scale IQ, VCI = Verbal Comprehension Index, PRI = Perceptual Reasoning Index, WMI = Working Memory Index, PSI = Processing Speed Index, se = standard error of the estimate.

CCoommmmoonnaalliittiieess aanndd ddiiffffeerreenncceess wwiitthhiinn ttwwoo aaggee ggrroouuppss aanndd ssaammppllee In the interest of better understanding the relationships between executive functioning and

individual domains of intelligence, differences in the correlations between the EFI and indi-

vidual WAIS-IV index scores within the age groups and sample were investigated. The analy-

sis resulted in only one significant difference: in the older age group, the correlation between

the EFI and VCI was stronger than the correlation between the EFI and PRI, z = 2.23, p =

.013. Interestingly, in the younger age group and in the entire sample, there were no signifi-

cant differences in the correlations between the EFI and any of the WAIS-IV indices. As sug-

gested by Friedman et al. (2006), Gc and Gf may be more strongly intercorrelated at younger

than at older ages.

In addition, the focus of attention was on the relationship between EFs and g as com-

pared to the relationship between EFs and individual domains of intelligence. As expected,

the correlation pattern between the EFI and the FSIQ differed according to age group as well.

Not surprisingly, in the sample, the correlation of the EFI with the FSIQ was significantly

stronger than the correlations of the EFI with any of the WAIS-IV indices: VCI, z = 2.88, p =

.002; PRI, z = 3.90, p < .001; WMI, z = 2.57, p = .005; and, PSI, z = 3.71, p < .001. However,

in the younger age group, the correlation of the EFI with the FSIQ was stronger only when

compared to the correlations of the EFI with the VCI, z = 2.86, p = .002 and PSI, z = 2.17, p =


.015; and in the older group, the correlation of the EFI with the FSIQ was stronger only when

compared to the correlation of the EFI with PRI, z = 3.55, p < .001, and PSI, z = 3.04, p <

.001. Hence, in both age groups, as well as in the sample, the EFI was more strongly corre-

lated with the FSIQ than with the PSI. In addition, in both age groups the correlation of the

EFI with the FSIQ was not stronger than the correlation of the EFI with the WMI.

As with previous research (Crawford et al., 2010; Duncan et al., 1995; Duncan et al.,

1996; Obonsawin et al., 2002; Salthouse & Davis, 2006), the present findings demonstrate a

strong relationship between EFs and g. Furthermore, EFs may be more strongly related to g

than to processing speed. However, strong relationships between EFs and specific compo-

nents of intelligence might also exist – EFs may be as strongly related to g as to WM. How-

ever, taking age into account might change the relationship pattern between EFs and specific

components of intelligence. In line with the present research, EFs do not appear to be more

strongly related to g than to Gf in younger adults, or to Gc in older adults.

DDeetteerriioorraattiioonn ppaatttteerrnn aanndd iinntteerriinnddiivviidduuaall vvaarriiaabbiilliittyy In the current study, commonalities and differences between two different age groups in re-

spect to the relationship between EFs and intelligence are in the focus of attention. Thus, dete-

rioration pattern and variability in performance within both age groups should be considered.

That is, combining individuals of different ages into one group may affect the strength of the

relationship between two variables. For example, if there are no considerable differences be-

tween age groups in interindividual variability on two variables, similar deterioration patterns

for different individuals can be expected. Consequently, substantial differences between age

groups in respect to the relationship between these two variables are not likely. In contrast,

considerable differences in interindividual variability between those age groups may affect the

strength of the relationship between the two variables.

The results of the study on age-related differences in the NAB Executive Functions

Module presented in the first section of the present chapter may help interpret the current

findings. That study demonstrated that in the youngest age group (i.e., 18-29 years), Catego-

ries, Word Generation, and Letter Fluency had higher interindividual variability than the other

NAB Executive Functions Module subtests. However, in the oldest age groups (i.e., 80-84

and 85-99 years), there was only a small increase in variability for Letter Fluency and Word

Generation, and moderate to substantial increase in variability for Categories. This may in

part explain the consistent correlation and prediction pattern between the three NAB subtests


and the WAIS-IV in both age groups. Conversely, Judgment and Mazes had relatively modest

variability in the youngest age group (i.e., 18-29 years). In Judgment, however, a moderate

increase in variability with advancing age was observed, whereas Mazes exhibited the highest

increase in variability among all subtests. Moreover, in the current study Judgment and Mazes

demonstrated the best age-dependent predictive validity for the WAIS-IV among all other

NAB subtests. However, despite of only moderate increase in variability, there were signifi-

cant differences in the correlationship of Judgment and the WAIS-IV between the two age

groups; whereas the high increase in variability in Mazes did not produce higher correlations

in the 60-88 age group. Nevertheless, the source of variability must also be considered. In the

older age group, Mazes correlated moderately with the PSI, while the correlations between

Mazes and the other WAIS-IV indices were not significant. Specific skills that are required

for Mazes, such as processing speed, might be shared with the PSI to a much greater degree

than with the other WAIS-IV indices. It is widely accepted that processing speed substantially

decreases with advancing age (Schaie, 1994). Consequently, dispersion in Mazes at older ages

might be accounted for by interindividual differences in processing speed. Furthermore, non-

cognitive factors might influence performance on cognitive tasks at older ages, too

(Salthouse, 2010b). Visual (Bertone, Bettinelli, & Faubert, 2007; Salthouse, 2010a) and motor

impairments (Seidler et al., 2010) especially, might affect performance on such highly speed-

dependent paper-pencil tasks as Mazes. These factors might affect variability in Mazes more

than variability in the WAIS-IV indices VCI, PRI, and WMI. As a result, in the older age

group of the current study, the increase in variability in Mazes did not considerably change

the size of the correlations between Mazes and the WAIS-IV.

