Graduate Thesis Or Dissertation
Working memory: cognitive and genetic decomposition public deposited.
- Working memory (WM) capacity – the ability to store and process information in primary memory – is a core ability that is central to most human behavior. Theoretical characterizations of WM capacity aim to identify the cognitive constructs that underlie this ability and to explain the close relationship between WM and short-term memory (STM). These characterizations are heavily based on behavioral research within the field of cognitive psychology. A relatively unexplored method that may provide a deeper understanding of the cognitive constructs that underlie WM is the use of a twin design. Twin designs allow for more detailed examination of underlying processing in that common and unique variance across constructs can be further decomposed into variance that is driven by the same or distinct sets of genes or the same or distinct environmental factors. This dissertation contributes to existing theories of WM in three ways: 1) The first aim was to determine whether existing research suggests that genes more strongly influence individual differences in WM task performance than STM task performance. Results of the meta-analysis revealed a tendency for WM tasks to be more heritable than STM tasks. 2) The second aim was to determine whether WM and STM constructs are distinguishable by decomposing their relationship into candidate cognitive subprocesses - attention control (AC) and long-term memory (LTM) - and then further decomposing those findings into genetic and environmental influences. The majority of the overlapping variance between WM and STM also covaried with AC and was almost entirely genetic in nature. 3) The final aim evaluated how much of the WM-intelligence relationship can be accounted for via mediation with the candidate cognitive constructs from Aim 2 and then determines the extent to which genes versus environmental factors are contributing to that relationship. AC and LTM significantly mediated the WM-intelligence relationship and STM played a negligible role. The genetic models revealed that the majority of overlapping processing for WM and intelligence was driven by genetics, which was mainly due to overlap with AC and only minimally due to STM.
- Altamirano, Lee Judith
- Psychology and Neuroscience
- Munakata, Yuko
- Olson, Richard
- Mozer, Michael
- Friedman, Naomi P.
- Curran, Timothy
- University of Colorado Boulder
- short-term memory
- working memory
- In Copyright
- English [eng]
- Reference Manager
- Simple TEXT file
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Focused review article, how is working memory training likely to influence academic performance current evidence and methodological considerations.
- Pearson Clinical Assessment, Clinical Research, Stockholm, Sweden
Working memory (WM) is one of our core cognitive functions, allowing us to keep information in mind for shorter periods of time and then work with this information. It is the gateway that information has to pass in order to be processed consciously. A well-functioning WM is therefore crucial for a number of everyday activities including learning and academic performance ( Gathercole et al., 2003 ; Bull et al., 2008 ), which is the focus of this review. Specifically, we will review the research investigating whether improving WM capacity using Cogmed WM training can lead to improvements on academic performance. Emphasis is given to reviewing the theoretical principles upon which such investigations rely, in particular the complex relation between WM and mathematical and reading abilities during development and how these are likely to be influenced by training. We suggest two possible routes in which training can influence academic performance, one through an effect on learning capacity which would thus be evident with time and education, and one through an immediate effect on performance on reading and mathematical tasks. Based on the theoretical complexity described we highlight some methodological issues that are important to take into consideration when designing and interpreting research on WM training and academic performance, but that are nonetheless often overlooked in the current research literature. Finally, we will provide some suggestions for future research for advancing the understanding of WM training and its potential role in supporting academic attainment.
At the center of all conscious information processing lies working memory (WM) —a fragile system responsible for processing and temporarily storing information. It is fragile because it can only keep track of a few pieces of information at the same time, and this information can fade away after just a short period of time (seconds) or be pushed out by distracting stimuli ( Goldman-Rakic, 1996 ; McNab and Klingberg, 2008 ). WM capacity (WMC) predicts school performance years later ( Gathercole et al., 2003 ; Bull et al., 2008 ; Alloway and Alloway, 2010 ; Geary, 2011 ). Furthermore, WMC in preschoolers has been shown to predict future risk of dropping out of high school ( Fitzpatrick et al., 2015 ). Children with reading and math difficulties often exhibit WM deficits ( Siegel and Ryan, 1989 ; Swanson and Jerman, 2006 ) and developmental studies of children with poor WMC have also reported findings to suggest that the effect is cumulative across development, resulting in greater decrements in learning as a child gets older ( Alloway et al., 2009 ). This notion has instigated a quest for effective early interventions—all with the higher purpose of supporting the learning of these children.
KEY CONCEPT 1. Working memory (WM) The brain's processing system that allows us to mentally work with a limited amount of information in the now. It is fundamental to all advanced thinking and in order to learn facts or skills, that information must first pass through working memory—our mental workbench, before becoming more stable long-term representations.
This review will expand on the results from Söderqvist and Bergman Nutley (2015) showing improvements in reading and math performance two years following Cogmed Working Memory Training (CWMT) , put these results in a wider theoretical context and discuss these in relation to conflicting results from other studies. As cognitive training programs differ in both their content and implementation, consequently showing significantly different effects between intervention types ( Melby-Lervåg et al., 2016 ), this review will focus on the most widely studied WM training program, CWMT™ ( Klingberg et al., 2002 ).
KEY CONCEPT 2. Cogmed Working Memory Training (CWMT) A computerized intervention entailing 12 different visuo-spatial and verbal WM span tasks that are presented in a rotating schedule and adapt to the capacity level of the trainee. Training is typically implemented during a period of 5–7 weeks, 30–40 min/day, 5 days/week, with weekly support from a certified coach that ensures compliance to the program.
The overarching question is whether an increased WMC following CWMT will transfer to increased school performance. A straightforward question perhaps, but what does it actually entail? Are we asking whether training affects the skills and content already learned, or whether training will help acquiring new skills and content in the learning to come? Earlier generations of “cognitive training” research struggled with similar questions and for instance Sidney Strauss described this complexity as follows ( Strauss, 1972 , p. 331):
“…the level of a child's structural development determines the concepts he will learn. That is, the intellectual structure sets the limits for what can be learned. In this sense learning is subordinate to development.”
In Söderqvist and Bergman Nutley (2015) , we discussed two theoretical routes through which improved WMC could impact academic outcomes, the learning route and the performance route . These two theoretical routes are in no way exclusive from one another, but they are nonetheless important to distinguish when designing an intervention study as their interaction and effects are otherwise likely to be overlooked. Since WM training does not include actual teaching or practice of reading or mathematical skills, their development will depend largely on the education students are or have been receiving, along with the development of other cognitive functions required for skill proficiency.
KEY CONCEPT 3. Learning route The mechanism by which WM training can influence academic performance through improving learning capacity. This can result from increased attention in class and from an increased capacity to digest new knowledge. Effects acting through this route would be evident in the long-term with outcome measures matching the curricular content.
KEY CONCEPT 4. Performance route The mechanism by which WM training can influence academic performance through WM's direct involvement in academic tasks. Effects impacting this route would accompany the increased WM capacity and be evident on outcome measures of already learned skills on tasks tapping WM to its limits.
While the field of cognitive training is still young, some researchers have been tempted to summarize the current results in meta-analyses, reaching the conclusion that WM training does not transfer to academic outcomes ( Melby-Lervåg and Hulme, 2013 ; Melby-Lervåg et al., 2016 ). Although meta-analyses can add value in assessing the current status of a field, the conclusions will only reflect the individual studies and the design choices therein. It is important to remember the theoretical assumptions in these studies and how they have been operationalized in order to understand what some of these, perhaps premature, conclusions are telling us. Since most studies that have assessed transfer effects to academic performance have focused on reading and mathematics we will here briefly summarize the literature of WM's role for the two.
The Role of WM for Reading Ability
The cognitive mechanisms underlying acquisition of reading ability have been studied extensively over the past decades ( Daneman et al., 1980 ) and the complexity of its nature is well-demonstrated ( Carretti et al., 2009 ; Kudo et al., 2015 ). Becoming reading proficient requires a delicate interplay between different cognitive abilities along with formal instruction and practice. For instance, one study of beginning readers (7-year olds) found that phonological awareness predicted reading accuracy, while phonological awareness and verbal WM predicted reading comprehension ( Leather and Henry, 1994 ). As reading proficiency involves both cognitive abilities and skill acquisition, the strength of the relation between WM and reading is likely to vary over time depending on both cognitive maturity and skill development ( Christopher et al., 2012 ). In one such study, the relations between reading comprehension and different cognitive components were assessed in a longitudinal design in grades 1–3 ( Seigneuric and Ehrlich, 2005 ). The results indicated that WM became an increasingly important predictor of reading comprehension with age and it was not until grade 3 that WMC independently explained variance in reading comprehension after controlling for decoding and vocabulary. At earlier reading stages, the influence of WM could be explained by its shared variance with decoding and vocabulary skills. This main finding has also been reported in slightly older children, where verbal WM predicted reading comprehension after controlling for WM's shared variance with other verbal abilities ( Cain et al., 2004 ; Kibby et al., 2014 ), however WM did not predict word identification ( Kibby et al., 2014 ). Thus, WM is on an independent predictor of reading comprehension once word reading ability has been mastered.
