NORTHWESTERN UNIVERSITY

The Effects of Task Features and Executive Functioning on Prospective Memory Performance of
Adults with ADHD

A DISSERTATION

SUBMITTED TO THE GRADUATE SCHOOL

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

for the degree

DOCTOR OF PHILOSOPHY

Field of Communication Sciences and Disorders

By

Daniella Karidi

EVANSTON, ILLINOIS

June 2013
 UMI Number: 3563748

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© Copyright by Daniella Karidi 2013

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 3

ABSTRACT

The Effects of Task Features and Executive Functioning on Prospective Memory Performance of

Adults with ADHD

Daniella Karidi

Adults with ADHD commonly report problems with time management and organization,

such as missing meetings or forgetting to pay bills. These are examples of prospective memory

(PM) failures. Prospective memory refers to the ability to plan intended actions, retain them in

memory and initiate their execution at an appropriate point in the future. Researchers have

proposed that PM failures are related to difficulties in Executive Function (EF). Difficulties in

EF are also among the critical manifestations of ADHD. At present the extent of EF

involvement in PM difficulties in adults with ADHD remains unclear, as is the impact of

different features of PM tasks (time-based, event-based and regularity). The first experiment

investigated PM performance in adults with ADHD and controls with the aim of exploring PM

errors and the differential effects of PM task features on memory. Nineteen adults with ADHD

and 24 controls completed the Virtual Week task, a computer simulation of common PM tasks

encountered in everyday life. Virtual Week requires individuals to remember PM tasks that vary

in their retrospective memory demands and include time- and event-based tasks. In this

experiment, adults with ADHD had significantly fewer correct PM responses across all task

types, demonstrating a persistent deficit in performance as compared to controls. Across all

participants, event-based and regular tasks were found to yield better PM performance than time-
 4

based and irregular tasks. The second experiment focused on exploring the relationships among

EF, attention and PM. In addition to the PM measures included in the first experiment, this study

also assessed EF through multiple measures. Overall, findings also indicate that adults with

ADHD underperformed compared to controls on EF and PM measures. As hypothesized,

working memory was significantly related to performance on PM tasks. However, results did

not reveal significant relationships between performance on PM tasks and the EF measures of

shifting and inhibition. Importantly, after controlling for working memory ability, ADHD status

was no longer a predictor of PM performance. These studies add to our current understanding of

the relationships among PM, attention and EF.

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Acknowledgment

I would like to acknowledge several people for their support over the last several years

while completing my doctoral program and this dissertation. First, I would like to express my

deepest gratitude to my doctoral adviser and committee chair, Dr. Steve Zecker for his support

and guidance. I cannot thank him enough for the strong basis in research methods and statistics

he has provided me. To my committee members, Dr. Doris Johnson, Dr. Amy Booth and Dr.

Paul J. Reber, I thank them each for their encouraging words, supportive guidance, and valuable

time. I extend my appreciation to Dr. Peter Rendell for allowing me the use of the Virtual Week

software and providing his support and guidance.

This is a great opportunity to express my thanks to the subjects who participated in the

study, for their dedication to research and their genuine concern for others in their same

situation. Their participation was partially supported by a Graduate Research Grant from the

University Research Grants Committee at Northwestern University. Furthermore, I would like to

thank Courtney Coburn, my research assistant, for helping both with data collection and

providing support and assistance as needed.

I would like to thank my family for their encouragement and for always believing that I

could do this. I thank my parents and my parents-in-law for constantly supporting me along the

way, with a special thank you to my mother, Dr. Beth Erez, for consistently being my first and

foremost supporter and inspiring me to pursue a doctoral degree. To my amazing husband and

best friend, Dror, without whom this accomplishment would never have been possible. He has

believed in me during the times when my motivation and inspiration waned. He always
 6

encouraged me to push limits and excel beyond what I thought was possible. I would like to

thank my children, Noa and Itai for providing me with the best motivation to complete this

dissertation and for inspiring me to overcome obstacles; their love, smiles and laughter are the

best reward one can receive.

I would like to acknowledge Northwestern University and the Office of Services for

Students with Disabilities and thank them for providing me with essential tools and support

during this doctoral program. In addition, I would like to thank Elizabeth Lenaghan from The

Writing Place for her assistance in editing and reviewing this dissertation.

My great appreciation to my dear friends and colleagues, Nicole Rogus-Pulia, Nogah

Nativ, Julia Jones Huyck, Ellyn Riley, Kate Dunckley and Daphne Sajous-Brady, that joined me

throughout this process and helped me so much. I would also like to thank Roscoe Nicholson for

his insightful comments on this thesis. To my special friend, Donna Schatt, thank you for being

there for me whenever I needed it and for sharing in my excitement over this accomplishment.

Finally, I am indebted to many people for making the time working on my Ph.D. an

unforgettable experience and would like to thank them all.

 7

I would like to dedicate this dissertation to my wonderful family, especially to my loving and

dedicated husband, Dror and our exuberant, sweet, and kind-hearted kids.

“How did it get so late so soon? It's night before its afternoon.

December is here before its June.

My goodness how the time has flewn.

How did it get so late so soon?”

― Dr. Seuss
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Table of Contents

ABSTRACT ................................................................................................................................... 3

Acknowledgment ........................................................................................................................... 5

Table of Contents .......................................................................................................................... 8

List of Tables and Figures .......................................................................................................... 12

List of Figures .......................................................................................................................... 12

List of Tables ............................................................................................................................ 12

Chapter 1: Attention Disorders ................................................................................................. 14

Attention Disorders and Executive Functions .......................................................................... 15

Attention Disorders and Prospective Memory ......................................................................... 16

Chapter 2: Prospective Memory................................................................................................ 21

Prospective Memory Retrieval and Attentional Resources ...................................................... 22

Prospective Memory and Executive Functions ........................................................................ 25

Prospective Memory Assessment ............................................................................................. 30

Virtual Week ........................................................................................................................ 31

Prospective and Retrospective Memory Questionnaire ....................................................... 33

Chapter 3: Study Aims and Research Questions ..................................................................... 34

The Aim of the Study ............................................................................................................... 34

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Research Questions and Hypotheses: Experiment 1 ................................................................ 34

Research Questions and Hypotheses: Experiment 2 ................................................................ 35

Participants ............................................................................................................................... 36

Chapter 4: Experiment 1: Am I Forgetting Something? Prospective Memory in Adults

with ADHD .................................................................................................................................. 37

Abstract .................................................................................................................................... 37

Introduction .............................................................................................................................. 37

Prospective Memory and ADHD ......................................................................................... 39

Retrospective Memory ......................................................................................................... 42

Prospective Memory Assessment ........................................................................................ 46

Methods .................................................................................................................................... 48

Participants ........................................................................................................................... 48

Measures .............................................................................................................................. 49

Procedures ............................................................................................................................ 52

Results ...................................................................................................................................... 53

Virtual Week ........................................................................................................................ 53

Virtual Week Errors ............................................................................................................. 61

PRMQ .................................................................................................................................. 62
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Discussion ................................................................................................................................ 64

Chapter 5: Experiment 2: Prospective Memory and Executive Function in Adults with

ADHD ........................................................................................................................................... 72

Abstract .................................................................................................................................... 72

Introduction .............................................................................................................................. 73

Methods .................................................................................................................................... 79

Participants ........................................................................................................................... 79

Measures .............................................................................................................................. 81

Procedures ............................................................................................................................ 85

Results ...................................................................................................................................... 86

Executive Functions ............................................................................................................. 86

Prospective Memory ............................................................................................................ 88

Prospective Memory and Executive Functions.................................................................... 89

Discussion ................................................................................................................................ 94

Chapter 6: General Discussion ................................................................................................ 102

References .................................................................................................................................. 112

Appendix A List of PM Task Included in Virtual Week......................................................... 123

Appendix B Computer Screen Displays of Instructions for Time Check Task ...................... 125
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Appendix C Prospective and Retrospective Memory Questionnaire...................................... 127

Appendix D Characteristics of Participants with ADHD ....................................................... 131

Appendix E Computer Screen Display of Sample List of PM Tasks (Trial Day) .................. 132
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List of Tables and Figures

List of Figures

Figure 1. Computer Screen Display of the Computerized Virtual Week Task............................ 32

Figure 2. Overall Virtual Week Performance as a Function of Prospective Memory Cue for

the ADHD and Non-ADHD Participants ...................................................................................... 55

Figure 3. Overall Virtual Week Performance as a function of Task Regularity for ADHD

and Non-ADHD Participants ........................................................................................................ 56

Figure 4. Overall Virtual Week Performance as a Function of Task Regularity and Prospective

Memory Cue ................................................................................................................................. 57

Figure 5. Results of the 2 Group (ADHD, Non-ADHD) x 5 Virtual Week Task Tape Mixed-

Model ANOVA ............................................................................................................................. 59

Figure 6. Scores on the Prospective Memory and Retrospective Memory Subscales for the

ADHD and Non-ADHD Participants............................................................................................ 63

List of Tables

Table 1. Participant Characteristics .............................................................................................. 48

Table 2. Means and Standard Deviations for Virtual Week Error Categories for ADHD

Participants and Non-ADHD Controls. ........................................................................................ 62

Table 3. Means and Standard Deviations of the Prospective Retrospective Memory

Questionnaire for ADHD Participants and Non-ADHD Controls ................................................ 63

Table 4. Participant Characteristics .............................................................................................. 80

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Table 5. Means and Standard Deviations and Group Comparisons for Executive Function

Measures. ...................................................................................................................................... 87

Table 6. Correlations among Executive Function Scores and Virtual Week Scores for All

Participants. ................................................................................................................................... 91

Table 7. Results from Regression Analysis of Composite Working Memory and ADHD

Status to predict PM ability........................................................................................................... 92

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Chapter 1: Attention Disorders

Attention Deficit Hyperactivity Disorder (ADHD) is a developmental disorder of the

central nervous system characterized by difficulties in the areas of attention, hyperactivity and

impulse control (American Psychiatric Association, 2000). Symptoms present themselves in

situations that require paying attention, restricting movement, inhibiting impulses and regulating

one’s own behavior (Barkley, 2006). The Fourth Edition of the Diagnostic and Statistical

Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 2000) divides ADHD

into three possible subtypes—Primarily Inattentive Type, Primarily Hyperactive-Impulsive Type

and Combined Type. While attention disorders are generally associated with childhood, they are

now recognized as persisting into adulthood in approximately one half of all individuals

diagnosed when they are young (Barkley, 1997). In fact, ADHD prevalence in the adult

population overall is estimated at about 3%- 4% of all adults (Faraone, Sergeant, Gillberg, &

Biederman, 2003; Kessler et al., 2006).

The symptoms of ADHD create special challenges for adults, in the workplace, at home

and in social environments. Evidence also suggests that while many children who have ADHD

symptoms do not outgrow them, the ADHD symptoms exhibited may change across the lifespan,

in part because adults with ADHD may have developed strategies to overcome or mask some of

their earlier symptoms (Barkley, 1997, 2006). Hyperactivity symptoms are frequently reduced in

adulthood, but symptoms related to executive functions typically become more apparent as the

demands for executive functioning increase in adulthood (Resnick, 2005).

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Attention Disorders and Executive Functions

Executive functioning (EF) has been interpreted in a variety of ways, but most definitions

consider EF a general term encompassing a range of skills that include the mental processes

involved in planning and executing goal-directed behavior, such as working memory, shifting

and self-monitoring (for reviews see Chan, Shum, Toulopoulou, & Chen, 2008 and Gioia,

Isquith, Kenworthy & Barton, 2002). Executive functions manage the brain’s cognitive

processes; they provide the mechanism for “self-regulation” (Vohs & Baumeister, 2004).

Executive function deficits have been suggested to be an important (perhaps the essential)

impairment in individuals with ADHD. Barkley (1997, 2006) theorizes that EF depends upon

self-regulation and inhibition, both of which are impaired in individuals with ADHD; therefore

individuals with ADHD demonstrate EF deficits.

An increasing number of studies have reported that individuals with ADHD tend to

perform poorly on measures used to assess EF (for example, Antshel et al., 2010; Barkley 2006;

Bramham et al., 2009; Brown, 2006; Brown, Reichel & Quinlan, 2009; Clark, Prior, & Kinsella,

2000). Willcutt, Doyle, Nigg, Faraone, & Pennington, (2005) conducted a meta-analysis of 83

studies that compared performance of individuals with and without ADHD on EF tasks. The

authors concluded that children and adolescents with ADHD demonstrated significant general

impairments on EF tasks; effect sizes for all measures were in a range that is typically considered

medium (d=.46-.69). Aspects of EF that were consistently observed to be deficient in

individuals with ADHD included response inhibition, vigilance, spatial working memory and

some aspects of planning. It is important to note that these EF deficits are not explained by
 16

group differences in intelligence, academic achievement or symptoms of co-morbid disorders

(Willcutt et al., 2005). Similarly, Antshel et al. (2010) compared the performance of adults with

high levels of mental ability, with and without ADHD, on measures of EF and demonstrated that

despite their high intelligence, the adults with ADHD had significantly lower scores on EF tests

compared to control subjects. The adults with ADHD also reported a lower quality of life as

well as less positive functional outcomes (Antshel et al., 2010).

Attention Disorders and Prospective Memory

Adults with ADHD commonly report problems in time management, organization and

planning (Barkley, Murphy, & Fischer, 2008), all of which contribute to problems in daily living

such as forgetting doctor appointments, missing meetings and failing to remember the birthdays

of loved ones. These are all examples of prospective memory (PM) failures. Prospective

memory is defined as the ability to plan intended actions, retain them and carry them out at an

appropriate point in the future (Graf & Grondin, 2006; McDaniel & Einstein, 2007).

There are several reasons to suspect that individuals with ADHD have PM deficits. First,

there is anecdotal evidence from clinical settings that adults with attention disorders frequently

report PM failures and thus have not outgrown PM difficulties, as well as evidence from studies

of individuals with ADHD in which they report time management and organization difficulties

(Abikoff et al., 2009; Barkley et al., 2008).

The second reason to hypothesize that adults with ADHD may demonstrate PM

difficulties lies in studies that demonstrate that children with ADHD have significantly more PM

failures than control children without ADHD (Kerns & Price, 2001; Kliegel, Ropeter, &
 17

Mackinlay, 2006; Siklos, & Kerns, 2004; Zinke, et al., 2010). The difficulties shown in children

with ADHD may be affected by specific PM task features, such as the cueing method of the PM

tasks (i.e., whether the task is time-based or event-based). When a task is to be performed at a

specific time, such as participating in a 9 a.m. conference call, it is categorized as time-based.

When the successful execution of a task is cued by an event’s occurrence, such as seeing an

ATM and subsequently remembering to withdraw cash, it is categorized as event-based.

Kerns and Price (2001), looked at both event-based PM and time-based PM tasks in

children with ADHD (age 6-13). Using a computerized time-based driving game

(CyberCruiser), they found that children with ADHD experienced significantly more PM failures

than controls. The children with ADHD erred by running out of gas significantly more often

than subjects without ADHD, demonstrating deficits in PM. However, when event-based PM

tasks were administrated (e.g., when the experimenter snapped his/her fingers, the child was to

remember to get up, walk to the door and turn the doorknob) the researchers found no significant

differences between the children with and without ADHD.

Comparable results were found in a study that investigated PM in children with ADHD

(age 8-10) while using a different computerized time-based PM task (Zinke et al., 2010). Here,

children were asked press a button every two minutes while simultaneously engaging in another

ongoing task. Children with ADHD made significantly more PM errors than control children

and there was a significant relation between the number of reported ADHD symptoms and PM

performance. An analysis of performance on the ongoing task indicated that the children with

ADHD did not differ from control participants in their performance on the ongoing task
 18

suggesting that it is not the ongoing task per se that drives the difference between the groups on

the PM task. The researchers propose that their results indicate that ADHD symptomatology

(e.g., difficulties in time estimation, forming intentions and working memory) may underlie the

PM difficulties (Zinke et al., 2010).

Similarly, Brandimonte, Filippello, Coluccia, Altgassen, and Kliegel (2011) compared

the performance of children with ADHD, children with Autism Spectrum Disorder (ASD) and

matched controls on an event-based PM task and a response inhibition task (Go/No-Go). Their

results revealed that the performance of the children with ADHD was impaired relative to

controls on the task requiring response inhibition, but differences between groups were not

observed on the event-based PM task. The participants with ASD showed the reverse pattern of

impairments: significant difficulties on the event-based PM task and no differences on the

Go/No-Go task compared to controls. Overall, taking into account the studies investigating PM

in children with ADHD, compared to controls, children with ADHD have been shown to exhibit

deficits on certain types of PM tasks (time-based), but have not displayed deficits on other PM

tasks (event-based).

Only one published study has investigated PM in adults with ADHD, and this study

yielded a pattern of results similar to that seen in children with ADHD (Altgassen, Kretschmer,

& Kliegel, 2012). Participants included 25 adults with ADHD and 25 non-ADHD control

participants (age 20-57). In this study a computerized version of the Dresden Breakfast Task

was employed. In this PM test, participants are asked to prepare breakfast based on a set of rules

(e.g., first putting down the tablecloth, then setting the table) and time restrictions. Prior to
 19

beginning the task, participants were asked to create a plan for its successful completion and to

write it down (participants did not have access to their written plan during the execution phase).

Here, adults with ADHD created less consistently accurate plans and they did not follow their

plans to the same extent as the control participants. During the computerized task itself, adults

with ADHD exhibited significant PM impairments compared to control participants on time-

based tasks, while no impairments were evident on event-based tasks. The researchers conclude

that these findings suggest that PM impairments in adults with ADHD are task-specific. In

particular, they propose that the impairments of adults with ADHD are limited to only time-

based PM activities (Altgassen et al., 2012).

