Education, Faculty of
Educational and Counselling Psychology, and Special Education (ECPS), Department of
DSpace
UBCV
Maja, Rachel A.
Master of Arts - MA
University of British Columbia
Working Memory (WM) is a complex higher order function that is important for completing complex tasks such as learning, comprehension, and reasoning (Baddeley, 2010). Due to the importance of this ability, many cognitive training programs aim to improve individual WM capacity. One cognitive training program that is unique to others is the Arrowsmith Program. The Arrowsmith Program has an independent classification procedure, which involves an entry level assessment using multiple cognitive tasks. Performance across these tasks then produce an Initial Learning Profile, which identifies areas of cognitive strength and weakness (Barbara Arrowsmith-Young, 2012). The purpose of the present study was to evaluate how Arrowsmith measures relate to valid measures of WM. Six participants from an archival data set completed a WM measure from the WJ III NU COG (Numbers Reversed), parental questionnaires using the WM subscale from the BRIEF, Arrowsmith measures, and demographic information. A trend was observed in the results, in which better overall Arrowsmith performance was related to higher WM ability on a performance-based measure, but lower parental ratings of WM (higher parental concern of WM problems). Large positive and negative correlations were found between one Arrowsmith measure (Symbol Relations) and both the performance-based and parental ratings of WM, indicating that better performance on Symbol Relations was related to better WM on both measures. The implications of these findings and consideration for future research with the Arrowsmith Program are discussed.
AN EVALUATION OF ARROWSMITH PROGRAM MEASURES IN RELATION TO WORKING MEMORY by RACHEL A. MAJA B.Sc., (Hons.), The University of Victoria, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in The Faculty of Graduate and Postdoctoral Studies (School and Applied Child Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2020 © Rachel A. Maja, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: An evaluation of arrowsmith program measures in relation to working memory submitted by Rachel Maja in partial fulfillment of the requirements for the degree of Master of Arts in School and Applied Child Psychology Examining Committee: Rachel Weber, School and Applied Child Psychology Supervisor William McKee, School and Applied Child Psychology Supervisory Committee Member Laurie Ford, School and Applied Child Psychology Supervisory Committee Member iii Abstract Working Memory (WM) is a complex higher order function that is important for completing complex tasks such as learning, comprehension, and reasoning (Baddeley, 2010). Due to the importance of this ability, many cognitive training programs aim to improve individual WM capacity. One cognitive training program that is unique to others is the Arrowsmith Program. The Arrowsmith Program has an independent classification procedure, which involves an entry level assessment using multiple cognitive tasks. Performance across these tasks then produce an Initial Learning Profile, which identifies areas of cognitive strength and weakness (Barbara Arrowsmith-Young, 2012). The purpose of the present study was to evaluate how Arrowsmith measures relate to valid measures of WM. Six participants from an archival data set completed a WM measure from the WJ III NU COG (Numbers Reversed), parental questionnaires using the WM subscale from the BRIEF, Arrowsmith measures, and demographic information. A trend was observed in the results, in which better overall Arrowsmith performance was related to higher WM ability on a performance-based measure, but lower parental ratings of WM (higher parental concern of WM problems). Large positive and negative correlations were found between one Arrowsmith measure (Symbol Relations) and both the performance-based and parental ratings of WM, indicating that better performance on Symbol Relations was related to better WM on both measures. The implications of these findings and consideration for future research with the Arrowsmith Program are discussed. iv Lay Summary The purpose of this study was to evaluate how an alternative cognitive training program’s measures relate to well-established measures of working memory. To do this, the researcher examined the relationship between performances across well accepted measures of working memory with measures from the cognitive training program. The present findings may provide the cognitive training program with valuable information on how their measures relate to working memory, and therefore better tailor their program to each individual. The preliminary findings may also guide future research to better understand this cognitive training program, and provide more information to other researchers studying the nature of working memory. v Preface The following writing is based on the unpublished research of Rachel Maja, a graduate student, under the supervision of Dr. Rachel Weber. This research used an archival dataset from a larger study that was previously approved by the Clinical Research Ethics Board (CREB) at the University of British Columbia under certificate number H13-03232. vi Table of Contents Abstract ............................................................................................................................. iii Lay Summary ................................................................................................................... iv Preface ................................................................................................................................ v Table of Contents ............................................................................................................. vi List of Tables ................................................................................................................... viii Acknowledgements .......................................................................................................... ix Introduction ....................................................................................................................... 1 History and Models of Working Memory ................................................................... 1 The Role of Long-term Memory and Attention ......................................................... 3 WM and Brain Regions ................................................................................................ 4 Individual Differences................................................................................................... 6 Cognitive Training Programs....................................................................................... 9 The Arrowsmith Program .......................................................................................... 12 The Current Study ...................................................................................................... 15 Research Question #1 ............................................................................................... 16 Research Questions #2 .............................................................................................. 16 Research Question #3 ............................................................................................... 16 Significance of Present Study ................................................................................... 16 Methods ............................................................................................................................ 18 Approach...................................................................................................................... 18 Participants .................................................................................................................. 18 Ethical Considerations and Consent ......................................................................... 18 Procedure ..................................................................................................................... 19 Recruitment and Data Collection. ............................................................................. 19 Materials ...................................................................................................................... 20 Demographic Survey. ................................................................................................ 20 WJ III NU COG ........................................................................................................ 20 BRIEF ....................................................................................................................... 21 vii Arrowsmith Classification ........................................................................................ 21 Statistical Analysis ...................................................................................................... 22 Nature of Data. .......................................................................................................... 22 Preparation of Data. .................................................................................................. 22 Analysis..................................................................................................................... 23 Results .............................................................................................................................. 25 Sample Characteristics ............................................................................................... 25 Research Question One .............................................................................................. 25 Spearman Correlations. ............................................................................................. 26 Research Question Two .............................................................................................. 26 Spearman Correlations .............................................................................................. 26 Research Question Three ........................................................................................... 27 Mann-Whitney U test. ............................................................................................... 27 Discussion......................................................................................................................... 29 Overview ...................................................................................................................... 