Others have found that different aspects of WM seem related to different aspects of reading ( Oakhill et al., 2011 ; Gathercole et al., 2016 ) and that this relation also varies between languages ( Arina et al., 2015 ). The relation observable between WM and reading ability will thus depend partly on the types of assessments used to measure each construct. For example in the study by Seigneuric and Ehrlich (2005) , correlation between WMC and reading in grade 3 was more prominent when using passage comprehension compared with a sentence comprehension outcome. Taken together, it seems that WM may be differentially supporting the two different phases of “learning to read” and “reading to learn” ( Chall, 1996 ), in that WM may be transitioning from being one of several essential parts of the puzzle in reading acquisition to later becoming a crucial function in content understanding.
How is WM Training Likely to Influence Reading?
As we have reviewed above, WM is only one of several capacities that are necessary for reading proficiency. Thus, the right question to ask may not be whether CWMT is likely to influence reading proficiency or not, but rather explore the stages where CWMT is likely to have effects on word decoding, reading fluency and reading comprehension respectively, depending on the baseline profile (cognitive and skill acquisition) of the child. For instance, phonological awareness has a more significant impact on reading acquisition in the early stages than WM. However, if WM is also impaired, then additional struggles are likely to cause hindrance in decoding progress, which could then be improved with CWMT. Conversely, if another ability than WM is acting as a bottleneck for a specific skill acquisition, then CWMT alone is not likely to have an effect on that skill. Since previous research has shown WMC to be predictive of certain aspects of reading at certain ages only, selecting appropriate outcome measures for training studies is crucial. Part of the variance seen between controlled published CWMT studies may indeed lay in the assessments chosen (see Table 1 ).
Table 1. Effect sizes (Cohen's d ) extracted from published studies on CWMT on developing samples reporting data on different aspects of reading .
For example, as WMC has not been shown to predict simple word recognition (e.g., Kibby et al., 2014 ) and reading comprehension when using simple sentences (compared to longer texts; Seigneuric and Ehrlich, 2005 ), these types of measures are not likely to be influenced by an improved WMC over and beyond a level that can be explained by variance common to verbal and phonological abilities. This is possibly reflected in a recent study ( Roberts et al., 2016 ) that report no improvements in reading following CWMT in a large sample of 6 to 7-year-olds. The measures used to assess reading [from the Wide Range Achievement Test (WRAT)-4] at a 12 month follow up was word reading, sentence comprehension and spelling and at a 24 month follow up: word reading and spelling. Thus, considering the theoretical background discussed above it is not surprising that improvements were not observed on these measures. However, it would be inappropropriate to draw any strong conclusions regarding reading capacity in general as it remains unclear from this study whether effects would have been observed with other reading measures that are known to be more reliant on WMC ( Dunning et al., 2013 ; Katz et al., 2016 ). When reading is assessed with passage comprehension on the other hand, it seems that CWMT more often than not has produced positive effects in both clinical and non-clinical samples, in line with what would be expected based on the literature reviewed above ( Cain et al., 2004 ; Seigneuric and Ehrlich, 2005 ; Carretti et al., 2009 ; See Figure 1 ).
Figure 1. Depicts the effect sizes (Cohen's d ) extracted from the studies using CWMT divided between outcomes of decoding (A) , sentence comprehension (B) , and passage comprehension (C) . Studies with clinical samples are color coded in a red shade whereas studies with samples without a clinical diagnosis are depicted in a blue shade.
While the figure offer no clear explanation to the circumstances under which CWMT transfers to measures of reading, it highlights some of the complexities in interpreting the literature, namely the types of assessments used, the wide age range within studies, the learning status between studies and the time of the assessment. As previously discussed, WM seems to play a different role in supporting reading acquisition than it does reading comprehension, meaning that studies including students on both sides of becoming reading proficient (for instance in Phillips et al., 2016 ranging from age 8 to 16) are likely to see differential effects on the same outcome after training. It also seems that studies assessing reading proficiency in the early stages do tend to find effects on a group level on decoding or phoneme awareness ( Foy and Mann, 2014 ; Fälth et al., 2015 ), whereas studies assessing older children on this measure tend to find effects primarily in impaired samples ( Dahlin, 2010 ; Egeland et al., 2013 ), although not consistently ( Chacko et al., 2014 ). This could perhaps reflect the different levels of restrainment WM has caused (on average) on the measured skill in the various study samples and to which degree other factors are hindering performance.
The Role of WM in Mathematics
WMC predicts measures of current and future mathematical abilities ( Passolunghi et al., 2007 ; De Smedt et al., 2009 ; Raghubar et al., 2010 ; Dumontheil and Klingberg, 2012 ; Peng et al., 2016 ) and their partly overlapping neuroanatomical correlates have been suggested to account for at least some of this observable relation ( Zago et al., 2002 ; Swanson et al., 2008 ; Metcalfe et al., 2013 ). However, longitudinal studies have found different components of WM to be related to mathematics performance at different ages ( De Smedt et al., 2009 ; Holmes et al., 2009 ; Raghubar et al., 2010 ) and between aspects of mathematics within the same age ( Wiklund-Hörnqvist et al., 2016 ). The developmental stage of the participants does not only relate to the cognitive development (linked closely to age) but also to the quality and quantity of exposure participants have had to mathematical training ( Morrison et al., 1997 ; Roberts et al., 2015 ). During early stages of learning arithmetic most children use counting strategies, at first often with the aid of fingers before developing verbal counting ( De Smedt et al., 2009 ). Finally, the counting will gradually be replaced by forming categorical representations in long-term memory (LTM; Noël et al., 2004 ; De Smedt et al., 2009 ). The use of these different strategies put different demands on cognitive functioning, including that of WM. This is reflected in a changing pattern in how WM components relate to mathematics in that executive and visuo-spatial capacities appear to be mostly recruited for learning and application of new mathematical skills whereas the phonological loop/verbal WM becomes more important once the skill is learned ( McKenzie et al., 2003 ; Raghubar et al., 2010 ). Thus, although the task is held constant between participants, the strategies used to solve it might differ and as a result associations with WM will differ between participants. Similarly, there might be intra-individual differences in cognitive demand if an individual is measured with a longitudinal design (or following an intervention) if the individual has switched strategy e.g., from verbal counting to automatized solutions between the measurement points. This is supported by a study by Meyer et al. (2010) , who observed that while grade 3 students performed significantly better than grade 2 students on measures of operations and mathematical reasoning, no significant differences were observed in the WM measures found to correlate with these mathematical measures. This possibly reflects a development of strategies as a result of 1 year of formal education. Although these skills are reliant on WM, they are not necessarily reliant on a matched development of WM. Rather, a good fundamental WMC will allow for the learning and development of new skills and strategies ( Bull et al., 2001 ) but once these are established WM will play a different role in performance ( Imbo and Vandierendonck, 2007 ).
In addition, the term “mathematics” can include a large variety of skills. Geary (2011) describes two core domains: numerical facility (including skills such as arithmetic, number, and counting knowledge) and mathematical reasoning (representing more abstract mathematical knowledge). These two domains both rely on a number of cognitive functions, of which WM (and its different components) has been demonstrated to be one ( Swanson and Jerman, 2006 ; Swanson et al., 2008 ; Geary, 2011 ). How WM relates to mathematics does not only depend on which domains of WM and mathematics are being assessed ( Peng et al., 2016 ; Wiklund-Hörnqvist et al., 2016 ) but also on other aspects such developmental stage of the subjects and how tasks are presented ( DeStefano et al., 2004 ).
How is WM Training Likely to Influence Mathematical Abilities?
As outlined above, WM is consistently found to relate to mathematical performance but the specific patterns of this relation are complex and not fully understood. Consequently, the effects of CWMT will be dependent on many different factors. For example, the effects from training are sensitive to the measures used, specifically as to which degree they tap WM. As we have discussed above, the load on these measures can differ across ages and specific strategies the child is using. The observation that the phonological loop seems to play a more important role for retrieving already learned skills in children ( McKenzie et al., 2003 ; Raghubar et al., 2010 ) is important since capacity of the phonological loop is shown to be largely unaffected by CWMT ( Holmes et al., 2009 ; Dunning et al., 2013 ). Thus, CWMT might have its potentially largest influence on the process of learning new skills or during more complex mathematical reasoning tasks shown to be more reliant on visuo-spatial resources ( Holmes et al., 2006 ). These points have been largely overlooked in the majority of studies on CWMT to date and as can be seen from Table 2 and Figure 2 , many studies have a wide age range of participants included. This will not only induce large variance due to development and strategies used, but in the cases where standardized assessments such as WRAT and Wechsler Individual Achievement Test (WIAT) have been used this will also mean that the actual tasks performed by the participants within the same study will differ due to the assessments wide inclusion of different mathematical domains ( Raghubar et al., 2010 ) and the start and stop rules typically used within these assessments. These points make it difficult to interpret and generalize these results.
Table 2. Effect sizes (Cohen's d ) extracted from published studies on CWMT developing samples reporting data on different aspects of mathematics .
Figure 2. Effect sizes (Cohen's d ) extracted from published studies on CWMT reporting data on number operations (A) and mixed maths tasks (B) . Studies with clinical samples are color coded in a red shade whereas studies with samples without a clinical diagnosis are depicted in a blue shade.