The deterioration pattern and interindividual variability of Judgment should be consid-

ered as well because Judgment is the subtest with significant differences between the two age

groups. In the older age group, Judgment shared 7% to 22% of its variance with the WAIS-IV

indices, which is over twice as much as the variance shared by the two measures in the

younger age group (i.e., 1% to 8%). As there was only a moderate increase in variability

across age, the increase in the shared variance is apparently not the result of the differences in

variability between both age groups. However, the focus of attention should also be on the

contrast in the variance shared by Judgment and the other EF subtests between the older age

group (i.e., from 3% to 17%) and the younger age group (i.e., from 0.04% to 8%,), implying

that, at older ages, Judgment may require the executive skills that are also involved in per-

formance on the other NAB EF subtests. Conversely, at younger ages, Judgment may assess


somewhat different abilities than the other EF subtests. Consequently, the relationships be-

tween Judgment and other executive abilities, as well as between Judgment and intelligence,

may become stronger as age advances.

Variability in performance should also be discussed with regard to the contrast be-

tween the two age groups in the total variance explained by the prediction models. Particular

attention should be directed to the difference in the total variance explained for the FSIQ

(72% in participants aged 60-88 and 45% in participants aged 18-59) and for the VCI (75% in

participants aged 60-88 and 28% in participants aged 18-59). On the one hand, these findings

could be partially explained by higher variability in performance at older ages, which might

result in stronger correlations between the two assessment tools. However, as stated previ-

ously, the results of the study on age-related differences in the NAB Executive Functions

Module revealed that the predictors of the FSIQ and VCI best-fitting models in the present

study were not the NAB subtests with the highest variability of performance in the older age

group. On the contrary, Letter Fluency, Word Generation, and Judgment, were the subtests

with a modest to moderate increase in variability across ages. Only in Categories, there was a

moderate to substantial increase in variability. Importantly, Mazes, which is the subtest with

the highest increase in variability, was included neither in the FSIQ nor in the VCI best-fitting

model of the older age group. By contrast, it was included in the FSIQ, PRI, and PSI best-

fitting models of the younger age group. Thus, the different extent of variability does not

seem to be sufficient explanation for the substantial difference between the two age groups in

the total variance accounted for. Consequently, a more theoretical approach seems more ap-

propriate and is as follows.

DDiiffffeerreennttiiaattiioonn--ddeeddiiffffeerreennttiiaattiioonn--hhyyppootthheessiiss aanndd iinnvveessttmmeenntt tthheeoorryy The substantial difference in the total variance accounted for in the FSIQ prediction between

the examined age groups can be further explained by the differentiation-dedifferentiation-

hypothesis (see chapter three for details). Since some evidence exists on dedifferentiation

processes occurring with increasing age (Baltes & Lindenberger, 1997; de Frias, Lövdén,

Lindenberger, & Nilsson, 2007; Deary, Whiteman, Starr, Whalley, & Fox, 2004; Li et al.,

2004), the dedifferentiation processes might also affect the relationships between EFs and

intelligence. Consequently, that relationships may become stronger as age advances; as a re-

sult, at an older age both constructs might reflect similar aspects of cognitive functioning. In

particular, EFs and Gc may be more strongly associated at an older age – the correlation be-


tween the EFI and VCI was stronger in the older than in the younger age group. Moreover,

the contrast in the total variance explained by the VCI best-fitting models between older

(75%) and younger participants (28%) demonstrates that Gc-related abilities may be predicted

more precisely with EFs at older than at younger ages. Interestingly, there was no such effect

in older participants for the PRI. On the contrary, the PRI was the WAIS-IV index with the

highest variance accounted for in younger participants. It has already been postulated that the

association between EFs and Gf versus Gc may differ as a function of age (Friedman et al.,

2006). Yet in a study conducted with a college student sample, EFs were neither more strong-

ly correlated with Gf nor with Gc (Friedman et al., 2006). The authors suggested that this

might be the result of a strong relationship between Gf and Gc in young adults. In frontal lobe

patients and older adults, however, due to Gf’s sensitivity to brain damage and executive dys-

function (Duncan et al., 1995; Friedman et al., 2006), EFs might be more strongly related to

Gf than to Gc. Nevertheless, the current study showed the opposite. Since there is not much

research in this area, more theoretical rather than an evidence-based explanation can be pre-

sented. First, potential differences between healthy participants and frontal lobe patients must

be taken into account. Second, the Gc level may become more meaningful with advancing

age. As postulated by Cattell (1963, 1987) in the investment theory (see chapter three for de-

tails), the Gc level in children and young adults could be mostly affected by Gf. However, as

Gf is considered to rapidly decrease with advancing age, the influence of potential changes in

Gf on Gc must be taken into account as well. Additionally, the contribution of non-ability

factors to the Gc level may increase as age advances. Adults usually become more different

from one another as a result of different lifestyles, occupations, learning opportunities, leisure

activities, health conditions, and personal goals (Schneider & McGrew, 2012). For example,

people who can create good learning opportunities for themselves, or are involved in intellec-

tually stimulating environments throughout their lives, may be able to improve their Gc. Thus,

the given Gc level might have an influence on EFs. In contrast, at younger ages, the relation-

ship between Gc and EFs may be weaker because young adults have not yet reached the high-

est level of their Gc. Moreover, the contribution of the given level of Gc towards performance

on EF tasks may increase only when a considerable reduction in executive functioning occurs.

