Birney, D. P. (2015). Challenges for an interdisciplinary consideration of cognitive training. In E. L. Grigorenko (Ed.), The global context for new directions for child and adolescent development. New Directions for Child and Adolescent Development, 147, 21–32.
3 Challenges for an Interdisciplinary Consideration of Cognitive Training Damian Patrick Birney Abstract Whether fluid cognitive functions are malleable has been a topic of ongoing debate for at least the past 100 years. Ever-evolving technology has led to new and diverse fields of investigation entering this debate. There are significant advantages to be gained by integrating different scientific paradigms, but there are also significant challenges. Cross-paradigm differences in levels of analysis, nomenclature, and expectations of training outcomes complicate interpretation of training results. It is argued that further investigations of (a) cross-paradigm intricacies and (b) strategy versus process training, particularly with spatial abilities, are needed. © 2015 Wiley Periodicals, Inc.
This research was supported under Australian Research Council’s Discovery Projects funding scheme (project DP140101147). The views expressed herein are those of the authors and are not necessarily those of the Australian Research Council. NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT, no. 147, Spring 2015 © 2015 Wiley Periodicals, Inc. Published online in Wiley Online Library (wileyonlinelibrary.com). • DOI: 10.1002/cad.20087
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Introduction Fluid cognitive function (FCF) is an umbrella term representing a diverse range of cognitive attributes including attention, memory, and fluid intelligence (Gf). The extent to which FCFs are malleable across the lifespan has been the focus of ongoing investigation for at least the past 100 years. For instance, Binet (1905) conceptualized intelligence as malleable and from this perspective developed intelligence tests to identify children who might benefit from special education. He linked increased academic performance as a function of his “mental orthopedics” training to an increase in intelligence. Others, such as Lewis M. Terman, took a more fixed view of cognitive abilities (Hinton, 1984). As technology has developed to assess and train FCFs, researchers from a broad range of scientific backgrounds have contributed to the debate on the mutability of cognition. The increasing breadth of interdisciplinary research into FCF introduces an ever-changing “multilingual challenge” as researchers from different backgrounds grapple with differences in the theoretical subtleties of domains they are not experts in (Ansari, De Smedt, & Grabner, 2012). This article considers the more salient interdisciplinary challenges to FCF research and argues that a greater awareness of the intricacies of different investigative paradigms may be useful for understanding some of the apparent inconsistencies in recently reported cognitive training outcomes. The article is presented in three sections. The first section considers conceptual challenges associated with paradigm differences in levels of analysis, nomenclature, and expectations of training outcomes. The second section attempts to highlight these conceptual challenges more specifically by considering two exemplar FCFs that have recently been the focus of training: working memory (WM) and spatial ability. WM is considered to reflect a domain general attentional resource and has been shown to have a substantial impact on everyday success. As such, improvements achieved by training WM are assumed to have a broad impact individually and socially. However, as discussed next, other than for a few isolated studies (e.g., Chein & Morrison, 2010; Jaeggi, Buschkuehl, Jonides, & Perrig, 2008), WM has proven to be rather resistant to training (e.g., Redick et al., 2013), particularly over the longer term. Spatial ability, on the other hand, has an equally important but more specific role in many aspects of everyday functioning. Although spatial ability has not yet been investigated as comprehensively across as many domains as WM, a recent meta-analysis by Uttal et al. (2013) reports considerable robust malleability of spatial skills. This finding is in contrast to the relative resistance of WM to training (Melby-Lerv˚ag & Hulme, 2013). The third and final section makes recommendations for future investigations cognizant of specific FCF paradigm differences.
