Investigating the efficacy of attention and working memory training for preschoolers

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Preschoolers

by Buse N. Bedir

Bachelor of Arts, University of Victoria, 2017

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of

MASTER OF SCIENCE in the Department of Psychology

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Supervisory Committee

Investigating the Efficacy of an Attention and Working Memory Training for Preschoolers

by Buse N. Bedir

Bachelor of Arts, University of Victoria, 2017

Supervisory Committee

Dr. Sarah Macoun, Department of Psychology Supervisor

Dr. Catherine Costigan, Department of Psychology Departmental Member

Dr. Todd Milford, Department of Curriculum and Instruction Outside Member

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Abstract

Supervisory Committee

Dr. Sarah Macoun, Department of Psychology Supervisor

Dr. Catherine Costigan, Department of Psychology Departmental Member

Dr. Todd Milford, Department of Curriculum and Instruction Outside Member

The effectiveness of attention and working memory (A/WM) training programs in improving executive functions (EFs) is heavily debated. The objective of the current study was to evaluate the efficacy of a game-based process-specific cognitive

intervention program (Dino Island; DI), on improving attention, working memory (WM), and pre-literacy skills in preschoolers. A secondary objective was to evaluate the

feasibility of delivering DI intervention in community settings. Dino Island is an intervention program that consists of five hierarchically structured tasks that target attention and WM. The intervention also involves the teaching of metacognitive strategies to facilitate transfer effects to daily activities. The DI intervention was delivered to preschoolers (ages four to six years) during regular school days. Ten preschoolers were randomly assigned to either an active DI intervention group, or an educational games control group, with fiveparticipants in each group. All participants completed 12 hours of intervention over an eight to ten-week period. Children’s attention, EF and school readiness was assessed pre and post intervention using cognitive measures, rating scales and interviews. Non-parametric test results found significant changes in working memory for the DI intervention group (p = .03), however, results did not show

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iv significant gains in other abilities. A case study approach was then utilized to further explore outcomes for children in the DI intervention condition. The results suggest that DI training can potentially lead to gains in WM among preschool children, providing preliminary evidence of its efficacy within this age groups. DI is also feasible to be delivered within school settings during regular school hours.

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List of Tables

Table 1. Participant Demographic Characteristics... 32

Table 2. Summary of Non-Parametric Test Results ... 52

Table 3. Summary of BC’s Pre-Intervention Test Results ... 57

Table 4. Strategy Use by Type on Wave for Participant #1 ... 60

Table 5. Strategy Use by Type on Mining Cave for Participant #1 ... 63

Table 6. Strategy Use by Type on Shell Machine for Participant #1 ... 65

Table 7. Summary of BC’s Pre- and Post-Assessment Results ... 68

Table 8. Summary of FZ’s Pre-Intervention Test Results ... 75

Table 12. Summary of FZ’s Pre- and Post-Assessment Results ... 85

Table 13. Summary of CN’s Pre-Intervention Test Results ... 91

Table 17. Summary of CN’s Pre- and Post-Assessment Results ... 100

Table 18. Summary of LE’s Pre-Intervention Test Results ... 106

Table 22. Summary of LE’s Pre- and Post-Assessment Results ... 115

Table 23. Summary of FD’s Pre-Intervention Test Results ... 121

Table 27. Summary of FD’s Pre- and Post-Assessment Results ... 130

Table 28. Summary of Sustained Attention Changes ... 134

Table 29. Summary of Inhibition Changes ... 135

Table 30. Summary of Working Memory Changes ... 136

Table 31. Summary of Parent-Reported Attention Changes. ... 136

Table 32. Summary of Parent-Reported EF Changes. ... 138

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List of Figures

Figure 1. Flow Chart of Participants and Procedures in the Study ... 46

Figure 2. Progression in Wave for Participant #1 ... 60

Figure 3. Progression in Mining Cave for Participant #1 ... 62

Figure 4. Progression in Shell Machine for Participant #1 ... 64

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Acknowledgments

First, I would like to thank my supervisor, Dr. Sarah Macoun, for her support, guidance and endless feedback. Without her time and contributions, this project would not have been possible. I would also like to thank Dr. Catherine Costigan for giving her time, support and thoughtful contributions throughout this project. Lastly, I am grateful to Dr. Todd Milford for graciously for providing his input, comments and time by serving as an external committee member.

Thank you to Laura Ellis for her tremendous amount of work during data collection, and all members of the Child Development Lab for their support during this process. Additionally, I am incredibly thankful to the staff, parents, and children at Parkdale Early Childhood Centre for their participation in this project.

Finally, from the bottom of my heart, thank you to my family and friends for their love, support and encouragement. Thank you for being the best support network out there. This would not have been possible without them.

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Dedication

This thesis is dedicated to my parents, who are there for me and whose support was also instrumental in completing this journey. I am very lucky to be your daughter.

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Introduction

Executive functions (EF) refer to a set of higher-level cognitive processes that allow for controlled and purposeful goal-directed behaviour (Friedman & Miyake, 2017). This term is generally used as an umbrella term for a set of component processes that are necessary for selection and guidance of purposeful behaviour to attain goals. The term “function” in the name of EF brings questions about what it means in this specific context. Zelazo et al. (1997) explained it as:

“The term function in this context refers to a complex activity with a constant outcome that is capable of being affected in a variety of ways” (p.199).

Attention and EFs are crucial for long-term academic, social, and life outcomes for children and attention/EF deficits can lead to significant cognitive, academic and mental health problems and negative life-trajectories (Alloway & Alloway, 2010; Mueller et al., 2008). Given that the healthy development of EFs is crucial for long-term life outcomes, targeted early training for EFs in preschoolers may help to prevent

negative trajectories that may result from un-remediated EF difficulties, and improve a series of important life domains such as academic achievement, self-regulation,

occupational achievement and physical and mental health (Blakey & Carroll, 2015; Henry, Messer, & Nash, 2014). In addition, EF training holds promise for children who do not have EF deficits and lead to improvements in attention and EFs. Henry and colleagues (2014) found that for a group of 5-8-year-old typically developing children, working memory (WM) training led to significant gains in WM post-intervention. Similarly, Blakey and Carroll (2015) reported that with a group of 4-year-old preschoolers, a brief EF training program led to gains in WM and mathematical

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2 reasoning. Given the potential effectiveness of EF training with children, and particularly preschoolers, this study set out to investigate the outcomes and effectiveness of an EF/attention training program in typically developing preschoolers.