Outcome Measures to Assess the Performance Route
As for all research, a crucial point to consider both when designing and interpreting research is whether the study design and outcome measures used actually answer the question(s) being asked. Most of the studies reviewed here set out to investigate whether effects from CWMT “transfers” or “generalizes” to academic performance ( Gray et al., 2012 ; Egeland et al., 2013 ; Chacko et al., 2014 ; Foy and Mann, 2014 ) thus implicitly focusing on what has been discussed here as the performance route. This implies an assumption that WM would have been acting as a bottleneck for pre-existing skills and that increasing its capacity would unlock previously constrained academic potential. Within this line of reasoning, such effects would be apparent only on academic tasks with a WM load close to each subject's limits. Due to the complexity of both reading and mathematical learning it is also not obvious that improving WMC will lead to linearly associated improvements in the academic skills measured. One can imagine two alternatives, one in which a minimum level of capacity is required for simple mathematics or reading skills, such as for example identifying letters or reading and understanding a short and simple sentence. In this case, having a WMC above this threshold might not provide additional benefits on such tasks. On the other hand, more advanced reading tasks such as reading and understanding a whole paragraph of more complicated text might benefit from a higher WMC independently of the baseline, thus exhibiting a linear pattern of improvement with an increased WMC. Similarly to what Raghubar et al. (2010) have argued, the complexity of the relation between WMC and academic performance points to the need for task content analyses of outcome measures if we are to better understand when, how and for whom CWMT leads to significant transfer effects.
Outcome Measures to Assess the Learning Route
Although learning itself takes time to manifest, there are other ways to study the process of learning as was done in a randomized, controlled study of children with ADHD ( Green et al., 2012 ). After CWMT, children in the intervention group were observed to have fewer occurrences of looking away and playing with objects during an academic task compared with the children in the control group, concluding that CWMT had indirect impact on academic learning. Another study explicitly set out to assess both hypothetical routes of impact with assessments directly after training as well as after 12 months ( Dunning et al., 2013 ). Other studies have investigated the learning route ( Holmes and Gathercole, 2014 ; Söderqvist and Bergman Nutley, 2015 ; Roberts et al., 2016 ) only, that is, that CWMT would positively influence the learning capacity of the participants. This hypothesis can be simplified as:
CWMT + education > education alone
Within this premise, great care must be taken in selecting outcome measures that match the content of the education part of the equation. For example, while a well-functioning WM can help a child understand geometry, only specific instruction and practice will enable that child to solve a problem using pythagoras theorem (an example from the WIAT-II numerical operations subtask). Most of the studies discussed in this review have used short standardized assessments such as the WRAT and WIAT, and results from these have been used to generalize conclusions to the much wider term “academic achievement.” Although these are good measures for their own purpose, such as identifying individuals with specific learning difficulties, it is important to keep in mind that they only provide a snapshot of a student's academic abilities. It is therefore surprising that most studies using these have not included a discussion on how the particular tasks included (a) relate to WM and (b) for math primarily, match the curriculum to reflect what the students have been learning in school since the completion of training.
One approach that is more likely to capture progress on curricular content is to use metrics that schools already use, such as exams and national achievement measures, since these are specifically designed to capture learning progress. So far there have been two studies implementing CWMT in a school environment that have also used outcome measures based on assessments that the schools choose themselves as part of their typical academic assessment ( Holmes and Gathercole, 2014 ; Söderqvist and Bergman Nutley, 2015 ). Both these studies stand out as finding significant improvements on mathematics and reading performance at long-term follow-up. It should however be noted that while year 6 students in the Holmes and Gathercole study demonstrated significant increases in both mathematics and English, the effects for year 5 students were less clear. Using established school metrics also has the benefit of the assessments being salient to the students since these contribute to grades and/or are presented in the usual educational context. Students might therefore be more motivated when performing these tests compared to tests performed for a research study only. Another potential benefit is reducing the risk of placebo effects driving the results. Although these studies have employed no-contact control conditions, placebo effects are unlikely to explain the results when using regular school based assessments administered by the teachers, about 10–24 months after the training, and with no obvious link to the study ( Holmes and Gathercole, 2014 ; Söderqvist and Bergman Nutley, 2015 ).
Time of Assessment
Another requirement to assess the learning route is to allow for sufficient time between training and assessment for learning to take place. Although time increases the risk of introducing confounding factors, this should be avoided with well-controlled designs and should not hinder investigating effects on learning. This point is often disregarded in the literature. For example, a recent meta-analysis concluded that WM training (different programs bundled together) does not “generalize to important real-world cognitive skills, even when assessments take place immediately after training” ( Melby-Lervåg et al., 2016 ). We believe that statements like these illustrate a lack of consideration for the different mechanisms underlying WM's role in learning, and thus how training is likely to influence academic performance.
Another overlooked factor when interpreting results from training studies is to consider how the training is implemented, not only with regards to compliance but also tracking the effort levels invested. Just as one would not expect to build muscles by going to the gym and simply sitting there, or lifting weights that are not putting a strain on the muscle, one should not expect effects from CWMT if a large portion of the training has been performed with low effort.
A factor that has been more widely discussed is use of control groups ( Morrison and Chein, 2011 ; Shipstead et al., 2012 ; Green et al., 2013 ). While active control groups are appropriate to control for test-retest and expectancy effects, their inclusion also warrants a close dissection of potential side-effects. For instance in a randomized, controlled trial studying 5–7 year old children with ADHD, the control condition consisted of the same tasks as the intervention group but with memory load set to 2 throughout the training ( van Dongen-Boomsma et al., 2014 ). Baseline assessments showed that the sample had an average WMC of 2.6–2.8 indicating that the control group would have been training on a level that is likely to have trained their WMC. Consistent with this speculation, both groups showed improvements on many outcomes but the contrast between groups was scarce. Similarly, other samples of impaired individuals have used similar control conditions ( Chacko et al., 2014 ) and even though the contrast may have been larger in older samples, it is still rather likely that training for 40 min/day on a low level task (similarly to a sustained attention task) could in fact lead to training effects, thus diluting the statistically measureable effects from the intervention. Such findings have been observed in brain activity after CWMT, showing similar changes in both groups, simply more pronounced in the intervention group ( Brehmer et al., 2011 ). This emphasizes the importance of identifying the active ingredient one wishes to study, which will then guide the selection of an appropriate control condition.
If one wants to investigate whether CWMT + education > education, one should ensure that the education given to both groups is comparable for reliable conclusions to be drawn. This aspect might have influenced the results from the Roberts et al. (2016) study. In this study students were screened for WMC and those with low WMC were identified as at risk for academic underachievement. Half of these children were then informed of their deficits and selected to take part in a CWMT intervention. When doing so these children were taken out of class to perform the intervention, thus missing out on fundamental instruction. In this sense the Roberts et al. study rather investigated the hypothesis: CWMT > education. Improving the WMC of these students is unlikely to replace the education they have missed out on. Rather it runs the risk of them falling behind in their knowledge and skills, potentially resulting in negative influences on their self-esteem and motivation. Better design options to avoid this possibility is either performing the training outside of the school day as in Holmes and Gathercole (2014) or training whole classes and scheduling training to take time from several different subjects instead of one (as in Söderqvist and Bergman Nutley, 2015 ). A benefit of training whole classes is that all students miss the same content and the teachers can therefore compensate for this and no single student will suffer from falling behind in relation to their peers.
Clinical vs. Typically Developing Samples
The vast majority of studies reviewed herein have included children with some sort of cognitive deficit, such as low WMC, ADHD, or children receiving special education. Since these categories tend to include heterogeneous samples, often with high comorbidity with other deficits ( Gray et al., 2012 ; Chacko et al., 2014 ) it is of particular importance to perform more in-depth analyses of the participants' characteristics and response to the intervention if we are to gain a deeper understanding of the results. A recent study including children with the age of 8–12 years and diagnosed with ADHD indicate that medication status and co-morbidity can both act as moderators for the effect following CWMT ( van der Donk et al., 2016 ). Furthermore, baseline cognitive capacities have been found to be a predictor of both training improvements and transfer effects in a sample of children with intellectual disability ( Söderqvist et al., 2012 ). As we have discussed above, if other deficits are present but not improved either by CWMT or by another parallel intervention, it is unlikely that any great improvements on transfer measures will be observed after CWMT alone. On the other hand, if WMC is the only or the most serious hindrance for performance, then CWMT is more likely to lead to noticeable improvements. However, such improvements run the risk of being missed in a classical group comparison design if there are other subgroups for which little or no improvements are observed. Performing in-depth analyses to understand inter-individual differences are necessary in order to understand when CWMT can lead to improvements in academic performance, and ultimately inform on how to create better individualized interventions.
In contrast, since our study (2015) included typically achieving children with no apparent deficits hindering their learning progress, it is more likely that effects from training will be more homogenous across the group. This study suggests that typically performing students can also benefit academically from CWMT.
An issue that most young intervention research fields suffer from is that of running studies with insufficient power to actually detect a true signal ( Green et al., 2013 ). While small pilot studies can produce directionally informative results, their effect sizes will naturally rely on the heterogeneity of the sample and can thus be expected to vary between studies. Some have raised the issue of drawing invalid conclusions due to type-I errors (see for example Simons et al., 2016 ). The risk of type-II errors on the other hand is unfortunately seldom discussed in the cognitive training literature and as can be seen in Tables 1 , 2 some studies report effect sizes in the range of 0.4–0.7 that are non-significant due to the small sample sizes in the studies ( Dunning et al., 2013 ; Foy and Mann, 2014 ). However, these studies have concluded that “there were no effects” on these measures rather than stating that the results are inconclusive. This magnitude of effects has been deemed as relevant in educational interventions ( Hattie, 2008 ) and highlights the importance of running studies with sufficient power to statistically detect such effects (as in for instance Bergman-Nutley and Klingberg, 2014 ; Roberts et al., 2016 ).