29 Overall Arrowsmith Performance and WM ............................................................. 29 Arrowsmith Dysfunction Measures and WM........................................................... 32 Overall Conclusions .................................................................................................... 36 Research with Arrowsmith Population ..................................................................... 36 Limitations and Strengths of the Present Study ...................................................... 38 Limitations. ............................................................................................................... 38 Strengths. .................................................................................................................. 39 Considerations for Future Research and Practice ................................................... 39 References ........................................................................................................................ 41 Appendix A: Demographic Survey ................................................................................ 48 viii List of Tables Table 1: Demographic Information ............................................................................................... 19 Table 2: Central Tendency Data .................................................................................................... 25 Table 3: Spearman rank-order Correlations .................................................................................. 27 Table 4: Summary of Mean Ranks ................................................................................................ 28 ix Acknowledgements I would like to thank the participants of the present study for their willingness to take part. I would also like to thank the administrators and staff at the Arrowsmith Program who supported my ability to carry out this study. Thank you to my supervisory committee, Dr. Bill McKee, Dr. Laurie Ford, and my research supervisor, Dr. Rachel Weber, for their support and encouragement throughout my Master’s degree. 1 Introduction Working memory (WM) can be described as a system or systems that allow(s) one to sustain a set of mental representations for further altering and processing (Carruthers, 2013). In a practical sense, WM is important for keeping things in mind while completing complex tasks such as learning, comprehension, and reasoning (Baddeley, 2010). In other words, WM is the ability to constantly monitor and code incoming information, and to update irrelevant information with newer, more relevant information (Morris & Jones, 1990). It is important for goal-directed behaviour, and allows individuals to hold information needed for cognition in the present moment (Eriksson, Vogel, Lansner, Bergstrom, & Nyberg, 2015; Oberauer & Hein, 2012). Therefore, WM is a critical skill that is required to function optimally and successfully in daily life. The term WM has been used in many different fields of research and clinical practice, such as cognitive psychology, neuroscience, and education (Baddeley, 2010). Since this time, a vast amount of research has been conducted to understand how working memory is defined, why it is important, and what brain regions are activated during WM tasks. A number of WM frameworks have evolved over time and now contribute to current, modern models. History and Models of Working Memory Earlier models of WM were conceptualized in a unitary fashion, and WM was often thought of as being synonymous with short-term memory (Baddeley, 2010). The concept of WM evolved from earlier understandings of short-term memory (STM), and eventually STM and WM were viewed as two separate constructs (Melby-Lervag & Hulme, 2012) with the important distinction being that WM manipulates information over a short period of time, whereas STM only stores and codes the incoming information. Baddeley and Hitch (1974) proposed a highly recognized three-component model, which was the first model that distinguished WM from STM 2 and provided a framework to conceptualize how temporary information is stored whilst simultaneously completing complex cognitive tasks (Baddeley, 2000). Their model was a multicomponent system that emphasized complex cognitive processes, rather than identifying WM as being solely memory based. The model outlined three main components, which included an attentional control system, known as the ‘central executive’, the phonological loop, and the visuospatial sketchpad. Each component of this model is responsible for different aspects of WM. The phonological loop is assumed to hold verbal and acoustic information, and likely plays an important role in long-term phonological learning, such as the development of vocabulary in children or the ability to acquire a new language (Baddeley, 2000). The visuospatial sketchpad is assumed to hold visuospatial information, separated into visual, spatial, and kinesthetic components which are represented in the right hemisphere areas (Baddeley, 2000). Approximately 26 years later, Baddeley proposed a revision and identified the limits of his previous model. One such limit is that research has demonstrated that visuospatial information can modify phonological memory, which indicates a relationship between the phonological loop and visuospatial sketchpad (Baddeley, 2000). Due to this connection, Baddeley proposed a fourth component of the model, known as the episodic buffer. The episodic buffer acts as a temporary storage system that is able to integrate information from a variety of sources (Baddeley, 2000). As it only holds information temporarily, the episodic buffer is assumed to have limited capacity for overall storage (Baddeley, 2000). It is theorized to be controlled by the central executive, and operates as an interface between the phonological loop, visuospatial sketchpad, and long-term memory (Baddeley, 2000). Baddeley’s framework emphasizes that WM involves multiple components, and thus integrates information between each component, rather than viewing each component in isolation (Baddeley, 2000). Although 3 WM has been frequently tied to long-term memory, Baddeley emphasizes that WM is different, and that long-term memory is regarded as a separate system that involves its own storage process (Baddeley, 2000). The Role of Long-term Memory and Attention Research consistently acknowledges that both long-term memory and attention have a large influence on WM processes, yet are considered as separate systems from WM (Baddeley & Hitch, 1974; Baddeley, 2000; Eriksson et al., 2015; Oberauer & Hein, 2012). Specifically, the activated portion of long-term memory allows for relevant information to be available during a particular task (Oberauer & Hein, 2012). In other words, long-term memory acts as a standby that holds relevant information that may be needed. In addition, ‘chunking’ information, or making sense of information, requires the support of long-term memory in making sense of what is presented (Baddeley, 2000). It is also suggested that the chunks of information are largely stored in our long-term memory, however others have argued that the phonological loop or the episodic buffer are responsible for this (Baddeley, 2000). Finally, others have indicated that long-term memory has a substantial role in relation to the central executive (Baddeley & Andrade, 2000). Although long-term memory plays a substantial role during WM processes, attentional control, or sustained attention, largely affects WM capacity. WM representations are vulnerable to distraction and interference, thus some aspect of attentional control and selection is required (Eriksson et al., 2015). During complex tasks, information is encoded into WM processes due to the interactions between attention and the perceptual objects that trigger our memory (Eriksson et al., 2015). After this occurs, sustained attention is crucial for maintaining the information in WM (Eriksson et al., 2015). Other models support the importance of attention during WM 4 processes, and, in particular, the importance of broad and narrow focus (Oberauer & Hein, 2012). Broad focus of attention has a limited capacity for storage, and is assumed to hold approximately four chunks of information at a time, which is widely supported (Eriksson et al., 2015; Oberauer & Hein, 2012). It is also known to select relevant representations in long-term memory, whereas narrow focus selects the information needed for an upcoming cognitive operation (Oberauer & Hein, 2012). Oberauer and Hein (2012) also identify that both broad and narrow focus can be supplemented by information in activated long-term memory. Therefore, attention plays a critical role in WM tasks, and is also supported by long-term memory. In conclusion, research indicates that WM is a diverse process that is influenced by long-term memory and attention. The diversity of WM suggests that various neural networks may be responsible for executing such complex processes. WM and Brain Regions Neuroimaging and lesion studies have assisted in determining the neural networks that are involved during WM processes. Research has indicated that brain networks associated with WM processes span across the cerebrum, including the prefrontal cortex, inferior and superior parietal lobe, as well as specific sensory cortices (Lazar, 2017). Lesion studies have demonstrated that lesions to the temporal cortex impact visual WM (Owen, Morris, Sahakian, Polkey, & Robbins, 1996), and parietal lesions impact spatial WM (Pisella, Berberovic, & Mattingley, 2004). Visual WM is often thought of as a process that allows us to maintain and manipulate visual information over short periods of time, whereas spatial WM is involved in maintaining and manipulating spatial information (Heuer, Schubo, & Crawford, 2016). Lesion studies have also demonstrated that lateral temporal lobes and the temporoparietal cortex are related to verbal WM performance (Bormann, Seyboth, Umarova, & Weiller, 2015). Further 5 research has demonstrated activation of posterior parietal cortex during WM tasks (Todd & Marois, 2004), along with the superior parietal cortex, which is said to be important for attentional control (Collete et al., 2005; Koenigs, Barbey, Postle, & Grafman, 2009). A meta-analysis established different clusters that were activated in children during a WM task, and the highest activation was found in the left superior frontal gyrus (Yaple & Arsalidou, 2018). The clusters that followed were observed in parietal areas, the right inferior parietal gyri, and bilateral superior parietal gyri (Yaple & Arsalidou, 2018). Additionally, significant activation was observed in the right insula, the precentral gyrus, and the inferior frontal gyrus (Yaple & Arsalidou, 2018). Baddeley indicates that the episodic buffer and the central executive are likely to be influenced by frontal areas, a common region of interest in WM research (Baddeley, 2000). Specifically, a region of interest in the frontal lobe is the prefrontal cortex (Eriksson et al., 2015). Due to the importance of preserving relevant information and ignoring task-irrelevant stimuli, the prefrontal cortex is said to play a critical role in information maintenance during WM tasks (Eriksson et al., 2015). Research demonstrates that electrical or magnetic stimulation of the prefrontal cortex affects WM performance overall (Brunoni & Vanderhasselt, 2014; Feredoes et al., 2011). Specifically, meta-analyses have demonstrated that there is some aspect of regional specificity in the prefrontal cortex, such that the left ventral prefrontal cortex is more involved in verbal WM tasks, whereas the right ventral prefrontal cortex is more involved in spatial WM tasks (Nee et al., 2013; Owen et al., 2005; Wager & Smith, 2003). In conclusion, research indicates that the prefrontal cortex plays an important role during WM processes. Research describing the anatomy and organization of the prefrontal cortex may further support why this region is often recruited during complex cognitive tasks. 