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Challenges of Multiple Domains of Investigation Three conceptual challenges to multidomain integration are considered: differences in levels of analysis, nomenclature, and expectations of training outcomes. Levels of Analysis. Figure 3.1 represents five broad (not mutually exclusive) domains in which FCF interventions have been investigated, along with a selection of FCFs typically targeted: (a) neuroscience, (b) experimental, (c) differential, (d) clinical, and (e) educational. Of course, there has been fruitful collaboration across these domains over time. For instance, clinical and educational psychologists draw on assessments developed by differential psychologists, and neuroscientists draw on tools and theories of clinical, experimental, and differential psychologists. However, there remains a tendency for cross-domain studies to be biased toward one domain or another, with theorists in one domain drawing evidence from others to bolster theoretical claims without a full appreciation of the paradigm subtleties of that other domain (Birney, Bowman, & Pallier, 2006). Consider the different domains in Figure 3.1. Educational researchers are interested in mitigating the impact cognitive deficits and learning disabilities may have on successful life outcomes. Neuroscience researchers on the other hand are interested in a level of analysis that is several orders of magnitude more detailed. Neuroscience relies on animal models or is constrained to very small human samples due to ethical considerations and resource costs. Outcome variables of interest to neuroscientists are often changes in physiology after some intervention—for example, electrochemical activation, blood flow, or cell density. Yet, the challenge of synthesizing paradigm differences continues to be worthwhile. For instance, understanding the dynamics of a maturing neural system has implications for educational research—particularly to those investigating critical periods in brain development where FCF training might be most beneficial (Ansari et al., 2012; Jolles & Crone, 2012). Nomenclature. An additional complication of the diversity of domains investigating FCFs is that it is not always clear that a function in one study is the same as in another study, even if the function has the exact same name. As a rather trivial example, WM defined in animal models (e.g., as measured by the Morris water–maze problem) is necessarily quite different from WM conceptualized in humans (e.g., as measured by the n-back task). Such comparative differences are well acknowledged, accepted, and arguably, in and of themselves, uninteresting. However, there are sufficient differences across domains for nontrivial concern. Consider three prototypical FCFs—fluid intelligence (Gf), working memory (WM), and executive functions (EFs). Gf is empirically defined in differential psychology as the latent trait extracted from the factor analysis of various tests thought to tap Gf. WM theory, on the other hand, was
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Figure 3.1. Domains in Which Fluid Cognitive Functions Have Been Investigated, and Exemplar Fluid Cognitive Functions
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developed within the cognitive-experimental paradigm (Baddeley & Hitch, 1974), mostly using dual-task methodologies to dissociate various storage and processing systems. Recent research has more explicitly brought WM into the differential psychology hierarchy (Ackerman, Beier, & Boyle, 2005), with some theorists suggesting that the statistical commonality between WM and Gf reflects a controlled-attention capacity (Birney & Bowman, 2009; Kane, Bleckley, Conway, & Engle, 2001). Finally, the conceptualization of executive functions has a more recent history largely stemming from cognitive neuropsychology and developmental cognitive psychology. The tasks used in these related but distinct research programs have been developed with different purposes in mind. For instance, differential psychology uses complex-span tasks to assess WM (Redick et al., 2012). Neuropsychology often uses the n-back task to assess a function with the same name (Gray, Chabris, & Braver, 2003), but which is different in many other respects (see Figure 3.2). Birney et al. (2006) have argued that it is not always clear how to differentiate cases where different theoretical accounts and operationalizations of processes referred to by different paradigms overlap. This is important to understand because one of the critical hypotheses underlying training and expectations of transfer is that training and transfer tap common processes (Shipstead, Hicks, & Engle, 2012) localized to common (or at least contiguous) brain regions (Perrig, Hollenstein, & Oelhafen, 2009). The task of mapping domain similarities and differences is necessarily prior to the task of identifying and investigating common processes across domains (Birney et al., 2006). As a case in point, the complex-span and n-back WM tasks referred to previously do not correlate highly with each other (rs = −.08 to .22), despite them both being considered WM tasks (Kane, Conway, Miura, & Colflesh, 2007). The authors argued that this is presumably because they tap different processes, although this account is far from simple with different methodological issues (e.g., systematic sample and task modality effects) at play (Redick & Lindsey, 2013). As another case in point, the reverse digit span task is a common standardized clinical assessment of WM used in several cognitive ability batteries. The task requires participants to recall a list of digits in the reverse order to show how they were presented. However, the status of the reverse digit span task as an indicator of WM is not uncontroversial. For instance, Engle, Tuholski, Laughlin, and Conway (1999) present evidence that in adults simple span performance reflects short-term memory and not WM, regardless of whether it is presented in a forward or backward format. Although we know of no research that has investigated analogous trends in child development, if it is the case that the same task assesses WM at one age and short-term memory at another, this needs to be reflected more clearly in assessment batteries and developmental theories. Outcome Expectations. Not all findings are reported at the same level of analysis, nor with equal focus and clarity, which makes understanding the diversity of effects associated with cognitive training at times NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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Figure 3.2. Example Working Memory Tasks
Notes: (A) spatial n-back task (where n = 2) and (B) complex span task.