In summary, PM impairments have been demonstrated in some but not all studies of

individuals with ADHD when compared to controls. In the Altgassen et al. (2012) study of

adults with ADHD, the results indicated difficulties on time-based PM tasks but not on event-

based PM tasks. Similarly, children with ADHD and controls did not exhibit significant

differences on event-based PM tasks (Brandimonte et al., 2011; Kerns & Price, 2001). Overall,

these findings suggest that PM difficulties of individuals with ADHD are not consistent across

all types of PM tasks.

It is also important to note that none of the above studies directly explored how adults

with ADHD perceive their PM abilities and how accurate they are in evaluating their own

abilities. Thus, the degree to which adults with ADHD experience PM difficulties and PM errors

requires further investigation. The present study is the second study to explore prospective

memory in adults with ADHD, and the first to extend the investigation to the classification of
 20

PM errors and the differential effects of PM task features in adults with ADHD. Better

understanding of PM errors and the effects of PM task features on PM performance in adults

with ADHD may provide important information to aid in the development of successful

interventions designed to improve PM functioning.

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Chapter 2: Prospective Memory

Ellis (1996) proposed a model of PM that includes five phases: 1) Intention Formation—

when future activity is planned (for example, forming the intention to call the doctor’s office

when it opens at 9 a.m.); 2) Intention Retention—the time during which the intended action is

retained in memory while other ongoing activities are performed (for example, keeping the

intention in mind during breakfast); 3) Intention Initiation—the point in time at which the

execution of the intention is initiated (for example, noticing that it is 9 a.m. and then initiating

the intention); 4) Intention Execution—the actual execution of the intended action (for example,

actually calling the doctor's office); and 5) Result Assessment—evaluating the outcome (Ellis &

Kvavilashvili, 2000). According to this model, a breakdown at any of the phases will result in

PM failure.

Prospective memory focuses on intended future events (for example, remembering to

register to vote) and thus differs from retrospective memory, which emphasizes memory of

events that have previously occurred (for example, recalling the date of the elections).

Successful prospective memory requires detecting an event or specific time and interpreting it as

a cue for action as well as remembering the specific action to be performed (retrospective

component) (McDaniel & Einstein, 2007). However, retrospective memory is a necessary but

insufficient condition for successful prospective memory (Kliegel, Jäger, & Phillips, 2008). In

particular, Ellis (1996) suggests that PM requires retrospective memory during the first stage of

Intention Formation. Retrospective memory is a crucial part of effective PM, but additional

cognitive processes are also required during the PM process (Burgess & Shallice, 1997). The
 22

degree to which specific cognitive processes are required will vary according to the type of PM

task being performed (McDaniel & Einstein, 2007).

As previously described, researchers frequently analyze PM tasks based on the methods

of cueing or targeting the retrieval of a previously defined plan. Planned retrieval that is

indicated by the occurrence of a specific event is categorized as event-based. By contrast,

planned retrieval that is initiated based on a specific clock time or an amount of elapsed time is

categorized as time-based (Graf & Grondin, 2006; McDaniel & Einstein, 2007). There appear to

be fundamental differences between the processes underlying event-based and time-based tasks.

It has been suggested that event-based tasks rely less on executive control processes and more on

spontaneous or automatic processing with a minimal need for cognitive resources, including

executive functions, while time-based tasks rely more heavily on self-initiated and executive

processing (Einstein, McDaniel, Richardson, Guynn, & Cunfer, 1995; Groot, Wilson, Evans &

Watson, 2002; Mäntylä, Carelli, & Forman, 2007). In situations when there are no external aids

(such as alarms or timers) available to assist in remembering time-based PM tasks, successful

performance depends on the ability to remember the task, monitor the passage of time, and

initiate a plan at the correct time. By contrast, event-based tasks do not necessarily require

monitoring, and thus presumably require fewer executive control processes.

Prospective Memory Retrieval and Attentional Resources

Although the cognitive mechanisms involved in PM are not fully understood, it has been

suggested that both attention and EF are crucial for PM (Kerns & Price, 2001; McDaniel &

Einstein, 2007). The extent to which PM uses attentional resources to initiate cue retrieval and
 23

perform PM tasks successfully is at the center of current debate. Some theorists propose that PM

always requires the use of attentional resources for successful cue retrieval (Smith, 2003; Smith

& Bayen, 2004). For these theorists, the assumption is that retrieval occurs through the resource-

demanding attentional process of monitoring the environment for a cue to initiate the PM task

(Mäntylä & Carelli, 2006). Many experiments investigating prospective memory use a classic

cognitive psychology paradigm— the dual-task—which is based on the assumption that the

attentional system has limited capacity. When performing two tasks simultaneously, one of the

tasks will require attention that will reduce the attentional resources available for the second task

(Graf & Grondin, 2006). In support of the attentional resources approach to retrieval, several

studies that used the dual-task strategy indicated that adding a PM component to a task

significantly affected performance of the ongoing activity (i.e., a prospective interference effect;

Einstein et al., 2005; Marsh & Hicks, 1998; Smith, 2003; Smith & Bayen, 2004).

An alternative theory of PM retrieval, referred to as Spontaneous Retrieval Theory,

suggests that PM tasks can be performed with minimal or no attentional resources (Einstein et

al., 2005). In PM tasks where the delay between the formation of the intended action and the

time for carrying out the intention is hours or days long, it is unlikely that one constantly

monitors the environment using attention-demanding resources. Instead it is likely that less

demanding, more spontaneous processes or automatic processes (requiring minimal executive

resources) support PM in many everyday settings (McDaniel & Einstein, 2007). Additional

evidence for spontaneous retrieval processes in PM can be found in self-reports of participants in

PM experiments. Subjects report the intention “popping into mind” when the retrieval cue
 24

appeared (Einstein & McDaniel, 1990). An essential assumption here is that people do not

necessarily monitor their environment for target events during the time preceding task

performance, meaning that no executive resources are devoted to PM initiation. Remembering

occurs when a cue for the target event initiates successful retrieval (Einstein et al., 2005;

McDaniel & Einstein, 2000).

To summarize, there is evidence that attention-demanding processes are involved in PM

and that utilizing such processes can improve PM (Smith, 2003). In contrast, there is also

evidence that successful PM retrieval can occur spontaneously and without a significant

allocation of attention (Einstein et al., 2005). In an attempt to explain this apparent

contradiction, McDaniel and Einstein (2000, 2007) introduced an integrated multiprocessing

model, which suggests that a complex process such as PM must have multiple of underlying

mechanisms. According to this model, several different kinds of processing can support PM,

ranging from attention-demanding monitoring to minimally demanding spontaneous retrieval

processes. The process employed in a given situation may depend on a variety of factors,

including the nature of the ongoing dual-task and the features of the PM task, as well as

motivation and other individual characteristics. The multiprocessing view also assumes that

some task features are likely to necessitate monitoring and attentional resources for accurate

prospective memory retrieval, while others are not. The multiprocessing theory of PM suggests

that task features (e.g., time- vs. event-based), may mediate the need for EF involvement in PM,

and thus predicts that individuals with EF and attentional deficits will have more difficulty on
 25

those tasks requiring substantial attentional demands than on tasks for which spontaneous

retrieval is likely to be used (McDaniel & Einstein, 2007).

Prospective Memory and Executive Functions

There are several reasons to suspect a relationship between EF and PM. First, available

neuropsychological and neuroimaging evidence points to frontal lobe structures as being

intimately involved in both PM and EF (Graf & Grondin, 2006; McDaniel & Einstein, 2007).

Prospective memory has been linked to frontal lobe systems, strongly associated with the ability

to self-initiate, plan, control, and monitor goal-directed behavior (Kerns & Price, 2001; Mäntylä,

Carelli, & Forman, 2007). Further, goal-directed activities such as PM require temporal

integration and the monitoring of action sequences, which have also been localized in the frontal

lobes (Mäntylä & Carelli, 2006).

A second source of evidence supporting the relationship between EF and PM originates

from studies of patients with frontal lobe brain injuries who demonstrate difficulty on PM tasks

when compared to control participants (Groot et al., 2002; Fleming et al., 2008). Groot et al.

(2002) explored the performance of participants with and without brain injuries on the

Cambridge Behaviour Prospective Memory Test and on behavioral attention and EF measures.

The results confirmed that participants with brain injuries demonstrated significantly more PM

failures than control participants. However, their results indicated that PM differences in

performance could be explained by performance on a combination of tests assessing

retrospective memory, attention and EF. Relationships among PM failures, EF, and frontal lobe

function have also been demonstrated in studies investigating users of the drug Ecstasy, which
 26

negatively affects frontal lobe functioning and impairs executive functioning. Ecstasy users self-

report significantly more errors in PM (based on their responses on the Prospective Memory

Questionnaire) and demonstrate executive difficulties (using fluency tasks) when compared to

Ecstasy-free control subjects (Heffernan, Jarvis, Rodgers, Scholey, & Ling, 2001).

The involvement of the frontal lobe in PM performance leads to the prediction that

individuals with deficits in frontal lobe systems will concurrently demonstrate difficulty on PM

tasks (Kerns & Price, 2001). Frontal lobe involvement has been consistently documented in

attention deficit disorders (Anderson & Jacobs, 2008). For example, in adults with ADHD,

functional imaging has revealed abnormal frontal lobe metabolism (see Schneider, Retz, Coogan,

Thome & Rösler, 2006 for a review of the literature). Further, significant cortical thinning of the

attention and EF networks—including dorsolateral prefrontal and the anterior cingulate

cortices—has been demonstrated in adults with ADHD when compared to adults without the

disorder (Makris et al., 2007). These findings strongly suggest that individuals with ADHD will

have difficulties in cognitive functions that rely on frontal lobe processes such as PM.

Third, a number of studies provide direct support for the relationship between PM and EF

(Burgess, Veitch, Costello, & Shallice, 2000; Kerns, 2000; Mäntylä, Carelli, & Forman, 2007;

Martin, Kliegel, McDaniel, 2003). For example, Mäntylä et al. (2007) compared the

performance of school-age children (ages 8 to 13) with that of college students on time-based

PM tasks. Subjects watched a short movie during which they were requested to press a button

every five minutes. Three components of executive functions were assessed: inhibition (via

performance on stop signal and Stroop tasks), shifting (through connections and category fluency
 27

tasks), and updating (with n-back task and matrix monitoring tasks). In both children and adult

subjects, PM performance was significantly related to the inhibition and updating components,

but not to the shifting component. Schnitzspahn, Stahl, Zeintl, Kaller, and Kliegel (2012)

explored the relationship between event-based PM and EF tasks by assessing shifting, updating

and inhibition in young and older adults without ADHD. Their results demonstrated that

inhibition and shifting correlated with PM performance; however updating measures showed no

significant relationship with PM.

Finally, studies demonstrating age-related differences in prospective memory have

yielded results consistent with the notion that EF is related to PM performance (Mackinlay,

Kliegel, & Mäntylä, 2009; Mäntylä & Carelli, 2006; Martin et al., 2003). Age-related

differences in PM tasks have been demonstrated with children and older adults (McDaniel &

Einstein, 2007). Normal aging is associated with decrements in EF; thus, older adults would be

expected to demonstrate less efficient goal-directed behavior and monitoring strategies than

younger adults (Mäntylä & Carelli, 2006). This in turn would predict that older adults would

perform more poorly on PM tasks than younger adults. As an example of support for this

prediction, EF performance predicted scores on two complex PM tests in a sample of 80 adults

(ages 20 to 80) (Martin et al., 2003).

Researchers have reported that older adults (typically those over 60 years of age)

demonstrate difficulties in both event- and time-based PM when compared to younger adults

(Einstein & McDaniel, 1996; Henry, MacLeod, Phillips, & Crawford, 2004). These age-related

effects on PM performance have been attributed to difficulties in self-initiated retrieval (Craik,

 28

1986). When event- and time-based PM tasks are compared within the same individual, age-

related difficulties are more consistently associated with time-based PM rather than event-based

PM (see for example, Einstein & McDaniel, 1996). Researchers propose that this difference

reflects the substantial reliance of time-based PM on self-initiated retrieval (Henry et al., 2004;

Hicks, Marsh & Cook, 2005). McDaniel and Einstein (2007) suggest that older adults are able to

utilize automatic processes better than attentional resources (including EF), thus age-related

effects provide support for an EF component to PM failures.

As observed in older adults, individuals with ADHD who have attention and EF difficulties

may be expected to demonstrate more difficulties in time-based PM tasks than event-based PM

tasks. As noted in Chapter 1, prior research has demonstrated that children with ADHD performed

similarly to controls on event-based PM tasks but demonstrated significant difficulties compared

with controls on time-based PM tasks (Kerns & Price, 2001; Zinke et al., 2010). However, it is

important to note that these studies report using a highly salient cue (such as the researcher

clapping) to signify the appropriate moment to start the intended action. The literature suggests

that the saliency of the prospective cue will affect the success of prospective remembering; that is,

the more salient the cue, the higher likelihood of recall (McDaniel & Einstein, 2007). Thus,

studies that used a very salient cue and non-ecologically valid cue may have artificially increased

the likelihood of children with ADHD remembering to execute the event-based task. As a result,

further comparison of the performance of individuals with ADHD on event-based tasks and to

time-based tasks is needed (Zinke et al., 2010).

 29

While investigating age differences between 7- and 12-year-olds on a time-based PM

task, Mackinlay et al. (2009) found that the majority of age-related variance in performance on

the PM task could be explained by executive function measures (working memory, task

switching and planning measures). Their results demonstrated no additional independent

contribution of time estimation ability. On the basis of this, Mackinlay et al. (2009) propose that

their results clearly support an important role of executive functions in explaining individual and

developmental differences in children's performance on time-based PM tasks.

It is important to note that there is no “gold standard” for measuring executive function—

no single measure of executive function is agreed on by most researchers and clinicians

(Barkley, 2012). As a consequence, the above studies vary significantly in the executive

function measures they used. For instance, when investigating the relationship between PM and

EF, some researchers measured PM and EF using tasks designed to evaluate each of the

constructs separately. For example, Mäntylä et al. (2007) measured subjects’ performance on a

PM task and assessed EF using six different measures of EF (a Stop Signal task, Stroop task,

Connection and Category Fluency task, N-Back task and Matrix Monitoring task). Other studies

manipulated the PM task demands in order to measure EF. For example, Smith (2003)

demonstrated that the magnitude of the prospective interference affect (the effect of the PM task

on the ongoing task) was correlated with the accuracy of prospective memory as well as with

individual differences in working memory capacity. As another example, Smith and Bayen

(2005) created groups of participants with high and low working memory on the basis of their

performance on a counting-span task. They then found that participants with good working
 30

memory skills performed significantly better than those with poor working memory in their

overall prospective memory performance.

Prospective Memory Assessment

When investigating PM performance in the laboratory it is important to reflect on what

constitutes a PM task. McDaniel and Einstein (2007) proposed that a PM task must be

embedded in an ongoing activity, that the execution of the intended PM action must be delayed

and that the window for opportunity during which the intended action should be performed must

be clearly defined and limited. Because of reliability issues associated with employing a single

measure to evaluate a characteristic and due to the inability of a single measure to fully assess

complex psychological constructs, when measuring PM it is important to employ multiple

measures. The present study employs the Virtual Week computerized task to test the differences

between groups on time- and event-based PM and regular and irregular PM tasks, as well as a

self-report measure, the Prospective and Retrospective Memory Questionnaire (PRMQ).

Studies indicate that Virtual Week is sensitive to PM difficulties in normal and abnormal

adult aging (Rendell & Craik; 2000; Rose, Rendell, McDaniel, Aberle, & Kliegel, 2010), and in

various clinical populations, including substance abuse (Rendell, Gray, Henry, & Tolan, 2007),

multiple sclerosis (Rendell, Jensen, & Henry, 2007), schizophrenia (Henry, Rendell, Kliegel, &

Altgassen, 2007) and Parkinson disease (Foster et al., 2013). Preliminary reliability findings

with a computerized short version of Virtual Week have been promising, with a split-half

reliability between .66 and .74 (Henry et al., 2007), and evidence for other desired psychometric

properties as well (see Rose et al., 2010). By virtue of its features and the body of work
 31

associated with it, Virtual Week seemed an ideal choice for exploring the relationship between

PM and ADHD in the current study.

Virtual Week

Virtual Week is a computerized board game that simulates a 5 day week with a series of

tasks to remember and decisions to make (for a review see Rendell & Henry, 2009). Each circuit

around the game-like board represents one day. Movement around the board is determined by

rolling two dice (see Figure 1). Examples of to-be-remembered activities include taking

medication, paying bills, and meeting friends for appointments, among others (for a list of all PM

tasks included in Virtual Week see Appendix A). As is the case with other instruments designed

to evaluate PM performance, Virtual Week has both time- and event-based tasks. In addition, as

outlined in previous studies, Virtual Week includes a manipulation of retrospective memory

demands by including both regular tasks and irregular tasks (Foster et al., 2013; Rendell et al.,

2007a; Rendell & Henry, 2009, Rose et al., 2010).

Prospective memory tasks that are the same each virtual day (regular) and others that are

different each virtual day (irregular) are examples of the variability of retrospective memory

demands. Regular tasks reduce retrospective memory demands in several ways. The regular

tasks are repeatedly cued with the same time or event; in contrast, irregular tasks are cued with

unique non-habitual targets. Regular tasks are introduced at the beginning of the game with

explicit instructions to try and remember these tasks, whereas irregular tasks are introduced as

the game progresses. In addition, the regular tasks include relatively simple actions related to

one topic (health) and therefore may be considered less complex than the irregular tasks that
 32

include multiple topics. Regular tasks thus receive enhanced encoding relative to irregular tasks

and as a result may require less use of retrospective memory for successful performance.

Figure 1. Computer Screen Display of the Computerized Virtual Week Task.