6 Frontal cortex neurons have been documented to have high dimensionality, which allows them to process a wide variety of item and task-related information (Rigott et al., 2013). This high dimensionality supports the prefrontal cortex with cognitive control functions that are goal-oriented, such that goals are complex, multi-faceted and involve diverse information (Miller & Cohen, 2001). Furthermore, as WM is important for attending to and processing different stimuli (e.g., verbal, visual, and spatial information), it is evident that the networks involved in responding to this information would be versatile. The organization of the prefrontal cortex may also support the ability to process complex information. Some researchers argue that the functional organization of the prefrontal cortex is based on the content of the task, rather than process type (Fuster, 2009; Miller & Cohen, 2001). Others propose that the prefrontal cortex is organized based on how abstract the task is, noting that, as tasks are increasingly more abstract, activity peaks in rostral areas and decreases toward posterior parts (Badre & D’Esposito, 2009; Fuster, 2009; Koechlin & Summerfield, 2007). Furthermore, researchers also propose that there is greater right frontal activation when information is integrated, and posterior activation when there is unintegrated retention of information (Prabhakaran, Narayanan, Zhao, & Gabrieli, 2000). Sub-divisions of the prefrontal cortex have been widely discussed in regard to WM, however there is no consensus of the functional organization (Eriksson et al., 2015). In conclusion, there are various brain regions that are involved during WM processes that allow an individual to execute diverse tasks efficiently. However, each individual has a unique WM capacity which can be influenced by multiple factors. Individual Differences Working memory capacity is something that is unique to each person, such that some individuals will have greater capacity than others. These factors are known as individual 7 differences. Individual differences are highly stable over time, and are thought of to be a core cognitive trait of the individual (Eriksson et al., 2015). Some researchers state that these distinctions likely reflect differences in the ability to hold representations in the broad focus, which can be represented by the number of items held at one time, as discussed previously (Cowan et al., 2005; Fukuda, Vogel, Mayr, & Awh, 2010; Oberauer & Hein, 2012). Additional researchers emphasize that individual differences are determined by the variability in their attentional control, rather than the amount of available storage (Adam et al., 2015; Kane & Engle, 2002). For example, low capacity individuals have more difficulty ignoring distracting information when compared to high capacity individuals (Eriksson et al., 2015). Therefore, attentional differences are likely due to how quickly an individual can disengage from something that captures their attention when it is not relevant (Eriksson et al., 2015). Due to this attentional component, individuals with high working memory capacity will also perform better on tasks that require the inhibition of distracting or irrelevant material (Melby-Lervag & Hulme, 2012). In conclusion, attentional capacity may help to explain individual differences in WM capacity. This is not surprising due to the large influence of attention on WM, as discussed previously. Individual differences in WM have also been related to the presence of neurodevelopmental disorders, which represent underlying differences in nervous system development. For example, WM deficits contribute to the learning and behavioural functioning of people with disorders such as attention-deficit hyperactivity disorder (ADHD) and learning disorders, such that individuals with both of these conditions demonstrate WM deficits (Maehler & Schuchardt, 2016; Melby-Lervag & Hulme, 2012). For example, children with dyslexia, an impairment in the development of reading skills, perform more poorly and slowly on WM tasks than controls (Beneventi, Tonnessen, Ersland, & Hugdahl, 2010; Swanson, 2006). Additional 8 research has demonstrated that WM deficits are common in children with a mathematics learning disability, often referred to as dyscalculia (Maehler & Schuchardt, 2016; Passolunghi, 2006). In sum, research supports that individuals with neurodevelopment disabilities may differ from their typically developing peers in terms of their WM capacity. These differences are also associated with corresponding differences in neural activation during WM tasks. Neuroimaging research has indicated that similar brain regions are activated during WM tasks in children with dyslexia and healthy controls, however the degree of activation differed (Beneventi et al., 2010). For example, in one study the control group demonstrated significantly more activation than the group with dyslexia bilaterally in the posterior middle frontal gyrus, the superior parietal lobule, and the left cerebellum. In contrast, the group with dyslexia demonstrated significantly more activation than the controls in the right anterior middle frontal gyrus, indicating that this area is sensitive to increasing WM demands in the group with dyslexia. In conclusion, an individual’s WM capacity can be influenced by individual differences in attention or other related cognitive abilities, as well as their neurodevelopmental history. Understanding WM capacity is important given the overall importance of WM in real life. As previously discussed, WM assists individuals in maintaining focus, ignoring distracting stimuli, and responding to relevant information (Melby-Lervag & Hume, 2012). In real terms, WM assists children to stay focused in class, inhibit inappropriate behaviour, and follow multi-step directions. Additionally, WM capacity is of interest because it plays a role in the development and demonstration of other cognitive abilities, such as fluid intelligence, abstract reasoning, and in overall academic performance, including mathematics and language abilities (Cowan et al., 2005; Daneman & Carpenter, 1980; Unsworth et al., 2014). For these reasons, WM has been a 9 target of many cognitive intervention programs, due to the importance of WM skills in academics and real-life, and its vulnerability to neurodevelopmental differences. WM has also been a target in cognitive intervention programs due to the frequency with which individuals experience WM difficulties. WM weaknesses may be common for a few reasons. One key aspect of WM is that it has a limited capacity, indicating that individuals are only able to store a small number of items (Luck & Vogel, 2013). Due to the limited capacity, it can be argued that WM may be vulnerable to disruptions, and therefore frequently results in WM weaknesses across individuals. Secondly, WM capacity varies substantially across individuals for several reasons, including attentional capacity, long-term memory, as well as maturation and aging (Baddeley, 2000; Eriksson et al., 2015; Lazar, 2017; Oberauer & Hein, 2012;). Due to the high frequency of factors that influence WM, this makes WM increasingly more vulnerable to disruptions that may result in weaknesses. Therefore, in conjunction with the importance of WM skills in academia and in real-life, the frequency of WM weaknesses increases the incentive for cognitive training programs to target WM. Cognitive Training Programs As mentioned, WM is often a target for improvement in cognitive training programs, because of its broad importance and the high frequency of WM weaknesses in individuals with neurodevelopmental disabilities or neurological impairment. Cognitive training programs are based on the theory of transfer, such that near- and far-transfer effects are expected (Barnett & Ceci, 2002). Near-transfer effects are effects on tasks that are similar to what is being trained, whereas far-transfer effects are effects on tasks that are different than those that are being trained (Melby-Lervag & Hulme, 2012). For example, if one were to achieve near-transfer effects, there would be expected improvements on tasks that are similar to the WM tasks being practiced. If 10 WM training programs successfully demonstrated far-transfer effects, there would be expected improvements in real world skills such as attentional skills, behavioural inhibition, and school performance (Melby-Lervag & Hulme, 2012). This is also known as generalization, such that the trained WM skills may generalize to skills areas that have not been trained (Melby-Lervag, Redick, & Hulme, 2016). In the particular case of individuals with learning disorders, training programs that target WM may help to alleviate their learning difficulties (Melby-Lervag & Hulme, 2012). While achieving these far-transfer effects has become a primary objective in many WM training programs, research has been inconsistent in producing positive far-transfer results. In their meta-analysis of WM training programs, Melby-Lervag and Hulme (2012) found that, overall, programs demonstrate large immediate gains on verbal and non-verbal WM, with larger gains shown in younger children (below age 10) than studies with older children (11-18 years). However, these effects were not sustained when the participants were reassessed after the program had ended. Furthermore, there was no evidence that WM training successfully produced generalized gains to other skills (e.g., verbal ability, word decoding, or arithmetic). In other words, the results indicated that WM training generalizes to other WM tasks, but did not show evidence of far-transfer (Melby-Lervag & Hulme, 2012). A separate meta-analysis found that WM training led to significant and large improvements in the performance of the trained task in older adults, as well as large near-transfer effects (Karbach & Verhaeghen, 2014). The results also indicated significant, but smaller, far-transfer effects, in opposition of Melby-Lervag and Hulme’s (2012) findings. Karbach and Verhaeghen (2014) indicate that their results may differ from others due to their exclusion criteria for their analyses, such that they only included older adults. 