confusing. Take for instance Cogmed, a commercially available cognitive training intervention for children advertised on the publisher’s website as “ . . . a computer-based solution for attention problems caused by poor working memory.”1 In discussing the effects of cognitive training, Diamond (2012) presented what seems to be a very optimistic outlook on the evidence for cognitive training generally, and Cogmed in particular. Diamond NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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states that executive functions can be improved and that Cogmed computerized training has been found to be successful in this regard. This is in stark contrast to what other researchers, such as Shipstead et al. (2012), have reported. Different fields may be more or less prepared to advocate support of cognitive training not (necessarily) because of differences in the understanding of what scientific rigor means, although this is always a possibility. Differences across domains can be a function of the outcomes most valued in that domain. Chacko et al. (2013) have related concerns, albeit ones more directly targeted at the use of Cogmed to treat ADHD symptoms. They argue, as have others (e.g., Green & Bavelier, 2008; Shipstead et al., 2012), that interventions are often confounded to an unknown extent with therapist and parent effects. Placebo- and active-control groups that do not take such effects into consideration are likely to overestimate intervention effects. This is particularly important because the broader advantages children gain from collaborative problem solving (for instance), or even the mere interaction with an adult interested in their welfare, can be difficult to estimate and control experimentally (Chacko et al., 2013; Diamond, 2012). Changes in mood, level of motivation, or desire to please can lead to apparent shortterm improvements that without careful experimental designs can easily be mistaken for true transfer (Green & Bavelier, 2008). Problems arise when intervention effectiveness is subjectively assessed using self-reports. Although self-report outcomes might be considered meaningful at one level of analysis (e.g., educational practitioners and parents), they are notoriously sensitive to biases and thus considered meaningless to researchers who have different expectations about what constitutes an appropriate outcome assessment (e.g., calibrated tests contrasted against theoretically aligned active-control groups in a randomized trial).