Attention

Attention refers to selectively concentrating on one aspect of information or stimuli while ignoring other stimuli (Posner, 2012). Most researchers agree that

attentional control is necessary for EFs and performance on non-routine tasks (Diamond, 2006; Wiebe, Espy, & Charak, 2008). It is argued that lower-order attention abilities such as basic alertness and shifting are required for EFs (Diamond, 2006). According to this view, the basic ability to control attention makes goal-directed behaviour and planning possible and hence, is necessary for EFs. Factor analytic (CFA) studies support this claim and reveal a common variance across all EF tasks, which is thought to reflect the role of attention control abilities during EF tasks (McCabe, Roediger, McDaniel, Balota, & Hambrick, 2010). This demonstrates that attention control can be thought of as a unique EF component that is predictive of higher cognitive control. Different terms, including but not limited to executive attention (Kane, Conway, Hambrick, & Engle, 2007;

McCabe et al., 2010), attentional control (Anderson, 2002), and cognitive control (Depue, Banich, & Curran, 2006), are used to refer to the attention that is required for all EF tasks. Consistent with this notion, Anderson (2002) proposed that attentional control is one of the four components of the “executive system model”. Similarly, Posner and DiGirolamo (1998) argue that executive attention is responsible for conflict resolution that may arise in EF-demanding situations, and hence constitute it as a unique part of

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3 EFs. Thus, given the close relationship between attention and EF abilities, attention will be described below.

Theoretical Models of Attention:

Attention is an important cognitive ability that encompasses multiple processes that start to develop in the first year of life and develop rapidly during the preschool years (Mahone & Schneider, 2012). Attention has many component processes that are sub-served by different brain networks (Petersen & Posner, 2012), including orienting towards and selecting stimuli to attend to, maintaining a state of alertness, regulating thoughts, focus and responses (Pozuelos, Paz-Alonso, Castillo, Fuentes, & Rueda, 2014). Although attention has been conceptualized in different ways (Baddeley, 1996;

Neumann, 1996; Sturm & Zimmermann, 2000), Posner and Petersen (1990) define attention as consisting of three networks. The first of these networks is the alerting network, which refers to the ability to maintain attention (sustained attention) and one’s readiness to respond to external cues (Pozuelos et al., 2014). Areas of the frontal and parietal lobes and locus coeruleus are involved with the alerting network, as well the right hemisphere of the brain (Posner & Petersen, 1990). The second network is referred to as the orienting network, which supports switching attention and re-orienting to different internal or external cues in the environment (Waszak, Li, & Hommel, 2010). Frontal eye fields, superior parietal cortex, temporoparietal junction and subcortical structures such as the superior colliculus are involved in the orienting network, and damage to these brain areas result in deficits in the ability to shift attention (Posner, Petersen, Fox, & Raichle, 1988). Finally, the last network is referred to as the executive attention, which is a person’s ability to control their attention. The brain areas involved in executive attention

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4 include the anterior cingulate cortex, lateral and ventral prefrontal cortex, and the basal ganglia (Pozuelos, Paz-Alonso, Castillo, Fuentes & Rueda, 2014). The functions of the executive attention network include selecting the appropriate response to different situations, maintaining goal-relevant responses and suppressing goal-irrelevant information (Rueda, Posner, & Rothbart, 2005). Out of the three networks described, executive attention is considered as the entry to conscious state and is involved with conflict resolution in conflict tasks such as the Stroop or Flanker tasks (Petersen & Posner, 2012). Thus, this is the higher-order executive attention network that is most closely associated with EFs (Rueda et al., 2005).

Development of Attention:

Within the first year of life, children start to develop the ability to sustain, shift and inhibit attention, and learn to use these skills to update and hold information in mind (Wiebe & Karbach, 2018). It has been shown that the ability to switch attention and attend to relevant cues start to emerge during the first four months of life in typically developing children (Hunnius & Geuze, 2004; Johnson, Posner, & Rothbart, 1991). Even though basic attention abilities start to develop in the first year of life, more complex skills such as orienting and using strategy for visual search don’t reach adult-level

performance until around six years of age (Mulder, Pitchford, Hagger, & Marlow, 2009). The development of the executive attention network starts at the end of the first year of life and has a more protracted developmental course compared to the other networks (Rueda, Checa & Cómbita, 2012; Rueda et al., 2005). It has been shown that infants younger than 12 months are not able to inhibit their previously learned responses (Diamond, 2006). On a spatial conflict task that induced a conflict between object’s

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5 identity and location, assessing children’s self-monitoring, error detection and error correction (Jones, Rothbart, & Posner, 2003), researchers found that the ability to inhibit pre-potent responses progressed from a total inability to inhibit responding to accurate performance between the ages of two to four years (Gerardi-Caulton, 2000).Even though basic attention and inhibition start to develop in the first years of life, more complex abilities such as attention control abilities continue to develop and go through important changes in middle childhood until the ages of 14-15 years (Conners, Epstein, Angold, & Klaric, 2003; Huizinga, Dolan & van der Molen, 2006). Children learn to resist irrelevant and distracting stimuli during middle childhood years, and selective attention becomes more effective (Fisher, Godwin, & Seltman, 2014). These findings show that executive attention starts to develop in the first year of life and continues to develop into early and middle childhood. Additionally, these studies suggest that basic attention is necessary and is a precursor to the development of executive attention that is closely associated with EFs and self-regulation.

Implications of Attention Deficits:

As attention control is essential for the execution of purposeful, complex behaviour and behavior regulation, attention problems have serious behavioural and emotional consequences for children and their families. Friedman and Miyake (2017) found that attention problems were associated with lower performance on cognitive and IQ tests. Additionally, attention problems negatively impact academic achievement of children. In a meta-analysis conducted by Frazier, Youngstrom, Glutting and Watkins (2007), it was reported that participants who had attention problems or Attention Deficit Hyperactivity Disorder (ADHD) had significantly lower academic achievement than

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6 typically developing participants. Similarly, attention and EF abilities are strongly

associated with high school graduation rates and post-secondary educational attainment, with strong attention and EF abilities leading to better outcomes (Barkley, Fischer, Smallish, & Fletcher, 2006). Thus, attention and EF capacity are associated with

immediate and long-term outcomes for children, with stronger attention and EF abilities leading to better outcomes and trajectories.

Executive Functions:

Even though there is no clear consensus on what exactly constitutes EFs, most researchers agree that inhibition, working memory and flexibility are core EFs that are needed for healthy cognitive development, success in all types of environments, and well-being/health across the lifespan (Alloway & Alloway, 2010; Bull, Espy, & Wiebe, 2008; Miyake et al., 2000; Muller, Baker, & Yeung, 2013). In the context of this study,

inhibitory control is defined as the inhibition of a prepotent response, stopping of an ongoing response, or resisting distractions (Barkley, 1997). Working memory refers to the systems that are involved in keeping information in mind and manipulating it while performing complex tasks (Baddeley, 2010). Individual differences in working memory tasks and abilities are highly correlated with general cognitive abilities such as reasoning and verbal comprehension, and academic performance (Kane & Engle, 2002). Finally, cognitive flexibility is defined as “activation and modification of cognitive processes in response to changing task demands” (Deak, 2003, p. 275). As the demands of the environment change rapidly, it is important to shift attention and select the appropriate response. Cognitive flexibility is important for planning, social interaction, spatial navigation, and creative thinking as well as use of language (Varanda & Fernandes,

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7 2017). While these three EF abilities are considered core, as discussed previously, many researchers also consider attention (particularly higher order attention) as an aspect of EFs (Anderson, 2002). On the other hand, others argue that basic attention is needed for EFs and that it is a precursor to EF, although not necessarily a core EF (Wiebe et al., 2008). Regardless of whether one considers attention to be an EF component or not, attention and working memory (A/WM) and flexibility have been found to uniquely contribute to a number of very important aspects of cognitive, academic and social functioning.