Conclusion and Future Directions
In this review we have highlighted some important points to consider when designing future WM training studies, as well as when interpreting their results. As a whole, we believe the field would benefit from refocusing on the theoretical and functional underpinnings of the expected effects (e.g., Green et al., 2012 ), with design choices that reflect the complexity of the area. Based on the literature reviewed above, we consider WM's role in the learning route to be a promising notion to investigate further. In order to advance our understanding of how CWMT supports learning, we need to run larger studies that include more careful mapping of individuals' baseline profiles, and track long-term scholastic learning. Operationally, this would include consideration of how the intervention and outcome measures are matched with the education given to the participants along with in-depth analyses of inter-individual differences in responsiveness to training, and acknowledgement of how additional cognitive and educational skills interact with the outcome performance. This is particularly important for studies with clinical samples or academically underachieving children.
It is important to recognize that CWMT is not suggested, nor is it likely, to be a “magic pill” that solves all cognitive problems. Some of the effects are well-established whereas others are still in its early research days. Thus, there is still much to be learned about what an individual should expect from CWMT and grand scale studies are needed to answer the remaining questions outlined above. Caution should be given to not overstate effects, but just as importantly we should recognize that concluding at this early stage that WM training is ineffective without trying to understand the theoretical or functional mechanisms behind the effects, is premature. This runs the risk of leading to fewer intervention options for individuals who could benefit from them and cause stagnation of the research field and thereby our knowledge of training induced neuroplasticity. Let us instead move forward and look for solutions and deeper understanding of the mechanisms behind training and its effects.
All authors listed, have made substantial, direct, and intellectual contribution to the work, and approved it for publication.
Conflict of Interest Statement
At time submission SN and SS are both employees of Pearson Clinical Assessment, the distributors of Cogmed Working Memory Training.
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Keywords: working memory training, reading, mathematics, working memory, learning, academic performance
Citation: Bergman Nutley S and Söderqvist S (2017) How Is Working Memory Training Likely to Influence Academic Performance? Current Evidence and Methodological Considerations. Front. Psychol . 8:69. doi: 10.3389/fpsyg.2017.00069
Received: 29 September 2016; Accepted: 11 January 2017; Published: 07 February 2017.
Copyright © 2017 Bergman Nutley and Söderqvist. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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Working Memory Performance among Childhood Brain Tumor Survivors
Heather m. conklin.
1 Department of Psychology, St. Jude Children's Research Hospital, Memphis, Tennessee
Jason M. Ashford
Robyn a. howarth, thomas e. merchant.
2 Division of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
Robert J. Ogg
3 Division of Translational Imaging Research, St. Jude Children's Research Hospital, Memphis, Tennessee
4 Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
Wilburn E. Reddick
5 Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee
While longitudinal studies of children treated for brain tumors have consistently revealed declines on measures of intellectual functioning, greater specification of cognitive changes following treatment is imperative for isolating vulnerable neural systems and developing targeted interventions. Accordingly, this cross-sectional study evaluated the performance of childhood brain tumor survivors ( n = 50) treated with conformal radiation therapy, solid tumor survivors ( n = 40) who had not received CNS-directed therapy, and healthy sibling controls ( n = 40) on measures of working memory [Digit Span and computerized self-ordered search (SOS) tasks]. Findings revealed childhood brain tumor survivors were impaired on both traditional [Digit Span Backward- F (2, 127)= 5.98, p < .01] and experimental [SOS-Verbal- F (2, 124)= 4.18, p < .05; SOS-Object- F (2, 126)= 5.29, p < .01] measures of working memory, and performance on working memory measures correlated with intellectual functioning (Digit Span Backward- r = .45, p < .0001; SOS- r = − .32 − − .26, p < .01). Comparison of performance on working memory tasks to recognition memory tasks (computerized delayed match-to-sample) offered some support for greater working memory impairment. This pattern of findings is consistent with vulnerability in functional networks that include prefrontal brain regions and has implications for the clinical management of children with brain tumors.
Survivors of brain tumors (BTs) diagnosed in childhood are at significant risk for cognitive impairments secondary to disease- and treatment-related factors. As survival rates improve, efforts to optimize long-term cognitive outcomes take on added importance. Longitudinal studies of children treated for BTs most consistently reveal declines in intellectual functioning ( Mulhern et al., 2004 ). Progressive IQ loss likely reflects a decreased rate of learning compared to peers rather than a loss of previously acquired knowledge ( Palmer et al., 2001 ). Risk factors associated with IQ decline most reliably include younger age at treatment, longer time since treatment, female gender, treatment intensity, and complicating medical factors (as reviewed in Mulhern & Butler, 2004 ).
Historically, the cancer survivorship literature has been limited by over reliance on global cognitive measures not functionally specific enough to facilitate identification of vulnerable neural systems or development of targeted cognitive interventions. In more recent years, investigations have begun to identify specific areas of cognitive impairment including attention ( Dennis et al., 1998 ; Reeves et al., 2006 ), working memory (WM; Dennis et al., 1992 ; Dennis et al., 1998 ; Kirschen et al., 2008 but not Mabbott et al., 2008 ) and processing efficiency ( Waber et al., 2006 ; Mabbott et al., 2008 ) that may be proximal contributors to global IQ declines. Emerging areas of core deficit are informative as nearly half of age-related improvements in IQ can be accounted for by developmental improvements in WM and processing speed ( Fry & Hale, 1996 ). WM may be particularly vulnerable to radiation effects as indicated by findings revealing WM impairment larger than predicted based on reduced processing speed ( Schatz et al., 2000 ).
A primary method of treatment for childhood BTs is radiation therapy, which is a well-established cause of change in cerebral white matter ( Filley & Kleinschmit-DeMasters, 2001 ). There is accumulating evidence to suggest reduced cerebral white matter accounts for a notable proportion of the observed decline in IQ among BT survivors ( Mulhern et al., 1999 ; Reddick et al., 2000 ). Total dose of irradiation plays a significant role in cognitive outcomes following radiation therapy ( Grill et al., 1999 ). Thus, there is strong rationale for reduction in radiation dose and volume when appropriate tumor control can be maintained. Accordingly, conformal radiation therapy (CRT) encompasses sophisticated planning and delivery techniques developed to limit highest radiation doses to volumes at risk while sparing surrounding normal tissues ( Merchant et al., 2004 ). Preliminary evidence suggests CRT results in a high rate of disease control and better preservation of cognitive abilities ( Conklin et al., 2008 ; Merchant et al., 2004 ). Further characterization of cognitive outcomes following therapy, including exploration of specific processes that may be more sensitive to radiation effects, is warranted to understand risks and benefits of this treatment approach.
WM is an ideal system to study in this regard as it is well defined behaviorally, in keeping with Baddeley and Hitch's tripartite model ( Baddeley, 1998 ), and neuroanatomically, with convergent evidence for involvement of the dorsolateral pre-frontal cortex from both primate ( Goldman-Rakic, 1995 ) and functional neuroimaging ( Petrides, 1995 ; Smith & Jonides, 1998 ) studies. WM is a limited capacity system that facilitates online maintenance and manipulation of information used to guide cognition and behavior ( Baddeley, 1998 ; Goldman-Rakic, 1995 ). It is an important cognitive system that has been shown to subserve many cognitive and academic skills including language comprehension ( Hanten, Levin & Song, 1999 ), mathematical computation ( Ayr, Yeates & Enrile, 2005 ), reading and writing ( De Jonge & de Jong, 1996 ; Swanson, 1999 ). Given protracted myelination of the prefrontal cortex ( Giedd, 2004 ; Sowell, et al., 2001 ), and radiation induced cerebral white matter changes, WM may be particularly vulnerable to radiation-related neurotoxicity.
Studies conducted by Dennis and colleagues in the 1990s provided the first evidence for WM impairment among childhood BT survivors. Using clinical measures, they identified WM deficits associated with a history of radiation therapy and principal tumor site involving thalamic/epithalamic brain regions ( Dennis et al., 1992 ). Subsequently they found risk for WM impairment depended on the interaction between tumor location and treatment, with worst outcomes for patients receiving radiation therapy for a posterior third-ventricle tumor ( Dennis et al., 1998 ). More recently, investigators have found WM impairment in patients with tumors in other locations, including cerebellar tumors ( Kirschen et al., 2008 ). However, these findings have not been ubiquitous, with other investigators failing to find WM impairment in children treated for posterior fossa tumors ( Mabbott et al., 2008 ). Further investigation is warranted using experimental measures that may better isolate specific cognitive processes and offer clues regarding neurological processes based on prior empirical studies.
Whereas clinical WM tasks typically require free recall of information, recognition memory tasks involve identification of previously encountered information using a forced-choice matching procedure. Thus, recognition memory tasks exert a relatively low level of WM executive control dependent on frontal brain regions ( Luciana et al., 2005 ). Recognition memory has traditionally been thought to rely on more posterior, medial temporal lobe, memory systems ( Nelson, 1995 ), which is supported by recent functional neuroimaging studies ( Olsen et al., 2009 ; Schon et al., 2004 ). Research with typically developing children has revealed a dissociation in findings whereby performance improves during adolescence on WM tasks but not recognition tasks, in keeping with protracted frontal lobe development ( Conklin et al., 2007 ). These findings suggest WM, not fully matured by adolescence, may be more vulnerable than recognition memory in childhood BT survivors. To our knowledge, this is the first study that assesses BT survivors using a battery of memory measures, including traditional and experimental measures, WM and recognition memory, grounded in the prevailing cognitive and neuroscience models.