These two task dimensions are crossed, creating four combinations of task along the

time/event and regular/irregular dimensions. In addition, a time-check component is included in

which participants are asked to stop a clock at a specified amount of time (two and four minutes)

after they begin each virtual day (see Appendix B for Time Check task instruction screens). The

primary variables of interest include the proportion of correct responses in each task category
 33

and the reason for failures of PM (i.e., misses where PM tasks were not executed or early

responses where intentions were executed before the specified time). Hence, Virtual Week

provides an opportunity to explore each participant’s PM errors in some detail. Understanding

the specific patterns of PM errors made by the adults with ADHD may provide important

information for considering possible intervention strategies to address PM difficulties.

Prospective and Retrospective Memory Questionnaire

In addition to using a test that directly assesses performance on tasks requiring PM,

questionnaires are a very common method for measuring perceived PM proficiency, especially in

clinical situations (Thone-Otto & Walther, 2008). One such questionnaire is the Prospective and

Retrospective Memory Questionnaire (PRMQ) (Smith, Della Sala, Logie, & Maylor, 2000). The

PRMQ consists of 16 self-report questions designed to assess an individual’s perception of his or

her prospective and retrospective memory abilities (see Appendix C). Prior research supports the

internal consistency, predictive validity (Smith et al., 2000; Kliegel & Jäger, 2006) and factor

structure (Crawford , Smith, Maylor, Della Sala, & Logie, 2003; Kliegel & Jäger, 2006) of the

PRMQ. The use of this instrument is of particular interest since research to date has not fully

addressed the question of whether adults with ADHD possess accurate self-awareness of their

PM abilities. To answer this question, the current study will investigate the self-reported PM

abilities of adults with ADHD and compare them to their performance on a computerized PM

task.
 34

Chapter 3: Study Aims and Research Questions

The Aim of the Study

The aim of the present study was to explore, for the first time, the relationship between

EF and PM in adults with attention disorders. Comparing the performance of adults with and

without ADHD on PM and EF tasks may provide insight into the relationships among PM,

attention and EF, thereby increasing our understanding of the cognitive processes that are part of

effective PM performance. It is important to explore these questions in adults with ADHD

because previous studies have primarily focused on children with ADHD despite the fact that

adults with ADHD commonly report PM difficulties and find these impairments adversely

impacting daily functioning. Exploring the relationship between different features of PM tasks

and adult ADHD may also provide important information for clinicians and could provide

empirical support for the use of objective PM measures such as Virtual Week.

Research Questions and Hypotheses: Experiment 1

The PM performance in adults with ADHD and controls was examined to investigate the

differential effects of task regularity and task type on PM performance. Importantly, it is the first

study of individuals with ADHD to extend the investigation of PM to PM errors and self-reports

of prospective and retrospective memory. This study was designed to investigate the following

questions: (1) Will adults with ADHD, who typically have monitoring and attentional difficulties

among their symptoms, demonstrate more PM errors than adults without ADHD, and will the
 35

two groups differ in the type of PM errors they commit? (2) Will PM task type affect the

performance of both adults with ADHD and control subjects? (3) Will adults with ADHD

accurately self-report their deficits in prospective memory? Based on the multiprocessing theory

that proposes that PM task demands may influence performance, it is hypothesized that PM task

type will affect the performance of both adults with ADHD and control subjects, although it is

unknown how task type may interact with group. While everyday PM tasks—whether time- or

event-based—can also be regular or irregular, to date an examination of the regularity effects on

PM tasks has not been systematically investigated in adults with ADHD. It is hypothesized that

regular tasks, which reduce retrospective memory demands, will yield increased PM accuracy

compared to irregular tasks for all subjects. Task regularity is expected to differentially benefit

the participants with ADHD by allowing them to reduce the use of their limited executive

resources in order to successfully perform the tasks, thus increasing accuracy. It is expected that

adults with ADHD will be able to accurately self-report their PM deficits, as evidenced by a

significant relationship between PRMQ scores and Virtual Week performance.

Research Questions and Hypotheses: Experiment 2

Despite the evidence that both EF deficits and PM failures may negatively affect

functioning and quality of life, the empirical relationship between EF and PM has not been

investigated in adults with ADHD. At present, it is still unclear whether and to what extent EF is

involved in PM difficulties in adults with ADHD. This study aimed to explore the following

questions: (1) To what extent does EF performance predict PM performance in adults with

ADHD? (2) Are certain EF measures better predictors of PM performance than others? (3) Do
 36

different PM task features modulate the relationship between EF and ADHD? Prospective

memory performance of both groups is expected to relate to EF measures; specifically, better EF

scores are expected to be associated with more accurate PM performance.

Participants
Both experiments included adult participants with and without ADHD (ages 18-41).

Subjects were recruited from the community by posting signs in public spaces; additionally,

several participants in the ADHD group were recruited from the office for students with

disabilities at a Midwestern university.

In order for participants to be included in the ADHD group, they needed to report being

diagnosed with ADHD by a qualified professional within the last five years. In addition, all

ADHD subjects reported having at least five current symptoms on the ADHD Rating Scale for

Adult Symptoms and ADHD Rating Scale (Barkley & Murphy, 2006). All participants in the

control group reported three or fewer symptoms on the same scale. All participants reported

normal corrected or uncorrected vision and hearing, and none reported evidence or history of

deafness, blindness, aphasia, or psychiatric disturbance as indicated on a medical history

questionnaire. For additional information on the ADHD participants’ characteristics, see

Appendix D.
 37

Chapter 4: Experiment 1: Am I Forgetting Something? Prospective

Memory in Adults with ADHD

Abstract

The current study investigated prospective memory (PM) performance in adults with and

without ADHD with the aim of assessing the type of PM errors made and the differential effects

of task regularity on PM performance. Nineteen adults with ADHD and 24 controls completed

the Virtual Week task, which simulates common PM tasks encountered in everyday life. Virtual

Week includes both regular and irregular tasks, which exert varying demands on retrospective

memory as well as tasks that are time- and event-based. Adults with ADHD demonstrated

significantly fewer correct PM responses across all task types, thus demonstrating a broad deficit

in PM performance as compared to controls. Across all participants, event-based and regular

tasks were found to yield better PM performance than time-based and irregular tasks. Overall,

these findings support the hypothesis that adults with ADHD have broad impairments in PM

performance.

Introduction

Attention Deficit Hyperactivity Disorder (ADHD) is a condition characterized by

inattention, hyperactivity and impulsivity (American Psychiatric Association, 2000). While most

often associated with children, ADHD is being increasingly recognized as a significant problem

among adults, with an estimated ADHD prevalence in the adult population of 3%-4%
 38

(Kessler et al., 2006). Adults with ADHD commonly report problems in time management,

organization and planning (Barkley et al., 2008), all of which contribute to problems in daily

living such as forgetting a coaching session, missing meetings and failing to refill a prescription.

All of these difficulties can be classified as prospective memory (PM) failures.

Prospective memory refers to the ability to plan intended actions, to retain these actions

in memory while being involved in other ongoing activities, and to self-initiate the execution of

the planned actions at an appropriate point in the future (Graf & Grondin, 2006; McDaniel &

Einstein, 2007). Prospective memory failures can result in negative consequences across a wide

range of everyday situations, as most occupational and social activities require some degree of

PM. Prospective memory also plays an important role in maintaining health; for example,

individuals need to employ PM to monitor their diet and remember to take medications (Wilson

& Park, 2008).

Numerous researchers have proposed that PM failures are related to difficulties in

Executive Function (EF) (Cherry & LeCompte, 1999; Kerns, 2000; Mackinlay et al., 2009;

Marsh & Hicks, 1998; Rose et al., 2010). Executive function encompasses a wide range of skills

that include the mental processes involved in planning and executing goal-directed behavior,

such as working memory, shifting and self-monitoring (Gioia et al., 2002). For example,

Kliegel, Martin, McDaniel, and Einstein (2002), demonstrated a significant relationship between

PM performance and performance on executive measures of planning, cognitive flexibility and

problem-solving in adults.
 39

Importantly, EF deficits have also been suggested to be a central (and perhaps the

essential) impairment in individuals with ADHD (Barkley, 1997, 2006). In support of the

theoretical connection between weaknesses in EF and ADHD, an increasing number of studies

have reported that individuals with ADHD tend to perform poorly on a range of measures used to

assess EF (see, for example, Antshel et al., 2010; Barkley, 2006; Bramham et al., 2009; Brown,

2006; Brown et al., 2009; Clark et al., 2000). Thus, ADHD and PM failure have both been

linked to deficits in EF.

Prospective Memory and ADHD

To date, only a few studies have explicitly explored PM performance and its relation to ADHD,

and nearly all of these have focused on children (Brandimonte et al., 2011; Kerns & Price, 2001;

Kliegel et al., 2006; Zinke et al., 2010). In these studies, children with ADHD have been shown

to have significantly more difficulties in performing PM tasks than non-ADHD controls (Kerns

& Price, 2001; Kliegel et al., 2006; Zinke et al., 2010). The difficulties shown in these ADHD

children were primarily influenced by whether the cueing method of the PM tasks was time-

based or event-based. When a task is to be performed at a specific time, such as participating in

a 9 a.m. conference call, it is categorized as a time-based task. When the successful execution of

a task is cued by an event’s occurrence, such as seeing a bank and subsequently depositing a

check, it is categorized as an event-based task. Using a computerized game (CyberCruiser) that

includes five repeating time-based PM tasks, Kerns and Price (2001) demonstrated that children

with ADHD performed less accurately when compared to control participants on these time-

based tasks. In contrast, the participants with ADHD completed non-repeating event-based PM
 40

tasks as accurately as controls. In addition, a study by Zinke et al. (2010) investigated repeated

time-based PM in children by asking them to press a button every two minutes while working on

an ongoing task. Results indicated that the children with ADHD were less accurate than control

children on this time-based task. Further the researchers found a significant relationship was

between the number of ADHD symptoms reported (metrics of severity) and accuracy on PM

tasks.

Brandimonte et al. (2011) compared the performance of children with ADHD, children

with Autism Spectrum Disorder (ASD) and matched controls on an event-based PM task and a

response inhibition task (Go/No-Go). Results demonstrated that compared to their matched

controls, the performance of the children with ADHD was impaired on the response inhibition

task but did not differ on the event-based PM task. The participants with ASD demonstrated the

opposite pattern of impairments: they experienced significant difficulties compared to controls

on the event-based PM task but did not differ their performance on the Go/No-Go task. These

results are consistent with Kerns and Price (2001) findings, which indicated that event-based

tasks resulted in no differences in performance compared to children without ADHD. In sum,

across the studies investigating PM in children with ADHD, on certain types of PM tasks

children with ADHD have exhibited deficits, while on other types of PM tasks they have not.

Specifically, a performance deficit for children with ADHD is evident on time-based PM tasks,

while no such deficit compared to controls is noted on event-based tasks.

Despite evidence showing PM deficits in children with ADHD and research showing that

ADHD symptoms continue to manifest in adulthood (Barkley & Murphy, 2010), only one
 41

published study has investigated PM in adults with ADHD (Altgassen et al., 2012). Their study

of PM in adults used a computerized version of the Dresden Breakfast Task in which participants

prepare breakfast based on a set of rules (e.g., first putting down the tablecloth, then setting the

table) under time restrictions. Before participants (age 20-57) began the PM task they were

asked to develop and write a plan for its successful completion. These written plans were not

accessible during the performance of the PM task, but were used to evaluate the quality of the

plans made and how well such plans were followed when performing the task itself. Results

indicated that the adults with ADHD created less consistently accurate plans and were less

precise in following them than control participants. On the time-based tasks in the Dresden

Breakfast Task (e.g., remembering to take the tea-bag out of the tea after 3 min), the adults with

ADHD had significant PM impairments compared to control participants. However, adults with

ADHD did not demonstrate difficulties on the event-based tasks (e.g., switching off the egg

cooker when it beeped). The researchers propose that their findings support a task-specific PM

impairment in adults with ADHD; specifically, they suggest a deficit only on time-based PM

activities (Altgassen et al., 2012).

It has been proposed that adults with ADHD are not deficient on event-based tasks

because event-based PM relies less on executive control and monitoring processes and more on

spontaneous processing that appears to be less dependent on EF. In contrast, the time-based

tasks on which adults with ADHD have been shown to have deficits are theorized to rely more

heavily on self-initiated executive processing, which is an important aspect of EF (Einstein et al.,

1995; Groot et al., 2002; Mäntylä et al., 2007).

 42

These explanations of adult ADHD PM performance also fit with the claims of the

multiprocessing theory of PM by McDaniel and Einstein (2000, 2007), which suggests that the

extent to which EF is involved in PM is influenced by features of the prospective memory task

itself. This theory further proposes that some task features necessitate monitoring and attentional

resources for accurate PM retrieval, while other task features encourage spontaneous retrieval

that does not require significant amounts of executive processes. For example, time-based tasks

are more likely to require monitoring a clock or in some way keeping track of time and therefore

place greater demands on EF than event-based tasks that do not typically require such active

monitoring (McDaniel & Einstein, 2007).

In summary, some but not all studies that have investigated PM in individuals with

ADHD have demonstrated impairments in PM when compared to controls. The results of the

Altgassen et al. (2012) study of adults with ADHD indicated difficulties only on time-based PM

tasks and not on event-based PM tasks. Similarly, Brandimonte et al. (2011) did not find

significant differences between children with ADHD and controls on event-based PM tasks.

Together, these findings suggest that PM difficulties of individuals with ADHD are dependent on

the types of tasks required.

Retrospective Memory

The distinction between time- and event-based tasks is not the only dimension that needs

to be considered when trying to understand PM difficulties in ADHD adults. In order to properly

assess PM performance and possibly treat PM difficulties, it is necessary to understand the full

range of factors influencing PM, which would include the effect of retrospective memory on
 43

performance. Unlike PM, retrospective memory emphasizes the memory of events that have

previously occurred (e.g., remembering a list of words previously presented). Retrospective

memory is closely linked to PM, because certain aspects of retrospective memory are required

for successful PM (Einstein & McDaniel, 1996).

The role of retrospective memory in ADHD remains the subject of some debate because

research on retrospective memory performance in individuals with ADHD has produced mixed

results. For example, a recent study suggests that retrospective memory is impaired in children

with ADHD, as only 75% of children with ADHD who had developed a plan could remember

their plan after a delay, while non-ADHD children’s plan retention levels were much better

(Kliegel et al., 2006). Additionally studies have supported the hypothesis that children with

ADHD perform poorly on retrospective memory tasks (Aloisi, McKone, & Heubeck, 2004;

Barnett, Maruff, & Vance, 2005), although others have reported evidence for intact or only

slightly impaired recall (Kaplan, Dewey, Crawford, & Fisher, 1998; Kibby & Cohen, 2008).

One approach to investigating the impact of retrospective memory on PM is through a

comparison of responses to regular and irregular tasks. Recently, researchers have proposed that

task regularity (the extent to which a task occurs repeatedly and with some predictability) may

also affect performance on measures of PM (Aberle, Rendell, Rose, McDaniel, & Kliegel, 2010;

Rose et al., 2010). Everyday living includes both regular tasks and irregular tasks. Regular tasks

(also referred to as repetitive tasks) include cues that are presented within the context of a

consistent routine and are therefore more predictable and expected to rely less on monitoring. In

contrast, irregular (non-repetitive) tasks have no consistent pattern or any of the associated cues
 44

that come with a pattern. Irregular tasks are claimed to require greater involvement of executive

functions (Aberle et al., 2010). Regular tasks, in addition to being part of a repetitive routine, are

also cued with the same target every time; in contrast, irregular tasks are cued with unique, non-

habitual targets. As a result of having consistent cues that become associated with a task over

time, regular tasks may receive enhanced encoding relative to irregular tasks and therefore are

proposed to require less use of retrospective memory for successful performance. In addition,

regular tasks are expected to have an increased likelihood of successful retrieval given the

greater strength of the memory trace.

Most laboratory studies investigating PM have included either regular tasks or irregular

tasks, but not both. Even those PM and ADHD studies that have examined performance on both

regular and irregular tasks have not manipulated regularity effects in their experimental design in

a manner that allows for unambiguous interpretation of results. For example, Altgassen et al.

(2012) included both regular and irregular tasks, but this distinction was confounded because the

regular tasks were always time-based, while the irregular tasks were all event-based.

Systematically manipulating regularity effects in combination with time- and event-based tasks

would help further the understanding of the potential role of PM.

Task regularity effects have been investigated in older adults without attention disorders

by using the Virtual Week task (Rendell & Craik, 2000). Virtual Week includes both regular and

irregular PM tasks and time-based and event-based tasks simulating everyday PM situations over

a fictional five-day period (for more details on Virtual Week tasks refer to the Methods section).

Two Virtual Week studies investigating age differences in PM have demonstrated that older
 45

adults (above age 60) have more PM errors on time-based and irregular tasks when compared to

controls, while older adults’ performance on the regular PM tasks was not significantly different

from that of younger controls (Aberle et al., 2010; Rose et al., 2010). Thus, the performance of

older adults diverged from that of control participants as the task difficulty and the demands on

EF and retrospective memory increased.

Although everyday PM tasks, whether time- or event-based, can be either regular or

irregular, to-date a systematic comparison of performance on regular and irregular PM tasks has

not been conducted in adults with ADHD. The multiprocess theory of PM suggests that features

of the PM task (i.e., regular vs. irregular and time- vs. event-based) may mediate the need for EF

involvement in PM. According to multiprocess theory, several different kinds of processing can

support prospective remembering, ranging from attention-demanding monitoring to less resource

demanding spontaneous retrieval processes. The process that is chosen in a given situation is

predicted to depend on a variety of dimensions, including PM task features and demands on

retrospective memory, in addition to motivation and individual characteristics. Based on this, it

is hypothesized that adults with ADHD, who typically have monitoring and attentional

difficulties among their symptoms, will demonstrate more PM failures when compared to control

participants, and that task characteristics will strongly impact performance. Based on existing

research, it is expected that PM task features impact performance differently for the two groups.