Theoretical Models of EFs:

For decades, researchers have created models with the goal of defining and explaining the nature of EFs (Kirkham, Cruess, & Diamond, 2003; Miyake et al., 2000; Zacks & Hasher, 1994). Some researchers have conceptualized EFs as a single, unitary process responsible for directing and regulating behaviour (Baddeley, 1996). Others, on the other hand, have argued that EF abilities consist of several distinct constructs that are independent from each other, but that rely on the functioning and coordination of one central EF (Barkley, 1997).

There remains debate about whether EFs are unitary or distinct constructs (Bull et al., 2008; Hughes & Ensor, 2007; Lerner & Lonigan, 2014; Miyake et al., 2000). Miyake et al.’s (2000) theoretical approach defines EFs as a multi-component system that include both independent and interrelated processes (Miyake et al., 2000; Stuss &

Alexander, 2000). In an influential study, Miyake et al. (2000) proposed that there are three core components of executive functions (e.g., inhibition, working memory and shifting) which are related but distinct constructs. In this model, Miyake et al. (2000)

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8 combined both unitary and distinct theories of EF and concluded that EFs reflect both unitary and non-unitary constructs. Distinct EFs tend to correlate highly with each other, which shows unity, but there is also evidence of diversity in that they tap into different abilities (Miyake & Friedman, 2012). Confirmatory factor analyses (CFA) using this model have confirmed that EFs appear to be distinct but interrelated constructs in adults, adolescents, and school-age children (Diamond, 2016; Lerner & Lonigan, 2014; Miyake & Friedman, 2012; Rose, Feldman, & Jankowski, 2011). On the other hand, CFA analyses support a single latent EF construct in studies with preschoolers (Wiebe et al., 2011). These results suggest that EFs start as a unitary construct, and start to differentiate during development. Consistent with the view that inhibition, working memory and flexibility/shifting represent core EFs (Jewsbury, Bowden, & Strauss, 2016; Miyake et al., 2000; Wiebe et al., 2008), this thesis uses the Miyake et al. (2000) model of EFs. For the purposes of this thesis, I will be focusing on attention and WM (A/WM) within Miyake’s model.

Neural Correlates of EFs:

EFs are strongly associated with the function of the frontal lobes in both adults and children. Luria (1973) identified the frontal lobes as “the essential apparatus for organizing intellectual activity as a whole, including the programming of the intellectual act and the checking of its performance” (p. 340). In this statement, he essentially

conceptualized what is currently known as EFs and localized EFs to the frontal regions of the brain.

Previously, it was thought that there was a homogenous involvement of the frontal lobe during activities that involve EFs. However, later on it was shown that EFs

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9 are associated with different regions of the frontal lobes(Henri-Bhargava, Stuss &

Freedman, 2018) and are distributed over a wide network of areas and pathways (Monchi, Petrides, Strafella, Worsley, & Doyon, 2006). To support this idea, a meta-analysis of neuroimaging studies showed that the cingulate cortex and the temporal cortex were activated for WM and updating tasks (Niendam et al., 2012). The same study showed that the cognitive control network (CCG) was activated in the prefrontal and parietal areas during inhibition and updating tasks (Niendam et al., 2012). The cognitive control network, activated heavily during performance on EF tasks, consists of anterior cingulate cortex, dorsolateral prefrontal cortex, superior and inferior parietal lobes, prefrontal cortex, temporal cortex and occipital cortex (Kim, Wittenberg, & Nam, 2017). EFs in Children.

EF Development:

Researchers have been trying to understand the nature of EFs in children, and whether the factor structure of EFs in children resembles that of adults for years (Shing, Lindenberger, Diamond, Li, & Davidson, 2010; Wiebe et al., 2011). CFA studies have shown that the nature of EFs in 10 to 16-year-old children is similar to that of adults; that is working memory, inhibition and shifting appear to be distinct but interrelated

components of EF in this age group (Shing et al., 2010). However, less is known about the nature of EFs in younger children, particularly prior to school entry. Research with preschool-age children suggest that a single, unitary EF factor may be present, and that working memory and inhibitory control in this age group may be tapping into the same ability (Wiebe et al., 2008). Consistent with this idea, Wiebe et al. (2011) reported that there was no distinction between working memory and inhibition skills at three years of

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10 age. Further, Wiebe et al. (2008) found that a simple, single executive control factor model was supported over multifactor executive control models in preschoolers.

Additionally, Wiebe et al. (2011) found that in a group of 228 three-year-old children, the fit of a single latent EF construct was supported over a multifactor EF construct. This research suggests that EFs may start out as an undifferentiated unitary process and become differentiated and distinct as children develop and mature over the years (Huizinga et al., 2006; Wiebe et al., 2008).

Executive functions have a protracted developmental course across the lifespan and different EF components are thought to come on-line at different ages (Anderson, Anderson & Lajoie, 1996). Currently, it is thought that the development of EFs occurs in a stage-like fashion with spurts in EF development starting as early as a child’s first year of life (Anderson et al., 1996; Bull, Espy, & Senn, 2004). The development trend is not linear; instead, there seems to be periods of acceleration, plateauing and deceleration in the development of EFs (Huizinga et al., 2006). EF development follows the maturation of the frontal lobes, and particularly prefrontal cortex, which is one of the latest brain areas to mature (Giedd et al., 1999). This is due to the slower myelination of fibers in the prefrontal cortex, which is not complete until early adulthood years (Paus,

Castro-Alamancos, & Petrides, 2001). Because of the slow maturation of the underlying brain substrate, EFs are one of the last cognitive functions to develop, not fully reaching adult levels until the early 20s (Li et al., 2004). As prefrontal cortex continues to develop and mature into adulthood, EFs follow a similar path.

It has been shown that core EFs such as inhibition and working memory start to develop in the first year of life and go through important changes in preschool years,

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while more complex EFs such as planning and problem solving take a longer time to develop, and reach maturity into early adulthood (Espy, 1997; Espy, Kaufmann, Glisky, & McDiarmid, 2001). Studies have shown that by the age of 14 years, inhibition skills reach adult levels of maturity in typically developing children (Fan, McCandliss,

Fossella, Flombaum & Posner, 2005; Huizinga et al., 2006). Similarly, working memory abilities go through important changes in early and middle childhood, and do not reach adult levels until around age 15 years (Huizinga et al., 2006). Inhibition and working memory, then, support the later development of more complex EFs and higher order cognitive processes such as organization and planning, which continue to develop through adolescence and into the early 20s (Garon, Bryson, & Smith, 2008; Romine & Reynolds, 2005).