What Is Being Trained? A Tale of Two FCFs Two exemplar FCFs that have recently been the focus of training are working memory (WM) and spatial ability. Each is considered in turn. Working Memory. The most broadly understood model of WM is that derived from the early Baddeley and Hitch (1974) account of a simultaneous processing and storage cognitive architecture. WM is an encompassing, multifaceted concept that has facilitated a successful line of experimental research premised on relatively fixed cognitive capacity limits (Apter, 2012). The original model specified three systems, two that focus on dissociable phonological and visuospatial stores, and a common central executive system that “acts” upon and integrates information from these stores. Within the individual differences literature, these distinctions are conceptualized as verbal WM and spatial WM. Verbal WM is the storage and processing of verbal information, whereas spatial WM is the storage and processing of spatial information (Hegarty & Waller, 2005). Melby-Lerv˚ag and Hulme’s NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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(2013) meta-analytic review suggested that although WM training resulted in reliable short-term improvements in verbal WM and more persistent spatial WM improvements (both of which are examples of near transfer2 ), the utility of WM training programs as methods to improve more distal cognitive function (i.e., far transfer2 ) in healthy children and adults remains questionable.3 Apter (2012) argues that to conceptualize change, the subcomponents of the WM model need to be more completely understood and that the cross-modality, simultaneous processing, and storage features need to be more highly specified than they currently are. Apter’s views are that classic WM models operate more as theoretical metaphors (of assessment) than dynamic theories capable of conceptualizing change. To understand processes underlying change, accounts of WM need to go beyond models of assessment. Given WM is multifaceted, considerable overlap in the FCFs targeted by a given assessment is likely to exist. When such assessments are used for training, this overlap might obscure identification of the “active ingredient.” As mentioned previously, complex-span and n-back tasks both tend to correlate with Gf but are poorly correlated with each other (Kane et al., 2007; Redick & Lindsey, 2013). This is in spite of the fact that they both assess WM. This overlap between FCF measures is not yet completely understood, but attempts to do so will likely be important for targeting cognitive training. Spatial Abilities. Unlike the largely inconsistent results of WM training (Melby-Lerv˚ag & Hulme, 2013), spatial skills training has been shown to produce very robust near and far transfer gains (Uttal et al., 2013). Unpacking this dissociation will be important for the FCF malleability debate. The extent to which spatial skills training gains transfer to general WM processes requires particular attention. From an individual differences perspective, the general spatial ability factor has been shown to include spatial-orientation and visualization subfactors (Hegarty & Waller, 2005). Uttal et al. (2013) however based their meta-analysis into the malleability of spatial abilities on a 2 × 2 framework of spatial skills: intrinsic versus extrinsic and static versus dynamic. Intrinsic spatial activities require distinguishing between characteristics of a single object, such as hidden figures, mental rotation, and paper folding (Figure 3.3A–C, respectively). Extrinsic activities require participants to distinguish between the spatial relations among objects in a group, such as in navigating with a map. In static activities, the object of focus remains stationary throughout the task (e.g., hidden figures). In dynamic activities, the object of focus moves either physically or mentally in the mind of the participant (e.g., mental rotation). Tasks can be cross-classified. For instance, the mental rotation task would be considered an intrinsic-dynamic task, as would be the spatial n-back task (Figure 3.2A). Map reading would be considered an extrinsic-static task. NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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Figure 3.3. Example Spatial Ability Tasks
Notes: (A) hidden figures, (B) mental rotation, and (C) paper folding.
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The Uttal et al. (2013) meta-analysis was of nonclinical studies and excluded research that focused on rehabilitation of high-risk or at-risk populations. The studies considered were predominately educationally focused, and because of this, it may be the case that training improved far transfer performance by simply encouraging the use of strategies without inducing any structural process changes in the brain that increase capacity directly (Jolles & Crone, 2012). Strategy training aims to provide individuals with the strategies necessary to better exploit existing cognitive resources. Process training aims to make structural changes to underlying neural mechanisms. In practice, the two approaches are often intermixed to varying extents meaning that causes of any improvement in performance may not always be clear (e.g., Cogmed entails process training but often also includes one-on-one coaching). However, it may the case that even if brain changes are observed, these changes may be just as meaningfully associated with strategy learning and increased metacognitive awareness as with increases in capacity (Apter, 2012). With these conceptual issues in mind, it remains the case that the most remarkable finding from the Uttal et al. (2013) research was the pervasiveness of improvements observed, even after controlling for study design, type of control group, age, gender, and starting level of spatial skills. That is, the evidence presented suggests that a wide range of training interventions improve spatial skills. This is a particularly important finding in relation to acquisition and application of strategies or rules, particularly for STEM4 related domains and related policy, as the authors point out. However, there are also implications for the trainability of basic cognitive functions (e.g., WM and attention) via a spatial training route that are yet to be fully unpacked. This is particularly so if training-related gains in spatial ability have occurred through “basic cognitive pathways, such as improved attention and memory,” as Uttal et al. (2013, p. 368) propose.