Specifically, it is predicted that on irregular and time-based tasks individuals with ADHD will

demonstrate greater deficits compared to controls due to the greater involvement of EF in these

tasks. Conversely, because these tasks require less EF involvement, it is predicted that
 46

individuals with ADHD will demonstrate smaller differences from controls on regular and event-

based tasks.

Prospective Memory Assessment

When measuring psychological constructs such as PM it is necessary to use multiple

indicators because of reliability and validity issues that arise from the use of a single measure. In

addition to the objective measures of PM, that have been described previously, subjective

measures such as rating scales can also yield important information (Crawford, Henry, Ward, &

Blake, 2006). As a result, subjective assessment of PM proficiency was also incorporated in the

present study. Questionnaires are the most common method for measuring perceived PM

proficiency, especially in clinical situations (Thone-Otto & Walther, 2008). One such

questionnaire is the Prospective and Retrospective Memory Questionnaire (PRMQ) (Smith et al.,

2000). The PRMQ consists of 16 self-report questions designed to assess individuals’

perceptions of their prospective and retrospective memory abilities. The validity of self-report

measures of general memory functioning has been questioned (Rabbitt, Maylor, Mclnnes, Bent,

& Moore, 1995) and research to date has not examined whether adults with ADHD possess

accurate self-awareness of their PM abilities. There are important benefits to be derived from

knowing whether self-reports of PM are accurate. For example, Thone-Otto & Walther (2008)

reviewed strategies and techniques available to improve PM in patients with brain injuries and

found that one of the most important determinants of success of such interventions was whether

or not patients were aware of their difficulties. In addition, Macan, Gibson, and Cunningham

(2010) observed a significant correlation between scales measuring components of time

 47

management and PRMQ, indicating that adults without disabilities that report managing their

time well also report successful prospective and retrospective memory. This suggests that

determining whether or not adults with ADHD are aware of their own PM abilities will provide

clinically relevant information. The current study will investigate the self-reported PM abilities

of adults with ADHD and compare these subjective impressions to actual performance on the

computerized Virtual Week PM task.

To fill in gaps that exist in the current research and to overcome methodological

limitations of past research, the present study employs the PRMQ and the Virtual Week

computerized task to test the differences between adults with ADHD and control groups on time-

and event-based PM tasks and on regular and irregular PM tasks. Although its validity for

individuals with ADHD has not been previously explored, Virtual Week has been found to be a

reliable tool for measuring PM performance in clinical and nonclinical populations (Rendell &

Henry, 2009) and is especially well suited for evaluating regularity effects across time- and

event-based PM. It is predicted that adults with ADHD will demonstrate more PM errors on

Virtual Week than adults without ADHD. Based on the multiprocess theory it is further

hypothesized that PM task type will affect the performance of adults with ADHD in similar ways

as the performance of control subjects, while larger effect size are expected in the ADHD group.

In addition, it is expected that adults with ADHD will be able to accurately self-report their PM

deficits, as evidenced by a significant relationship between PRMQ scores and Virtual Week

performance. Determining the extent to which adults with ADHD are able to accurately estimate
 48

their PM abilities will inform about the utility of self-report questionnaires in evaluating PM

ability in clinical and research environments.

Methods

Participants

Nineteen adults with ADHD and 24 control adults without ADHD (ages 18 – 41) (total

N=43) were included. Sample size was based on a power analysis that assumed an effect size of

d=.40 (based on previous research) and power of .80. The two groups did not differ significantly

in age, IQ or gender (see Table 1). All participants reported normal corrected or uncorrected

vision and hearing and no evidence or history of deafness, blindness, aphasia, or psychiatric

disturbance was indicated on a medical history questionnaire. Subjects were recruited from the

community through posted signs in public spaces and several participants in the ADHD group

were recruited from the office for students with disabilities at a Midwestern university. Subjects

were paid for their participation or received class credit; all were able to withdraw at any time

without prejudice or penalty. All subjects demonstrated an understanding of English as

evidenced by the ability to read and comprehend the consent forms.

Table 1. Participant Characteristics

ADHD Non- ADHD Group
(n=19) (n=24) Comparisons
M SD M SD t Sig
Gender 11 Male 8 Female 16 Male 8 Female - -
Age (years) 25.58 6.04 26 6.35 .220 .83
Composite Intelligence Index 111.21 10.46 105.5 9.10 1.98 .06
 49

Measures were taken to exclude from the study participants with ADHD who did not

report significant current symptomology and those without an ADHD diagnosis who reported

evidence of the disorder. In order for participants to be included in the ADHD group, they must

have received an ADHD diagnosis from a qualified professional within the last five years. In

addition, all ADHD subjects were required to report having at least five current symptoms on the

ADHD Rating Scale for Adult Symptoms and ADHD Rating Scale (Barkley & Murphy, 2006).

All the participants in the control group reported three or fewer symptoms on the same scale.

Given the evidence that among children with ADHD, certain medications used to treat

the disorder may enhance EF and improve performance on tasks assessing time perception

(Baldwin et al., 2004), subjects with ADHD were asked about their use of such medications.

Only one subject reported never having taken medication for ADHD. Of the remaining with

ADHD, 85% (16) reported currently taking medication and 10% (2) reported having taken it in

the past. Frequency of current and past medication use varied; 56% (10) reported taking

medication daily, 27% (5) reported at least once per week use and 17% (3) reported using

medication less than once a week. Only 20% (3) had taken medication in the 24 hours preceding

participation in the experiment. No significant demographic differences or performance

differences were observed between the ADHD subjects who were taking medication at the time

of testing and those who were not.

Measures

ADHD measures: Two rating scales commonly used to assess ADHD in adults, the

Current Symptoms Scale - Self-Report Form (Barkley & Murphy, 2006) and the Childhood
 50

Symptoms Scale - Self-Report form (Barkley & Murphy, 2006) were administered to all

participants. The ADHD Current Symptoms Scale for adult symptoms includes information

regarding the number and severity of current ADHD symptoms and the Childhood Symptoms

Scale provides information regarding the presence of symptoms in childhood. These scales each

contain 18 items based on the diagnostic criteria for ADHD in the DSM-IV-TR (American

Psychiatric Association, 2000). Each item is rated on a scale from 0 (rarely or never) to 3 (very

often). An item marked as "often" or "very often" (2 or 3 on the scale) indicates the presence of

a symptom. The scales each contain nine items relating to Inattention and nine items relating to

Hyperactive-Impulsive symptoms, thus representing the ADHD subtypes.

Reynolds Intellectual Assessment Scales: (RIAS) (Reynolds & Kamphaus, 2003): The

RIAS is an individually administered test of intelligence normed on individuals from 3 to 94

years old. Each of the four RIAS subtests yields a T-score with a mean of 50 and a standard

deviation of 10. Combining these subtest scores in various ways yields a two-subtest verbal

intelligence index, a two-subtest nonverbal intelligence index, and a composite intelligence

index. Performance on the three indices produced by this test is expressed as standard scores

with a mean of 100 and a standard deviation of 15. The current study included the RIAS

measure as a way to assess participant’s intelligence and to confirm that the groups do not differ

in their mental ability. Several features of the RIAS made it especially appropriate for the

current study. The RIAS is a standardized measure that can be administered in 20 minutes and

possesses technical characteristics similar to other measures of intelligence. Further, because it

 51

is less commonly used than some other measures of mental ability, the likelihood of participants

having been previously exposed to the RIAS is low.

Prospective memory measures: Both a subjective questionnaire, the Prospective and

Retrospective Memory Questionnaire (PRMQ) (Smith et al., 2000), and a behavioral PM test,

Virtual Week, were both utilized to assess PM. The PRMQ consists of 16 items equally divided

between a Prospective Memory subscale and Retrospective Memory subscale. For example, “Do

you fail to do something you were supposed to do a few minutes later even though it’s there in

front of you, like take a pill or turn off the kettle?” is categorized as measuring PM, while “Do

you forget what you watched on television the previous day?” is an item assessing retrospective

memory. Respondents indicate the frequency of forgetting in these situations on a five-point

Likert scale (5= very often; 1= never). Higher PRMQ scores indicate a perceived higher

prevalence of memory errors, that is, poorer memory performance. Prior research supports the

internal consistency, predictive validity (Kliegel & Jäger, 2006; Smith et al., 2000) and factor

structure (Crawford et al., 2003; Kliegel & Jäger, 2006) of the PRMQ.

The Virtual Week task (Rendell & Craik, 2000) possesses a number of characteristics that

its creator states are representative of everyday PM behavior (for a review of Virtual Week see

Rendell & Henry, 2009). Each circuit around the game-like board, determined by rolling two

dice, represents one virtual day (see Figure 1). Participants complete five days (five circuits

around the board) and throughout each day the participant has ten activities to remember.

Examples of to-be-remembered activities include taking medication, paying bills, and meeting

friends for appointments. Studies have indicated that Virtual Week is sensitive to PM difficulties
 52

in normal and abnormal adult aging (Rendell & Craik, 2000; Rose et al., 2010) as well as

discriminating among various clinical conditions, including substance abuse (Rendell et al.,

2007a), multiple sclerosis (Rendell et al., 2007b) and schizophrenia (Henry et al., 2007).

Virtual Week activities include time- and event-based tasks and regular and irregular

tasks. These two task types are crossed, creating four combinations of time/event and

regular/irregular. In addition, a “Time-Check” component is included in which participants are

asked to stop a clock at a specified point in time (two and four minutes) after they begin each

virtual day. Participants carry out each PM task by remembering to press the “perform task”

button and then selecting the correct task from a list of possible PM tasks. The list of PM tasks

includes both the tasks to be remembered and distractor tasks (see Appendix E for a computer

screen display of a sample list of PM tasks). The primary matrixes of performance on Virtual

Week include the proportion of correct responses in each task category and the reason for

failures of PM (i.e., misses where PM tasks were not executed or early responses where

intentions were executed before the specified time).

Procedures

This study was part of a larger study that lasted for approximately four hours and was

divided into two experimental sessions. Both control and ADHD subjects were administered all

measures in the same sequence. The Prospective and Retrospective Memory Questionnaire was

completed by all participants before they performed the Virtual Week task. Participants

completed Virtual Week while seated in front of a laptop computer, responses where indicated

by using a mouse. Each participant completed one practice day (one circuit of the board) while
 53

receiving verbal instructions and feedback from the experimenter. After completing the practice

day, participants received additional instructions and demonstrated to the experimenter that they

understood all aspects of the task. Following completion of the practice day, the experimenter

was present in the room but did not provide any additional assistance or instructions on the

Virtual Week task.

Results

Virtual Week

The impact of PM task as a variable was evaluated through two analyses. First, a 2

(Group: ADHD vs. non-ADHD) x 2 (Regularity: Regular vs. Irregular) x 2 (PM Cue: Event vs.

Time) mixed ANOVA with repeated measures on the last two factors was conducted. Secondly,

a 2 (Group: ADHD vs. non-ADHD) x 5 (PM task) mixed-model ANOVA was conducted. The

PM task variable features five levels: Regular-Event, Regular-Time, Irregular-Event, Irregular-

Time and Time-Check. In both analyses the dependent variable was PM accuracy, expressed as

proportion correct.

The first analysis of participants’ performance t (see Figures 2-4) revealed a main effect

of group; ADHD subjects were less accurate than non-ADHD subjects, [F(1,41) = 9.13, p = .004,

η2p = .18, (ADHD, M = .69, SD = .15 and Non-ADHD M = .81, SD = .10)]. There were also

main effects of regularity, [F(1,41) = 18.36, p < .001, η2p = .31], and PM Cue, [F(1,41) = 32.47,

p <.001, η2p = .44], as well as a two-way interaction between these variables, [F(1,41) = 17.30,

p < .001, η2p = .3]. The main effect of cue obtained because Event-Based cues yield better PM
 54

performance (M =.86, SD =.02) than Time-Based cues (M =.70, SD =.03). The main effect of

regularity was obtained because the Regular queues yield better PM performance (M =.87,

SD =.02) then the Irregular queues (M =.73, SD =.03). Importantly, Group did not interact with

either of the other variables, either individually or in combination (all Fs ≤ 0.16 and ps ≥ .688).

Simple effects tests on the significant Cue x Regularity interaction showed that both groups of

participants were more accurate on Event- than Time-Based tasks for both Regular,

[F(1,41) = 6.34, p = .016, η2p = .13] (Event-Based M = .87, SD = .18; Time-Based M = .79,

SD = .19) and Irregular PM tasks, [F(1,41) = 37.62, p < .001, η2p = .50] (Event-Based M = .87,

SD = .14; Time-Based M = .61, SD = .28). The effect size was much larger for Irregular tasks,

reflecting a larger difference between Event- and Time-Based tasks for Irregular compared to

Regular tasks. Further tests of simple effects showed that for Event-Based tasks there was no

difference between Regular and Irregular PM tasks across all participants, [F(1,41) = 0.34,

p = .855, η2.01], but on Time-Based tasks participants as a whole were more accurate on Regular

than Irregular PM tasks, [F(1,41) = 31.66, p < .001, η2p = .44] (Regular M = .79, SD = .19;

Irregular M = .61, SD = .28].

 55

Figure 2. Overall Virtual Week Performance as a Function of Prospective Memory Cue for the
ADHD and Non-ADHD Participants
 56

Figure 3. Overall Virtual Week Performance as a function of Task Regularity for ADHD and
Non-ADHD Participants
 57

Figure 4. Overall Virtual Week Performance as a Function of Task Regularity and Prospective
Memory Cue
 58

In the second analysis, a 2 x 5 mixed Group (ADHD, Non-ADHD) x Overall PM task

ANOVA was conducted (see Figure 5). Results revealed a main effect of group [F(1,42)=9.76,

p<.005] and a main effect of task [F(1,38)=18.38, p<.001]. However, no interaction effects

between group and PM task variables [F(1,38)=.10, p=.92]. The main effect of group obtained

because the ADHD group performed significantly more poorly (M=.69, SD=.15) than the non-

ADHD group (M=.81, SD=.10). Post hoc tests on the main effect of task type revealed that the

means for both the Time Check (M=.62, SD=.24) and the Irregular Time-Based tasks (M=.62,

SD=.27), which did not differ, were significantly lower than the other three PM tasks, which

also did not differ from each other [Regular-Event (M=.87, SD=.17), Regular-Time (M=.79,

SD=.18) and Irregular-Event (M=.87, SD=.14)]. Further analysis of the non-significant Group

x PM task interaction revealed significant deficits for the ADHD group compared to controls

across four of the five PM tasks types (Regular-Event, Regular-Time, Irregular-Event, and

Irregular-Time). There was no significant difference between the ADHD and the non-ADHD

groups on the Time Check PM task.

 59

Mean

1.00
Propo ADHD
Non- ADHD
0.90rtion
ADHD
Corre
0.80
ct
0.70
Virtu

0.60 al

Week
0.50

0.40
Regular / event- Irregular / Regular / time- Irregular/ time- Time-check
based tasks event-based based tasks based tasks tasks
tasks
PM Task Type

Figure 5. Results of the 2 Group (ADHD, Non-ADHD) x 5 Virtual Week Task Tape Mixed-
Model ANOVA
 60

An additional analysis was conducted to assess the degree of relationship between

symptomatology and Virtual Week performance. This analysis revealed that the number of

ADHD symptoms was significantly correlated with overall performance on Virtual Week

(r=-.60, p<.001). Thus, as the number of reported ADHD symptoms increased, PM scores

decreased. The coefficient of determination (r2=.36) indicates that 36% of the variability in

Virtual Week performance can be accounted for by the number of reported ADHD symptoms.

In addition, Overall performance on Virtual Week was significantly correlated with both

Hyperactive-Impulsive scores (r=-.50, p<.001) and Inattentive scores(r=-.55, p<.001).

Performing the same PM task across successive days on the Virtual Week was expected

to result in performance becoming increasingly habitual, thus resulting in higher accuracy as the

task progressed through the five days. To examine this hypothesis, subjects’ performance on

Regular tasks in the first two and one half Virtual Week days was compared with their

performance on Regular tasks in the second two and one half days of Virtual Week. A

2 (Group) X 2 (Half: First and Second) mixed model ANOVA was conducted. The main effect

of Group was significant [F(1,41)=6.895, p<.005]. As predicted, the ADHD participants were

significantly less accurate overall than the control participants. Results also revealed that there

was a significant main effect for task half [F(1,41)=2.291, p<.05]. As expected, performance on

the Regular tasks in the first half (M=.37, SD=.09) was less accurate than the second half

(M= .43, SD= .08). There was no significant interaction between Group and task half

[F(1,1)=2.077, p=.157]. Hence, while both groups showed improved performance as the Virtual
 61

Week progressed, the difference in performance between the two groups remained the same

throughout the task.

Virtual Week Errors

Categorization and analysis of participant errors on the Virtual Week was also conducted,

and the mean proportions of errors for the two groups in each error category are presented in

Table 2. Errors on Virtual Week can be classified into several types including Early (a correct

response before the appropriate time), Late (a correct response after the appropriate time) and

Misses (a failure to respond, i.e., an omission error). There was a significant difference between

the two groups on the proportion of Misses, with subjects with ADHD making significantly

more omission errors. There were no significant differences between the groups in the

proportion of Late or Early errors.

Repetition errors (in medication terms “double dosing”) occur when a participant repeats

a task that has already been performed. While this type of error has been addressed in previous

studies of PM (Einstein, McDaniel, Smith, & Shaw, 1998; McDaniel, Bugg, Ramuschkat,

Kliegel, & Einstein, 2009; Rose et al., 2010), in the current study, there was a very low

frequency of these errors. No subject committed more than one Repetition error, and no

between-groups differences were noted.