Early and middle childhood is considered as one of the most important development periods for the maturation of EFs as seen behaviourally in children’s

improved ability to resist impulses and follow social rules (Wiebe & Karbach, 2018). EFs start to develop in the early years of life, and follow an inverted U-shaped curve across the lifespan (Zelazo, Craik, & Booth, 2004). This suggests that EFs rapidly change in the early years of development and have a spurt in the preschool years. By fiveyears of age, children show improved ability to inhibit previously learned responses (Clark et al., 2013) and to delay gratification (Carlson, Moses, & Claxton, 2004), and these abilities continue to develop and improve with age. Taken together, research in this field indicates that EFs start to develop in the first year of life and continue to develop through early and middle childhood, with a spurt in preschool years (Zelazo, Qu, & Müller, 2005). Early

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rudimentary EFs set the stage for the development of more complex EFs later on, highlighting importance of healthy EF development in early childhood.

EF abilities in preschool years are related to a number of very important

developmental milestones including the development of social skills, behavioural control, school readiness, and academic achievement (Alloway & Alloway, 2010). In typically developing children, EF abilities during preschool years are strongly predictive of pre-literacy/numeracy skills at school entry, and also predict later academic achievement (Alloway & Alloway, 2010; Bull et al., 2008). Further, atypical development of EFs is associated with the presence of neurodevelopmental disorders such as autism spectrum disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and learning disorders (LDs; Blair & Razza, 2007; Loe, Chatav, & Alduncin, 2015).

Assessment of Executive Functions:

Although a focus of considerable research, the assessment of EFs has proven to be a challenge across all age groups (Miyake & Friedman, 2012), due to the many different definitions of EFs and the contributions of other cognitive abilities (such as language, attention, memory, etc.) to EF tasks, making EF tasks relatively impure or nonspecific (Anderson, 2002). In adult populations, additional challenges include the fact that EF tests administered in controlled lab settings often do not mirror EF demands in day to day life.

Even though EFs are essential for successful childhood development and considered as a marker for atypical development in many cases, finding ways to

accurately measure EFs in the early childhood (particularly preschool years) has proven to be a major challenge, even more so than for adults. Given the importance of healthy

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13 EF development and detecting challenges early on to prevent further problems, there has been an increase in research regarding preschool EF, its assessment and implications in the previous years (Carlson, 2005, 2012; Garon et al., 2008).

EF assessment in preschoolers is even more challenging for several reasons, including the lack of age-appropriate tasks (Garon et al., 2008), and the fact that the factor structure of child EF is different than adult EF (Zelazo & Carlson, 2012).

Additionally, assessment is further complicated by the fact that other cognitive abilities that are built into EF tasks in children (memory, language, etc.,) are still developing in this age group. Initially, at least in part as a result of the limitations of assessment tools for this age range, it was felt that preschool-age children do not have functional EFs (Zelazo, Qu, & Muller, 2005). However, more recently, using sophisticated measures that are specifically tailored to this age range, we can see evidence of early EFs in young children (Bull et al., 2004; Zelazo et al., 2005). Measures that are associated with WM, inhibition and rule learning are used extensively with young children to assess EF

capacity (Gardiner, Hutchison, Muller, Kerns & Iarocci, 2017). Similarly, flexibility tasks such as categorizing stimuli or sorting tasks are used with preschool aged children. In addition to these measures, informant ratings are used to show some evidence of EF capacity in preschool aged children.

Despite recent advancements in the assessment of EFs, there are still some major challenges associated with EF assessment in young children. As stated above, a primary challenge in this age group is the lack of age appropriate assessment tools. The tasks and measures used for young children must be age-appropriate and not simply downward extensions of adult measures, as child EF abilities are thought to be structurally and

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14 functionally different than adult EF abilities (Howard & Okely, 2015). EFs are less

differentiated in preschoolers (Muller, Kerns, & Konkin, 2012), and for this reason, the nature of most adult-modified EF tasks may make it difficult to observe EFs in clinical or research settings. Additionally, many childhood EF measures are simply downward extensions of adult tests, with very high language and focus demands, and not suitable for young children. Thus, it is important to develop and use developmentally sensitive

measures for young children (Barkley & Fischer, 2011; Loe et al., 2015; Payne, Hyman, Shores, & North, 2011).

EF Deficits and Implications:

As previously discussed, EFs are necessary for coping with and adapting to novel situations and are essential for optimal functioning. In an ever-changing environment, EFs allow us to adapt to new and diverse situations, and inhibit inappropriate responses to our surroundings (Jurado &Roselli, 2007). Frontal lobe dysfunction, as a result of age-related structural and functional declines or damage to the brain, affects cognition and EFs in adults (Phillips & Della Sala, 1998). Frontal lobe damage has been shown to be associated with a variety of EF deficits including inflexibility, attention and WM

problems, lack of awareness, and social deficits. In a study by Rodríguez-Bailón, Triviño and Lupiáñez (2012), the researchers found that patients with frontal lobe damage

showed impairments in the executive attention network proposed by Posner and Dehaene (1994). They also found that frontal lobe damage was associated with personality change characterized by a lack of behavioural control. The same pattern was found in a different study that examined the relationship between frontal lobe damage and attention (Hu et al., 2013); the researchers found that patients with frontal lobe damage showed reduced

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15 efficiency and impairments of the executive attention network. Similar to the deficits in the attention network, frontal lobe lesions are associated with declines in WM as well. In a study by Thompson-Schill et al. (2002) with adults, the researchers found that frontal lobe damage was associated with impairments in WM that is not related to decline due to normal aging. Additionally, EF deficits are found to be correlated with behavioural disinhibition, which is thought to underlie behaviours such as externalizing problems, attention deficits (such as in individuals with ADHD), risk taking and novelty seeking, substance use and conduct disorders in adults (Young et al., 2009).

Deficits in EF have also been implicated in a variety of neurodevelopmental disorders, including ADHD (Castellanos, Sonuga-Barke, Milham, & Tannock, 2006), ASD (Robinson, Goddard, Dritschel, Wisley, & Howlin, 2009), Fetal Alcohol Syndrome Disorder (FASD) (Mattson, Crocker, & Nguyen, 2011), traumatic brain injury (TBI; Levin & Hanten, 2005), LDs (Schuchardt, Maehler, & Hasselhorn, 2008), and language impairments (Im-Bolter, Johnson, & Pascual-Leone, 2006). With respect to ADHD, working memory, inhibition and cognitive flexibility are impaired in both school-aged children and adolescents with this diagnosis (Alderson, Rapport & Kofler, 2007; Willcutt. Doyle, Nigg, Faraone, & Pennington, 2005). These deficits are associated with

behavioural problems including inattention, poor impulse control, inflexibility,

disorganization, poor planning, and low working memory (Anderson, 2002). Problems with aspects of EFs have been implicated in children with ASD as well (Gilotty,

Kenworthy, Sirian, Black, & Wagner, 2002; Ozonoff, & Strayer, 1997), including deficits in flexibility, working memory and planning (Ozonoff & Cathcart, 1998). Similar to these two previous populations, deficits in attention, including selective, orienting,

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16 shifting and sustained attention are seen in children with FASD (Kodituwakku &

Kodituwakku, 2014; Kooistra, Crawford, Gibbard, Kaplan, & Fan, 2011). In the FASD population, deficits in EF abilities are also associated with problems with communication and social relationships (Gilotty et al., 2002). Additionally, EF deficits lead to serious secondary disabilities such as school failure (Mayes & Calhoun, 2007), health problems and social and emotional functioning deficits (Kenworthy, Black, Harrison, della Rosa & Wallace, 2009), and adaptive behaviour impairments (Gilotty et al., 2002).