11 Researchers have also evaluated WM training programs in regard to clinical populations. A meta-analysis evaluated WM training programs explicitly with individuals with an intellectual disability (ID). The results indicated overall significant effects on WM training, with larger effect sizes when training targeted both verbal and visuospatial WM (Danielsson, Zottarel, Palmqvist, & Lanfranchi, 2015). The study demonstrated that the results were greater when combining verbal and visuospatial WM training, in comparison to only targeting one aspect of WM. A drawback to this meta-analysis is that the authors did not report whether there was transfer of WM gains to other skills areas in this population. More studies are needed to better understand how to design an effective WM intervention for this group and to understand how short-term effects remain over time and transfer to everyday activities (Danielsson, et al., 2015). A separate study assessed whether a WM training program could enhance skills in children diagnosed with ADHD. Results indicated that the WM training improved performance in WM in the ADHD group in comparison to the control group (Klingberg, Forssberg, & Westerberg, 2002). There were also significant increases in scores measuring intelligence after WM training, demonstrating far-transfer effects (Klingberg, Forssberg, & Westerberg, 2002). However, the study did have limitations, such as a small sample size (i.e., 7 children per group). In contrast, a randomized clinical trial of a WM intervention for children found that there were no far-transfer effects (Roberts et al., 2016). For example, there were no academic improvements, or improvements in other domains such as attention, social-emotional difficulties, and quality of life at 12 or 24 months following the intervention (Roberts et al., 2016). There was, however, evidence of near-transfer. In conclusion, current research evaluating the efficacy of WM training programs is inconsistent, indicating that further research is necessary to better understand the outcomes of 12 these programs. Near-transfer effects have been consistently demonstrated across WM training programs, however whether far-transfer effects can be achieved is still up for debate. Conflicting research on the matter may be due to the differences in duration of the intervention, the nature of participants involved, and the nature of WM training itself (Melby-Lervag et al., 2016). A meta-analysis indicated that some studies have focused on verbal or visuospatial tasks, or both (Melby-Lervag et al., 2016), which has been documented to influence the outcomes of the training (Danielsson, et al., 2015). Overall, WM training programs yield short-term improvements on verbal and visuospatial WM tasks, where visuospatial improvements are sustained longer. Moreover, there is a lack of evidence suggesting that WM training produces effects that generalize to important real-world cognitive skills (Melby-Lervag et al., 2016). This may be due to the short duration of training, and the streamlined approach of administration. For example, the WM training programs evaluated above were not individualized to each person. As previously discussed, individual differences impact WM capacity, along with many other variables. Creating an individualized program for each student may impact both near- and far-transfer effects during WM training and may produce more favorable outcomes. Evaluating how a WM training program is individualized would be advantageous for this field of literature, and would be the first step in identifying the overall results. The Arrowsmith Program The current study is an exploration of the WM correlates of a cognitive training program’s measures. This program, the Arrowsmith Program, maintains the philosophy that cognitive areas may be improved through strenuous cognitive exercises, resulting in strengthened learning abilities. The Arrowsmith Program claims to be different from other cognitive training programs in that it is capacity based, rather than compensatory based, such that the program’s 13 goal is to change the student’s capacity to learn, rather than compensate or work around their weaknesses. There are a variety of cognitive exercises implemented in the program that target multiple cognitive processing weaknesses, with WM being an important target. Each student receives a personalized schedule of exercises that are tailored to their learning profile to target their identified weaknesses. The exercises utilized in the Arrowsmith Program include written, visual, auditory, and computer exercises, and were created by Barbara Arrowsmith-Young (2012) to strengthen and enhance the cognitive capacities underlying the identified Learning Dysfunction. The Arrowsmith Program also differs from others in that each student is evaluated upon entrance to the program in order to develop their Initial Learning Profile (ILP). The ILP outlines areas of strength and weakness for each student, classifying their weaknesses as Learning Dysfunctions. A student may exhibit all, or only some, of the of Learning Dysfunction identified by the program. Through this identification process, the Arrowsmith Program uses their own measures used to assess each student’s strengths and weaknesses among 19 different cognitive areas. After the classification, the Arrowsmith Program then administers cognitive exercises to address the identified Learning Dysfunction. The students can attend the program anywhere from three to four years, which is typically longer than most cognitive training programs (Klingberg et al., 2002; Roberts et al., 2016). After the completion of the program, the aim is to reintegrate students into a traditional school setting. The students enrolled in Arrowsmith are diverse in demographics, varying from kindergarten to grade twelve, and have a variety of difficulties including reading, writing, mathematics, memory, or attention. Therefore, it is important to note that the majority of students attending Arrowsmith are not typically developing. 14 In comparison to other cognitive training programs, there is no available information to demonstrate the effectiveness of the cognitive exercises or the validity of the measures used in the Arrowsmith Program. A possible explanation for this is that Arrowsmith’s methodology is proprietary information, meaning that a large portion of the program is not openly shared with researchers. This limited information makes it difficult to compare results, support ideas, and derive conclusions about the program itself. Therefore, researchers examining the Arrowsmith program are not able to use previous research as a foundation to support their findings. Rather, new research would first have to provide a foundation to begin the examination of the program overall. Due to the aforementioned limited availability of Arrowsmith research, this provides an opening for new research to be conducted. Importantly, despite the fact that concrete conclusions cannot yet be made, new research may begin to lay groundwork for future researchers. In terms of Arrowsmith’s measures, there is also no available research to review the validity or reliability of these measures. Therefore, one cannot yet conclude if the measures are reliable, or if they are measuring what they are said to evaluate. A possible way to begin establishing the validity of Arrowsmith measures is to compare performances across these metrics with empirically valid and reliable measures that are already well-established. However, in doing so, one would also need to examine cognitive theory and use that theory to hypothesize what cognitive abilities are being measured by the Arrowsmith Program. Examining the validity of Arrowsmith measures would provide foundational knowledge of the program itself by focusing on the classification of Learning Dysfunctions. As previously mentioned, there are 19 different Learning Dysfunctions that are identified by Arrowsmith’s measures. The following of Arrowsmith-Young’s (2012) Learning Dysfunctions likely relate to WM, based on current WM theory and Arrowsmith-Young’s definitions. First, 15 Memory for Information or Instructions is the capacity for remembering verbal information. An auditory exercise that involves remembering information is used to measure this area. As previously stated, holding chunks of information requires focus and sustained attention, which has been demonstrated to be achieved through WM (Eriksson et al., 2015; Oberauer & Hein, 2012). Thus, students with difficulties in this area would also be expected to have difficulties in WM. Next, Symbol Relations is the capacity to understand relationships among two or more ideas (Arrowsmith-Young, 2012). To measure this ability, a computer-based exercise is administered which involves analyzing and solving conceptual relationships. The task requires the student to read an analog clock and understand which additional hands represent additional levels of time (e.g., the calendar date, seconds, milliseconds). Making comparisons between ideas is a form of reasoning, which has also been demonstrated to be largely influenced by WM (Baddeley, 2010). Finally, the last Learning Dysfunction that is likely related to WM capacity is Quantification Sense. To measure this, a computer-based exercise that requires continuous mental calculation is administered. Deficits in this area will affect the ability to perform internal sequential mental operations, such as completing mathematics in their mind (Arrowsmith-Young, 2012). Research has demonstrated that WM plays a vital role in mathematics, specifically when numerical values are being manipulated and there are various steps required to problem solve (Ashcraft & Krause, 2007). The Current Study The purpose of the current study is to examine whether performance across measures created by Barbara Arrowsmith-Young (2012) are related to performance on empirically validated performance-based and parental rating scale measures of WM. This study is the first 16 attempt to begin to understand how Arrowsmith measures relate to empirical theory and valid cognitive measures. The current research questions are described below. Research Question #1. Does the performance on Arrowsmith measures relate to performance on a performance-based task of WM? Research Questions #2. Does the performance on Arrowsmith measures relate to parental ratings of WM? A second aim of this study is to identify whether there is a significant relationship between the overall performance across WM-related Arrowsmith tasks and performances across all measures of WM. Research Question #3. Do low and high performing Arrowsmith students differ in terms of their WM task performance and/or parental ratings of WM? Significance of Present Study. Due to the dimensionality of WM, it is not surprising that several of the identified areas of Learning Dysfunction are hypothesized to be related to WM, or involve WM in some aspect. This study represents the first step in evaluating and understanding the Arrowsmith approach to cognitive intervention. Specifically, this study examines how Arrowsmith measures relate to empirically validated cognitive measures. In doing so, this research will attempt to reconcile this small piece of Arrowsmith classification system with current theory regarding cognitive and neurodevelopment. Additional understanding of the Arrowsmith classification system will, in turn, allow future research to evaluate the cognitive exercises involved in this program as prescribed treatments for specific Learning Dysfunctions. In conclusion, this study seeks to establish an important link between the underpinnings of the Arrowsmith Program and current cognitive theory. This research has the potential to serve as a foundation for further research into the way that the Arrowsmith Program identifies Learning 17 Dysfunctions, the Arrowsmith Program’s exercises, and the potential benefits of participation in this cognitive intervention program. In conclusion, the current study will construct foundational knowledge of the Arrowsmith Program, and serve as a precursor to future research in understanding how the Arrowsmith exercises are related to and may improve cognitive areas. This study will also add to the current literature of WM cognitive training programs by adding more depth and breadth of knowledge. 18 Methods Approach This study used an archival dataset from a larger study examining the neurocognitive characteristics of Arrowsmith students and exploring the outcomes of the Arrowsmith Program. Arrowsmith students from that data archive who met criteria were contacted to obtain consent to access their Arrowsmith Initial Learning Profile and to obtain demographic information. A total of six participants responded to these efforts to obtain additional consent. Participants As mentioned, six children and youth, ages 10 to 16 years old (M = 12.8, SD = 2.13) were included in the analysis. The demographic information is summarized in Table 1. Participants from the data archive were contacted if they had completed the WM measures of interest in this study. Participants were included in the larger study if they were between the age of 9 and 17 years and right handed. Participants were excluded if they were 1) outside the age range of 9-17; 2) diagnosed with a history head trauma, a major psychiatric diagnosis, neurodegenerative disorder or substance abuse; 3) taking any drugs (e.g., GABAergic, NMDA-receptor antagonists, noradrenergic reuptake inhibitors) known to influence neuroplasticity; or 4) diagnosed with Down Syndrome. Ethical Considerations and Consent Participants provided written informed consent if they were 14-17 years old, and participants between the ages of 9-13 provided assent, in addition to collecting parent consent for their participation. All participants were assessed at baseline of enrolment in the Arrowsmith Program. The university clinical research ethics board approved this work due to the neuroimaging portion of the larger study. 19 Procedure Recruitment and Data Collection. The participants from the larger study were recruited through the Arrowsmith Program and materials were distributed by Arrowsmith program staff. Recruitment occurred via sending out flyers to all families with enrolled children and youth, and families chose to participate voluntarily by contacting the research team directly. Participants were invited to UBC campus where the cognitive and parental rating data was collected by graduate research assistants. As mentioned, all relevant participants were contacted via email to obtain the data of interest in this study, which was obtained online via the University of British Columbia’s Survey Tool, Qualtrics. Of the 40 participants contacted, six participants responded, forming this study’s sample. Table 1 Demographic Information Participant Age Child Ethnicity Parental Education Parental Occupation Country of Birth Diagnoses 1 13 Northern and Western European Origins High School Diploma Lifeguard Australia ADHD 2 13 Other Masters/PhD Registered Clinical Counsellor Australia Mild Cerebral Palsy, ADHD, language delay 3 10 Eastern and Southern European University Degree CEO/Engineer United States Dysgraphia, Dyslexia 4 14 Other University Degree Physiotherapist Australia Anxiety, Depression, language delay 5 11 East Asian Origins High School Diploma Accountant United States Dyslexia and Dysgraphia 20 6 16 Northern and Western European University Degree Accredited Investment Fiduciary United States Dysgraphia Note. n = 6 Materials Demographic Survey. The demographic survey included questions regarding each participant’s parents’ current occupation, ethnicity, and highest education level (used as a proxy for socio-economic status). The survey also included questions regarding the child’s ethnicity, and whether the child had developmental delays or learning problems, or had received services such as Speech and Language, Supported Child Development Program, Learning Resource Support, Special Education, or any other services. WJ III NU COG (Woodcock, McGrew, Schrank, & Mather, 2007). The Woodcock-Johnson Third Edition Test of Cognitive Abilities, Normative Update (WJ III NU COG; Woodcock et al., 2007) is a cognitive test battery that provides an overall estimate of intelligence, as well as a measurement of broad and narrow cognitive abilities. The WJ III NU COG (Woodcock et al., 2007) utilizes the Cattell-Horn-Carroll (CHC; McGrew, 2005) model as its foundation, and produces seven CHC broad ability composite scores, including Fluid Reasoning, Comprehension-Knowledge, Processing Speed, Short-Term Working Memory, Long-Term Retrieval, Visual Processing, and Auditory Processing (McGrew, Schrank, & Woodcock, 2007). The normative update was standardized using a sample of 8,782 individuals aged 2-90 years old from the United States and stratified to match the 2005 U.S. population (McGrew et al., 2007). The WJ III NU COG has also been demonstrated to be a psychometrically reliable and valid measure of intelligence at both the cluster and test level (McGrew et al., 2007). In the current study, standard scores were used from the Numbers Reversed test, a measure of auditory 21 WM, which required the participant to listen to a string of digits and then repeat them backwards. A higher standard score indicated better WM performance. BRIEF (Gioia, Isquith, Guy, & Kenworthy, 2000). The Behaviour Rating Inventory of Executive Function, Parent Form (BRIEF; Gioia et al., 2000) is an 86-item questionnaire that provides eight clinical scales (Inhibit, Shift, Emotional Control, Initiate, Working Memory, Plan/Organize, Organization of Materials, and Monitor), that together produce an overall score, referred to as the Global Executive Composite. For the purpose of the current study, only the Working Memory clinical scale was included in the analyses. One parent of each participant completed the BRIEF Parent Form, which then produced a T-score. For the working memory clinical scale, a higher T-score indicated a higher frequency of problems with working memory (assumed to be poorer WM skills). The BRIEF was standardized using a normative sample of 1419 parents and 720 teachers. Furthermore, it has high test-retest reliability and internal consistency, demonstrating to be psychometrically reliable and valid (Gioia et al., 2000). Arrowsmith Classification (Arrowsmith-Young, 2012). As described above, there are three Arrowsmith Learning Dysfunctions that are theorized to be related to WM. Each participant obtained a performance score for each of these areas of dysfunction (Memory for Information or Instructions, Symbol Relations and Quantification Sense), from the Arrowsmith-administered entrance evaluation. Each score was based on a 12-point scale that measured the proficiency of their functioning in that cognitive dysfunction area. A higher score indicates better performance in the given area. The Arrowsmith measures have not been empirically evaluated, and thus lack established psychometric properties. The current study will seek to correlate scores across the Arrowsmith measures with valid and reliable measures of WM, which will be the first step in examining an aspect of their validity. 