Among the core clinical characteristics observed in individuals with ADHD is increased

distractibility (see American Psychiatric Association, 2000). This characteristic requires

consideration of an additional type of error—“distractors”—which occur when a participant

remembers to perform a task but chooses an incorrect task from the list of possible tasks (i.e., a
 62

distractor is selected). In the current study distractor errors were very infrequent—less than 0.5

distractor errors per participant were observed. There were no significant differences in the

frequency of distraction errors between the two groups (p>.05). In sum, the pattern of errors

shown in Table 2 indicates that omission errors were the predominant error type, and the only

one that differentiated the two groups.

Table 2. Means and Standard Deviations for Virtual Week Error Categories for ADHD
Participants and Non-ADHD Controls.
ADHD Non- ADHD Group Comparisons
M SD M SD t Sig.
Misses .10 .08 .05 .04 2.54 <.05
Late Errors .16 .04 .12 .03 1.60 .12
Early Errors .04 .03 .02 .01 1.70 .10

PRMQ

A 2 (Group: ADHD vs. non-ADHD) x 2 (Memory type: Prospective Memory vs.

Retrospective Memory subscale) mixed-model ANOVA was conducted (see Table 3 and

Figure 6). Results revealed a main effect of group [F(1,40) = 34.2, p = .001], a main effect of

memory type [F(1,40) = 45.23, p = .001], and a significant interaction between group and PRMQ

[F(1,40) = 18.23, p = .001]. Post hoc test indicated that the interaction obtained because there

was a significant difference between reported prospective memory and retrospective memory for

the adults with ADHD (p<.01), but for non-ADHD subjects self-reported retrospective memory

and prospective memory did not differ (p>.05). Further analysis reveals a statistically significant

correlation between PRMQ score and number of ADHD symptoms (r=.792, p<.01).
 63

Table 3. Means and Standard Deviations of the Prospective Retrospective Memory

Questionnaire for ADHD Participants and Non-ADHD Controls
ADHD (n=18) Non- ADHD (n=24)
M SD M SD
Total PRMQ 53.39 10.61 35.46 8.16
Prospective Memory Questionnaire 29.28 5.76 18.42 3.69
Retrospective Memory Questionnaire 23.11 5.52 17.04 5.05

Figure 6. Scores on the Prospective Memory and Retrospective Memory Subscales for the
ADHD and Non-ADHD Participants
 64

To examine whether the PRMQ scores are related to actual performance in the Virtual

Week PM task, the relationship between the two measures was investigated using Pearson

correlation coefficients. For all subjects combined, there was a significant negative correlation

between Total PRMQ scores and Overall performance on Virtual Week (r= -.56, p<.05).

However, when examined separately, this statistical significance did not hold for both groups.

That is, non-ADHD subjects demonstrated a non-significant correlation between Virtual Week

measures and the PRMQ scores (r= -.27, p=.10), while subjects with ADHD showed a

significant negative correlation between PRMQ scores and Virtual Week performance (r= -.45,

p<.001). Further examination of the significant correlation for the ADHD subjects revealed that

only the Irregular tasks significantly correlated with Total PRMQ scores, while Regular PM

tasks showed a non-significant correlation with Total PRMQ.

Discussion

The present study, designed to investigate PM performance in adults with ADHD, is the

first to investigate time- and event-based PM and regularity effects on PM in adults with ADHD.

As hypothesized, findings demonstrate that adults with ADHD made significantly more PM

errors as compared to controls without ADHD. Correspondingly, the more ADHD symptoms a

participant reported, the more PM difficulties were observed. These findings are consistent with

Zinke et al. (2011), who showed a significant relationship between the number of ADHD

symptoms and PM performance in children. The present study thus extends these results by

indicating that ADHD symptoms continue to relate to PM difficulties in adults with ADHD.

Further research is needed to examine the extent to which the number of symptoms or types of
 65

symptoms impacts PM difficulties and how this knowledge might be utilized by clinicians to

develop better intervention strategies.

The second goal of the current study was to explore the effect of PM task features on the

performance of adults with ADHD. Regular tasks and event-based tasks were easier than

irregular and time-based tasks for both groups. The findings relating to task regularity and time-

and event-based tasks support the multiprocess theory of PM, which proposes that regular tasks

and event-based tasks are associated with more accurate PM performance than time-based and

irregular tasks (McDaniel & Einstein, 2007). In addition, these results support the proposal that

regular tasks are less demanding on retrospective memory then irregular tasks.

Although previous studies of older adults without ADHD (Aberle et al., 2010) have

shown significant difficulties when compared to younger participants, these differences were

reduced on tasks that were less demanding of EF (i.e., event-based and regular). The present

results demonstrate that the difference in performance between the ADHD and controls remained

relatively consistent across PM task type. This finding that adults with ADHD underperformed

compared to control participants even on what were pursuant to be the less demanding tasks

differs from findings comparing aging adults without ADHD with young adults (Rose et al.,

2010). In attempting to explain this finding, it is possible that adults with ADHD might have

never fully developed the broad range of EF skills that need to be utilized in all situations

requiring PM, while older adults may have experienced a selective decline in certain previously

developed EF skills. Also, the consistent PM deficits in the performance of adults with ADHD

observed in the present study may be attributable to the specific demands of the PM tasks in the
 66

current study. Event-Based and Regular tasks used in this study reduce the demands on EF, yet

they do not fully eliminate EF involvement in PM. As a result, poor EF skills can still play a role

in PM, even on what are believed to be tasks with less EF involvement. In addition, the current

study included a PM task that had multiple intentions (10 PM tasks each virtual day). The

number of different intentions in a PM paradigm has been proposed by previous research to

likely influence the amount of EF required for successful PM performance (Kliegel et al., 2011).

More PM tasks should lead to an increase in attentional demands and a corresponding increase in

task difficulty, thus resulting in adults with ADHD underperforming compared to control

participants even on what were perceived to be the less demanding tasks. Further research is

needed to examine the extent to which specific EF mechanisms are responsible for the consistent

PM performance deficit found between individuals with ADHD and controls. More broadly, in

light of the different patterns of PM impairment for those with ADHD and individuals with age-

related impairments, these findings illustrate that PM deficits should not be assumed to manifest

in the same manner across different clinical populations experiencing EF impairments.

As previously described, regular PM tasks may include helpful characteristics such as

predictability and other available cues that may result in higher PM accuracy compared to

irregular tasks. These benefits are seen in the results of the current study, in which both groups

improved their performance on the regular tasks as the Virtual Week progressed from the first

half of the Virtual Week to the second half.

An additional critical feature of regular PM tasks that needs to be considered is the

tendency to commit repetition errors. In repetitive tasks, difficulty in remembering whether or

 67

not one has already performed the action may occur (McDaniel et al., 2009). That adults with

ADHD did not commit more repetition errors then non-ADHD participants suggests that

remembering if a task had already been performed is not a significant problem for ADHD

subjects. An analysis of omission errors can also inform about the process by which errors

occur. Omission errors are typically believed to represent a subject forgetting to perform a PM

task. However, it is also possible that some omission errors represent a false belief that the task

had already been completed and therefore did not need to be performed again. However, in the

present study, it was not possible to distinguish between these two potential causes of omission

errors. This will remain an important question for later studies.

With regard to event-based tasks, data indicate that adults with ADHD experienced

significant difficulty compared to controls on Event-Based tasks, a finding that is in contrast with

previous studies that demonstrated no deficit relative to controls on such tasks (Altgassen et al.,

2012; Kerns & Price, 2001). Altgassen et al. (2012) proposed that their event-based tasks

lowered the need for EF by using task cues that were distinct and salient and therefore reduced

processing demands. It is likely that the explicitness and salience of cues signaling the initiation

of event-based PM tasks affects the demands on inhibitory control processes requiring EF.

These concerns regarding cue salience were addressed in the current study by using the Virtual

Week task, which included both event-based and time-based PM tasks that were designed to

have cues with comparable levels of salience and distinctiveness. In contrast with previous

studies, the current study used event-based tasks that were more naturalistic and complex and

thus possibly presented an increased demand on executive functions that are commonly impaired
 68

in individuals with ADHD. These results suggest that not all event-based tasks lower the

demand on EF equally and that as a result, adults with ADHD may encounter greater difficulty

than adults without ADHD on certain types of everyday event-based PM tasks. Future research

should consider exploring further the relationship between EF processes, ADHD, cue salience

and task complexity on PM performance.

Of additional interest is the finding that the two groups did not differ in accuracy on the

Time Check PM task. Both groups performed equally poorly on the Time Check measure,

remembering to perform the time check task only about 50% of the time. These results are in

line with previous studies, such as Altgassen et al. (2012) that demonstrated that adults with

ADHD monitored the clock as frequently as controls. These results demonstrate that time-based

tasks requiring direct clock monitoring necessitate the use of different executive processes than

time-based tasks that do not include direct clock monitoring. Moreover, results indicate that the

PM deficits seen in adults with ADHD can not to be attributed to a fundamental deficit in time

monitoring. Left unanswered is whether a deficit in time estimation ability underlies difficulties

experienced by adults with ADHD on time-based tasks.

From a clinical perspective understanding the pattern of PM errors made by the adults

with ADHD may provide important information about possible intervention strategies to address

PM difficulties. For example, significant group differences based on error type were only

evident for omission errors; adults with ADHD forgot to perform tasks significantly more often

than controls, but they were not excessively early or late when they did perform them. In

addition, adults with ADHD did not differ from controls in the frequency of repetition errors and
 69

were not more likely to select a distractor from the list of possible tasks in Virtual Week. Taken

together, these results suggest that interventions designed to improve PM difficulties in adults

with ADHD may benefit by focusing on task completion rather than performing the task at the

wrong time or performing it twice.

An important finding of the current study is the significant relationship between

perceived and actual PM performance in adults with ADHD. Performance on Virtual Week was

significantly related to scores on the Prospective and Retrospective Memory Questionnaire

(PRMQ) for all participants, but the relationship was largely driven by the participants with

ADHD. This finding indicates that adults with ADHD are generally aware of the nature and

extent of their prospective memory failures. Therefore, such questionnaires appear to have

clinical validity for adults with ADHD and may provide a useful and effective tool for

identifying PM problems and in evaluating the effectiveness of PM interventions. The results of

the current study that self-reports of adults without ADHD did not significantly correlate with

PM performance are in line with Uttl & Kibreab (2011) study, which found that self-report of

PM ability is not significantly correlated with performance on PM tasks in young adults without

disabilities. Rabbitt et al. (1995), warned against interpreting self-report scores as measures of

ability, as they propose that self-reports of cognitive functioning depend on several variables,

including the cognitive function evaluated, individual differences in self-confidence, anxiety, and

use of memory aids.

Furthermore, in examining the scores on the two subscales of the PRMQ it was observed

that participants with ADHD reported more difficulties on both the subscales compared to
 70

controls. Additionally, the adults with ADHD reported significantly more difficulties on the

Prospective Memory subscale as compared to the Retrospective Memory Subscale. In support of

the Virtual Week task manipulation of retrospective memory, Irregular tasks were statistically

significantly related to self-reports on the PRMQ, but no such relationship existed between

PRMQ scores and Regular PM tasks. These findings suggest that regular tasks were easier to

perform and were not significantly related to one’s perceived difficulties.

The present results must be viewed in the context of some limitations. First, participants’

use of stimulant medication could not be manipulated in the current study. However, no

significant differences were noted between those participants who were taking medication and

those who were not. Nonetheless, the effect of medication on PM performance merits further

investigation. A second limitation may have derived from the ADHD sample including both

community-based participants and clinically-referred participants. While including a diverse

group of participants is desirable, the small sample size limited the ability to examine potential

differences between these samples. Finally, the observed between groups PM differences may

be at least in part due to differences in retrospective memory (recall) abilities. At the conclusion

of each virtual day, participants’ recall of the PM tasks that were to be performed was not

evaluated. More recent versions of the Virtual Week software have been adjusted to allow for

such evaluation in future studies.

In conclusion, the present study found a consistent difference between PM performance

in adults with ADHD and controls. The lower PM accuracy seen in adults with ADHD is one

manifestation of more general time-management and planning difficulties. Results support

 71

Virtual Week as a valid measure for evaluating PM performance in that it can be predicted by

self-report on the PRMQ. Such objective measures of PM may provide an avenue for further

studies investigating treatment that is aimed at addressing PM difficulties in individuals with

ADHD.
 72

Chapter 5: Experiment 2: Prospective Memory and Executive

Function in Adults with ADHD

Abstract

The present study investigated prospective memory (PM) performance in adults with

attention deficit hyperactivity disorder (ADHD), with the aim of exploring the relationships

among executive functioning (EF), attention and PM. Eighteen adults with ADHD and 20

controls performed the Virtual Week task, which simulates PM tasks encountered in everyday

life by requiring individuals to remember tasks that vary according to their regularity, as well as

whether they are time-based or event-based. Executive function was assessed with measures of

working memory, shifting and inhibition. Adults with ADHD demonstrated fewer correct PM

responses across all task types, thus demonstrating a broad deficit in PM performance as

compared to controls. As expected, ADHD participants also demonstrated significantly lower

working memory scores than control participants. Working memory scores were strongly related

to performance on PM but no relationships were observed among PM and other EF measures.

These results suggest that not all EF measures assess vital mechanisms for PM and importantly,

that working memory is an independent contributor to PM that is at least as important as ADHD

status in discriminating among individuals based on their PM abilities.

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Introduction

Attention deficit hyperactivity disorder (ADHD) is being increasingly recognized as a

significant problem among adults because ADHD symptoms continue into adulthood for about

one-third to two-thirds of children with ADHD (Barkley, 1997; Resnick, 2005). Attention deficit

hyperactivity disorder is a condition characterized by a range of symptoms, including inattention,

hyperactivity and impulsivity (American Psychiatric Association, 2000). As a result of these

symptoms, adults with ADHD experience attention difficulties that interfere with their daily

functioning in areas such as school, work, family interactions, and social activities.

Many of these difficulties experienced by adults with ADHD can be linked to Prospective

Memory (PM) failures, such as forgetting to take medication on time, missing important

meetings or not remembering to return someone's phone call. Prospective memory refers to the

ability to plan intended actions, to retain these actions in memory while being involved in other

ongoing activities and to self-initiate the execution of a plan to behave at an appropriate point in

the future (Graf & Grondin, 2006; McDaniel & Einstein, 2007). Everyday activities are filled

with and are sometimes overflowing with PM demands; thus, insight into PM failures may have

important implications for a wide range of daily functions. For example, empirical evidence

suggests that PM plays an important part in medication adherence, and thus can have important

health consequences (Zogg ,Woods, Sauceda, Wiebe, & Simoni, 2012).

To date, only a handful of studies have explicitly explored PM performance and its

relation to ADHD, and nearly all of these have focused on children (Brandimonte et al., 2011;

Kerns & Price, 2001; Kliegel et al., 2006; Zinke et al., 2010). Findings from these studies have
 74

consistently demonstrated that individuals with ADHD have significantly more difficulties on

PM tasks than non-ADHD controls (Kerns & Price, 2001; Kliegel et al., 2006; Zinke et al.,

2010). Only one published study has investigated PM in adults with ADHD demonstrating a

similar pattern as seen in children (Altgassen et al., 2012). In that study, Altgassen et al. (2012)

used a computerized version of the Dresden Breakfast Task in which participants (age 20-57)

prepare breakfast based on a set of rules (e.g., first putting down the tablecloth, then setting the

table) and time restrictions. Before participants began the task they were asked to develop and

write a plan for its successful completion. Results indicated that the adults with ADHD created

less consistently accurate plans and followed them less than non-ADHD controls. Adults with

ADHD had significant PM impairments compared to control participants on the time-based tasks

in the Dresden Breakfast Task (when the performance of a task is cued by a specific time), but

the two groups did not differ in their performance on event-based tasks (when the PM task was

cued by an event) (Altgassen et al., 2012).

As seen in Altgassen and colleagues’ study, researchers frequently divide PM tasks based

on the methods of cueing or targeting the retrieval of the previously defined plan (2012). A

time-based PM task involves remembering to perform a planned action at a predetermined time

or after a certain amount of time has elapsed. In contrast, event-based PM involves a planned

retrieval that is to be performed concurrent with or following a specific event (Graf & Grondin,

2006, McDaniel & Einstein, 2007). It has been proposed that event-based tasks rely less on

executive control processes and more on spontaneous or automatic processing (with a lesser need

for cognitive resources such as executive functions), while time-based tasks rely more heavily on
 75

self-initiated and executive processing (Einstein et al., 1995; Groot et al., 2002; Mäntylä et al.,

2007). Thus, the varying cognitive demands of PM tasks are proposed to affect the degree to

which these tasks involve executive functioning (EF) (McDaniel & Einstein, 2007; Schnitzspahn

et al., 2012).

Executive functioning (EF) encompasses a range of skills that includes the mental

processes involved in planning and executing goal-directed behavior. These EF processes

include working memory, shifting, inhibition and self-monitoring (Gioia et al., 2002). Executive

function deficits have been proposed to be a key impairment in individuals with ADHD

(Barkley, 1997, 2006). It has been further suggested that PM deficits in individuals with ADHD

are related to the degree of involvement of executive functions (Kerns & Price, 2001; Kliegel et

al., 2006). Evidence from brain-imaging studies strongly links EF and PM (Martin et al., 2003).

Moreover, and importantly for the study of ADHD, studies have demonstrated that the successful

performance of PM tasks relies on the same prefrontal systems in the brain that regulate attention

(McDaniel, Glisky, Rubin, Guynn, & Routhieaux, 1999).