Summary and Recommendations To summarize, the objective of this article was to draw attention to general challenges of interdisciplinary investigations and to consider how these challenges present themselves when attempting to consolidate cognitive training intervention outcomes. Challenges associated with differences in levels of analysis, nomenclature, and expectations of training outcomes were identified. Researchers will need multilingual competencies to be able to synthesize the intricacies of different investigative paradigms. Understanding these challenges will open up opportunities for new ways to conceptualize cognitive training. Two implications are particularly salient. First, as suggested by Apter (2012), a focus on training singlefacet functions, such as attention/inhibition, will help mitigate complexities of training multifaceted functions like WM (a proposal consistent with Harrison et al., 2013). However, while single-facet training may be NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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experimentally more tractable in that components are highly specified, a reductionist approach runs the risk of overlooking benefits brought about by the synergy of functionally complex tasks—that is, complexity per se (Birney & Bowman, 2009). A balanced but well-informed cross-domain approach, particularly focusing on spatial abilities, is recommended. Second, there is a need for further work to separate strategy training and process training and to consider the possibility that spontaneously acquired strategies lead to apparent changes during or as a consequence of process-only training. This is particularly the case given the success of predominately strategy-based spatial ability training. Future investigations that address these issues will continue to inform and hopefully further clarify the FCF malleability debate.
Notes 1. Extracted from http://www.cogmed.com/program, September 22, 2014. 2. Near transfer entails concomitant gains on tasks similar to the training task; far transfer is in relation to gains on substantially different tasks. 3. A variety of tasks have been used in cognitive training. Cogmed for instance uses short-term memory tasks, like the digit-span task; others have used tasks as represented in Figures 3.2 and 3.3. 4. STEM = science, technology, engineering, and mathematics.
References Ackerman, P., Beier, M. E., & Boyle, M. O. (2005). Working memory and intelligence: The same or different constructs? Psychological Bulletin, 131, 30–60. Ansari, D., De Smedt, B., & Grabner, R. H. (2012). Neuroeducation—A critical overview of am emerging field. Neuroethics, 5, 105–117. Apter, B. J. B. (2012). Do computerised training programmes designed to improve working memory work? Educational Psychology in Practice, 28, 257–272. Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47– 89). New York, NY: Academic Press. Binet, A. (1905). New methods for the diagnosis of the intellectual level of subnormals. L’Ann´ee Psychologique, 12, 191–244. (Translated by Kite, E. S. (1916). The development of intelligence in children. Vineland, NJ: Publications of the Training School at Vineland) Birney, D. P., & Bowman, D. B. (2009). An experimental-differential investigation of cognitive complexity. Psychology Science Quarterly, 51, 449–469. Birney, D. P., Bowman, D. B., & Pallier, G. (2006). Prior to paradigm integration, the task is to resolve construct definitions of gF and WM. Behavioral and Brain Science, 29, 127. Chacko, A., Feirsen, N., Bedard, A., Marks, D., Underman, J. Z., & Chimiklis, A. (2013). Cogmed working memory training for youth with ADHD: A closer examination of efficacy utilizing evidence-based criteria. Journal of Clinical Child & Adolescent Psychology, 42, 769–783. Chein, J. M., & Morrison, A. B. (2010). Expanding the mind’s workspace: Training and transfer effects with a complex working memory span task. Psychonomic Bulletin & Review, 17, 193–199. NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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Diamond, A. (2012). Activities and programs that improve children’s executive functions. Current Directions in Psychological Science, 21, 335–341. Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. (1999). Working memory, short-term memory, and general fluid intelligence: A latent-variable approach. Journal of Experimental Psychology: General, 128, 309–331. Gray, J. R., Chabris, C. F., & Braver, T. S. (2003). Neural mechanisms of general fluid intelligence. Nature Neuroscience, 6, 316–322. Green, C. S., & Bavelier, D. (2008). Exercising your brain: A review of human brain plasticity and training-induced learning. Psychology and Aging, 23, 692–701. Harrison, T. L., Shipstead, Z., Hicks, K. L., Hambrick, D. Z., Redick, T. S., & Engle, R. W. (2013). Working memory training may increase working memory capacity but not fluid intelligence. Psychological Science, 24, 2409–2419. Hegarty, M., & Waller, D. A. (2005). Individual differences in spatial abilities. In P. Shah & A. Miyake (Eds.), The Cambridge handbook of visuospatial thinking (pp. 121–169). New York, NY: Cambridge University Press. Hinton, H. L. (1984). The Iowa child welfare research station and the 1940 debate on intelligence: Carrying on the legacy of a concerned mother. Journal of the History of the Behavioral Sciences, 20, 160–176. Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences of the United States of America, 105, 6829–6833. Jolles, D. D., & Crone, E. A. (2012). Training the developing brain: A neurocognitive perspective. Frontiers in Human Neuroscience, 6(76), 1–13. Kane, M. J., Bleckley, M. K., Conway, A. R., & Engle, R. W. (2001). A controlled-attention view of working-memory capacity. Journal of Experimental Psychology: General, 130, 169–183. Kane, M. J., Conway, A. R., Miura, T. K., & Colflesh, G. J. H. (2007). Working memory, attention control, and the n-back task: A question of construct validity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 615–622. Melby-Lerv˚ag, M., & Hulme, C. (2013). Is working memory training effective? A metaanalytic review. Developmental Psychology, 49, 270–291. Perrig, W. J., Hollenstein, M., & Oelhafen, S. (2009). Can we improve fluid intelligence with training on working memory in persons with intellectual disabilities? Journal of Cognitive Education and Psychology, 8, 148–164. Redick, T. S., Broadway, J. M., Meier, M. E., Kuriakose, P. S., Unsworth, N., Kane, M. J., & Engle, R. W. (2012). Measuring working memory capacity with automated complex span tasks. European Journal of Psychological Assessment, 28, 164–171. Redick, T. S., & Lindsey, D. R. B. (2013). Complex span and the n-back measures of working memory: A meta-analysis. Psychonomic Bulletin & Review, 20, 1102–1113. Redick, T. S., Shipstead, Z., Harrison, T. L., Hicks, K. L., Fried, D. E., Hambrick, D. Z., . . . Engle, R. W. (2013). No evidence of intelligence improvement after working memory training: A randomized, placebo-controlled study. Journal of Experimental Psychology: General, 142, 359–379. Shipstead, Z., Hicks, K. L., & Engle, R. W. (2012). Cogmed working memory training: Does the evidence support the claims? Journal of Applied Research in Memory and Cognition, 1, 185–193. Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A. R., Warren, C., & Newcome, N. S. (2013). The malleability of spatial skills: A meta-analysis of training studies. Psychological Bulletin, 139, 352–402.
DAMIAN PATRICK BIRNEY is a senior lecturer in the School of Psychology at the University of Sydney, Australia. NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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3 Challenges for an Interdisciplinary Consideration of Cognitive Training Damian Patrick Birney Abstract Whether fluid cognitive functions are malleable has been a topic of ongoing debate for at least the past 100 years. Ever-evolving technology has led to new and diverse fields of investigation entering this debate. There are significant advantages to be gained by integrating different scientific paradigms, but there are also significant challenges. Cross-paradigm differences in levels of analysis, nomenclature, and expectations of training outcomes complicate interpretation of training results. It is argued that further investigations of (a) cross-paradigm intricacies and (b) strategy versus process training, particularly with spatial abilities, are needed. © 2015 Wiley Periodicals, Inc.