For the current study, we used a controlled, cross-sectional experimental design to test our hypotheses about WM performance. The primary objective was to assess childhood BT survivors, treated homogeneously with CRT, on experimental measures of WM and recognition memory in order to identify cognitive processes that may contribute to the well-established decline on global cognitive measures. We predicted: 1) childhood BT survivors would perform significantly worse than solid tumor (ST) survivors who had not received CNS-directed therapy and healthy siblings on measures of WM, 2) performance on measures of WM would have a modest but significant association with performance on a measure of intellectual functioning and 3) childhood BT survivors would demonstrate a significantly higher incidence of WM impairment than recognition memory impairment. In the event WM deficits were identified among BT survivors, we planned exploratory analyses to identify demographic or clinical factors predictive of WM performance.
Participants were recruited to form three groups: BT survivors treated with CRT, healthy siblings of children treated for a BT, and ST survivors. A sibling control group was included given the use of experimental measures without published norms, and to increase group similarity on potentially contributory variables. A ST control group was included to facilitate identification of cognitive effects specific to CNS-directed therapy while accounting for the childhood cancer experience including frequent school absences. Participants were required to be English speaking and between 8 and 18 years of age. All three groups were stratified by gender and age (8–12; 13–18). The BT group was further stratified by tumor location (infratentorial; supratentorial). The study was approved by the Institutional Review Board; written informed consent was required prior to participation. Eligible participants were contacted in the order of upcoming hospital visits for routine medical care.
All BT patients were treated for a primary CNS tumor (ependymoma, low grade glioma or craniopharyngioma) on an institutional phase II trial of CRT (RT-1). They were required to have initiated radiation therapy at least two years prior to enrollment, with no evidence of recurrent disease, to characterize late rather than acute effects of treatment. Patients received CRT, including intensity-modulated radiation therapy, over six to seven weeks with a prescribed dose of 54–59.4 Gy. The irradiated clinical target volume included a 10 mm margin surrounding the tumor, tumor bed or both, in order to treat microscopic disease. An additional 3 to 5 mm was included to account for uncertainty in patient positioning and image registration. Target volume definitions and treatment parameters have been previously reported ( Merchant, 2002 ). All ST survivors were treated for a primary ST (Ewing sarcoma, osteosarcoma, soft tissue/rhabdomyosarcoma, neuroblastoma or Wilms' tumor), without CNS-directed therapy (i.e., cranial radiation therapy, intrathecal chemotherapy or high dose methotrexate), and were diagnosed at least two years prior to study enrollment. Sibling control participants were healthy siblings of St. Jude BT patients (15 of which participated in this study).
Individuals were excluded from participation for significant impairment in global intellectual functioning to maximize the ability to complete cognitive tasks. All BT patients were excluded for an IQ less than 70 as indicated by prior RT-1 testing. ST and sibling control participants were excluded if they had a history of pull-out special education services. In the unlikely event current study testing revealed an IQ less than 70, data from that participant was removed from analysis. Participants were also excluded for a history of CNS injury or disease (predating cancer diagnosis in BT patients), documented Attention Deficit Hyperactivity Disorder (predating cancer diagnosis for BT patients), treatment with psychostimulant or psychotropic medication within two weeks of study participation, or a major sensory or motor impairment that would preclude valid testing.
Assessment of Working Memory
All participants were administered the Digit Span Task from the age appropriate Wechsler Scale [Wechsler Intelligence Scale for Children- Fourth Edition, Integrated (WISC-IV Integrated; Wechsler, 2003 ) or Wechsler Adult Intelligence Scale-Third Edition (WAIS-III; Wechsler, 1999 )]. Internal consistency reliability for this task is high (WISC-IV Integrated, r = .87). Z-scores were computed separately for longest Digit Span Forward and Digit Span Backward using published normative data so WISC-IV and WAIS-III scores could be combined.
Two experimental WM measures were also administered: Self-Ordered Search-Verbal (SOS-V) and Self-Ordered Search-Object (SOS-O), modeled after examiner administered tasks by Petrides and Milner (1982) . They have been used previously in behavioral studies of typically developing children ( Conklin et al., 2007 ; Luciana et al., 2005 ) and patients with schizophrenia ( Conklin et al., 2005 ), as well as neuroimaging studies with healthy adults ( Curtis, Zald & Pardo, 2000 ). Positron emission tomography indicates performance on SOS-O is associated with an increase in regional cerebral blood flow in the dorsolateral prefrontal cortex and the frontopolar region, with better performing participants demonstrating a greater amount of blood flow to the prefrontal cortex ( Curtis et al., 2000 ).
For the SOS-V task, words are presented in an array on a computer monitor. Four array size trials were used: 2 × 2 for four words, 3 × 2 for six words, 3 × 3 for nine words and 4 × 3 for 12 words (see Figure 1 ). Participants select each word once, and only once, in any order. After each response, the words randomly rearrange cueing initiation of the next response. The goal is to complete each trial with as few responses as possible, thus requiring the participant to hold previously selected words in WM. All words have an age of acquisition of ≤ eight years of age ( Gilhooly & Logie, 1980 ) and were chosen to be low in visual imagery to encourage verbal mnemonic strategies ( Gilhooly & Logie, 1980 ). The computer program precludes an alphabetization strategy or selecting the same location multiple times in a row, thus capitalizing on randomized spatial presentation of words. For each difficulty level, the trial ends when the participant selects all words or after 3N responses (where N is the number of words in a trial), whichever occurs first. The dependent variable of interest is the error score (E) which is the number of erroneous attempts per word. E= (R−N)/N where R is the number of responses.
Screen shot of the self-ordered search-verbal task (6 word version).
The SOS-O task is parallel in format to the SOS-V task but includes geometric objects rather than words as stimuli. Four array size trials were used: 2 × 2 for three objects, 3 × 2 for five objects, 3 × 3 for eight objects and 4 × 3 for eleven objects (see Figure 2 ). During this task, the location of the most recently selected object is muted with a black square that does not accept a response for the subsequent response. Random rearrangement of objects and muting of previous responses limits the use of spatial mnemonic strategies. Objects were selected that are not readily nameable in order to limit verbal mnemonic strategies.
Screen shot of the self-ordered search-object task (11 object version).
Assessment of Recognition Memory
To assess recognition memory, analogous verbal and face recognition tasks were administered. Both tasks require recognition of previously presented stimuli using a forced-choice, delayed match-to-sample, procedure. For the Verbal Recognition Memory Task, a series of twelve words is presented one-by-one on a computer monitor. Subsequently, the participant is presented with twelve word pairs and must indicate which of two presented words was on the list of twelve previously viewed words. Two trials of twelve words were administered. The dependent variable of interest is the percent of correct responses.
The Face Recognition Memory Task is the same as the Verbal Recognition Memory Task but includes twelve faces rather than words. The faces are neutral in facial expression and derived from the MacBrain Stimulus Set (developed by Nim Tottenham at the University of Minnesota). Two trials of twelve faces were administered.
Assessment of General Cognitive Ability
All participants were administered the Vocabulary and Matrix Reasoning subtests of the Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999 ). This abbreviated IQ is highly correlated with full-scale IQ (correlation with WISC-IV is .82; Wechsler, 2003 ). The Reading subtest of the Wide Range Achievement Test, Third Edition (WRAT-3; Wilkinson, 1994 ) was administered to estimate reading level of participants and verify adequate ability for completing verbal tasks.
Assessment of Demographic Characteristics
Caregivers completed a demographic and developmental questionnaire that includes questions for derivation of an index of socioeconomic status (SES). The Barratt Simplified Measure of Social Status was used ( Barratt, 2006 ). Scores can range from 8 to 66 with higher scores indicative of higher SES.
Identification of Clinical Variables
Clinical variables were extracted from the medical record. Extent of surgical resection was categorized as biopsy, subtotal resection, near total resection or gross total resection based on gross residual disease on post-operative neuroimaging. Hydrocephalus was categorized as present/not present and tumor location as infratentorial/supratentorial based on neuroimaging scans at diagnosis. Medical records were also reviewed to identify BT patients who received a ventriculo-peritoneal shunt for hydrocephalus management.
Data Analytic Plan
Descriptive statistics of demographic and clinical variables were calculated to characterize participant groups. Groups were compared using binomial tests, analyses of variance (ANOVA) or chi-square to establish group similarity. ANOVAs were used to investigate group differences on the Digit Span and Recognition Memory tasks, with appropriate post hoc comparisons to evaluate significant findings. For the SOS tasks, linear mixed models were used to examine main effects of age, task difficulty (array size) and group, as well as their interactions. When significant main effects were identified, post hoc comparisons were conducted. To address multiple comparisons and risk of Type I error, the Adaptive Holm method was used to ensure statistically significant findings remained significant after adjusting for number of group comparisons for each outcome measure.