Studies exploring the relationship between PM and EF in adults without ADHD have

yielded a mixed pattern of results. Some findings suggest that the PM difficulties are not

necessarily related to EF (Bisiacchi, 1996; Martin et al., 2003), while a number of other studies

have provided support for the relationship between PM and EF (Burgess et al., 2000; Kerns,

2000; Mäntylä et al., 2007; Martin et al., 2003). For example, Mäntylä, Carelli, and Forman

(2007) compared the performance of school-age children (ages 8 to 13) with that of college

students on time-based prospective memory tasks. Subjects watched a short movie during which
 76

they were requested to press a button every five minutes. Three components of EF were

assessed using standardized measures: inhibition, shifting and updating. In both children and

adult subjects, PM performance was significantly related to inhibition and updating, but not to

shifting. While a number of other aspects of EF have been examined, the aspect of EF that has

been most commonly related to PM has been working memory (Schnitzspahn et al., 2012). For

example, Rose et al. (2010) found a strong association between working memory and PM that

was stronger for PM tasks demanding more monitoring processes. They also demonstrated that

older adults have stronger correlation between working memory and PM than young adults. For

more information on the role of age and individual differences see McDaniel and Einstein,

(2007).

Furthermore, studies demonstrating age-related differences in PM are consistent with the

notion that EF is related to PM performance (Mackinlay et al., 2009; Mäntylä & Carelli, 2006;

Martin et al., 2003). Age-related differences in PM tasks have been demonstrated with both

children and older adults (McDaniel & Einstein, 2007). In adults, declines in EF are associated

with normal aging; thus, older adults would be expected to demonstrate less efficient goal-

directed behavior and monitoring strategies (examples of EF) than younger adults (Mäntylä &

Carelli, 2006). This decline in EF in return would predict that older adults would perform more

poorly on PM tasks, because goal directed behavior and monitoring, among other EF skills, are

necessary for successful PM performance. Evidence in support of this prediction comes from

Martin et al. (2003) who showed that EF performance predicted scores on two complex PM tests

in a sample of 80 non-disabled adults (ages 20 to 80 years) and that older adults demonstrated
 77

significantly more PM difficulties then young adults. Age-related deficits on PM tasks in the

laboratory, such as those used by Martin et al., are suggested to be largely related to the specific

features of the PM task. That is, research has shown that older adults demonstrate significantly

more difficulties on time-based PM tasks than on event-based tasks as well as on PM tasks that

demand extensive involvement of working memory resources (Einstein, McDaniel, & Scullin,

2012).

While task characteristics (such as event- and time-based PM) and EF are expected to

impact the likelihood of successful PM performance, other factors are likely involved as well. In

order to fully assess PM performance it is necessary to understand a broad range of factors

influencing it, including the effect of task regularity. Recently, researchers have suggested that

task regularity (the extent to which tasks occur repeatedly and with some predictability) may also

affect performance on PM tasks. Regular tasks (also referred to as repetitive tasks) include cues

that are presented within the context of a consistent routine and therefore are more predictable.

As a result, they are expected to rely less on processes such as monitoring that are aspects of

executive functioning. In contrast, irregular (non-repetitive) tasks have no consistent pattern that

includes associated cues; therefore these irregular tasks are claimed to require greater

involvement of EF in order to be successfully remembered (Aberle et al., 2010, Foster et al.,

2013).

Executive functioning, is a multi-faceted construct and as a result there is no “gold

standard”—no single measure that is agreed upon by researchers and clinicians as capturing the

essence of EF (Royall et al., 2002). Because of this lack of consensus, studies have varied
 78

significantly in the EF measures they have used. When investigating the relationship between

PM and EF many researchers have measured PM and EF with behavioral measures specifically

aimed at assessing the construct. For example, Mäntylä et al. (2007) measured subjects’

performance on a PM task and assessed executive functions using six measures of EF (a Stop

Signal, Stroop task, Connections and Category Fluency task, an N-Back task and a Matrix

Monitoring task). Other studies have manipulated PM task demands in order to vary the

presumed contribution of EF. For example, Smith (2003) demonstrated that the magnitude of the

PM interference effect (the effect of the PM task on the ongoing task) was correlated with PM

accuracy and with individual differences in working memory capacity.

Despite the evidence that both EF deficits and PM failures may negatively affect

everyday functioning and quality of life, the empirical relationship between EF and PM has not

been adequately investigated in adults with ADHD. As a result, at present it is still unclear

whether and to what extent EF is involved in PM difficulties in adults with ADHD. Based on the

review of current literature, this study utilized a set of EF measures—those assessing working

memory, shifting and inhibition—because these aspects of EF have been significantly implicated

in PM in previous studies and because past research has demonstrated that individuals with

ADHD often experience difficulties on these aspects of EF (e.g., Barkley & Murphy, 2011;

Klingberg, Forssberg, & Westerberg, 2002; Rapport, Van Voorhis, Tzelepis, & Friedman, 2001).

The present study aimed to explore the degree to which EF performance may predict PM

performance and whether certain executive functioning measures are better predictors of PM

performance than others. Additionally, this study was designed to assess whether different
 79

features of PM tasks modulate the relationship between EF and ADHD. Exploring the

relationship between PM and EF in special population with well-documented EF deficits such as

individuals with ADHD may provide essential insight on the role of EF in successful PM

performance and contribute important information to the debate about whether PM difficulties

are related to EF deficits.

Specifically, this study investigates whether task regularity (Regular vs. Irregular) and

PM Cue (Event vs. Time) affect the relationship between EF and ADHD. Based on past findings

demonstrating EF deficits in individuals with ADHD, as well as their frequent reports of

everyday PM failure, it is predicted that adults with ADHD will perform more poorly on PM

tasks than controls. Performance of both groups is expected to relate to EF measures, with better

EF scores predicted to be associated with more accurate PM performance.

Methods

Participants

Eighteen adults with ADHD and 22 control adults (ages 18 – 41) without ADHD served

as participants (total N=40). All participants reported normal corrected or uncorrected vision and

hearing, and no evidence or history of deafness, blindness, aphasia, or psychosis was indicated

on the medical history questionnaire. Subjects were recruited from the community through signs

posted in public spaces; additionally, several participants in the ADHD group were recruited

from the office for students with disabilities at a Midwestern university. Subjects were paid or

received class credit for their participation and all were able to withdraw at any time without

prejudice or penalty. All subjects demonstrated an understanding of English as evidenced by the

 80

ability to read and comprehend the consent forms. The ADHD group did not differ from the

control group in age, gender or IQ (see Table 4).

Table 4. Participant Characteristics

ADHD Non-ADHD Group
(n=18) (n=22) Comparisons
M SD M SD t Sig
Gender 10 Male 8 Female 16 Male 6 Female - -
Age (years) 25.6 5.75 26.05 6.41 .74 .46
Composite Intelligence Index 111.06 10.74 105.95 9.03 1.63 .11

Measures were taken to exclude from the study participants with ADHD who did not

report significant current symptomology and those without an ADHD diagnosis who did report

evidence of the disorder. In order for participants to be included in the ADHD group, they had to

have reported being professionally diagnosed with ADHD within the last five years. In addition,

all ADHD subjects reported having at least five current symptoms on the ADHD Rating Scale

for Adult Symptoms and ADHD Rating Scale (Barkley & Murphy, 2006). All the participants in

the control group reported three or less symptoms on the same scale.

Given the evidence that among children with ADHD certain medications used to treat the

disorder may enhance EF performance (Baldwin et al., 2004), subjects were asked about their

use of such medications. Only one subject with ADHD reported never taking medication for

ADHD; 15 subjects with ADHD reported currently taking medication for their ADHD and 2

reported taking it in the past. Frequency of such medication use varied; 58% (10) reported

taking medication daily, 23% (4) at least once per week and 17% (3) reported using medication

less than once a week. Only 3 subjects had taken medication in the 24 hours before participating

in the experiment. No demographic differences or performance differences were observed

 81

between the ADHD subjects who were taking medication at the time of testing and those who

were not.

Measures

Reynolds Intellectual Assessment Scales: (RIAS) (Reynolds & Kamphaus, 2003): The

RIAS is an individually administered test of intelligence normed on individuals from 3 to 94

years of age. Each of the four subtests yields a T-score with a mean of 50 and a standard

deviation of 10. Combining individual subtests yields a two-subtest Verbal Intelligence Index, a

two-subtest Nonverbal Intelligence Index, and a Composite Intelligence Index. The three indices

produced by the RIAS are expressed as standard scores with a mean of 100 and a standard

deviation of 15.

ADHD measures: Two rating scales commonly used to assess ADHD in adults, the

Current Symptoms Scale - Self-Report Form (Barkley & Murphy, 2006) and the Childhood

Symptoms Scale - Self-Report Form (Barkley & Murphy, 2006) were administered to all

participants. The Current Symptoms Scale for adult symptoms includes information regarding

the number and severity of current ADHD, while the Childhood Symptoms Scale provides

retrospective information regarding the presence of symptoms in childhood (ages 5 to 12).

These scales each contain 18 items based on the diagnostic criteria for ADHD as defined in the

DSM-IV-TR (American Psychiatric Association, 2000). Each item is rated on a scale from 0

(rarely or never) to 3 (very often). An item marked as "often" or "very often" (2 or 3 on the

scale) indicates the presence of a symptom. The scales contain nine items that relate to
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Inattention and nine items that relate to Hyperactive-Impulsive symptoms, thus representing the

two ADHD subtypes.

Executive function measures: From among the many neuropsychological tests of EF

that are available, this study includes measures of EF that have been demonstrated by previous

studies to be sensitive to the difficulties experienced by adults with ADHD (Barkley & Murphy,

2011). The measures chosen evaluate three important aspects of EF: working memory, shifting

and inhibition. Two standardized measures of working memory were chosen from the Wechsler

Memory Scale-III (WMS-III): Letter-Number Sequencing and Spatial Span. Letter-Number

Sequencing is a test of auditory working memory that measures the ability to hold a string of

letters and numbers in working memory and then reorder them in a prescribed manner. The

Spatial Span subtest is designed to measure the subject’s ability to hold a visual-spatial sequence

in working memory and then reproduce it. The subject is shown a three-dimensional board with

10 cubes placed randomly on it and the examiner points to the cubes in a prescribed sequence.

The subject is then required to reproduce the order in which the examiner pointed to the cubes.

Subsequently, the examiner points to the cubes a different sequence and the subject is requested

to point to the cubes in reverse order. A Composite Working Memory score is created based on

combination scores from both subtests.

Two sub-tests from Delis–Kaplan Executive Function System™ (D–KEFS™: Delis,

Kaplan & Kramer, 2001)—the Trail Making Test and the Color-Word Interference Test—were

also chosen for the current study. An adaptation of the traditional Trail Making Test (connect-

the-circle tasks) was used to measure temporal sequencing and mental flexibility. The Trail
 83

Making Test consists of five conditions: Visual Scanning, Number Sequencing, Letter

Sequencing, Number-Letter Switching and Motor Speed. The scoring measure for each of the

five conditions of the Trail Making Test is the number of seconds that the subject takes to

complete each condition. The key executive-function task from among these five is the Number-

Letter Switching Condition, in which the subject alternates between numbers and letters (1, A, 2,

B, etc.). The primary score used in the current study is the standard score on Number-Letter

Switching Condition minus the standard score on Motor Speed; this score is said to best assess

flexibility of thinking in a visual-motor sequencing task.

The Color-Word Interference Test is an adaptation of the classic Stroop task (Golden,

2002) and assesses cognitive flexibility by requiring the subject to inhibit reading of words

denoting colors while naming the color of ink in which the word is written. Initially, the task

asks the subject to switch back and forth between naming the dissonant ink color and reading the

conflicting word. In Inhibition/Switching condition, the examinee is asked to switch back and

forth between naming the ink color and reading the words. The primary scoring measure that is

used in the current study is the number of seconds that the subject takes to complete each

condition. Previously, a meta-analysis that examined the use of the Stroop Task in studies

including subjects with ADHD (Homack & Riccio, 2004) demonstrated that individuals with

ADHD consistently exhibit difficulties as compared to normal controls, thus making the Color-

Word Interference Test an appropriate task for investigating the relationships among attention,

PM and EF.
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Prospective memory measures: Both a subjective questionnaire, the Prospective and

Retrospective Memory Questionnaire (PRMQ) (Smith et al., 2000), and a behavioral PM test,

Virtual Week, were utilized to assess PM. The PRMQ consists of 16 items equally divided

between a Prospective Memory subscale and a Retrospective Memory subscale. Respondents

indicate the frequency of forgetting in certain situations on a five-point Likert scale (5= very

often; 1= never). For example, the question: “Do you fail to do something you were supposed to

do a few minutes later even though it’s there in front of you, like take a pill or turn off the

kettle?” assesses PM. Higher scores in the PRMQ indicate a greater perceived difficulty on these

memory tasks. Prior research supports the internal consistency, predictive validity (Kliegel &

Jäger, 2006; Smith et al., 2000) and factor structure (Crawford et al., 2003; Kliegel & Jäger,

2006) of the PRMQ.

The Virtual Week task (Rendell & Craik, 2000) is a computerized task that attempts to

recreate a number of characteristics representative of everyday PM behavior (for a review of

Virtual Week see Rendell & Henry, 2009). Each circuit around the game-like board, controlled

by rolling two dice, represents one day (see Figure 1). Participants complete five days (five

circuits around the board) and on each day there are ten activities to remember. Examples of

to-be-remembered activities include taking medication, paying bills, and meeting friends for

appointments. Studies have indicated that Virtual Week is sensitive to PM difficulties in normal

and abnormal aging adults (Rendell & Craik; 2000; Rose et al., 2010) as well as in various

clinical conditions, including substance abuse (Rendell et al., 2007a), multiple sclerosis (Rendell,

et al., 2007b) and schizophrenia (Henry et al., 2007).

 85

Virtual Week activities include both time- and event-based tasks and regular and irregular

tasks. These two task types are crossed, creating four combinations of time/event and

regular/irregular. In addition, a time-check component is included in which participants are

asked to stop a clock after a specified amount of time has elapsed (two and four minutes)

following the start of each virtual day. Participants carry out a PM task by remembering to press

the “perform task” button and selecting the appropriate task from a list of possible PM tasks.

The list of PM tasks includes the tasks to be remembered as well as distractor tasks. The primary

variables of interest include the proportion of correct responses in each task category and the

reason for failures of PM (e.g., misses where PM tasks were not executed or early responses

where intentions were executed before the specified time).

Procedures

The study was approved by the Institutional Review Board at Northwestern University in

accordance with international principles of human research. It was part of a larger study that

lasted for approximately four hours and was divided into two experimental sessions. Both

control and ADHD subjects were administered all measures in the same sequence. The

Prospective and Retrospective Memory Questionnaire was completed by participants before they

performed the Virtual Week task. Participants completed Virtual Week while seated in front of a

laptop computer using a mouse. Each participant completed one practice day (one circuit of the

board) while receiving verbal instructions and feedback from the experimenter. After

completing the practice day, participants received additional instructions for the task and

demonstrated to the experimenter that they understood all aspects of it. Following completion of
 86

the practice day, the participants began the first day of the Virtual Week task. Once the task

began, the experimenter was present in the room but did not provide any additional assistance or

instructions on the Virtual Week task.

Results

Executive Functions

In order to evaluate the relationship among the three EF measures, Pearson product-

moment correlation coefficients were calculated. The results indicated a significant correlation

between Trail Making and the Composite Working Memory score (r=.364, p<.01). Correlations

between Trail Making and Color-Word Interference scores (r=-.046, p=.39) and between Color-

Word Interference and the Composite Working Memory scores (r=-.021, p=.45) were non-

significant.

T-tests were conducted to compare the performance of participants with ADHD with

control participants on EF measures (see Table 5 for summary statistics). As expected, the

control participants outperformed the participants with ADHD on both of the Working Memory

measures and on the Composite Working Memory score. In addition, the participants with

ADHD had significantly lower scores on the Color-Word Interference task compared to control

participants. There were no significant differences between the groups on the Trail Making

Task.
 87

Table 5. Means and Standard Deviations and Group Comparisons for Executive Function
Measures.
ADHD Non- ADHD Group Comparisons

M SD M SD t Sig.
Spatial Span 8.44 1.72 10.64 2.04 3.62 .001
Letter Number Sequencing 10.94 2.48 12.45 2.82 1.77 .042*
Composite Working Memory 19.39 3.09 23.09 4.13 3.15 .001
Color-Word Interference 8.88 1.96 10.27 2.21 2.04 .048
Trail Making 9.06 2.95 9.68 1.52 .86 .379
* Significant at the 0.05 level (1-tailed)
To further explore the relationship between EF proficiency and ADHD symptomology, a

Pearson product-moment correlation coefficient was calculated between the number of reported

ADHD symptoms and scores on EF measures. There was a significant negative correlation

between Composite Working Memory score and total number of ADHD symptoms (r=-.379,

p<.01). There was a significant correlation between Color-Word Interference scores and the

total number of ADHD symptoms obtained, with more ADHD symptoms associated with lower

Color-Word Interference scores (r=-.289, p<.05). There was a non-significant correlation

between Trail Making scores and total number of ADHD symptoms (r=-.183, p=.132).

To examine whether differences exist between the Hyperactive-Impulsive and Inattentive

symptoms of ADHD and their relationship to EF, separate correlations were calculated for each

of the scales assessing these subtypes. There was a significant correlation between Composite

Working Memory score and Inattentive score (r=-.469, p<.01). In contrast, there was a non-

significant correlation between Composite Working Memory scores and Hyperactivity-

Impulsivity scores (r=-.111, p=.268). A different pattern emerges when exploring the

relationship between performance on Color-Word Interference Test and the number of reported
 88

ADHD symptoms. While there was a significant correlation between Color-Word Interference

scores and Hyperactivity-Impulsivity scores (r=-.286, p<.05), a non-significant correlation was

observed between Color-Word Interference scores and Inattentive scores (r=-.243, p=068). Both

ADHD subtype scores showed a non-significant relationship with Trail Making scores

(Hyperactivity-Impulsivity r=-.009, p=.48, and Inattentive r=-.259, p=.055).