For most EFs, this does not occur before the age of 50-60 years (Brennan et al., 1997; De

Luca et al., 2003; Raz et al., 1998; Robbins et al., 1998).

7. Implications for theory and practice 59

77.. IImmpplliiccaattiioonnss ffoorr tthheeoorryy aanndd pprraaccttiiccee As expected, the results of the current research showed age-related differences in performance

on the NAB Executive Functions Module subtests. Generally, decreases in the mean and in-

creases in the dispersion with advancing age were observed. However, performance on execu-

tive function (EF) tasks connected to fluid intelligence (Gf) was associated with substantial

decreases in the mean scores and substantial increases in the dispersion from early adulthood.

The greatest decrease in the mean, the highest increase in the dispersion, and the earliest onset

of decline were observed in the Planning, Mazes, and Categories subtests. In contrast, per-

formance on EF tasks connected to crystallized intelligence (Gc) was associated with in-

creases in the mean scores, even in late adulthood, but only small increases in the dispersion.

Letter Fluency, Word Generation, and Judgment were those subtests that showed the lowest

decrease in the mean, the lowest increase in dispersion, as well as the latest onset of decline.

In addition, the current research demonstrated substantial relationships between the

NAB Executive Functions Module and the WAIS-IV; the composite score Executive Func-

tions Index (EFI) with the WAIS-IV Full Scale IQ (FSIQ) and index scores, in particular,

produced high correlations. In most cases, there were no significant age-related differences in

the correlations between the NAB subtests and WAIS-IV indices. Especially, Categories and

Word Generation showed consistent correlation pattern for both age groups. The two subtests

correlated substantially with most of the WAIS-IV indices and were most frequently included

in the WAIS-IV best-fitting prediction models. However, differences between age groups in

regard to the relationship between EFs and intelligence were identified, too. In all significant

differences between correlations, the older age group had the higher score. In particular, the

NAB Judgment subtest correlated more strongly with the Verbal Comprehension Index

(VCI), Perceptual Reasoning Index (PRI), Working Memory Index (WMI), FSIQ, and Gen-

eral Ability Index (GAI). Additionally, there was a stronger correlation between the EFI and

VCI. Among the NAB subtests, Mazes and Judgment exhibited the strongest age-related pre-

dictive validity for the WAIS-IV.

Hereafter, general conclusions drawn from the current research are presented. First,

potential implications for neuropsychological assessment within research and clinical practice

are discussed. Second, suggestions for theoretical framework are presented. Third, limitations

of the studies conducted and recommendations for further research are provided.


77..11 IImmpplliiccaattiioonnss ffoorr nneeuurrooppssyycchhoollooggiiccaall aasssseessssmmeenntt

The findings presented in the current thesis highlight potential implications for the interpreta-

tion of neuropsychological assessment outcomes and for the development of EF measures.

Conclusions derived from the current research in relation to those issues are presented in the

following section.

NNeeuurrooppssyycchhoollooggiiccaall aasssseessssmmeenntt wwiitthh tthhee NNAABB Several implications of the current research as to the age-related differences in performance

between the NAB Executive Functions Module subtests and the relationships between the

NAB and WAIS-IV emerge for neuropsychological practice.

First, the current research demonstrated unique developmental trajectories for the in-

dividual NAB Executive Functions Module subtests. Thus, the deterioration pattern of a given

subtest should be taken into account within neuropsychological assessments. Letter Fluency,

Word Generation, and Judgment were those NAB subtests that exhibited the lowest decrease

in the mean and the lowest increase in dispersion, as well as the latest onset of decline. Thus,

in these subtests, small deviations from the mean of the standardization sample might not

have any special meaning; whereas substantial deviations from the mean score may indicate a

meaningful deterioration. Consequently, these EF tasks might be less sensitive to age-related

decline. On the other hand, Planning, Mazes, and Categories were those NAB subtests that

showed the greatest decrease in the mean, the highest increase in the dispersion, and the earli-

est onset of decline. Thus, in these subtests, even small deviation from the mean may imply a

decrease in executive functioning.

Second, the intercorrelations among the individual NAB Executive Functions Module

subtests were low to moderate; thus, these tasks assess somewhat different abilities and are

not redundant. Consequently, the NAB Executive Functions Module appears suitable for the

assessment of multiple executive abilities.