The association between WM performance and intellectual functioning was characterized by Pearson correlation coefficients. To compare the rate of WM impairment to recognition memory impairment in BT survivors, each BT patient was categorized as impaired/not impaired based on the sibling control group (performance more than one standard deviation worse than siblings). A McNemar test was then used to compare the percentage impairment on WM tasks to recognition memory tasks. This process was repeated using the ST group to define impairment. Further, linear mixed models were used to investigate whether group differences on the SOS tasks remained after adjusting for performance on recognition memory tasks. Finally, univariate linear models were used to explore clinical predictors (variables listed in Tables 1 and and2) 2 ) of WM performance on the Digit Span Task and SOS tasks for the BT group.
Demographic Characteristics by Group
Clinical Characteristics of the Brain Tumor Group
Demographic and Clinical Characteristics
The three groups were balanced with respect to gender, race, SES and age at participation ( Table 1 ). The ST group was significantly younger than the BT group at time of diagnosis and was significantly further out from diagnosis than the BT group at time of study participation. While all three groups had estimated IQ means within the average range, the BT group had a significantly lower IQ than both the ST and sibling control groups. No individuals, in any group, were removed from data analysis for an IQ less than 70.
The BT group was balanced by diagnosis and tumor location ( Table 2 ). Only six patients received chemotherapy prior to radiation therapy; whereas, all but two patients had undergone surgical resection(s). Most patients undergoing chemotherapy received multiagent chemotherapy including cyclophosphamide, cisplatin or carboplatin, etoposide and vincrinstine.
There were no significant group differences on the Digit Span Forward Task ( p = .07). There were significant group differences on the Digit Span Backward Task. Post hoc comparisons indicated BT survivors performed significantly worse than both ST and healthy controls ( p < .01), who did not differ from one another ( p = .61). See Figure 3 .
Digit span task performance by group. Group means with standard error bars. ** p < .01
For the SOS-V task, two participants in the BT group were excluded from data analysis due to reading ability below the 8 year-old level. Linear mixed models did not reveal any significant interactions among array size, group and age. There were statistically significant main effects for array size ( p < .0001) and group ( p < .05), but not age ( p = .50). Post hoc comparisons indicated performance across groups worsened with increasing array size. Post hoc comparisons also indicated, after accounting for age and array size, the BT group performed significantly worse than the ST group ( p < .01) and worse than the sibling group ( p < .05), which did not differ from each other ( p = .49).
Similar to the SOS-V task, linear mixed models did not reveal any significant interaction among array size, group and age for the SOS-O task. There were statistically significant main effects for array size ( p < .0001) and group ( p < .01), but not age ( p = .20). Post hoc comparisons indicated across groups performance worsened with increasing array size. Post hoc comparisons also indicated, after accounting for age and array size, the BT group performed significantly worse than the ST group ( p < .01) and worse than the sibling group ( p < .05), which did not differ from each other ( p = .70). See Figure 4 .
Self-ordered search task performance by group. Group means with standard error bars. *On both task versions, there was a significant main effect for array ( p < .0001), with worse performance as number of items increased, and group ( p < .05), with brain tumor survivors performing worse than solid tumor survivors or siblings.
Working Memory and Intellectual Functioning
For data reduction purposes, a mean score for SOS-V and SOS-O was computed. There was a small, but significant, correlation between the SOS-V mean error score and estimated IQ for the combined sample ( r = −.26, p < .01). Likewise, there was a significant correlation between SOS-O mean error score and estimated IQ ( r = −.32, p < .001). For both Digit Span Tasks, there was also a significant correlation between estimated IQ and span length (Digit Span Forward- r = .35, p < .0001; Digit Span Backward- r = .45, p < .0001). When looking within groups, the correlations were all in the expected directions and generally of similar magnitude across groups. Despite a reduction in power to detect significance, correlations reached at least a trend for significance (p < .10) for the SOS-V task in the ST group, the SOS-O task in the ST and sibling groups, Digit Span Forward in the BT group and Digit Span Backward in the BT and sibling groups.
The same two participants excluded from data analysis for the SOS-V task were excluded for the Verbal Recognition Memory Task due to inadequate reading ability. For both Verbal and Face Recognition Memory Tasks participants completed two trials. Examiner observations revealed a small subset of participants misunderstood the directions to select the stimuli previously encountered and instead selected novel stimuli. When observed, the instructions were carefully re-explained for the second trial. Given this finding, only data from the second trial was used for all participants. Post hoc data inspection revealed there were still a few participants that appeared to perform the opposite task of the instructions resulting in performance levels of 0% or near 0% accuracy. Given this finding, data was excluded for any pariticipants with percent accuracy scores < 25%, which is significantly less than chance performance (50%) and suggestive of a failure to understand task instructions. This resulted in removal of data for two participants in the BT group and two in the sibling group from the Verbal Recognition Memory Task but none from the Face Recognition Memory Task.
There were significant group differences on the Verbal Recognition Memory Task ( p < .05). Post hoc comparisons indicated the BT group performed significantly worse than the sibling group ( p < .05), with a trend for worse performance than the ST group ( p = .08); there was no significant difference between the ST and sibling groups ( p = .57). There were no significant group differences on the Face Recognition Memory Task ( p = .30). See Figure 5 .
Recognition memory task performance by group. Group means with standard error bars.* p < .05
Working Memory vs. Recognition Memory
When defining impairment based on the sibling group (worse than 1 SD from their mean), rate of impairment among the BT group on the SOS-V Task (30%) was not greater than on the Verbal Recognition Memory Task (35%; p = .79). Likewise, rate of impairment among the BT group on the SOS-O Task (34%) was not greater than on the Face Recognition Memory Task (26%; p = .50). In contrast, when defining impairment based on the ST group (worse than 1 SD from their mean), rate of impairment among the BT group on the SOS-V Task (37%) was significantly greater than on the Verbal Recognition Memory Task (15%; p < .05). Likewise, rate of impairment among the BT group on the SOS-O Task (44%) was greater than on the Face Recognition Memory Task (14%, p < .01). Linear mixed models investigating group differences on the SOS-V and SOS-O tasks after adjusting for Verbal and Face Recognition Memory, respectively, produced convergent findings. On the SOS-V task, the BT group performed significantly worse than the ST group (p<.05) with a trend for worse performance compared to the sibling group (p= .07); whereas, on the SOS-O task the BT group performed significantly worse than both the ST and sibling groups (p< .05).
Exploratory univariate linear models were used to investigate demographic and clinical variables predictive of WM performance for the BT group on the Digit Span Task, SOS-V or SOS-O. Mean error scores for SOS-V and SOS-O continued to be used for data reduction purposes. No significant predictive relationships were identified.
Study findings were largely consistent with a priori hypotheses. Children treated for a BT with CRT performed significantly worse than control participants on traditional, clinical measures of WM as well as experimental, computerized measures of WM. Further, deficits were identified in comparison to healthy siblings and children who had been treated for a ST without CNS-directed therapy. Performance on WM tasks tended to correlate with estimated IQ suggesting WM problems may be a contributor to previously established declines in intellectual functioning. Given a significant proportion of age-related improvements in IQ can be accounted for by developmental improvements in WM, WM deficits may help explain the time-since-treatment effect whereby the gap in intellectual abilities between BT survivors and peers widens over time. There was mixed support for the hypothesis that BT survivors would demonstrate a significantly higher rate of WM impairment relative to recognition memory impairment, with more support for this prediction when using ST survivors as the control group.
Impaired WM performance on experimental measures, for which performance has been shown to be mediated by activation of the prefrontal cortex ( Curtis et al., 2000 ), adds behavioral support to the theory frontal brain regions are particularly vulnerable to radiation effects. Diffusion tensor imaging (DTI) has been used to investigate regional white matter vulnerabilities following CNS-directed therapy for childhood BTs. For example, Qiu and colleagues (2007) used DTI to study survivors of medulloblastoma treated with whole-brain irradiation. Fractional anisotropy (FA- a DTI-derived index of white matter integrity) of the frontal and parietal lobes was found to be significantly less compared with controls. Moreover, the frontal lobe was found to have a significantly larger difference in FA compared with the parietal lobe despite the same irradiation dose, suggestive of radiosensitivity of frontal brain regions. In another study comparing children treated for medulloblastoma with surgery, adjuvant chemotherapy and radiation therapy to children treated with surgery only for pilocytic astrocytoma, both groups were found to have significantly decreased FA values in cerebellar midline structures and the frontal lobes; however, the amount of decreased FA was greater in medulloblastoma survivors thus revealing distal effects of local cerebellar treatment and additional neurotoxic effects of adjuvant treatment ( Rueckriegel et al., 2010 ). Distal treatment effects are consistent with reports that WM performance is related to integrity of cerebello-thalamo-cerebral connections such that break down in myelin anywhere in this pathway may interrupt communication with the frontal lobes ( Law et al., 2011 ). Taken together these findings offer important clues to neuropathology underlying cognitive late effects that may inform further refinement of cancer-directed therapy.
CRT represents a significant advancement in the treatment of childhood BTs. Recent research suggests CRT results in a high rate of disease control and better preservation of intellectual functioning ( Merchant et al., 2009 ), academic skills ( Conklin et al., 2008 ), and learning and memory ( Di Pinto et al., 2010 ). However, current findings suggest risk remains for cognitive disruption that can be detected by WM measures. It is important that researchers and clinicians look beyond global measures to prevent missing specific difficulties that may impact functional outcomes. Measures with greater sensitivity to specific cognitive processes also provide better insight to underlying neurological processes and suggest areas for targeted intervention. It is likely we are approaching a plateau in the ability to limit neurotoxicity associated with cancer-directed therapies while maintaining high survival rates, highlighting the need to develop focused interventions that mitigate potentially unavoidable cognitive late effects.