It is clear that unremediated EF deficits may lead to serious social, cognitive, and mental-health related consequences for individuals, and particularly children, in the long term (Biederman et al., 2006; Diamantopoulou, Rydell, Thorell & Bohlin, 2007; Miller & Hinshaw, 2010). Healthy development of EFs has been linked to important life outcomes such as level of education completed, financial security, relationship status, health and substance use in adulthood (Hunter, Edidin, & Hinkle, 2012). Additionally, it has been shown that EF and attention skills at early ages predict school readiness and school performance at later ages (Alloway & Alloway, 2010; Best, Miller, & Naglieri, 2011). This is understandable as school-age children are expected to follow directions, inhibit inappropriate responses and behaviour, pay attention in class, and get along with other students, all of which are associated with EFs. Early EF abilities not only predict school readiness at the start of elementary school, but also predict later academic success. For example, it was shown that EF abilities in preschool children predict pre-literacy and numeracy skills at school entry and also predict long-term school attainment, even more so than intellectual ability (Alloway & Alloway, 2010). Difficulties with EF, and

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17 consequences for academic achievement and school success (Nisbett, Aronson, Blair, Dickens, Flynn et al., 2012; Sirin, 2005).

The lack of these essential skills is strongly associated with poor school

performance in children with and without neurodevelopmental disorders (Mueller et al., 2008). Children with EF problems may also have behavioural or emotional difficulties including lack of insight and intuition, and may present as unmotivated or unresponsive or impulsive (Raaijmakers et al., 2008). These behavioural and emotional difficulties may manifest as problems in social interactions, social functioning and peer relationships (Fahie & Symons, 2003). Problems with the development of early EF has been shown to predict children’s social problems, while later EF development predicts psychosocial outcomes in adolescence (Galambos, MacDonald, Naphtali, Cohen, & de Frias, 2005; Santor, Ingram, & Kusumakar, 2003). Deficits in the development of early EF skills may impede the development of academic and social competence at later ages and negatively impact an individual’s life.

In sum, childhood is characterized by significant changes in cognition and

behaviour that result from the maturation of brain areas responsible for higher order EFs. During the early years of life, in line with development of frontal lobe systems, children gain social, cognitive and emotional knowledge and experience that help them navigate different situations and environments. The EF network helps children navigate this environment and provide mechanisms for goal directed behaviour and decision making (Barrasso-Catanzaro & Eslinger, 2016; Eslinger, Flaherty-Craig, & Benton, 2004). Impairments in the development of EFs, such as in children with PFC damage, children with neurodevelopmental disorders (NDD), or children from low socio-economic

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18 backgrounds, pose challenges that may cause negative life trajectories and outcomes including cognitive, social, behavioural, academic and emotional difficulties. Without intervention, these deficits do not show significant improvement (Ozonoff & McEvoy, 1994), and have an impact on all the abilities discussed above. Thus, it is very important to promote healthy development of EFs in childhood and provide interventions to ameliorate cognitive deficits early on.

Cognitive Interventions for Attention and EF

Given the importance of attention and EFs for healthy development, promoting and strengthening EFs has been a focus of research in fields focused on early childhood development (Diamond, 2012; Diamond & Lee, 2011). As discussed previously, since strong EFs are essential for healthy development and successful academic, occupational and social life, it is important to remediate these skills early on in children who may be at risk for EF deficits. Additionally, a meta-analysis shows that, even in typically

developing children, EF training leads to gains (Sala & Gobet, 2017), which suggests that improving attention and EFs may benefit typically developing children as well. There are different types of intervention approaches for remediating EFs including pharmacological treatments, behavioural interventions and cognitive rehabilitation interventions

(Diamond, 2012; Diamond & Lee, 2011). Among the different types of intervention approaches, cognitive rehabilitation interventions are the focus of this current thesis.

There are different approaches to cognitive intervention in the literature, including compensatory, process specific and combined approaches (Mateer, Kerns, & Eso, 1996; Sohlberg & Mateer, 1987). One type of attention and EF training is referred to as the ‘compensatory approach’ (Conners, Rosenquist, Arnett, Moore, & Hume, 2008; Loomes

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19 et al., 2008). When using the compensatory approach for remediating attention and EFs, the individual is taught techniques and strategies to help ‘compensate for’ or ‘work around’ cognitive impairments (Sohlberg & Powell, 2011). In compensation interventions, the aim of the program is to change behaviour or activity to promote functional gains (Sohlberg & Powell, 2011). Specific techniques may involve learning to follow a checklist to engage in certain behavioural routines or learning metacognitive strategies to help a person reflect on their behavior and use strategies to improve functioning in real-life situations. Other tools that are considered compensatory include the use of technology such as memory aids, navigation devices and flow charts. Learning these strategies does not directly alter an individual’s cognitive abilities; however,

functional abilities improve with the consistent use of these techniques, and transactions with the environment may possibly alter cognition in the long term (Mateer et al., 1996).When using this model, compensatory strategies should be tailored to the

individual and their specific needs, and the behaviours learned should be relevant to and generalize to other real-life situations. Such techniques have been used with children in the literature, and it has been shown that they are effective when used consistently; however, their effectiveness was not maintained when they stopped the program (Loome et al., 2008).

Another approach to EF training is the direct training of these skills, also known as the process-specific approach (Sohlberg & Mateer, 1987). This approach utilizes repetition of hierarchically organized exercises in targeted areas to improve cognitive abilities through mass practice (Sohlberg & Mateer, 1987). The process-specific model of EF training involves targeting one specific cognitive ability through repeated exercises to

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20 reorganize the neural pathways in the brain and improve cognitive abilities (Sohlberg et al., 2003). This approach capitalizes on properties of experience dependent

neuroplasticity, in the sense that repetition and consistent use of one ability can lead to neural change and improve the functioning of that specific skill (Feuerstein, Feuerstein, Falik, & Rand, 2006; Kleim & Jones, 2008). Neuroscience literature has shown that process-specific approach is an effective method and is associated with changes at both the brain (neuroanatomical, neurochemical) and behavioural (functional changes) levels (Kelly, Foxe, & Garavan, 2006). Kleim and Jones (2008) provided core principles of experience-dependent plasticity that are relevant for this type of cognitive remediation. The core principles of “intensity”, “salience”, and “repetition” means that long-lasting neural change requires repetition of a salient behaviour at a high intensity. The activities also need to be hierarchically graded and adaptive, that is self-adjusting based on

individual’s performance. One of other the principles “use it and improve it” refers to the principle of targeting one specific ability leading to improvement of that ability through the engagement and change of underlying neural systems (Kleim & Jones, 2008). Neural changes elicited through process specific training have been associated with gains in WM, attention control, reduced symptoms of inattention and nonverbal reasoning post-intervention (Pozuelos et al., 2014; Rueda et al., 2012, 2005).