22 Statistical Analysis Nature of Data. Due to the small sample size, (n = 6), the purpose of the analysis is to look for trends, rather than make inferences. With a small sample size, it becomes problematic when using a test statistic to evaluate significant differences between groups; there is not enough statistical power to capture a significant difference if one is present. Therefore, a test statistic (p value) will not be reported when conducting the nonparametric tests. Rather, the analyses will be described qualitatively in terms of trends. Preparation of Data. To determine whether the data fit a normal distribution, a Shapiro-Wilks test was completed. To ensure the data was appropriate for a nonparametric test, all four assumptions were examined for the Mann-Whitney U test. The first assumption requires one dependent variable that is measured at the continuous or ordinal level, which is the case for this study. The second assumption requires one independent variable that consists of two categorical, independent groups. To prepare the data for the third research question, the six participants were sorted into two groups based on their total Arrowsmith performance score summed across the three different categories (i.e., Memory for Information or Instructions, Symbol Relations, Quantification Sense). A median score of 15.5 was calculated for the total Arrowsmith performance score, and was used to place participants into the two different groups (i.e., performing above or below the median). Therefore, this assumption was met. The third assumption for conducting a Mann-Whitney U test is independence of observations, meaning that there need to be different participants in each group, which is also the case for this study. Lastly, the fourth assumption is the need to determine whether the distribution of scores have the same or different shape. To evaluate this, two histogram graphs were created to examine the distribution of scores for each group. To further ensure the distributions were similar, a 23 nonparametric version of a homogeneity test using mean ranks will be computed to compare variances. In order to conduct Spearman rank-order correlations, there are two assumptions to meet: that the two variables are measured on an ordinal, interval or ratio scale; and that there is a monotonic relationship between the two variables. This is suitable for this study because the Arrowsmith performance score is rated on a scale from 1 – 12, ranging from very severe (1), to above average (12), making this ordinal data. The other measures of WM (Numbers Reversed, BRIEF) are measured on an interval scale. Thus, Spearman’s correlations are most appropriate. Analysis. The analysis of the different WM measures and the classification data was conducted using the IBM SPSS software (IBM Corporation, 2012). To address research questions 1 and 2, Spearman rank-order correlations were conducted to assess the strength and direction of the relationship between the three Arrowsmith WM performance scores, Numbers Reversed, and the BRIEF. A Spearman correlation statistic is between -1 and 1, with a value that is closer to zero indicating a weak relationship between the Arrowsmith performance scores and Numbers Reversed or the BRIEF. A positive value would indicate that as students experience better performance across the Arrowsmith measures, they tend to also have better performance across Numbers Reversed, or worse ratings on the BRIEF (more WM problems). A negative value would indicate that, as students experience better performance across Arrowsmith measures, they tend to also have worse performance on Numbers Reversed, or better ratings on the BRIEF (fewer WM problems). To address the last research question, two Mann-Whitney U tests will also be conducted to determine whether the two Arrowsmith groups (i.e., above or below median score) differ in WM task performance and parental ratings of WM. As noted above, the significance value is not 24 of interest, rather, the change in mean ranks will be examined to indicate which group is likely to have better WM. 25 Results Sample Characteristics Descriptive statistics for all variables were explored. Central tendency data are presented in Table 2. A series of Shapiro-Wilk’s tests (Shapiro & Wilk, 1965) and visual inspection of histograms were completed to determine if all scores were normally distributed. The Shapiro-Wilk’s test of normality indicated that all measures of WM, aside from Symbol Relations, were normally distributed. However, after inspecting the histograms, only one measure of WM appeared to fit a normal distribution (i.e., Numbers Reversed). A small sample size makes it difficult to establish accurate and meaningful distribution of the scores. Therefore, a nonparametric analysis was used due to the difficulty of determining normality. Table 2 Central Tendency Data Variable Mean Standard Deviation Range Median Numbers Reversed 94.83 8.80 24.00 95.00 BRIEF 72.33 7.53 17.00 74.00 Memory for Information or Instructions 3.33 0.82 2.00 3.5 Quantification Sense 7.17 3.49 9.00 7.00 Symbol Relations 4.00 3.63 9.00 2.00 Total Arrowsmith Score 14.5 4.32 12.00 15.5 Note. n = 6 Research Question One Does the performance on Arrowsmith measures relate to performance on a performance-based measure of WM? 26 Spearman Correlations. A Spearman’s rank-order correlation analysis was used to investigate the strength and direction of the relationship between Memory for Information or Instructions, Quantification Sense, and Symbol Relations, and an empirically valid measure of WM (i.e., Numbers Reversed). Small positive correlations were found between Numbers Reversed and Memory for Information or Instructions, rs (6) = .063, and Quantification Sense, rs (6) = .209. A strong positive correlation was found between Numbers Reversed and Symbol Relations, rs (6) = .772. For all Arrowsmith measures, as students performed better they also tended to perform better on Numbers Reversed. Research Question Two Does the performance on Arrowsmith measures relate to parental ratings of WM? Spearman Correlations. Additional Spearman rank-order correlations were used to investigate the strength and direction of the relationship between Memory for Information or Instructions, Quantification Sense, Symbol Relations, and a parental WM measure (BRIEF). A small positive correlation was found between the BRIEF WM ratings and Memory for Information or Instructions, rs (6) = .031. A strong positive correlation was found between the BRIEF WM ratings and Quantification Sense, rs (6) = .677, and a strong negative correlation between the BRIEF WM ratings and Symbol Relations, rs (6) = -.778. This means that as students performed better on Memory for Information or Instructions and Quantification Sense, their parents tended to report more WM concerns on the BRIEF, whereas the opposite trend was observed for Symbol Relations. A summary of the Spearman correlations from both research questions one and two are reported in Table 3. 27 Research Question Three Does the Arrowsmith performance score relate to performance on task-based and parental ratings of WM? Mann-Whitney U test. Two Mann-Whitney U tests were conducted to examine the change in mean rank scores between the two Arrowsmith groups on measures of WM. First, the distribution of scores on both dependent variables (i.e., Numbers Reversed, BRIEF) were examined between the two groups. The distribution of scores for Numbers Reversed appeared to look similar, however the distribution of scores on the BRIEF appeared to have a different shape. To further examine whether the two distributions were similar, a nonparametric homogeneity of variance test was conducted using mean ranks. Comparing the two groups based on their respective Numbers Reversed score, the nonparametric variance test indicated similar variances (p = .643). The same nonparametric analysis of variance was conducted using the BRIEF WM ratings and indicated similar variances across the two groups (p = .519). The first Mann-Whitney U test (U = 1.50) indicated an increase in mean rank scores on the performance based measure of WM (Numbers Reversed) in the higher scoring Arrowsmith group. This means that the students who performed higher across Arrowsmith measures tended to also perform better on the WM task, consistent with the earlier correlation findings. The second Mann-Whitney U test (U = 2.0) indicated an increase in mean rank scores on the BRIEF in the higher scoring Arrowsmith group. This means that students who performed higher across Arrowsmith measures tended to be rated worse by their parents on the BRIEF in terms of their WM. A summary of the change in mean rank scores are summarized in Table 4. Table 3 Spearman rank-order correlations 28 1 2 3 4 5 1. Numbers Reversed - 2. BRIEF WM -.319 - 3. Memory for Information .063 .031 - 4. Quantification Sense .209 .677 -.318 - 5. Symbol Relations .772 -.778 .201 -.366 - Table 4 Summary of Mean Ranks Group Numbers Reversed Mean Rank BRIEF WM Mean Rank 1 2.50 2.67 2 4.50 4.33 Note. Group 1 = median score of <15.5; Group 2 = median score of >15.5 29 Discussion Overview The purpose of this study was to examine Arrowsmith measures in regard to current theory and measurement of working memory (WM). Six participants (ages 10-16) were recruited from the Arrowsmith Program. All of the participants had recently enrolled in the program and were assessed at baseline. Performance-based and parental questionnaires were used to investigate the participant’s baseline WM upon entry into the program. In addition, demographic information was collected regarding ethnicity, parental occupation and education, country of birth, diagnoses and age. The Arrowsmith Program is a unique program that delivers cognitive training exercises to children and youth enrolled in the program. The individuals who attend the program typically have learning challenges and a unique set of strengths and weaknesses. Unfortunately, there is no empirical support for the training exercises used in this program, their model for understanding learning challenges, or the measures they use to identify these challenges. Without established psychometric properties in their measures, it becomes difficult predicting which cognitive abilities are being recruited. Without established validity of these measures, there is also no support in the classification of Learning Dysfunctions. In this chapter, the findings of this study are discussed, and how those findings are relevant to current literature in WM and cognitive training programs. Study strengths, limitations, and considerations for future research are also highlighted. Overall Arrowsmith Performance and WM The current study examined the relationship between Arrowsmith measures, used to identify Learning Dysfunctions in need of intervention, and current, valid measures of WM. 30 Findings indicated a trend in which higher performing Arrowsmith participants also did better on a performance-based task of WM, indicating they had stronger WM ability at baseline. This may suggest that the identified Arrowsmith measures actually recruit WM. As discussed previously, WM is a higher-order cognitive function that is often recruited when executing complex tasks (Eriksson et al., 2015). During such complex tasks, attention is required for encoding information into WM processes (Eriksson et al., 2015). It is likely that most of the Arrowsmith measures have a degree of complexity that also require attention. For example, Symbol Relations requires quickly reading an analog clock and solving for time using multiple hands. To complete this task accurately, it will require some aspect of visual spatial perception (conceptualizing where the hands are placed on the clock), working memory (holding