Prospective Memory

Both the Prospective and Retrospective Memory Questionnaire (PRMQ) and Virtual

Week, were analyzed to assess PM performance. On the PRMQ there was a significant

difference between the ADHD (M=52.39, SD=10.61) and non-ADHD (M=35.13, SD=8.09)

groups on the PRMQ Total scores [t(38)=5.8, p<.01]. The ADHD group also had significantly

higher scores than the non-ADHD group (denoting greater memory difficulties) on both the

Prospective Memory [t(38)=7.3, p<.001], and Retrospective Memory subscales [t(38)=3.7,

p<.001].

A 2 x 5 mixed-model ANOVA: Group (ADHD, non-ADHD) x PM task types (Regular-

Event, Regular-Time, Irregular-Event, Irregular-Time and Time-Check) was conducted to

explore the impact of ADHD on PM performance. There was a main effect of group

[F(1,39)=9.663, p<.005]; non-ADHD participants (M=.864, SD=.102) outperformed the

participants with ADHD (M=.683, SD=.147) in terms of overall Virtual Week accuracy. The

main effect of PM task was also significant, [F(1,35)=16.377, p<.001]. The interaction between

group and the PM tasks was non-significant [F(1,35)=.035,p=.997]. Follow-up analyses on the

main effect of task revealed that the means for both the Time Check (M=.62, SD=.24) and the
 89

Irregular Time-Based task (M=.62, SD=.27) were significantly lower than other PM tasks,

which did not differ significantly from each other.

The relationship between PRMQ and overall Virtual Week score was investigated using

Pearson correlation coefficients. For all subjects combined, there was a significant negative

correlation between Total PRMQ scores and Overall Virtual Week performance (r= -.55, p<.01).

However, when examined separately, this statistical significance did not hold for both groups.

That is, there was a significant correlation between the two variables only for the subjects with

ADHD (r=-.466, p=.026). For the non-ADHD subjects, the correlation between the Virtual

Week and PRMQ did not reach statistical significance (r=-.229, p>.05).

Prospective Memory and Executive Functions

The relationship among the three EF measures and five types of PM tasks (Regular-

Event, Irregular-Event, Regular-Time, Irregular-Time and Time-Check) was examined using

Pearson product-moment correlation coefficients (results presented in Table 6). Composite

Working Memory scores significantly correlated with four of the PM tasks. However, no

significant correlations were observed between Composite Working Memory and Regular Event-

Based PM. The Color-Word Interference scores correlated significantly only with the Regular

Time-Based PM, and Trail Making scores only correlated significantly with the PM Time Check

task.

In order to explore the extent to which EF performance could predict PM performance

and to determine which executive measures are the best predictors of PM performance, a

multiple regression analysis was performed. Prior to conducting the regression, the relationships
 90

among PM (as measured by Overall Virtual Week score) and EF tasks (as measured by Color-

Word Interference task, Composite Working Memory and Trail Making Test) for all participants

was determined using Pearson product-moment correlation coefficients (see Table 6). Overall

PM task performance significantly correlated with Working Memory score (r=.498, p<.001), and

the strength of this association was approximately equal for the two groups when analyzed

separately. However, the Color-Word Interference and Trail Making tasks did not significantly

correlate with overall Virtual Week score.

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Table 6. Correlations among Executive Function Scores and Virtual Week Scores for All
Participants.
Task Color-Word Composite Trail Overall Regular- Irregular- Regular- Irregular- Time-
Interference Working Making Virtual Event Event Time Time Check
Memory Week
Color- - -.02 -.05 .22 .08 .20 .34* .16 .01
Word
Interference
Composite - .36* .50** .25 .32* .42** .31* .36*
Working
Memory
Trail - .16 .03 .02 .10 -.03 .38*
Making
Test
Overall - .58** .59** .78** .81** .55**
Virtual
Week
Regular – - .33* .36* .31* .09
Event

Irregular – - .35* .36* .17

Event

Regular – - .66** .20

Time

Irregular- - .20
Time

Time- -
check

* Correlation is significant at the 0.05 level (1-tailed)

**Correlation is significant at the 0.01 level (1-tailed)
 92

Next, to assess the ability of Composite Working Memory and ADHD status (ADHD vs.

Non-ADHD) to predict PM ability (Overall Virtual Week score), a multiple regression analysis

was conducted. Given the lack of significant correlation between Color-Word Interference, Trail

Making tasks and overall PM performance, these variables were not included in the subsequent

regression. Descriptive statistics and regression coefficients are shown in Table 7 and in Figure

7. Both predictor variables had a significant correlation (p <.01) with PM scores, but only

Working Memory had significant effects in the full model (p <.05). Surprisingly, the ADHD

variable failed to reach significance in the full model (p =.074). The two-predictor model was

able to account for 33% of the variability in Virtual Week scores [F(2, 37) = 8.367, p < .001,

R = .56].

Table 7. Results from Regression Analysis of Composite Working Memory and ADHD Status to
predict PM ability.
Predictor Variable Unstandardized Standardized
Coefficients Coefficients
B Std. Beta t Sig.
Error
1 (Constant) .519 .121 4.280 .000
Composite Working Memory .012 .005 .370 2.417 .021
ADHD -.077 .042 -.282 -1.840 .074
 93

Figure 7. Relationship between Composite Working Memory Scores and Overall Virtual Week
Scores for Subjects With and Without ADHD

Given the significant correlation between Working Memory and PM performance and the

fact that the two groups differed significantly in working memory ability, it is reasonable to ask

whether the previously described between-groups differences on the Virtual Week task can be

accounted for by working memory differences. Thus, the previously described mixed-model

ANOVA 2 (group) x5 (PM task type) was re-analyzed as an ANCOVA using each participant’s

Composite Working Memory score as a covariate. When controlling for working memory there

were no longer significant main effects of ADHD group [F(1,33)=1.68, p=.18] or PM task

[F(1,33)=1.33, p=27]. However, even when controlling for working memory, the groups
 94

continued to differ significantly in their performance on both Regular Event-Based [F(1)=4.33,

p<.05] and Irregular Event-Based [F(1)=4.65, p<.05] tasks. The interaction between group and

PM task remained non-significant [F(1,4)=.025,p=.99].

Discussion

The aim of the present study was to explore for the first time the relationship between EF

and PM in adults with attention disorders and to further our understanding of the relationships

between EF and features of PM tasks, such as type of PM cue and regularity. Consistent with

predictions, adults with ADHD underperformed compared to the control participants on all PM

and EF measures, except for the Trail Making measure that assesses shifting. These findings

extend those of Zinke et al. (2011), who showed a significant relationship between the number of

ADHD symptoms and PM performance in children. The present study indicates that ADHD

symptoms continue to result in significant PM difficulties in adults with ADHD.

The second goal of the current study was to determine which EF measures are the best

predictors of PM performance. To this end, the relationships between PM and EF measures were

examined. Results indicated that of the three EF measures, only working memory performance

was significantly related to performance on Virtual Week. That is, participants with High

Working Memory scores had significantly more accurate PM performance than those with lower

scores. These results are consistent with previous studies that explored the role of working

memory in PM in older adults without attention disorders (Cherry & LeCompte, 1999; Rose et

al., 2010). These results also replicate outcomes from Smith and Bayen (2005) that created

groups of participants with high and low working memory on the basis of their performance on a
 95

counting-span task. Similar to the present study, Smith and Bayen (2005) found that participants

with good working memory skills were significantly better than those with poor working

memory in their overall PM performance. Also consistent with results of the current study is a

study that investigated the relationship between EF and PM in young children (ages 4 to 6 years)

and found that when controlling for age, working memory but not inhibition predicted PM

performance (Mahy & Moses, 2011).

Importantly, working memory is an essential aspect of PM independent of ADHD status.

Unexpectedly (as working memory deficits are just one of many aspects of functioning impacted

in ADHD), results indicated that working memory is a better predictor of overall PM

performance than ADHD status. As previously noted, there were significant differences between

the groups in terms of working memory; most of the low Working Memory scores were found

among the ADHD participants. However, working memory appears to be differentially related

to PM task type. That is, when controlling for working memory, the groups continued to differ

significantly in their performance on Event-Based tasks but were no longer different on the

Time-Based tasks. That the effect of ADHD was restricted to Event-Based PM is consistent with

the view that event-based tasks are based on more automatic processing and are therefore less

reliant on working memory (Mahy & Moses, 2011; McDaniel & Einstein, 2007).

These results suggest that knowing the working memory capacity of an individual with

ADHD may provide important information for clinicians that will allow them to deliver the most

effective interventions. Working memory capacity has traditionally been thought to be largely

unalterable; however, recent studies suggest that working memory can be improved by training
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(Holmes et al., 2010). Currently several commercially available interventions designed to

provide working memory training have been receiving considerable attention (Gray et al, 2012;

Holmes et al., 2010; Klingberg et al., 2005). Results of the current study suggest that working

memory training regimens could be beneficial for adults with ADHD, especially those with low

working memory. However, these training programs have not been fully supported by the

literature, with one of the major concerns being whether working memory training transfers to

everyday performance (Gray et al, 2012; Melby-Lervåg & Hulme, 2012; Shipstead, Hicks, &

Engle, 2012). Further, results suggest that improving working memory is not expected to

increase performance on all types of PM. Future research may benefit by using tasks such as

Virtual Week as proxy for everyday performance in evaluating the efficacy of working memory

training in individuals with ADHD.

Overall, studies reviewing the performance on EF measures in adults with ADHD have

not yielded consistent results. Several previous studies have demonstrated that adults with

ADHD have a range of difficulties when compared to controls, while others have not found

differences between adults with ADHD and controls on EF measures (Barkley & Murphy, 2011;

Murphy, Barkley, & Bush, 2001). The current study also shows a pattern of inconsistent

differences on EF tasks; the control participants outperformed the participants with ADHD on

measures that evaluated working memory and inhibition (Color-Word Interference task) but not

shifting (Trail Making task). When considering the relationship among EF measures in adults

with ADHD it is also important to explore the possibility that these relationships differ across the

ADHD subtypes — Combined, Hyperactive-Impulsive or Inattentive. In the current study the

 97

EF measures demonstrated different relationships with ADHD symptomatology. Specifically,

working memory performance was significantly correlated with inattentive scores, while

Hyperactive-Impulsive scores were significantly related to inhibition scores. This pattern is

consistent with the literature that suggest that individuals with the Predominantly Inattentive

subtype experience significantly less inhibition difficulties then do those in the Predominantly

Hyperactive-Impulsive subtype (Pasini, Paloscia, Alessandrelli, Porfirio, & Curatolo, 2007;

Seidman, 2006).

Inconsistencies in the ability of laboratory EF tasks to characterize performance deficits

in individuals with ADHD highlight the importance of supplementing these tasks with

questionnaires, which have been more consistently able to demonstrate differences between

controls and adults with ADHD (Barkley, 2012). In the current study, adults with ADHD

reported more PM difficulties then control participants and their self-reports correlated

significantly with performance on various aspects of Virtual Week. These results indicate that

adults with ADHD are relatively accurate in their ability to assess their PM difficulties and

suggest that questionnaires such as the PRMQ should be considered valid tools for researchers

and clinicians.

In a somewhat unpredicted finding, adults with ADHD demonstrated significant

difficulty compared to controls on both Event-Based tasks and Time-Based tasks. The

participants without ADHD performed at a very high level (close to ceiling) on Virtual Week

Event-Based tasks, while the participants with ADHD experienced some difficulties, a finding

that is in contrast with previous studies that have demonstrated a deficit in PM performance in
 98

ADHD with time-based but not event-based tasks (Altgassen et al. 2012; Kerns & Price, 2001).

An explanation of why the present study showed a deficit on event-based tasks may lie in the

nature of the tasks employed. That is, the explicitness and salience of cues that signal the

initiation of an event-based PM task are likely to affect the task’s demands on inhibitory control

processes that require EF, and as a result, the EF demands of event-based cues have varied across

studies. Notably, Altgassen et al. (2012) proposed that their event-based tasks lowered the need

for EF by using task cues that were highly distinct and salient and therefore were not as

challenging for individuals with ADHD. In contrast, in the current study the event-based tasks

were more naturalistic and complex, and thus possibly placed an increased demand on the EF

processes that are commonly impaired in individuals with ADHD. In addition, the Altgassen et

al. (2012) study included a planning stage that likely enhanced performance, whereas the current

study was designed without a planning stage. Taken together, these results suggest that not all

event-based tasks place comparable demands on EF processes; as a result, certain event-based

PM tasks may pose a difficulty for adults with ADHD in everyday life.

Of further interest is the finding that both adults with and without ADHD performed

comparably poorly on the Time Check task and that it was the only task type (of the five

included) that did not result in significant differences between the two groups. These results are

in line with previous studies that found no differences between ADHD participants and control

participants on time-monitoring tasks (Altgassen et al., 2012; Kerns & Price, 2001; Zinke et al.,

2010). Researchers propose that time-based tasks require a high degree of inhibition and

substantial working memory capacity because they force the individual to shift from the ongoing
 99

task in order to monitor time (Einstein & McDaniel, 1996). Altgassen et al. (2012) speculated

that the necessity of maintaining timing information in working memory or the need to inhibit an

ongoing task at a specific time could potentially be the underlying cognitive mechanisms that

differentiate time-based tasks from time monitoring tasks. Results of the current study support

the importance of working memory in maintaining timing information by demonstrating a

significant relationship between working memory and time-based task performance, while no

such relationship was noted between Time Check task performance and working memory.

Moreover, only the Time Check task was significantly related to the EF measure of shifting

(Trail Making). This suggests that shifting is necessary for successful time monitoring but may

not be required for successful time-based PM performance.

The limitations of this study should be considered in evaluating and interpreting its

results. First, participants’ use of stimulant medication could not be manipulated in the current

study. However, no differences were noted between those participants who were taking

medication and those who were not. Nonetheless, a systematic examination of the effect of

medication on PM performance merits further investigation. A second limitation may have

resulted from the ADHD sample including both community-based participants and clinic-

referred participants. While including a diverse group of participants is desirable, the small

sample size limited the ability to examine differences between these two samples. In addition,

participants in the current study had a limited range of working memory abilities, with most

falling within the average or high range. Such a restriction of range likely resulted in reduced

statistical power and a reduced ability to observe significant relationships among variables. It is
 100

likely, given the pattern of results that was observed that individuals with below average working

memory capacity would demonstrate even less accurate PM performance and more PM

difficulties in everyday life.

One of the challenges of evaluating PM in the laboratory is the use of semi-naturalistic

tasks, and while Virtual Week task is designed to mimic real-life PM situations, it differs from

everyday PM performance in important ways. The time frame of each PM task is shorter; the

number of external distractors is limited and the actual duration of the “week “is approximately

one hour. The above findings suggest that even with these limitations, adults with ADHD

demonstrate significant PM difficulties, which leads to the prediction that in more demanding

everyday situations PM difficulties may manifest to an even greater degree and result in even

more significant time management and organization problems.

In conclusion, the results demonstrate a consistent deficit in the abilities of adults with

ADHD when performing PM tasks. Hence, the present study provides further evidence for PM

deficits in ADHD which may underlie the everyday organization and time management deficits

of individuals with ADHD. Turning to the contribution of EF, the current results indicate that

not all EF measures reflect vital mechanisms for successful PM performance. Working memory

ability significantly predicted PM performance for both adults with and without ADHD.

However, results did not reveal a significant relationship between performance on Overall

Virtual Week tasks and the other EF measures of shifting and inhibition. Event-based and

regular tasks were easier to perform for both groups and showed a weaker relationship with EF

measures then time-based and irregular tasks. Furthermore, the results suggest that adults with
 101

ADHD can benefit from clinicians and researchers using tools such as Virtual Week and

questionnaires such as PRMQ to evaluate PM abilities and the effects of intervention aimed at

enhancing PM.
 102

Chapter 6: General Discussion

Taken together, the results of both experiments indicate that adults with ADHD have a

persistent deficit in PM performance as compared to controls. As predicted, non-ADHD

participants consistently outperformed the ADHD participants on the Virtual Week task. These

results are in line with previous studies that explored PM in children with ADHD (Kerns &

Price, 2001; Kliegel et al., 2006; Siklos & Kerns, 2004; Zinke et al., 2010), and yielded similar

results to those seen in adults with ADHD (Altgassen et al., 2012). Thus, these results extend

previous findings to adults and indicate that PM deficits in ADHD can be reliably assessed with

objective tasks such as Virtual Week. Moreover, the results further establish the relationship

between ADHD symptoms severity and degree of PM difficulties, as participants reporting the

most ADHD symptoms had significantly more PM difficulties. It is important to note that at

most only two of the 18 ADHD symptoms among those in the diagnostic criteria of DSM - IV

can be interpreted as specifically relating to PM. Thus, PM difficulties represent a deficit that is

substantially independent of the diagnostic criteria for ADHD. Furthermore, findings indicate

that ADHD symptoms continue to result in PM difficulties throughout one’s life and not just in

childhood. This finding may be especially important given that PM difficulties can negatively

affect many aspects of quality of life and may have significant health implications for some

(McDaniel & Einstein, 2007; Zogg et al., 2012).

In the current study, adults with ADHD experienced significant difficulties on both

event- and time-based PM tasks relative to controls. This finding is in contrast with previous

studies that demonstrated that participants with ADHD displayed difficulties only on time-based
 103

PM tasks and not on event-based PM tasks (Altgassen et al., 2012; Kerns & Price, 2001).

Researchers have proposed that the explicitness and salience of PM cues may reduce the level of

EF needed and therefore increase accuracy in individuals with ADHD. Previous studies that

explored the differences between time-based and event-based PM in individuals with ADHD

may have included event-based tasks that contained more explicit and salient cues, thus resulting

in the adults with ADHD demonstrating difficulties only on the less salient time-based tasks

(Altgassen et al., 2012; Kerns & Price, 2001). These concerns regarding cue salience were

addressed in the current study by using the Virtual Week task that included both event-based and

time-based PM tasks that were designed to have cues with comparable levels of salience and

distinctiveness. In contrast with previous studies, the current study used event-based tasks that

were naturalistic and complex and thus possibly presented an increased demand on the EF

processes that are commonly impaired in individuals with ADHD. Taken together the results

suggest that adults with ADHD may encounter greater difficulty than adults without ADHD on

certain types of everyday event-based PM tasks. Future research should consider further

exploring the effects of cue salience and task complexity on PM performance and the possibility

of manipulating these factors as a way to improve PM performance of adults with ADHD.