When undertaking process-specific training for children, the use of a videogame format can increase engagement (Hardy, Willard, & Bonner, 2011; Jaeggi, Buschkuehl, Jonides, & Shah, 2011), capitalizing on Kleim’s principle of salience as a key factor in

driving neural change. In fact, it has been shown that WM training in a game format is more engaging for children; children spend more time on the training program, have

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21 better training performance, and perform better on WM measures post-intervention when delivered in a computerized/game-based format (Prins, Dovis, Ponsioen, ten Brink, & van der Oord, 2011). Additionally, computerizedA/WM interventions can automatically adjust the difficulty level based on the individual’s performance, incorporating an ‘adaptive’ format which maximizes therapeutic outcomes. The adaptive approach maintains optimal levels of engagement as the difficulty is neither too easy nor too hard for the particular individual. Studies that compare adaptive to non-adaptive approaches (in which the level of difficulty does not adjust based on the user’s performance) show that an adaptive approach leads to a higher intensity training process and greater

improvement on post-intervention measures (Alloway, Bibile, & Lau, 2013; Klingberg et al., 2005). The process-specific approach has been effectively used for EF training in preschoolers and older children within both computerized and non-computerized training formats (Lee et al., 2016; Howard, Powell, Vasseleu, Johnstone, & Melhuish, 2017; Re, Capodieci, & Cornoldi, 2015; Kerns, Macoun, MacSween, Pei, & Hutchison, 2017; Thorell, Lindquist, Nutley, Bohlin, & Klingberg, 2009). These studies reported promising results and found that EF training with preschoolers have the potential to improve these very important abilities (Blakey & Carroll, 2015; Sala & Gobet, 2017; Kroesbergen, van’t Noordende & Kolkman, 2014; Passolunghi & Costa, 2016).

An important component of process-specific approach is the inclusion of metacognitive strategies to children, which refers to teaching an individual to

self-monitor, evaluate, and control their own cognition (Kerns et al., 2017). Researchers have found that teaching and using metacognitive strategies improve the generalization of learned abilities to other real-life tasks and increase the self-directed use of the strategies

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22 (Cicerone et al., 2011; Sohlberg et al., 2003; Tamm, Epstein, Peugh, Nakonezny, & Hughes, 2013; Ylvisaker & Feeney, 1998). In a study by Galbiati et al., (2009), the researchers combined a computerized attention training program with individual sessions with a therapist who taught metacognitive strategies to children with traumatic brain injury to improve attention deficits. They found that combining attention training with metacognitive strategies led to improvements in attention and adaptive skills such as social skills, communication and daily living (Galbiati et al., 2009). Additionally, a number of meta-analyses have demonstrated that process specific training is more

effective when the delivery is combined with teaching metacognitive strategies (Cicerone et al., 2011; Kennedy et al., 2008). These findings support the claim that process-specific EF training methods should be used with metacognitive strategies to maximize training outcomes and generalizability effects.

With respect to outcomes pertaining to A/WM training, there is strong evidence that A/WM abilities can be trained and improved in both adults and children, which then provides a foundation for gains in other abilities such as social relationships, cognitive performance and academic achievement (Kerns, MacSween, Vander Wekken & Gruppuso, 2010; Morrison & Chein, 2011; Thorell et al., 2009). Although not without controversy(Chenault, Thomson, Abbott, & Berninger, 2006; Melby-Lervag, Redick, & Hulme, 2016; Shipstead, Hicks, & Engle, 2012), there is a strong literature documenting the efficacy of A/WM training in children and adults to promote EF skills,

behaviour/emotion regulation and, in turn, academic achievement (Blair & Diamond, 2008; Kerns, Eso, & Thomson, 1999; Kerns et al., 2010; Morrison & Chein, 2011; Rueda et al., 2012; Shalev, Tsal, & Mevorach, 2007; Thorell et al., 2009).

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23 Despite differing opinions on whether A/WM training is truly effective and

generalizable, research has supported efficacy of A/WM training in typically and

atypically developing children (Blair & Diamond, 2008; Rueda et al., 2012, 2005). Rueda and colleagues (2012) delivered computerized attention training to a group of typically developing 5-year-old children and compared their outcomes to a non-trained group. They found that children in the training group were able to activate the attention network faster and more efficiently compared to the non-trained group of children, suggesting that attention abilities were improved after training. Further, based on longitudinal follow-up, this effect was maintained even two months after the training. In a study by Thorell et al. (2009), preschool children received visuospatial working memory or inhibition training for five weeks. When compared with a non-trained group, results indicated that working memory training improved the trained aspects of working memory, as well as

performance on non-trained measures of spatial/verbal working memory and attention. On the other hand, inhibition training led to improvement on trained inhibitory tasks, with no transfer effects to working memory or attention abilities. This suggests that WM training has more generalizability to other domains such as attention compared to

inhibition training. On the other hand, inhibition training is more specific and leads to gains in only inhibition domain.

The efficacy of A/WM training in clinical populations has also been documented in the literature. A study by Re, Capodieci, and Cornoldi (2015) investigated the

effectiveness of a non-computerized EF training program, delivered by a school psychologist to five-year-old children with ADHD symptoms. The children who participated in the program showed improvements on tasks that measured attention

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24 control, working memory and impulsive behaviours. Similarly, in a pilot study by

Salvaguardia, Re, Caponi, and Cornoldi (2009), with first-graders who showed ADHD symptoms, attention control and working memory training improved children’s EFs and decreased the presence of ADHD symptoms. In a different study, computerized A/WM training was delivered to children between the ages of six to 13 years who had FASD or ASD (Kerns et al., 2017). Post-intervention results showed significant gains on cognitive measures of A/WM, an academic measure of reading fluency, and behavioural regulation. These results suggest some far transfer of A/WM training to academic performance and behavioural regulation.

Although many studies have shown that EF training can be effective for children, other studies have not found positive effects of A/WM training, particularly with respect to generalizing to real-world situations. A meta-analysis by Melby-Lervag et al. (2016) reviewed the effectiveness of working memory training programs. They reported that the effectiveness of WM training did not transfer to non-trained abilities or real-word

cognitive skills. Similarly, another article by Shipstead et al. (2012) reviewed the effectiveness of a working memory training program (Cogmed). They concluded that while this WM program showed marginal improvements in working memory capacity on cognitive testing, the effectiveness was not maintained in the long term, and there were no transfer effects to other skills. It is important to note that meta-analyses may not be an ideal method for evaluating cognitive training outcomes as these approaches tend to collapse different diagnoses, intervention times, types of interventions, and age ranges and ignore individual differences.