Third, the current results showed considerable correlations between the NAB Execu-

tive Module subtests and the WAIS-IV. The NAB subtests Categories and Word Generation

were considerably correlated with all WAIS-IV indices and most frequently included in the

WAIS-IV best-fitting prediction models of both younger and older participants. Thus, when

using the two tasks, the IQ must be considered in the interpretation of the assessment out-

comes. The two subtests appear to be suitable for the prediction of intellectual abilities. In

contrast, Letter Fluency was the subtest least frequently included in the WAIS-IV best-fitting

models. Moreover, Letter Fluency was included in the FSIQ best-fitting model of neither age


group. Hence, Letter Fluency seems not to be a suitable predictor of general intelligence (g).

However, Letter Fluency was included in the VCI best-fitting models of both age groups;

thus, this NAB subtest might be a good predictor for crystallized abilities.

Fourth, the current research demonstrated differences between younger and older par-

ticipants in the relationship between the NAB Executive Functions Module and the WAIS-IV.

In particular, a significant difference between younger and older participants was observed in

the correlation of the EFI with the VCI. Thus, the EFI could be used as an indication of Gc at

older ages. Furthermore, as there was a substantial difference between younger and older par-

ticipants in the VCI variance accounted for by the NAB subtests, it can be concluded that the

VCI, and Gc in general, can better be predicted by the NAB EF tasks at older than at younger

ages. Additionally, the NAB Judgment subtest was more strongly correlated with the WAIS-

IV indices, as well as with the FSIQ, in the older than in the younger age group. Furthermore,

Judgment was a part of the FSIQ, VCI, and PRI prediction models in older participants. As a

result, the Judgment scores may be interpreted differently according to age. Thus, older

adults’ low scores on the Judgment subtest may reflect a decrease in general cognitive func-

tioning or a low premorbid IQ. By contrast, due to low correlation between Judgment and

WAIS-IV scores in the younger age group, the Judgment scores do not have any special

meaning in the interpretation of the overall assessment outcome at younger ages. However,

though the Judgment subtest seems to be suitable for the detection of age-related executive

and cognitive decline, the other NAB subtests were also substantially correlated with the

FSIQ in older participants, with the exception of Mazes. Thus, the other NAB subtests and the

EFI might be used as an indication of g at older ages. At younger ages, this would be possible

to a lesser degree, due to the weaker correlations between the scores on both measures. How-

ever, in contrast to older ages, the NAB Mazes subtest appears to be a good predictor of intel-

lectual abilities at younger ages. In younger participants, this more specific EF task was sig-

nificantly correlated with three WAIS-IV indices and FSIQ; furthermore, it was there a part of

the FSIQ, PRI, and Processing Speed Index (PSI) best-fitting models; whereas in the older

age group, it was included only in the PSI best-fitting model.

Fifth, the NAB EF tasks accounted for 45% of the FSIQ variance in the younger age

group and for 72% of the FSIQ variance in the older age group; thus, there might be middle to

high overlap between the NAB Executive Functions Module and WAIS-IV. Consequently,

the two measures may reflect partially the same or similar abilities. Additionally, the current

research revealed differences within the two examined age groups and the sample in respect

to the relationship between the EFI and WAIS-IV indices, as well as FSIQ. That is, in the


older age group, the correlation between the EFI and VCI was stronger than the correlation

between the EFI and PRI. Additionally, in the sample, the correlation of the EFI with the

FSIQ was significantly stronger than the correlations of the EFI with any of the WAIS-IV

indices. However, in the younger age group, that correlation was stronger only when com-

pared to the correlation of the EFI with the VCI, and in the older group, only when compared

to the correlation of the EFI with PRI. Hence, in both age groups the correlation of the EFI

with the FSIQ was not stronger than the correlation of the EFI with the Working Memory

Index (WMI). Consequently, when using the EFI as an indicator of performance on the FSIQ

and as an indicator of overall cognitive functioning in general, an age-related interpretation of

assessment outcomes is recommended as follows. The EFI, as a global measure of executive

functioning, may overlap not only with FSIQ, but also with VCI at older ages and with PRI at

younger ages. However, regardless of age, a substantial overlap with WMI may be expected.

Further, caution is warranted with the interpretation of the EFI as a measure of overall execu-

tive functioning. As previously presented, deterioration patterns and the relationships with

intelligence differ for the individual NAB Executive Functions Module subtests. Conse-

quently, when interpreting NAB assessment outcomes, the meaning of performance on the

individual subtests and the contribution of every single subtest to the EFI should be taken into

account.

DDeetteerriioorraattiioonn ppaatttteerrnnss iinn eexxeeccuuttiivvee ffuunnccttiioonnss The current research showed differences in performance on EF tasks between ten adult age

groups across a large life span. Thus, age should always be considered within neuropsy-

chological assessment. When standardizing EF measures especially, separate norms should be

provided for age groups that differ greatly in performance on particular tasks. For example, if

changes occur rapidly, tasks should be normed in separate small age ranges, according to the

pattern of deterioration. High variability in performance in healthy adults may also change the

interpretation guidelines for assessment outcomes in brain-damaged patients; for example, an

outcome rated beyond the limits of normal performance may not necessary mean any lesion-

related impairment; it might rather reflect an age-related deterioration pattern. Consequently,

base-rates for impaired performance on EF measures in normal population should be estab-

lished to help improve clinical diagnosis. Third, information about the approximate age at

which cognitive decline begins for different cognitive functions could be used to help prevent

or reverse age-related changes, for example by determining the optimum time for implement-

ing interventions (Salthouse, 2009).