The same pattern of difficulties in BT survivors was generally revealed irrespective of control group with BT survivors performing significantly worse than healthy and cancer control groups on IQ, Digit Span Backward, both experimental WM tasks, and the verbal forced-choice recognition task. These findings indicate WM problems are not simply the result of the childhood cancer experience but rather appear specific to CNS disease and/or treatment. There has been recent interest in studying cognitive late effects of systemic chemotherapy used to treat STs ( Minisini et al., 2004 ; Vardy & Tannock, 2007 ); the current findings are not suggestive of risk. The current study also highlights the importance of including control groups in study design. By relying on published normative data, group differences in IQ and Digit Span would have been missed as control groups performed better than published norms. This phenomenon has previously been reported in the leukemia literature (e.g., Janzen & Speigler, 2008 ).
While WM problems were prevalent among BT survivors, and independent of type of measure or control group, evidence for WM as a specific deficit for BT survivors was mixed. BT survivors performed similarly to both control groups on the nonverbal forced-choice recognition task, suggesting an area of spared functioning. However there were group differences on the verbal forced-choice recognition task, and there were no greater differences in WM performance relative to recognition memory performance when using sibling controls. This finding might indicate performance on the verbal forced-choice recognition task is mediated by frontal brain areas in addition to medial temporal lobe areas ( Ranganath, Johnson & D'Esposito, 2003 ) and/or reflect additional adverse impact of radiation on the hippocampus ( Nageswara Rao et al., 2011 ).
Potential study limitations exist. First, the experimental SOS tasks used in this study demonstrated good psychometric properties with respect to revealing group differences and parametric varying of task difficulty. However, in contrast to earlier studies with typically developing children ( Conklin et al., 2007 ; Luciana et al., 2005 ), improvement in WM performance with age was not identified. Age effects may have been obscured by treatment effects in the patient groups. Retrospective exploratory analyses revealed some age related trends among the sibling group that may have been difficult to detect with a small sample size. Second, it came to the study team's attention that some participants misunderstood directions for the forced-choice recognition tasks. While this issue was addressed retrospectively through data analysis, in future studies it will be important to confirm participant understanding of directions in real-time. Third, a stricter comparison of nonverbal working memory to recognition memory would include tasks using the same stimuli type (i.e., objects or faces). Fourth, performance on the Verbal Recognition Memory Task may have been associated with ceiling effects thus limiting comparison of verbal recognition and working memory tasks; however, this was in part countered by using the same reference group (healthy siblings or ST patients) for task comparisons and was not a factor in comparing nonverbal recognition and working memory tasks. Finally, cross-sectional study design precluded investigation of development of deficits over time. Prospective, longitudinal studies would afford the opportunity to investigate whether WM deficits precede the emergence of IQ deficits thus revealing a window of time during which intervention might prevent a sequential cascade of cognitive issues.
Current findings have clinical implications and suggest areas for investigation. Identification of WM as an area of difficulty following CRT for childhood BTs suggests vulnerability of underlying neural substrates, most notably a well-established frontal-parietal network including the dorsolateral prefrontal cortex. In evaluating newer treatment approaches such as proton beam therapy, which promise greater sparing of healthy brain tissue, it will be important to assess WM as a test of decreased neurotoxicity. Future work more directly assessing brain function with functional neuroimaging (fMRI and DTI) is needed to examine when and how development of anterior brain regions deviates from the typical trajectory, which may inform treatment delivery. Current findings also suggest WM may be a core deficit underlying the well documented decline in IQ. This finding may direct closer monitoring to facilitate earlier intervention. There is emerging support for interventions that mitigate the impact of cognitive late effects among cancer survivors ( Butler et al., 2008 ; Conklin et al., 2010 ). Current findings identify WM as a target for future intervention trials.
This work was supported in part by the National Cancer Institute (St. Jude Cancer Center Support [CORE] Grant number P30 CA21765), (H.C., grant number R21 CA131616); the International Neuropsychological Society (H.C., Rita Rudel Award); and the American Lebanese Syrian Associated Charities (ALSAC). The authors wish to thank Clay Curtis, Catalina Hooper and Matt Scoggins for their contributions to developing the experimental computerized tasks. We also thank the patients and their families who volunteered their time to participate.
Portions of this paper were presented at the annual meetings of the International Neuropsychological Society in Acapulco, Mexico, February 3–6, 2010 and the International Society for Pediatric Oncology in Boston, Massachusetts, October 21–25, 2010.
The authors have no conflicts of interest to disclose.
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Studies on Working Memory
By ellen r. doman m.a..
Baddeley’s model of working memory was published in 1974 (Baddeley, Hitch 1974) and continues to be used by professionals today. Prior to their work, the only part of thinking that had been clearly defined was the short term memory (Atkininson, Shiffrin 1971). According to Baddeley and Hitch (1974), the working memory was responsible for processing information and it had various components that were used to handle information. The working memory was also describe as having a capacity regarding the amount of information that it could handle.
In a study completed Graesser, Singer and Trabasso (1994) their constructionist theory discussed how an individual’s working memory related information that he or she was reading to information that was already known and stored. This had also been discussed in a study done by Daneman and Carpenter ( Daneman, Carpenter,1979), who established that the working memory determined how much information could be stored related to reading comprehension work.
Studies done more recently support the role of the working memory in attention and processing verbal information which are key to reading comprehension (Carretti, Borella, Cornoldi, DeBeni2009). Further studies showed that the performance of the working memory was a good predictor of how well individuals could answer inferential questions in long reading passages if they were not permitted to go back and reread the material (Andreassen, Braten 2010). Working memory was also found to be the key factor in understanding sentences being read in a second language (Kashiwagi 2011). In students demonstrating learning difficulties or deficits in reading comprehension, Pimperton’s study showed that these issues were tied to the working memory function (Pimperton, Nation 2010). In fact, the verbal working memory was found to be the best indicator of reading comprehension difficulties (Macaruso, Shankweiler 2010).
All parts of the working memory were found to be related to and able to predict success in not only reading comprehension but also problem-solving abilities and math (Zheng, Swansen, Marcoulides 2011). This study was the followed by a study published in 2012 supporting that several areas of the working memory were involved in mathematics as well (Nyroos, Wikland-Hornquist 2012). Alloway (Alloway, Passolunghi 2011) had also completed a study finding that several areas of working memory impacted on achievement in mathematics.
Working memory was found to be able to predict which students would have difficulty learning how to do math operations (Toll, Van de Ven, Krostberger, Van Luit 2011). These findings were then supported by Proctor who found that there was a positive correlation between working memory and mathematical reasoning in students with learning disabilities (Proctor 2012).
We see that the working memory has been related to problem-solving, reading comprehension in native languages and in second languages as well as in the comprehension of mathematics. Problems in working memory have also been cited in many of these studies as an accurate predictor of learning problems in these areas. The importance of working memory not just in academics but also in daily problem-solving are clear. Recently a study done by Dahlen supported that working memory training had a positive effect on reading comprehension and literacy (Dahlen 2011). During the school year 2007-2008, NACD worked with the Wasatch Peak Academy using digit spans and reverse digit spans activities with students. Pre and post testing completed independently by the school as well as testing completed by NACD showed a positive correlation between improvement in working memory as produced by improved scores on reverse digit span activities and standardized test scores (NACDFoundation 2009). A study completed in 2012 which is being published by NACD also found a positive correlation between reverse digit spans and the Simply Smarter Kids App which are both working memory activities.
Baddeley, A.D., Hitch, G. (1974) “Working Memory.” In G.H. Bower (Ed.) The Psychology of Learning and Motivation: Advances In Research and Theory. 1975: vol.8 47-89.
Atkinson, R.C., Shiffrin, R.N. “The Control Processes of Short Term Memory.” Technical Report 173 Psychology Series. 1971: Institute for Mathematical Studies in the Social Sciences, Stanford University, Stanford, CA.
Graesser, A.C., Singer,M., Trabasso, T. “Constructing Inferences During Narrative Text Comprehension.” Psychological Review 1974: Vol.101, Number 3, 391.
Daneman, M., Carpenter, P.” Individual Differences in Working Memory and Reading.” Carnegie-Mellon Universtiy, USA http://dx.doi.org/10.1016/S0022-5371(80)90312-6
Carretti, B., Borella, E., Cornoldi, C., DeBeni, R. “The Role of Working Memory in Explaining the Performance of Individuals with Specific Reading Comprehension Difficulites: A Meta-Analysis.” Learning and Individual Differences 2009: Vol.19,number 2, 246-251.
Andreassen, R., Braten, I. “Examining the Prediction of Reading Comprehension on Different Multiple Choice Tests.” Journal of Research in Reading 2010: Vol.33, Issue 3, 263-283 http://dx.doi.org/10.1016/S0022-5371(80)90312-6
Kashiwagi, A. “Relative Clauses in First and Second Language: A Case Study.” ProQuestLLC., Ph.D. Dissertation 2011: The Ohio State University. http://udini.proquest.com/view/processing-relative-clauses-in-pqid:2328649871/
Pimperton, H. Nation, K. “Suppressing Irrelevant Information from Working Memory: Evidence For Domain Specific Deficits in Poor Comprehenders.” Journal of Memory and Language 2010: vol 62, n4, 380-391.