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25 In addition to promoting EF development, A/WM training may also lead to

academic gains as a result of improvements in core cognitive abilities that are important for academic success. Although some studies have not shown academic gains based exclusively on A/WM training (Chenault et al., 2006; Melby-Lervag & Hulme, 2013), other studies have found that A/WM training can transfer to academic gains (e.g., arithmetic and reading fluency) both in typically developing children and those with attention deficits and special education needs (Bergman-Nutley & Klingberg, 2014; Dahlin, 2011; Loosli, Buschkuehl, Perrig, & Jaeggi, 2012). These mixed results show that A/WM training holds promise for improving academic skills and outcomes in certain children; however, more research is clearly needed to support these claims.

The positive effects of A/WM and EF interventions may be particularly strong when interventions are delivered early (i.e. during the preschool years), potentially leading to greater transfer of effects due to factors such as accessing critical periods of brain development and pre-empting failure experiences (Wass, Scerif, & Johnson, 2012). Although experience dependent neuroplasticity is a lifelong process, with changes in development of the brain continuing throughout the life span as a result of experience (Baltes, Lindenberger, & Staudinger, 2006), the brain has greater plasticity in the early years of development (such as preschool years) (Anderson, Spencer-Smith & Wood, 2011). Further, targeting critical EF skills early on may avoid the possible negative consequences that stem from unremediated EF deficits. Given that strong EFs are crucial or long term academic, social and emotional success, there is value to investigating the impact of A/WM training in this population to improve early EF abilities and academic skills.

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26 A Novel A/WM Training Program: Dino Island

Dino Island (DI) is a computerized cognitive intervention which targets A/WM and cognitive flexibility in children. DI is a therapeutic game based on a process specific approach, which systematically targets and trains specific EF skills through repetitive practice on self-adjusting and hierarchically graded exercises (Sohlberg & Mateer, 1987). This approach is supported by neuroscience as process-specific A/WM interventions are shown to promote brain plasticity and cognitive gains (Feuerstein, Feuerstein, Falik, & Rand, 2006; Kelly et al., 2006). DI is a revision of Caribbean Quest (CQ), which is another computerized cognitive intervention that targets A/WM. CQ was a laptop-based intervention that was programmed into a platform that was no longer supported. To work around this problem, DI was designed to be tablet-based to improve its ease of use, particularly for younger children such as preschoolers. In addition, CQ games had a sea creature theme that was mostly appealing to young children, however, older children did not find this interesting. To solve this problem, DI games were

designed to have artwork that appealed to a wider range of children. These changes from CQ to DI made the games feel more computer game-like, which is more motivating for most children.

The DI consists of five hierarchically graded and self-adjusting therapeutic computer ‘games’ that focus on different aspects of attention (sustaining attention, focusing attention), working memory (auditory and visual working memory), and cognitive flexibility (shifting attention). These games were initially designed for elementary students, however, changes to game progression and difficulty levels were made to make it more suitable for preschool-aged children. These changes included

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27 adjusting span lengths on WM tasks (shorter spans), decreasing length of each game (shorter game duration compared to the elementary school version) and adjusting the adaptability of each game. This means that the difficulty level of games decreased faster for preschoolers after they made an error, and the difficulty levels increased slower (had to master an easier level before progressing to a more difficult one). The self-adjusting and adaptive nature of the games increase the effectiveness of the intervention and make it more motivating for children. Additionally, of the five possible games, three were chosen to be a part of this intervention as they were more suitable for preschoolers. Santorini game was excluded as this game has high motor-control demands, and that it may be too challenging for preschoolers. Additionally, Volcano was excluded from this study as the game was not fully developed and ready to play at the start of this

intervention. These changes and adjustments were made based on trials using CQ in younger children. However, as explained above, DI is different than CQ in significant ways, thus, it is important to determine whether these changes are appropriate for a group of preschoolers.

DI combines both compensatory and process-specific approaches of cognitive interventions. DI is designed to be delivered with an interventionist present to teach metacognitive strategies to participants as they play the ‘games’, which teaches

participants to monitor their own thinking and performance to maximize performance. As mentioned earlier, process-specific interventions which combine direct intervention with an active metacognition component show better efficacy and greater transfer effects to other areas (Sohlberg et al., 2003); thus, the metacognitive component of this intervention

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28 theoretically maximizes generalizability and transfer effects associated with AWM

training.

Additionally, to maximize salience, DI includes internal motivators associated with each task; each game has fun bonus games that unlock after completing each level of a therapeutic game, where by a child can collect coins and can use them to customize their character or to buy trophies in the DI store to enhance motivation. The presence of such motivators in the game makes the intervention more engaging for children and may improve effectiveness. External motivators such as stickers and colouring books were also used to increase motivation during game play.

The Present Study

This study aimed to deliver a novel A/WM training program, DI, to preschoolers and investigate the effectiveness of the DI intervention on improving A/WMabilities, as well as looking at transfer effects to school readiness. This current thesis is the first delivery of DI to preschoolers, and thus it aims to serve as a feasibility study to

investigate the utility and appropriateness of DI with this particular age group within a community setting. Evidence supports the utility of childhood interventions, particularly in the area of A/WM training, in many different populations including children with and without neurodevelopmental disorders. However, less is known about the efficacy of such interventions in preschool-age children, in spite of recommendations that early intervention targeted towards this age range holds particular promise (Wass et al., 2012). Further, how and why these interventions may or may not work with preschool aged children is not well studied. Thus, this study aimed to examine the efficacy of DI in a typically developing preschool population.

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29 While this study was initially designed as a randomized control design, due to challenges with recruitment and unanticipated small sample size shifted to a case study approach. It was determined that by using a case study approach, the effectiveness of the DI program on improving attention and EFs would be better examined. As a result, multiple case studies approach was utilized with five typically developing preschoolers. In this approach, each case was treated as its own case study. Then, as recommended by Yin (2014), I examined the patterns and similarities and differences between the cases and determine whether these types of interventions can be used with young children. Using this approach, data from the DI intervention condition participants were collected. A comprehensive profile was developed for each case, including background and

demographic information, pre-intervention testing results on measures explained later in the methods section, and parent-reported behavioural scales assessing attention and EFs. Then, a detailed analysis of each participant’s game play and intervention performance was discussed. Finally, post-intervention testing results, observations from cases and how these changes between two assessment sessions relate to DI intervention were discussed. After the detailed analysis of all five cases, performance across participants was

synthesized to identify patterns and trends using the methods recommended by Yin (2014). Hypotheses are as follows:

Study Predictions

1. DI participants will show gains in attention, WM and inhibition post-intervention as indicated by improvements on objective performance measures.