multiple times in your mind while solving for the product), processing speed (ability to quickly compute and solve for time), and attention. There are likely other processes involved during this task, but because it recruits diverse abilities and requires a large amount of attention, one might describe this task as complex. In other words, due to the complexity and the amount of cognitive energy required, the Arrowsmith measures are likely related to other WM task-based measures. However, the degree of complexity likely differs between tasks, which may explain why some of the individual Arrowsmith measures related more strongly to WM than others. In contrast, a seemingly different trend was observed in parent ratings of WM, in that parents reported poorer WM ability (more WM problems) in the higher performing Arrowsmith group. There may be a few explanations as to why these results are contradictory. First, it is possible that the inconsistent results are reflective of a lack of psychometric properties across Arrowsmith measures. As both Numbers Reversed and the BRIEF have been shown to be reliable and valid measures of WM, the results may be indicative that the Arrowsmith measures 31 are lacking such properties, in that they do not relate consistently across measures of WM. Another explanation is that parental ratings of WM ability are different in nature than performance-based measures. The findings from this study demonstrated small correlations between Numbers Reversed and the BRIEF, indicating differences between the two measures. Previous research has also demonstrated a lack of association between performance-based tests and behavioral ratings of WM (Biggs et al., 2011; Herbrich, Kappel, Winter, & van Noort, 2019). This may be because task-based measures focus on the underlying skills while behavioural ratings measure the application of those skills in real-life situations, leading to different results (Herbrich et al., 2019). When examining higher order cognitive functions like executive functions or WM, it appears to be more common for researchers to use task-based measures (Packwood, Hodgetts, & Tremblay 2011). However, there is also evidence to suggest that behavioural measures, such as parental ratings, have higher ecological validity, meaning they are a better measure of WM in everyday life (Jurado & Rosselli, 2007). The differences between these measures may partially explain the difference in results in the present study. Another potential explanation could be that parental ratings of higher-order functions, such as WM, are based on parent perception of observation, meaning they are influenced by subjectivity. For example, parents may over or underestimate certain abilities. Moreover, if learning challenges, behavioural difficulties, and neurodevelopmental diagnoses are present, parents may be more likely to report poorer functioning as opposed to children who have not struggled in a typical school environment. In other words, parents that have chosen to enroll their children in an alternative educational program may be more likely to endorse poorer functioning across questionnaires. In conclusion, research supports the difference between task-based measures and parental ratings. Although parental ratings may be more ecologically valid, they 32 can be influenced by subjectivity. One method to ensure that behavioural questionnaires accurately capture WM is to use more than one rater, such as another parent or a teacher. Overall, performance-based measures and behavioural ratings each offer unique ways of capturing functioning such as WM. Therefore, it may be advantageous to use a mixture of performance-based and behavioural measures in research and clinical settings to fully capture WM capacity. Arrowsmith Dysfunction Measures and WM Spearman correlations indicated a strong positive correlation between Symbol Relations and Numbers Reversed. This indicates that better performance on Symbol Relations is related to better WM on a performance-based measure of WM. As discussed above, Symbol Relations is a complex task that requires diverse cognitive abilities, theorized to recruit WM and attention. As the findings indicated that this was the strongest correlation between measures, this means that using Symbol Relations in the Arrowsmith classification system may be an appropriate way to estimate WM ability. Therefore, if an Arrowsmith student performs poorly on Symbol Relations or is classified as having a Learning Dysfunction in the area of Symbol Relations, it is likely they have WM impairments. The correlation analysis also indicated that better performance on Symbol Relations was related to better parental ratings of WM (fewer WM concerns). Despite research demonstrating differences between performance-based measures and parental ratings of WM, these findings suggest that Symbol Relations was similarly related to parental reports of WM as it was a performance-based measure. These preliminary findings suggest that performance on Symbol Relations may be predictive of WM capacity amongst parental ratings and task-based ability. An explanation for Symbol Relations acting as the strongest correlate to WM could be the complexity of the task itself. As thoroughly described above, it may be the most complex task 33 that requires large amounts of cognitive energy in comparison to the other Arrowsmith measures. Symbol Relations could also be a diverse enough task to capture similar WM abilities as represented in performance-based and real-life environments. A small and strong positive correlation was observed between Quantification Sense and Numbers Reversed and parental ratings of WM, respectively. The strong correlation indicates that better performance on Quantification Sense was related to poorer parental ratings of WM (higher WM concerns). These findings are preliminary, however, suggest that Quantification Sense does relate to parental ratings of WM in the Arrowsmith population. This means that an Arrowsmith student could have a Learning Dysfunction in Quantification Sense, and have strong WM abilities as rated by their parents. In contrary, Quantification Sense did not strongly relate to Numbers Reversed, and related to WM in the opposite direction. Based on WM theory, a strong relationship was expected between Quantification Sense and WM measures, in the appropriate direction. As mentioned previously, there have been no empirical studies demonstrating the psychometric properties of the Arrowsmith measures, such as reliability and validity. The results in the current study suggest that Quantification Sense may not be a valid measure of WM, given the lack of correlation with established WM measures. Another possible explanation for this finding could be that arithmetic-based tasks, or Quantification Sense, do not accurately capture WM ability. For example, an individual could have strong arithmetic skills, but lack WM skills in real life, such as generating goals, plans, or essential steps to achieve said goals. Additionally, Numbers Reversed is an auditory WM task, and requires the ability to hold and manipulate auditory information. During Quantification Sense, it may not be necessary to hold and manipulate any auditory information, which may explain the small correlation. Moreover, the current sample did not have math learning challenges, and their learning challenges were 34 reportedly more literacy-based. This may indicate that Numbers Reversed (an auditory WM task) does not measure WM related arithmetic skills. Although research has suggested a relationship between WM and math skills (Ashcraft & Krause, 2007), the findings may suggest that the sample had no underlying WM issues in math. Additionally, the parental ratings indicated WM problems, which may indicate that Arrowsmith participants have WM impairments in everyday life that do not impact arithmetic skills. The findings also suggested that Memory for Information or Instructions was not related to Numbers Reversed or the BRIEF. Numbers Reversed is classified as an auditory WM task, in that it requires an auditory processing component. Similarly, Memory for Information is also presented in an auditory format. Both of these measures are tapping into the phonological loop component of WM (Baddeley, 2000). One would expect that there would be a strong correlation between these two measures, however findings suggest quite the opposite. An explanation for this could be that Memory for Information or Instructions could be more related to short-term memory (STM), rather than WM. As previously discussed, short-term memory and WM are viewed as separate constructs. STM is involved in storing and coding information, and does not manipulate information over time (Melby-Lervag & Hulme, 2012). This could be the case for Memory for Information or Instructions. This task may be presented in a way where the student must memorize and repeat exactly what they hear. If this is the case, there is no manipulation of information occurring, and is better classified as a STM task. However, this is only theorized. Only vague information is shared to researchers regarding Arrowsmith measures and procedures. Therefore, it becomes difficult to understand how each measure works when there is minimal information shared. 