As the study conducted demonstrated unique developmental trajectories for individual

EF tasks, performance on a given task may depend on the deterioration pattern of the underly-

ing abilities. Thus, when interpreting the outcomes of neuropsychological evaluations, the

nature of the underlying abilities and age-related differences in these abilities must be consid-

ered. Moreover, there might be EF tasks that are more useful for detecting age-related cogni-

tive decline than others. The current research demonstrated deterioration patterns for different

EF tasks similar to those known from intelligence research for Gc and Gf. Consequently, EF

tasks with a strong crystallized component might be less sensitive to age-related decline than

EF tasks with a strong fluid component. This might be particularly useful in the assessment of

subclinical executive dysfunctions in normal adults, which is considered difficult to detect as

compared with executive dysfunctions related to brain lesions (Bryan & Luszcz, 2000).

MMuullttiiffaaccttoorriiaall nnaattuurree ooff eexxeeccuuttiivvee ffuunnccttiioonn mmeeaassuurreess The multifactorial nature of most existing EF measures has previously been discussed (Stuss

& Alexander, 2000). The current research also highlights the meaning of various skills con-

tributing to performance on EF tasks. On the one hand, the current research demonstrated that

the NAB EF tasks assess somewhat different abilities; on the other hand, the current results

showed considerable correlations between the NAB EF tasks and WAIS-IV. Consequently, it

should be taken into account that EF and intelligence measures may assess similar abilities.

Furthermore, the multifaceted nature of these measures and specific components shared must

be considered. For example, EFs are considered a crucial part of cognition (Ardila, 1999);

hence, intelligence measures are likely to assess EFs to a certain extent. Furthermore, other

cognitive components shared by EF and intelligence measures may affect the strength of the

relationship as well. To illustrate, if two different measures mostly require spatial abilities, the

relationship between these measures may be considerable. By contrast, if there are addition-

ally other abilities involved, not shared by both measures, the relationship might be weaker.

Indeed, in the current research, the tasks that require similar skills were more strongly inter-

correlated, rather than the tasks that require different skills. The extent of complexity appears

meaningful as well – the current research suggests that EF tasks, which involve multiple abili-

ties, may be more strongly related to intelligence measures. Both the extent of complexity and

the individual components shared by measures used should be considered within neuropsy-

chological assessments; if there is a substantial relationship between two measures, only one

of the two measures could be used in the initial diagnostics; whereas the second one could be

applied within following assessments. In the assessment of EFs especially, the strength and


nature of the relationships between different measures appears crucial. As novelty is consid-

ered the core characteristic of EF tasks, many of the existing EF measures cannot be applied

more than once (Rabbitt, 1997). Thus, a more aware use of EF measures is indicated. Fur-

thermore, a more aware selection of the type of EF measure is indicated as well, especially, in

cases of brain lesions. That is, all specific cognitive functions should be evaluated prior to the

assessment of executive functioning and all known cognitive dysfunctions must be taken into

account when selecting assessment tools. In cases of language impairments, EF tasks without

a strong verbal component should be applied. Similarly, if there are prominent deficits in in-

formation processing speed, instead of time-restricted tasks, the use of EF measures without

speed components is indicated.

As to the extent of complexity, there are several factors that should be taken into ac-

count. Due to substantial relationships between complex EF tasks and intelligence measures,

the premorbid IQ should be considered; in cases of multifaceted EF tasks, which are strongly

correlated with general intelligence measures, a below average assessment outcome does not

necessarily indicate a clinically relevant impairment. Instead, it may correlate with the pre-

morbid level of intelligence. However, if an extensive assessment of executive functioning is

indicated, complex EF measures may substantially contribute to the overall neuropsychologi-

cal evaluation. In particular, the application of any EF measure is indicated, if its complexity

corresponds with the requirements of everyday life of the patient examined. Thus, information

in regard to the ecological validity of a given measure is essential.

RReellaattiioonnsshhiippss bbeettwweeeenn eexxeeccuuttiivvee ffuunnccttiioonnss aanndd iinntteelllliiggeennccee aanndd tthhee mmeeaanniinngg ooff aaggee As already noted, the multifactorial nature of EF tasks and the relationships between execu-

tive and non-executive functions should be considered within neuropsychological assessment.

In particular, the current research demonstrated substantial relationships between EFs and

intelligence. Strong relationships between the two constructs were particularly demonstrated

by the composite scores of the assessment tools used and by complex tasks rather than by

tasks assessing specific abilities. Furthermore, complex multifactorial tasks were significant

predictors of intelligence performance. Thus, when interpreting the composite and complex

task scores of EF measures, the current and premorbid IQs must be taken into consideration.

This appears to be a general rule, which can be applied regardless of age. By contrast, EF

tasks involving specific skills generally appear to be significantly related only to the intelli-

gence components involving similar skills. Hence, potential dysfunctions in those skills in-

volved must be considered rather than g. However, the current research also demonstrated


age-related differences in the relationships between specific EF tasks and individual intelli-

gence components as well as g. Consequently, the degree of overlap between the abilities as-

sessed by different measures may differ as a function of age. Furthermore, EF tasks involving

specific skills might also be good predictors of intelligence, for example, NAB Executive

Functions Module subtests – Mazes being a good predictor at younger ages and Judgment

being a good predictor at older ages.