Macaruso, P., Shankweiler, D. “Expanding the Simpler View of Reading in Accounting for Reading Skills in Commuity College Students.” Reading Psychology 2010: vol.33, n5, 454-471.
Zheng X, Swanson, H.L., Marculides, G. “Working Memory Components as Predictors of Children’s Mathematical Word Processing Abilities.” Journal of Experimental Child Psychology. Dec 2011: 110 n4, 481-98.
Nyroos,M., Wiklund-Hornquist,C. “The Association between Working Memory and Educational Attainment as Measuared in Different Mathematical Subtopics in the Swedish National Assessment Primary Education.” Educational Psychology 2012: vol.32, n2, 239-256.
Alloway, T. P., Passolunghi, M. “The relations between working memory and arithmetical abilities: A comparison between Italian and British children.” Learning and Individual Differences 2011: vol 21, 133-137.
Toll, S.W.M., Van der Ven, S.H.G.,Kroesbergen, E.H., Van Luit, J.E.H. Executive Functions as Predictors of Math Learning Disabilities .Journal of Learning Disabilities 2011: vol.44, n6, 521-532.
Proctor, B. “Relationships between Cattell-Horn-Carroll (CHC) Cognitive Abilities and Math Achievement within a Sample of College Students with Learning Disabilities.” Journal of Learning Disabilities 2012: vol.45, n3, 278-287.
Dahlen, K.I.E. “Effects of Working Memory Training on Reading in Children with Special Needs.” Reading and Writing: An Interdisciplinary Journal 2011: vol 24, n 4, 479-491.
NACD/Wasatch Peak Academy School Model Program(The NACD Foundation 2009: vol.22 n9. http://nacd.org/newsletter/0709_wpa.php
Reprinted by permission of The NACD Foundation, Volume 25 No. 9, 2012 ©NACD
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Example Of Working Memory Capacity Dissertation Conclusion
Type of paper: Dissertation Conclusion
Topic: Education , Capacity , Study , Dyslexia , Difference , Hypothesis , Theory , Speech
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In this paper the researchers have developed and discussed the question regarding the memory capacity of dyslexic individuals compared to those who don’t have dyslexia. The hypothesis that dyslexic individuals have a lower working memory capacity was checked in this study. To check this hypothesis the researchers have used basics of statistical methods and probability theory. The analysis was performed on a data sample comparing the working memory capacity in dyslexic and non-dyslexic individuals by using the symmetry span (working memory measure). The researchers have used descriptive statistics and Student’s independent samples t-test to check the hypothesis. All calculations were performed in SPSS 22 statistical software. It is appeared that the value of the symmetry span for those who has dyslexia was on average only 5.7 compared for 13.9 to those who haven’t it. This difference was checked by using Student’s t-test and the results of the test showed that the null hypothesis should be rejected. There is enough evidence to say that there is a significant difference in memory capacity between dyslexic and non dyslexic individuals. However, there was performed a two-tailed test which is used only to check the difference, but it doesn’t tell us about which of two mean values are significantly bigger and which one is lower. But based on the descriptive statistics it is enough evidence to conclude, that the working memory capacity is lower for dyslexic individuals. This study confirms a number of studies conducted earlier. Numerous studies have shown that dyslexic adults have symptoms of varying severity, which depend on the cause of the violation. A person suffering from dyslexia cannot quickly and correctly recognize words, read it often makes spelling mistakes. An individual is not formed a complete phonological component of language. A person with dyslexia may suffer memory and attention, but as a rule, it is connected with the violation of interhemispheric interactions. In this disorder may be a shortage of the reader's experience. A person with dyslexia usually has a bad understanding of the text. Intelligence thus violating saved. The individual reads with errors on the first syllable trying to guess what is written. People often do not understand what is written in the text, it is difficult to summarize the reading. Reading has been difficult, he quickly tired and irritated. These people make a number of grammatical or spelling errors in copying plain text, they find it difficult to correctly write the word perceived by ear. The individual with this neurological disorder has a lot of problems with handwriting, cannot perform the writing task in a short time. Dyslexia and dysgraphia lay "imprint" on the character of a person has high emotional, very irritable, impulsive makes mistakes. Very often the disease is accompanied by emotional instability. In character traits can manifest a strong sense of justice or refined aesthetic taste. The dominant feature of the right hemisphere may manifest as poor coordination of movements. The results of this study show first of all that the one of the factors which may cause the symptoms of dyslexia is a real problem with a working memory capacity. Most researchers studying the problem of dyslexia in children, says a history of pathological effects of biological factors that cause minimal brain dysfunction. That’s why the understanding the initial causes of the problem helps to understand better ways of treatment. Dyslexia prevention should begin in the preschool years, developing in children visuospatial functions, memory, attention, analytic-synthetic activity, fine motor skills. It plays an important role to overcome violations pronunciation of sounds, the formation of lexical and grammatical structure of speech. The need for timely identification of children with speech disorders, speech therapy and conducting classes, preparing for the development of reading and writing. However, there are some limitations of the study. The first limitation is related to that the experiment and comparison was performed only once. To be sure that the results of the study are accurate it is better to compare a number of different data samples to see that the tendency in a significant difference between memory capacity of dyslexic and non-dyslexic individuals stays from sample to sample. The second limitation is due to the fact that the problems with memory capacity may be caused by other diseases, such as Attention Deficit, Hyperactivity Disorder (ADHD), etc. To avoid this limitation it should assumed or checked that the participants of the study are not affected by such diseases. The third limitation is related to the assumptions of the statistical test: it should be assumed that there are no other significant factors which may affect the working memory capacity was omitted in this study. The people chosen for the research should not have any other causes on their memory capacity – only dyslexia (or its absence, for non-dyslexic individuals). If there are exist some “third” factors which had a significant effect on the values of working memory capacity, it may bias the result of this study. Finally, there are several assumptions related to the method of mean values comparison. It is known that there must be no significant outliers in the data and the dependent variable (working memory capacity) should be approximately normally distributed. These assumptions may be checked by performing some non-parametric tests for distributions, for example, Kolmogorov-Smirnov test of Shapiro-Wilk test. The last assumption is related to homogeneity of variances. However, it was checked in the analysis section by using Levene’s test. The study can be improved if all assumptions will be checked and met. The main issue to avoid is the influence of other significant factors. For this purpose it is better to make testing separately by gender, approximately for the same age group and so on. The influence of third factors must be reduced to a minimum.
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Can I pay someone to do my essay after it’s done?
Sadly, no. In an ideal world of perfectly honest people, you’d say, “I need help write my research paper”, and we’d have it ready for you for free and rely on your generosity. In the real world, our writers, editors, and support managers are real people who like to have a roof over their heads and meals on their tables. Our refund policy keeps you safe, but only your upfront payment protects our writers from scams. So whenever you ask, “Can you write my essay cheap?”, we say, “Sure”, but we ask you to cover the cost first.
Who will write my paper for me? How do I know they’re qualified to handle it?
Every writer on our team holds a degree in one or more majors, possesses years of academic writing experience, and has a solid reputation among our clients. You can be sure that whenever you run asking, “Write essay for me”, we’ll match you with an expert best suited to handling your academic level, class, and topic. Be safe in the knowledge that we only hire seasoned academics to write papers for you.
How do I choose the best writer to write my paper for me?
You can select a specific expert to deal with your “write my essay” issue or pick a top or pro-level writer. Although either of these options will add to the bottom line, you won’t have to wonder, “Who will write my essay?”. We recommend selecting one of our premium experts for critical assignments that need a special touch to score top grades and improve your class ranking or GPA. Contact our support team to ask, “Can someone write my paper for me with top results?” to learn more about writer options.
How do I know if you’ll make my essay original?
Your every “write my essay” order goes through a plagiarism checker to guarantee originality. After all, our writers know “write my paper” means crafting an original piece from scratch, not rewriting a stale sample found online. But if you want further proof, you’re welcome to order an official plagiarism report with a similarity percentage. All it takes is checking the box in the order form or asking a support agent to add it to the bottom line when you come asking, “I need you to write an essay for me.”
How can I lower the price when ordering an assignment?
Although we keep our online paper help rates as low as possible, you can play around with the order parameters to lower the price. For example, instead of crying, “I need you to write my essay in 12 hours”, set the deadline for two weeks, and your bottom line will be much more affordable. You can also wait for a seasonal promotion with discounts of up to 15% if you’re thinking, “I’m in no hurry to pay someone to write my essay.”
What do I do if you write my paper for me, and I don’t like it?
You can get a revision or a refund, depending on how much your “write my essay for me” order went off track. We know when you pay someone to write your paper you expect the best results, and we strive to follow every instruction to a T when we write a paper for you, but miscommunication can occur. In this case, don’t be shy about requesting a free revision or a new writer to rework your assignment. And if you feel the paper is unsalvageable, you may be liable for a partial or full refund.
How do I know you’ve finished writing my paper?
We’ll notify you via email the moment the writer uploads the first draft for your revision. You can then preview it and approve the piece to download an editable file or get it sent for a revision round with your comments about necessary corrections. Besides, you can always request a progress update from your writer or a support manager. Just ask them, “Any progress since I hired you to write my essay for me?”. As you see, you don’t need to fret, thinking, “How will I know when you write my essay, and it’s ready?”
What are you waiting for?
You are a couple of clicks away from tranquility at an affordable price!