35 When examining the three Arrowsmith measures individually, it is clear that their relationship to WM is inconsistent. Some Arrowsmith measures strongly relate to both poorer and better WM, whereas one Arrowsmith measure did not relate at all. The current study is one of few that have evaluated the Arrowsmith Program, and likely the first evaluating the Arrowsmith measures used to identify Learning Dysfunctions. Due to this, there is no available information regarding the reliability and validity of these measures. A possible explanation for the inconsistency in results is that the Arrowsmith measures are different in nature, and some may be more psychometrically sound than others. Although WM is recruited frequently in complex tasks, some of the Arrowsmith measures (Quantification Sense, Memory for Information or Instructions) may not be as valid as others (Symbol Relations). Furthermore, tasks that also recruit arithmetic and STM abilities may not relate as strongly to WM as others. This is common in performance-based tasks, in that they are often faced with task impurity. This refers to the task assessing different executive components than what is intended (Packwood et al., 2011). Another explanation of this inconsistency is the small sample size. With a sample of only six participants, it becomes difficult to capture accurate trends that can be generalized to the Arrowsmith population. Another explanation for the inconsistent results could be the nature of the WM measurements used. As previously mentioned, Numbers Reversed is an auditory WM task. WM literature suggest that WM includes three main components: the central executive, phonological loop, and the visuospatial sketchpad (Baddeley, 2000). As Numbers Reversed requires attentional control and holding verbal information, the visuospatial sketchpad is not represented. Increased diversity of WM measures may have established more consistent results between the Arrowsmith measures. Furthermore, the BRIEF WM subscale includes items regarding attention span, 36 distractibility, forgetfulness, and completion of tasks. It is a possibility that the subscale of WM in the BRIEF is more closely related to attentional difficulties, rather than WM. As research has supported the relationship and involvement of attention and WM (Eriksson et al., 2015), it may become difficult to parse out to the constructs in terms of measurement. Both of the WM measures used measure a different application of the ability, which may have influenced the findings. Overall Conclusions The present findings indicate that some aspects of the Arrowsmith classification system are related to valid measures of WM. Overall, Symbol Relations appears to be the best predictor for both performance based WM and parental ratings. When including the other Arrowsmith measures in the analysis, it changed the direction of the relationship, as evidenced in Table 4. Due to the opposing correlations, this likely impacted the direction of the mean rank change reported in the Mann-Whitney U test. In conclusion, using Symbol Relations to identify a WM-related Learning Dysfunctions appears to be acceptable in the Arrowsmith Program. This measure can likely be accurate at identifying WM capacity in tasks and in everyday life. Other measures used in the Arrowsmith classification system either did not relate to WM, or the direction of the relationship was not consistent with other measures. It is likely that some of the Arrowsmith measures would not be valid to use when examining WM ability. Furthermore, the lack of diverse measures of WM, the small sample size, and the use of performance-based and behavioural ratings may have impacted the findings. Research with Arrowsmith Population Conducting research with participants from the Arrowsmith Program is unique to other student populations. The Arrowsmith Program is an alternative method to education, and is 37 typically chosen because students are struggling in conventional brick and mortar schools. As discussed previously, the Arrowsmith Program implements minimal academic instruction; rather, it focuses on cognitive exercises. Therefore, the nature of individuals who are placing their children in an alternative program is a unique subset of people. This may have impacted results in a few ways. First, parents may be more inclined to endorse poorer performance of functioning when their child has struggled with aspects of school. This was found to be true in the current sample. For example, scores across the WJ III NU COG demonstrated that participants overall had average WM ability (M = 94.83, SD = 8.80). In contrast, the BRIEF WM subscale indicated that parent’s ratings suggested moderate concerns, with scores falling in the elevated range (M = 72.33, SD = 7.53). If the parents were more likely to endorse behavioural concerns, this would have falsely elevated WM concerns, and underestimate the participant’s true WM capacity. Another reason why conducting research with the Arrowsmith population is unique is due to the nature of participants. For example, most of the students enrolled in the Arrowsmith Program have learning challenges and other diagnoses. The participants in the current study had a range of cognitive challenges including poor executive functioning, processing speed, expressive and receptive language, attention deficit hyperactivity disorder (ADHD), mild cerebral palsy, and learning disabilities. This may have impacted the results because the children in the Arrowsmith Program are diverse, and may be difficult to compare to one another. This is evident in the Arrowsmith Program itself, in that they create an Initial Learning Profile for each student. Due to the unique subset of individual’s enrolling children in the Arrowsmith Program, this also creates a barrier of sample size. The number of Arrowsmith students in a classroom are much smaller than a typical classroom, which limits recruitment of participants. This impacted 38 the study because it made recruiting for a large sample difficult. Without a large enough sample, it is not feasible to draw inferences and determine if a significant result is present. Another barrier to working with the Arrowsmith population is that all information specific to Arrowsmith is protected. Specifically, this means that all exercises and the assessment process within the Arrowsmith Program is not able to be shared or replicated due to patents and ownership. This makes it difficult to fully understand the different measures and tasks implemented by the Arrowsmith Program because the information being shared with external researchers is limited. This impacted the evaluation of results because it is not completely known what the Arrowsmith measures are. Without detailed descriptions of the measures, it is difficult to explain why some measures strongly related to WM or did not relate to WM at all. Limitations and Strengths of the Present Study Limitations. The largest limitation of these findings is the small sample size. This limited how the data could be analyzed despite the proposed inferential design. As a result, it was not feasible for the analyses to capture a real significant difference between the two groups, if one was present. Due to this, the data had to be described in terms of trends and it was not appropriate to make inferences based on the findings. Certain claims about WM and how it relates to the Arrowsmith measures cannot be made without significantly more Arrowsmith participants. Another limitation of the current study is the lack of diverse WM measures. Numbers Reversed is one WM task relying specifically on auditory WM abilities. Although it has been shown to be a valid measure (McGrew et al., 2007), it would have been advantageous to use additional performance-based measures of WM, such as visual or spatial measures of WM. As WM is complex and influenced by many factors, the results may be more accurate or stable 39 when using additional measures. For example, having more than one WM measure allows for diversity in the way the performance-based tasks capture WM. This means that there is likely a higher opportunity of capturing WM as a whole. Strengths. An advantage of this study is the evaluation of an aspect of the Arrowsmith Program that has not been studied before. There have been various studies on cognitive training programs, however, few researchers have evaluated the assessment process or classification process that is implement by the cognitive training program at hand. Rather than just identifying whether the cognitive training program improves functioning, this study examines the program in more depth and breadth than most. Another strength of this study is that it approaches assessing the validity of the Arrowsmith measures by comparing them to other measures of WM. This is a large strength of this study as it is something that has not been done before with the Arrowsmith Program, and demonstrates preliminary information regarding their assessment tools and how similar they are to current measures of WM. Considerations for Future Research and Practice Further research in this area would be necessary to make inferences and better understand the strength of the relationship between the Arrowsmith measures and WM. Due to the small sample size, it is difficult to make inferences and suggest that the results are an accurate depiction of the Arrowsmith measures. With additional participants, the results will yield better conclusions in the evaluation of the Arrowsmith measures and how they classify their students. The current evidence suggests that one of the measures may be predictive of performance-based WM and more ecological valid measures of WM. Although the findings are preliminary, it suggests the presence of a strong relationship, and also indicates a lack of relationship between 40 WM (i.e., Memory for Information of Instructions). In having this information, it could better inform individuals implementing the Arrowsmith Program and how each domain relates to WM. Findings from this research can also add to the field of WM focused programs, and to help better understand cognitive training programs as a whole. Further, examining which Arrowsmith tasks recruit WM may help explain the lack of improvement in WM over time (Weber et al., 2019). Current research has demonstrated that over time, Arrowsmith participants make cognitive gains in areas such as learning, processing speed, and long-term memory, but do not make improvements in WM (Weber et al., 2019). The current study adds to this literature, and may be indicative of which Arrowsmith measures/exercises should be focused on in terms of improving WM. Lastly, general implications for clinical practice include the importance of evaluating current measures of WM. For example, findings from the current study suggested that the Arrowsmith sample at baseline did not have WM impairments as evidenced by their scores across the WJ III NU COG. However, parents endorsed elevated concerns in terms of everyday WM abilities. 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Other (please list): Does your child have any developmental delays or learning or behavioural problems that have been identified by a professional (please check one)? ________ Yes ________ No If Yes, please describe: Has your child received or participated in any of the following special health, educational, or community services (please check one for each item)? Yes No Don’t Know 1. Speech and Language 49 2. Supported Child Development Program (SCD) 3. Learning Resource Support 4. Special Education 5. Other (if yes, please describe) Please describe the reason for any of the services listed above: 1. Speech and Language 2. Supported Child Development Program (SCD) 3. Learning Resource Support 4. Special Education 5. Other (please describe)
Thesis/Dissertation
eng
School and Applied Child Psychology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
An evaluation of arrowsmith program measures in relation to working memory
Text