At older ages specifically, strong relationships between EFs and intelligence can be

expected and EF performance might reflect IQ levels. Particularly, Gc and g might be better

predicted by EF performance at older rather than at younger ages. Furthermore, while Gc and

g might be the aspects of intelligence best predicted with EF performance at older ages, Gf

and g appear to be the aspects of intelligence best predicted with EF performance at younger

ages. Consequently, the substantial contribution of EFs to Gc and g in older adults, and the

substantial contribution of Gf and g in younger adults, must be considered. However, the Gc,

Gf, and g levels might contribute to EF performance, too. There is a lack of a coherent and

widely accepted theory in respect to both EFs and intelligence. Furthermore, it is not well

investigated which mechanism is responsible for the mutual relationship between the two

constructs. Hence, the direction of this relationship is still required to be established. On the

one hand, it appears plausible that intelligent behavior could be the result of an interaction

between executive functioning and other cognitive domains. Thus, executive dysfunctions

might lead to disturbances in Gc, Gf, and g. However, since executive functioning is consid-

ered a significant component of intelligence, also intelligence dysfunctions might result in

disturbances in executive functioning. This should particularly be considered in cases of men-

tal retardation or low premorbid IQ.

Despite substantial relationships between EFs and intelligence, the contribution of

other, cognitive and non-cognitive factors to performance must be considered as well. For

example, in the WAIS-IV prediction models of the current research, a substantial amount of

variance remained unexplained. This was the case in the younger age group especially. As the

relationships between EFs and intelligence may be weaker at younger ages, particularly here,

attention should be directed to the contribution of other, non-executive cognitive components

to intelligence. Additionally, although the relationships between EFs and intelligence gener-

ally appear to be stronger in older adults, the source of interindividual variability must par-

ticularly be considered here. For example, the current research suggests that at older ages,

dispersion in the NAB subtest Mazes might be accounted for by interindividual differences in

processing speed. Moreover, non-cognitive factors might influence performance on Mazes,


too. In particular, visual and motor impairments might be crucial for performance on cogni-

tive tasks at older ages. This should be considered in speed-related paper-pencil tasks espe-

cially.

77..22 IImmpplliiccaattiioonnss ffoorr tthheeoorreettiiccaall ffrraammeewwoorrkk

The current findings demonstrated both age-independent and age-related relationships be-

tween the NAB Executive Functions Module and the WAIS-IV. These relationships were

moderate to substantial, depending on age group and subtest or index. Thus, both age and the

underlying abilities may play a role in the relationship between EF and intelligence measures.

Furthermore, as there might be executive and non-executive abilities, which are involved at

the same time, it appears difficult to clearly distinguish between EF and intelligence meas-

ures. Moreover, the previous and current research suggests partly considerable relationships

between the two kinds of measure. Hence, the NAB and WAIS-IV subtests might assess simi-

lar abilities to some extent. This has practical implications, but should also be considered

from theoretical perspective. As the WAIS-IV is mostly adherent to the Cattell-Horn-Carroll

(CHC) theory, an attempt can be made to put the NAB EF subtests into relation with the CHC

theory, given that CHC classifications for the WAIS-IV are available (e.g., Figure 1). This

will require further investigations; however, conclusions derived from the current research

might give some clues for a better understanding of this relation. At present, the CHC theory

represents the most popular hierarchical model of intelligence and since its creation has regu-

larly been updated in accordance with the current state of research. Furthermore, by offering a

general interpretation and classification system, the CHC theory has the potential to be better

implemented into the field of neuropsychological assessment (Schneider & McGrew, 2012).

Within the CHC classification, EFs, as the central executive mechanism composed of inhibi-

tion, shifting, and updating, are associated with the narrow ability working memory capacity

and placed within Stratum II short-term memory (Newton & McGrew, 2010; Schneider &

McGrew, 2012). In fact, the current research demonstrated a substantial relationship between

the NAB EFI and the WAIS-IV WMI, being comparable to the relationship between the EFI

and FSIQ. However, as depicted by Figure 4, the current research also revealed that in most

cases, three of five NAB EF subtests were moderately or strongly correlated with the WAIS-

IV scores, including subtest scores. Consequently, it appears that EFs contribute to the intelli-

gence performance that could be classified as associated with more than one CHC narrow

ability. Moreover, EFs might be better classified as a separate broad ability and as such im-

plemented into the CHC framework as an additional broad ability factor on stratum II. In that


case, different aspects of executive functioning could be represented as narrow abilities on

stratum I. Future research should thus focus on investigating the contribution of EFs to cogni-

tive abilities classified within the CHC theory on all three strata. In particular, factor-analytic

studies of EF measures and CHC-based intelligence batteries could reveal more about poten-

tial affiliations between EFs and the CHC model of cognitive abilities.