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30 2. Improvements in attention and EFs will show evidence of far transfer through

gains on parent reported questionnaires, parent/teacher interviews, and measures of school readiness

Methods Participants:

This study was approved by the Human Ethics Review Board of the University of Victoria. Participants were recruited from a daycare in the Greater Victoria area.

Daycares and preschools in Victoria were contacted first, and letters were sent out to preschool and daycare personnel and administrators to provide information about the study. Overall, 24 preschools and daycares were contacted about the study. Of these, five expressed initial interest; however, due to daycare policies limiting the use of technology and screen-based media, only two schools were eligible to participate. These schools agreed to distribute recruitment flyers and information packages to parents. Parents received recruitment flyers and those who were interested signed a consent to be

contacted form and returned it to school to indicate their interest. Once having expressed interest, families were contacted, and a telephone screening interview was conducted. As this study was focused on typically developing (TD) preschool-aged children, we

excluded children who were identified by their parents as having a diagnosis of any neurodevelopmental disorders such as ADHD, ASD, intellectual delays, language delays, learning disorders, medical or mental health/behavioural problems diagnoses, and also children who were going under an assessment at the time of interviews. Eligibility criteria include typically developing children between ages three and six years who did not hold any specific neurodevelopmental diagnoses. Families who met all the criteria, and signed

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31 the consent to be contacted form, were contacted to discuss the study in more detail, and those who were interested were enrolled in the study.

Overall, 16 parents expressed interest in the study by signing the consent to be contacted form. After completing screening interviews, only 10 children were eligible to participate in the study. With respect to excluded children, one child was currently going under an ASD assessment and one child had significant language delays. The other four children who were excluded only attended the preschool twice a week, and were not able to accommodate the intervention schedule.

Ten children participated in the current study. Participants were randomly assigned to either an active intervention group (DI) or a tablet-based educational games control group. The active intervention group was comprised of five children (two boys and three girls) aged three to five years (M = 4.00, SD = .71). The educational games group was also comprised of five children (two boys and three girls) aged three to four years (M = 3.80, SD = .45). The two groups do not differ significantly with respect to age, U = 10.00, p = .60.

All parents of participants in this sample spoke and understood English very well. None of the participants had any diagnosed psychological or psychiatric conditions. Additionally, none of the children took medication for any medical conditions. Only one participant in the control condition received support for Speech Language and

Occupational Therapy services for word pronunciation challenges. All parents indicated completing a minimum of some college education or an associate degree, which indicates that all children fell within a moderate to high socioeconomic status (SES) bracket in terms of parents’ education.

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32 Table 1. Participant demographic characteristics

Participant identification1

Age Sex Ethnic and Racial Background First Language Household Annual Income Parent Education Level

#1 4 Male Indian English

$60.001-$85.000

Bachelor’s degree

#2 4 Female White English $85,001 -

$110,000

Bachelor’s degree

#3 4 Female Half White, Half

Cambodian

English $60,001 - $85,000

Some College

#4 3 Female White English $85,001 -

$110,000

Bachelor’s degree

#5 5 Male White English

$60.001-$85.000

Bachelor’s degree

#6 4 Female Indian English $85,001 -

$110,000

Some College

#7 4 Male White English $85,001 -

$110,000

Bachelor’s degree

#8 3 Female Half White, Half

Indigenous

English Less than $40,000

Associate degree

#9 4 Female White English Less than

$40,000

Some College

#10 4 Male White English

$60.001-$85.000

Bachelor’s degree 1. Participant #1 to #5 are in the active intervention group; #6 to #10 are in the control group.

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33 Screening measures and standardized rating scales:

Prior to the intervention, parents completed a childhood history questionnaire (CHQ) that screened for demographic information, first language, language spoken at home, early childhood development, medical and mental health history, behavioural problems and medication use (Please see Appendix A for an example of the CHQ). All parent forms were left at the preschool in individual envelopes for parents to pick up with instructions on how to complete the forms.

In addition to the childhood history questionnaire, parents completed the

Behaviour Rating Inventory of Executive Functioning-Preschool Version (BRIEF-P) both pre and post intervention to assess executive functions (Gioia, Espy, & Isquith, 2003). The BRIEF-P is a norm-referenced, standardized measure that consists of fiveEF subscales, including Inhibit, Shift, Emotion Control, Working Memory and

Plan/Organize. The BRIEF-P consists of 63 statement where parents are asked to rate their children’s behaviour on a scale of 1 to 3 (1: Never; 2: Sometimes; 3: Often). Higher scores on this measure are associated with more difficulties with EF. This measure takes about 15 minutes to complete. The BRIEF-P parent version has high internal consistency reliability, with estimates ranging from .80 to .95 (Gioia, Espy, & Isquith, 2003).

Similarly, test-retest reliability is in the moderate to good range (.78-.90 for the parent sample; Gioia, Espy, & Isquith, 2003).

Parents also completed the Children’s Behaviour Questionnaire- Short Form (CBQ) provide information about their child’s temperament. This scale is developed specifically for research purposes and has 94 items across 15 different subscales that measure different aspects of temperament such as activity level, attentional control,

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34 impulsivity, inhibitory control and anger/frustration. Even though the full questionnaire was administered, the scores from the attentional control scale were used as a parent-reported measure of attention. The CBQ was developed for preschool aged children between the ages of three and seven years. On this scale, higher scores indicate better attentional control abilities. Even though this measure does not have published norms, Rothbart, Ahadi, Hershey and Fisher (2001) published average scores for children across the ages of three to six years. The CBQ showed good internal consistency across a variety of research studies. In a study by Ahadi, Rothbart and Ye (1993), it was found that the internal consistency coefficients for CBQ scales ranged from .67 to .93, with a mean estimate of .77 across all scales.

Intellectual Screening:

The Peabody Picture Vocabulary Test Fourth Edition (PPVT-IV) was used to provide an approximate estimate of intelligence by measuring receptive vocabulary. The PPVT-IV is an individually administered, untimed, norm-referenced, test which assesses receptive oral vocabulary in a wide range of ages. It yields an overall standard score (M = 100, SD = 15), and can be used as an estimate of general verbal ability in persons aged 2.5 years to 90 + years. The PPVT-IV is generally used to measure verbal ability and scholastic aptitude, and usually takes 15-30 minutes to complete even though there are no time limits. The PPVT-IV correlates well with IQ measures and can be used as a

screening tool for cognitive functioning as well (Campbell, Bell & Keith, 2001; Castellino, Tooze, Flowers & Parsons, 2011). In fact, in a study by Castellino and

colleagues (2011) with children with ASD; the correlation between the PPVT-IV and the Differential Ability Scale-2 (DAS-2) was .93 for verbal IQ and .80 for nonverbal IQ

Investigating the efficacy of attention and working memory training for preschoolers

Supervisory Committee

Abstract

Table of Contents

List of Tables

List of Figures

Acknowledgments

Dedication

Introduction