In both control and ADHD participants, these findings add to the growing body of

evidence that event-based and regular PM tasks are easier to perform than time-based and

irregular PM tasks. All participants’ performance on Virtual Week was more accurate on Event-

Based PM tasks than on Time-Based PM tasks, with the performance of control participants

close to ceiling levels. Regular PM tasks were also easier to perform then Irregular PM tasks for
 104

all participants. These results have been previously observed in studies exploring Virtual Week

with different clinical and nonclinical participants (Aberle et al., 2010; Rose et al., 2010) and are

consistent with the multiprocess model of PM that proposes that time-based and irregular tasks

are more demanding and thus more prone to impairment. According to the multiprocess model,

several different kinds of processing can support prospective remembering, ranging from

attention-demanding monitoring to spontaneous retrieval processes. Time-based and irregular

tasks require more EF and monitoring then the more spontaneous retrieval that may be part of

event-based and regular tasks (McDaniel & Einstein, 2007).

The current study was the first to examine PM error type in performance of adults with

ADHD, thus providing information that may assist in the development of interventions designed

to improve PM performance. As previously described, errors on Virtual Week can be classified

into several types including early responding, late responding and missed responses. In addition,

participants can err by committing repetition errors or distractor errors. Importantly, the adults

with ADHD only differ from non-ADHD participants on the number of missed responses

(omission errors). This result points to the importance of focusing on task completion and

reducing the likelihood of failing to perform a PM task when designing interventions to address

PM difficulties. Interventions focused on the timing of PM tasks (performing a task late or

early) would not necessarily be addressing ADHD-specific impairments, nor would they be

focusing on the most prevalent type of PM error.

A review of current literature indicates that there are no research-based PM interventions

specifically designed for adults with ADHD. Most research-based PM interventions were
 105

developed for individuals with brain injuries (Thone-Otto & Walther, 2008), normal aging

adults, or were designed to solely increase medication adherence (Zogg et al., 2012). Future

research should focus on possible interventions for improving PM performance in individuals

with ADHD. Results of the current study suggest that there are some important differences

between older adults without ADHD and young adults with ADHD in terms of their PM

difficulties and this information should be used to inform intervention. In the current study there

was no interaction between PM task type and group, while in other studies that compared

younger and older adults without ADHD, such an interaction was found. Thus, it appears that

the PM deficits experienced by adults with ADHD are broader in scope than those seen in

normal aging. The current study did not directly contrast the age-related PM difficulties of older

adults without ADHD with those of comparably aged adults with ADHD. Considering the

findings here, it may be beneficial for future studies to further explore the similarities and

differences between those populations, each of which may experience a unique pattern of PM

difficulties. That a distinct pattern of PM impairment appears to be seen in individuals with

ADHD suggests that interventions for PM difficulties that have been developed for other

populations such as the elderly may not necessarily be the most appropriate treatment for adults

with ADHD. This study also suggests Virtual Week is well-suited as a measure for evaluating

the treatment efficacy of interventions and training programs designed to improve PM

performance.

The second experiment explored the relationship between EF and PM in adults with

ADHD and the effects of different PM task features on the relationship among PM, EF and
 106

attention disorders. Researchers have proposed that PM deficits in individuals with ADHD are

related to the extensive involvement of EF in PM (Kerns & Price, 2001; Kliegel et al., 2006) and

taken together the results of this study provide clear support for the involvement of EF and PM.

The participants with ADHD demonstrated significant difficulties compared to controls on

measures that evaluated working memory and inhibition (Color-Word Interference task) but not

shifting (Trail Making task). The result demonstrating no significant between-groups difference

on Trail Making was not completely unexpected given results from previous studies. For

example, Wodka and colleagues (2008) demonstrated no significant differences between

children with and without ADHD on the same measure. This result provides further support for

the notion that not all EF tasks are sensitive to the deficits that individuals with ADHD

experience.

Several studies have shown that controls outperform subjects with ADHD on a variety of

EF measures (for example, Antshel et al., 2010; Barkley 2006; Bramham et al., 2009; Brown,

Reichel & Quinlan, 2009; Johnson et al., 2001; Lovejoy et al., 1999; Rapport et al., 2001; Woods

et al., 2002), while other studies found no significant between-groups differences on such tasks

(Sandson, Bachna, & Morin, 2000; Walker, Shores, Trollor, Lee, & Sachdev, 2000; Wodka et al,

2008). Seidman (2006) proposes that these inconsistences in EF performance among adults

ADHD suggests that not all persons with ADHD have EF deficits and that some may have

deficits in brain reward systems that are relatively independent of EF impairments.

The pattern of correlations among EF measures and PM performance was not entirely as

predicted. As hypothesized, results demonstrated that working memory was significantly related
 107

to performance on PM tasks. However, unexpectedly, results did not reveal a clear relationship

between performance on Virtual Week and the other EF measures of shifting and inhibition. It is

not surprising that results demonstrated a significant relationship between working memory

scores and performance on Virtual Week, as working memory has consistently been implicated

in the planning of an intended action as well as in maintaining the intentions while attending to

the ongoing tasks (Kliegel et al., 2002; McDaniel & Einstein, 2007; Smith, 2003).

In contrast, only Regular Time-Based tasks were significantly correlated with the

inhibition measure (Color-Word Interference task), suggesting that in order to accurately perform

Regular Time-Based tasks one must have successfully inhibited the ongoing task. Several

Regular Time-Based tasks needed to be performed at a specific time in Virtual Week

concurrently with another ongoing task. Conversely, the lack of a significant relationship

between inhibition and other PM task types suggests a minimal role of inhibition-related

processes in the successful completion of the task.

With regard to shifting (as measured by Trail Making measure), only the Time Check

task correlated significantly with this variable. Both groups performed equally poorly on the

Time Check measure, with subjects remembering to perform the Time Check task only about

half of the time. These results are in line with previous studies such as Altgassen et al. (2012)

who demonstrated that adults with ADHD had no time monitoring difficulties when compared to

control participants even though they exhibited deficits on time-based PM tasks. Results of the

current study demonstrated that time check performance was correlated with shifting and

working memory while time-based tasks only correlated with working memory ability. Taken
 108

together, these results suggest that the underlying cognitive mechanisms involved in time check

tasks may differ from those involved in more general time-based tasks. It is likely that time

check tasks require regular monitoring of a clock that may increase demands on available

cognitive resources (and thus increase task difficulty); while time-based tasks may be able to

benefit from the use of strategies to reduce the amount of clock monitoring needed, thus reducing

task difficulty.

Surprisingly the present findings suggest that working memory is a better predictor of

overall PM performance than ADHD status. This result was unexpected, as working memory

deficits are only one of a number of areas in which individuals with ADHD have deficits. Thus,

it was anticipated that the classification of individuals by group status would have allowed for

the many dimensions on which the two groups differ to collectively contribute to the prediction

of overall PM performance. Significant differences were observed between the groups in terms

of working memory, with most of the low Working Memory scores found among the ADHD

participants. When statistically controlling for working memory differences, the ADHD and

control groups continued to differ significantly in their performance on Event-Based tasks, but

not on Time-Based tasks. This pattern of findings also provides strong evidence that while

working memory was strongly correlated with PM performance, the PM impairments associated

with ADHD cannot be fully explained by working memory deficits.

The effect of ADHD status was restricted to Event-Based PM when working memory

was controlled for is also consistent with the view that event-based tasks are based on more

automatic processing and are therefore less reliant on working memory (McDaniel & Einstein,
 109

2007). In addition, findings reinforced the proposition that Regular Event-Based tasks, as

evaluated in the Virtual Week, are less demanding of EF involvement for successful performance

than the other types of PM tasks (Rose et al., 2010). Regular Event-Based tasks were the only

PM task type that did not correlate significantly with performance on any of the EF measures.

Executive function refers to a theoretical construct that controls and manages other

cognitive processes. A variety of assessment tools have commonly been used to evaluate EF.

Previous studies that explored the relationship between EF and PM and those that investigated

EF performance in individuals with ADHD varied significantly in the type and breadth of tasks

used in order to evaluate EF. This variation in methods used to evaluate EF, combined with

differing conceptualizations of what EF is have made comparing results studies difficult and

compromised the ability to generalize results across experiments. Based on the results of the

self-rating measure employed in the current study, adults with ADHD are substantially aware of

their PM difficulties. Such self-awareness is likely to bode well for treatment, as previous

studies have demonstrated that among patients with brain injuries, self-awareness of PM

difficulties is an important predictor of successful intervention (Thone-Otto & Walther, 2008).

Further, a study by Macan et al. (2010) demonstrated a significant correlation between self-

reports of time management difficulties and scores on PRMQ in adults without disabilities.

Performance on Virtual Week was significantly related to scores on the self-reported PRMQ for

all participants, but the strength of the relationship was largely driven by the participants with

ADHD. Furthermore, an examination of the scores on the two subscales of the PRMQ indicated

that compared to controls, participants with ADHD reported significantly more difficulties on
 110

both the Prospective Memory and the Retrospective Memory subscales. However, the subjects

with ADHD reported significantly more prospective than retrospective memory difficulties,

while the controls showed no such difference. Taken together, results provide further evidence

that PRMQ has clinical validity and may provide a useful and effective tool for identifying PM

problems and in evaluating the effectiveness of PM interventions for adults with ADHD.

Gaining understanding about the performance of adults with ADHD on the Virtual Week

task may be helpful for clinical practice as Virtual Week can assist in providing an additional

source of evidence for everyday functional impairment and thus fulfill one of the requirements

for a valid diagnosis in DSM-IV (Criterion D). In addition, results suggest that Virtual Week can

be a useful tool for evaluating the effectiveness of behavioral and psychopharmacological

treatments of ADHD. These results suggest that treatments that address executive function

deficits (such as working memory) may yield greater benefits than those that focus only on the

symptoms of ADHD (such as hyperactivity). In addition, there is a lack of tools designed to

assess PM within the context of memory evaluations for both individuals with and without

ADHD (Thone-Otto & Walther, 2008), and the current study supports the use of Virtual Week as

a part of a larger battery designed to evaluate overall memory abilities.

Attention deficit disorders are often comorbid with learning disabilities. The reported

comorbidity of ADHD and learning disabilities ranges from 10%-90% depending on the

definition of learning disabilities (Semrud-Clikeman et al., 1992), although most theorists

suggest that about a quarter to a third of individuals with ADHD also qualify for a learning

disabilities diagnosis. In an effort to include a sample of ADHD participants that reflects those
 111

in the general population, the current study did not exclude participants with ADHD who also

reported having been diagnosed with learning disabilities. However, only two participants in the

ADHD group also reported comorbid learning disabilities, making a statistical comparison

between participants with and without comorbid learning disabilities impossible.

While the Virtual Week task is designed to mimic real life PM situations, when exploring

PM in a well-controlled laboratory environment there are a few additional challenges that need

consideration. Despite the attempts to make Virtual Week task as ecologically valid as possible,

a number of differences between the task and real-world PM tasks exist. For example, an entire

virtual “week”, despite containing a large number of tasks that are typical of real world

situations, requires less than an hour to complete in the laboratory. Also, laboratory studies are

designed to reduce and control distractions, while everyday life includes a variety of distractions

(Zogg et al., 2012). However, the study’s findings suggest that even when examining very brief

time intervals and in situations with limited distractors, adults with ADHD demonstrate broad

and significant PM difficulties compared to controls, leading to the prediction that in more

demanding real life situations PM difficulties may manifest to an even greater degree.
 112

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Appendix A List of PM Task Included in Virtual Week

Event-based Regular Tasks

Each day:
• Take antibiotics at breakfast
• Take antibiotics at dinner

Time-based Regular Tasks

Each day:
• Use asthma inhaler at 11 am
• Use asthma inhaler at 9 pm

Time check Tasks

Each day:
• Test lung capacity at 2:00 on the stop clock
• Test lung capacity at 4:00 on the stop clock

Event –based Irregular Prospective Memory Tasks

• Drop in dry cleaning when out shopping

• Return to library a book borrowed by Brian when at Library
• Pick up your sister’s sports club membership pass when at the swimming pool/sports club
• Tell Kate that Margaret had a baby girl when you talk to Kate
• Buy some more paper when shopping next
• Invite David to dinner next week when out you meet David
• Ask Jill for book she borrowed when you have tea with Jill
• If using washing machine, set on gentle-wash cycle only
• Pick up dry cleaning when you go out to get photocopying done
• Buy gas for car when out later
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Time-based Irregular Prospective Memory Tasks

• Phone the bank to arrange an appointment at 12 noon

• At 5 p.m., put casserole in the oven for dinner
• Hair cut at 1 p.m.
• At 4 p.m., appointment at library for help with computer search
• Meet your friend Michael for coffee at 4 p.m.
• At 6 p.m., phone David’s sister about baby sitting
• Submit a report at 3 p.m.
• At 4 p.m. have an X-ray
• Physiotherapy appointment at 1 p.m.
• At 6 p.m., pick up photocopying from printing
 125

Appendix B Computer Screen Displays of Instructions for Time Check Task

126
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Appendix C Prospective and Retrospective Memory Questionnaire

Instructions

In order to understand why people make memory mistakes, we need to find

out about the kinds of mistakes people make, and how often they are made in
normal everyday life. We would like you to tell us how often these kinds of things
happen to you. Please indicate this by ticking the appropriate box. Please make
sure you answer all of the questions even if they don't seem entirely applicable to
your situation.
1. Subject number ______________

2. Do you decide to do something in a few minutes’ time and then forget to do it?

Never

Rarely

Sometimes

Quite Often

Very Often
3. Do you fail to recognize a place you have visited before?

Never

Rarely

Sometimes

Quite Often

Very Often
4. Do you fail to do something you were supposed to do a few minutes later even though it’s

there in front of you, like take a pill or turn off the kettle?

Never

Rarely

Sometimes

Quite Often

Very Often
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5. Do you forget something that you were told a few minutes before?

Never

Rarely

Sometimes

Quite Often

Very Often
6. Do you forget appointments if you are not prompted by someone else or by a reminder

such as a calendar or diary?

Never

Rarely

Sometimes

Quite Often

Very Often
7. Do you fail to recognize a character in a radio or television show from scene to scene?

Never

Rarely

Sometimes

Quite Often

Very Often
8. Do you forget to buy something you planned to buy, like a birthday card, even when you

see the shop?

Never

Rarely

Sometimes

Quite Often

Very Often
9. Do you fail to recall things that have happened to you in the last few days?

Never

Rarely

Sometimes

Quite Often

Very Often
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10. Do you repeat the same story to the same person on different occasions?

Never

Rarely

Sometimes

Quite Often

Very Often

11. Do you intend to take something with you, before leaving a room or going out, but

minutes later leave it behind, even though it’s there in front of you?

Never

Rarely

Sometimes

Quite Often

Very Often
12. Do you mislay something that you have just put down, like a magazine or glasses?

Never

Rarely

Sometimes

Quite Often

Very Often
13. Do you fail to mention or give something to a visitor that you were asked to pass on?

Never

Rarely

Sometimes

Quite Often

Very Often
14. Do you look at something without realizing you have seen it moments before?

Never

Rarely

Sometimes

Quite Often

Very Often
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15. If you tried to contact a friend or relative who was out, would you forget to try again

later?

Never

Rarely

Sometimes

Quite Often

Very Often
16. Do you forget what you watched on television the previous day?

Never

Rarely

Sometimes

Quite Often

Very Often
17. Do you forget to tell someone something you had meant to mention a few minutes ago?

Never

Rarely

Sometimes

Quite Often

Very Often

Source: Smith G., Della Sala S., Logie R. H & Maylor E. A. (2000). Prospective and retrospective memory

in normal ageing and dementia: A questionnaire study. Memory. 8 (5), 311-321

 131

Appendix D Characteristics of Participants with ADHD

# Gender Age Learning Used Age first Taking Frequency Education Currently Language
Difficulties ADHD diagnosed meds for of med. level in school
or LD with ADHD use
services ADHD
Some
5 F 22 Yes No 20 Currently Daily college Yes English
College
6 F 29 No No 21 Currently Daily graduate Yes English
Postgradu
7 M 23 No Yes 21 Currently Daily ate degree Yes English
Some English +
28 F 21 No Yes 20 Currently Daily college Yes other
Postgradu
29 F 34 No No 30 Currently Daily ate degree No English
More than Graduated
once a high
30 M 20 No Yes 19 Currently week school Yes English
More than
once a College English +
31 M 26 No Yes 20 Currently week graduate No other
College
32 M 41 No Yes 7 In the past Daily graduate No English
Some
33 M 21 Yes Yes 6 Currently Daily college No English
More than
once a College
34 M 26 Yes Yes 20 Currently week graduate No English
Some
35 M 24 No Yes 16 Currently Daily college Yes English
Less than
once a College English +
36 M 35 No Yes 20 Currently week graduate Yes Other
Some
high
37 M 20 No Yes 18 No school Yes English
More than
once a Some
38 M 21 No Yes 21 Currently week college Yes English
More than
once a Some
39 F 26 No No 12 Currently week college No English
Less than College
once a graduate
40 F 32 No Yes 20 Currently week Yes Other
College
41 F 24 No Yes 14 Currently Daily graduate No English
Past+ Some
42 M 19 No No 8 currently Daily college Yes English
College
43 F 22 No Yes 16 In the past graduate Yes English

Note. Subject 38 was included only in experiment 1 as he did not complete the second session.
 132

Appendix E Computer Screen Display of Sample List of PM Tasks (Trial Day)