77..33 LLiimmiittaattiioonnss aanndd ddiirreeccttiioonnss ffoorr ffuuttuurree rreesseeaarrcchh

Several limitations in respect to the research presented in the current doctoral thesis must be

considered. First, limitations in regard to data collection should be discussed. For the two

studies conducted, the samples were derived from the large standardization sample of the

German NAB; thus, due to the representative composition of the standardization sample, the

participants differed representatively in respect to demographic characteristics. However, as

the participants were recruited via public announces and letters and the participation was

based on voluntary consent, the requirements regarding random selection and broad cognitive

diversity could not be completely fulfilled. For the study on the relationship between EFs and

intelligence, the sample composition might have been biased by the intrinsic motivation of the

participants; all NAB From I participants were asked to voluntarily participate in an addition-

al WAIS-IV assessment. Especially because no additional remuneration but performance

feedback was offered, the intrinsic interests of potential participants might have influenced

the sample selection.

Second, caution is warranted in respect to the cross-sectional study design. Comparing

cognitive performance between different age ranges is thought to be confounded by cohort

influences. To illustrate, differences exist in educational attainment, health, nutrition, and per-

sonalities between participants of different ages. These generational differences might affect

cognitive performance. As such, this may affect the correlation between performance on dif-

ferent cognitive measures (Schaie & Willis, 2010). Longitudinal studies are thought to be

more informative in respect to cognitive changes over time since they are based on within-

person comparisons between different occasions (Schaie, 2005). In fact, discrepancies exist

between cross-sectional and longitudinal data with respect to age trends in cognitive perfor-

mance. Between-person comparisons usually demonstrate gradual declines from early adult-

hood. In contrast, within-person comparisons show stability or an increase in the perfor-

mance, which can partly be explained by participants’ prior test experience (Rönnlund &

Nilsson, 2006; Salthouse, 2009). Consequently, findings derived from longitudinal studies

should be interpreted with caution, too. Nevertheless, longitudinal studies will be necessary to


confirm whether interindividual variability exists in cognitive change and to explore whether

only some individuals show decline, whereas others remain stable. For a better understanding

of the influence of age on the relationship between EFs and intelligence, within-person com-

parisons will be required as well. It should be examined whether the pattern of relationships

between EFs and intelligence remains stable or change in individuals with advancing age.

Due to generational differences between participants in cross-sectional studies, vari-

ables affected by cohort influences are difficult to measure. In the current research, educa-

tional attainment differed between those aged 18-59 and 60-88. There were, nevertheless, no

considerable differences in the IQ between the two age groups. Consequently, the highest

educational attainment might not reflect real differences in education. This appears under-

standable due to the changes to the educational system that have occurred over the last dec-

ades. Thus, education could be measured more effectively than through educational attain-

ment; for example, through the number of years of educational qualifications, including voca-

tional training. However, since educational attainment may depend upon the culture, society,

and educational system, general guidelines regarding the operationalization of the education

variable might not be possible. In the current research, only the highest educational attain-

ment, as measured by the years of school completed, was used. Consequently, potential dif-

ferences in educational level between participants of different ages cannot be ruled out. Future

studies should pay more attention to the influence of education when comparing cognitive

performance between different age ranges. Moreover, the influence of other non-cognitive

factors should be better investigated; in particular, the influence of sex, state of health, and

lifestyle.

Third, the characteristics of the sample used should be considered. As the current re-

search findings are based on the data derived from healthy adults, caution is warranted in

terms of the potential implications for patients with brain damage. It must be bore in mind

that brain lesions can influence cognitive performance and change the relation pattern among

cognitive abilities, so it may differ from that of healthy people. Hence, further research with

patients will be required to provide clinicians with guidelines with which to make clinically

meaningful decisions.

Fifth, as age is the central aspect of the current doctoral thesis, limitations associated

with the age ranges examined must be discussed. In particular, the decision to divide a sample

into age groups might influence the study results. That is, when examining the adult life span,

comparing cognitive performance between several age groups might be affected by the age

range of the individual age groups. In the study on age-related differences in EFs, the sample


was divided into ten age groups; however, the age range across the groups differed. Especial-

ly, in greater age groups with substantial cognitive variability on a given function, only gen-

eral conclusions regarding age trends in performance are permitted. Furthermore, combining

participants of different ages into one or several groups may affect the strength of the rela-

tionship between cognitive functions measured. In particular, when comparing different age

groups composed of participants of different ages, caution is warranted with regard to the

generalizability of the findings, too. For the study on the relationship between EFs and intel-

ligence, the sample was divided into two age groups. However, due to real age differences

between participants within both age groups, variability in cognitive performance might be

substantial and as such influence the size of the correlations. Caution is warranted with the

interpretation as only conclusions pertaining general relation patterns may be derived. When

using a cross-sectional study design, studies with several small age groups across the adult

lifespan, with a sufficient number of participants in each age group, are necessary to deter-

mine specific age trends.

Sixth, the nature of the assessment tools used is another aspect that might be a limita-

tion of the current research. Although the NAB and WAIS-IV are thought to be comprehen-

sive assessment tools, they offer a selection of the potential tasks that might be implemented

to measure intelligence and EFs. Thus, some aspects of the two constructs may be pro-

nounced, whereas other may be neglected. To illustrate, although the WAIS-IV is based on

the CHC classification of cognitive abilities, in fact, it does not assess all aspects of cognition

classified there. The NAB Executive Functions Module is thought to measure multiple as-

pects of executive functioning; however, elementary EF components such as updating, shift-

ing, and inhibition are measured there to a lesser degree. Similarly, the contribution of cogni-

tive aspects, which are less construct specific, might differ as well. As an example, among the

six NAB EF subtests, only Mazes is a non-verbal task. Consequently, the NAB Executive

Functions Module tends to assess executive functioning rather via verbal modality. The pre-

dominant modality as well as the extent of complexity of the tasks used should be bore in

mind as this might influence both age-related performance and the relationships between

tasks. In both assessment tools used, the subtests are thought to involve multiple abilities.

Thus, conclusions regarding developmental changes in EFs and the relationship between EFs

and intelligence might be confounded by the multifaceted nature of the tasks used and the

mutual relationships between the underlying abilities. Due to practical implications, it is im-

portant to investigate EFs and intelligence by the means of multifaceted measures as they

seem to be ecologically more valid. However, in order to explore the meaning and nature of a


single ability, tasks involving single or a few clearly distinguishable abilities might be more

appropriate. Updating, shifting, and inhibition, as proposed by Miyake, Friedman, et al.

(2000) and integrated into CHC classification (Schneider & McGrew, 2012), may be used to

examine the basic abilities of executive functioning. On the one hand, a better differentiation

between single abilities as well as between complex and basic components of executive func-

tioning is needed. On the other hand, widely accepted terminology and taxonomy organising

the relations between the individual executive abilities should be implemented.

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Appendix A 90

AAppppeennddiicceess

AAppppeennddiixx AA:: PPuubblliiccaattiioonn 11

Buczylowska, D., & Petermann, F. (2016). Age-related differences and heterogeneity in ex-

ecutive functions: Analysis of NAB Executive Functions Module scores. Archives of Clinical

Neuropsychology, 31, 254-262. doi:10.1093/arclin/acw005

Appendix B 91

AAppppeennddiixx BB:: PPuubblliiccaattiioonn 22

Buczylowska, D., & Petermann, F. (2016). Age-related commonalities and differences in the

relationship between executive functions and intelligence: Analysis of the NAB executive

functions module and WAIS-IV scores. Applied Neuropsychology: Adult, 1-16. Advance

online publication. doi:10.1080/23279095.2016.1211528

Appendix C 92

AAppppeennddiixx CC:: PPuubblliiccaattiioonn 33

Buczylowska, D., Daseking, M., & Petermann, F. (2016). Age-related differences in the pre-

dictive ability of executive functions for intelligence. Zeitschrift für Neuropsychologie, 27,

159-171. doi:10.1024/1016-264X/a000179

Appendix D 93

AAppppeennddiixx DD:: SSttaatteemmeenntt ooff tthhee ccaannddiiddaattee’’ss ccoonnttrriibbuuttiioonn ttoo eeaacchh ppuubblliiccaattiioonn The current doctoral thesis was conducted by the candidate Dorota Buczyłowska according to

§ 6 subparagraph 5 of the formal requirements for doctoral candidates at the University of

Bremen. The thesis is based on three empirical articles, which were published by internation-

ally peer-reviewed journals. The preparation of all publications required several working

steps, which are necessary to accomplish empirical research. These steps include the concep-

tion of the study, the review of previous literature, data collection, data preparation, data anal-

ysis, and result interpretation, as well as the preparation and revision of the manuscript. Table

D1 shows the contribution of the candidate to each publication in each specific working step.

The extent of this contribution is indicated either as fully if the candidate solely contributed to a

specific working step, or as mostly if she made the most substantial contribution to a specific

working step, or as partly if she and at least one further contributor shared the same amount of

contribution. Nevertheless, it must be mentioned that all coauthors continuously supported the

current research by their suggestions and ideas and so contributed to the current doctoral thesis.

Table D1. The candidate’s contribution to the current research studies

Working step Publication 1 Publication 2 Publication 3

Conception Mostly Fully Mostly

Literature research Fully Fully Fully

Data collection Partly Partly Partly

Data preparation Partly Partly Partly

Data analysis Fully Fully Fully

Result interpretation Mostly Fully Fully

Manuscript preparation Fully Fully Fully

Manuscript revision Mostly Mostly Mostly

Appendix D 94

Dorota Buczyłowska and the other coauthors, Prof. Dr. Franz Petermann, and PD Dr. Monika

Daseking, herewith certify that the statement of the candidate’s contribution to each publica-

tion made by Dorota Buczyłowska is accurate and that permission is granted for these publi-

cations to be included in her doctoral thesis.

Bremen, February, 2017

____________________________

Prof. Dr. Franz Petermann

____________________________

PD Dr. Monika Daseking

____________________________

Dorota Buczyłowska, Dipl.-Psych.

Appendix E 95

AAppppeennddiixx EE:: DDeeccllaarraattiioonn ooff OOrriiggiinnaalliittyy

In accordance with § 6 subparagraph 5 of the formal requirements for doctoral candidates at

the University of Bremen, I hereby declare that the present dissertation represents my own

original work except where specifically acknowledged. Wherever contributions of others are

involved, every effort is made to indicate this clearly, with due reference to the literature. I

further declare that this dissertation does not contain any material which has previously been

accepted and is not currently considered for the award of any other university degree in my

name. In addition, I certify that no part of the present thesis will, in the future, be used in a

submission in my name, for any other degree in any university without the prior approval of

the University of Bremen

Bremen, February 2017

_____________________________

(Dorota Buczyłowska, Dipl.-Psych.)

executive functions across the adult life span: age...

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