This research was supported under Australian Research Council’s Discovery Projects funding scheme (project DP140101147). The views expressed herein are those of the authors and are not necessarily those of the Australian Research Council. NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT, no. 147, Spring 2015 © 2015 Wiley Periodicals, Inc. Published online in Wiley Online Library (wileyonlinelibrary.com). • DOI: 10.1002/cad.20087
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Introduction Fluid cognitive function (FCF) is an umbrella term representing a diverse range of cognitive attributes including attention, memory, and fluid intelligence (Gf). The extent to which FCFs are malleable across the lifespan has been the focus of ongoing investigation for at least the past 100 years. For instance, Binet (1905) conceptualized intelligence as malleable and from this perspective developed intelligence tests to identify children who might benefit from special education. He linked increased academic performance as a function of his “mental orthopedics” training to an increase in intelligence. Others, such as Lewis M. Terman, took a more fixed view of cognitive abilities (Hinton, 1984). As technology has developed to assess and train FCFs, researchers from a broad range of scientific backgrounds have contributed to the debate on the mutability of cognition. The increasing breadth of interdisciplinary research into FCF introduces an ever-changing “multilingual challenge” as researchers from different backgrounds grapple with differences in the theoretical subtleties of domains they are not experts in (Ansari, De Smedt, & Grabner, 2012). This article considers the more salient interdisciplinary challenges to FCF research and argues that a greater awareness of the intricacies of different investigative paradigms may be useful for understanding some of the apparent inconsistencies in recently reported cognitive training outcomes. The article is presented in three sections. The first section considers conceptual challenges associated with paradigm differences in levels of analysis, nomenclature, and expectations of training outcomes. The second section attempts to highlight these conceptual challenges more specifically by considering two exemplar FCFs that have recently been the focus of training: working memory (WM) and spatial ability. WM is considered to reflect a domain general attentional resource and has been shown to have a substantial impact on everyday success. As such, improvements achieved by training WM are assumed to have a broad impact individually and socially. However, as discussed next, other than for a few isolated studies (e.g., Chein & Morrison, 2010; Jaeggi, Buschkuehl, Jonides, & Perrig, 2008), WM has proven to be rather resistant to training (e.g., Redick et al., 2013), particularly over the longer term. Spatial ability, on the other hand, has an equally important but more specific role in many aspects of everyday functioning. Although spatial ability has not yet been investigated as comprehensively across as many domains as WM, a recent meta-analysis by Uttal et al. (2013) reports considerable robust malleability of spatial skills. This finding is in contrast to the relative resistance of WM to training (Melby-Lerv˚ag & Hulme, 2013). The third and final section makes recommendations for future investigations cognizant of specific FCF paradigm differences.
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Challenges of Multiple Domains of Investigation Three conceptual challenges to multidomain integration are considered: differences in levels of analysis, nomenclature, and expectations of training outcomes. Levels of Analysis. Figure 3.1 represents five broad (not mutually exclusive) domains in which FCF interventions have been investigated, along with a selection of FCFs typically targeted: (a) neuroscience, (b) experimental, (c) differential, (d) clinical, and (e) educational. Of course, there has been fruitful collaboration across these domains over time. For instance, clinical and educational psychologists draw on assessments developed by differential psychologists, and neuroscientists draw on tools and theories of clinical, experimental, and differential psychologists. However, there remains a tendency for cross-domain studies to be biased toward one domain or another, with theorists in one domain drawing evidence from others to bolster theoretical claims without a full appreciation of the paradigm subtleties of that other domain (Birney, Bowman, & Pallier, 2006). Consider the different domains in Figure 3.1. Educational researchers are interested in mitigating the impact cognitive deficits and learning disabilities may have on successful life outcomes. Neuroscience researchers on the other hand are interested in a level of analysis that is several orders of magnitude more detailed. Neuroscience relies on animal models or is constrained to very small human samples due to ethical considerations and resource costs. Outcome variables of interest to neuroscientists are often changes in physiology after some intervention—for example, electrochemical activation, blood flow, or cell density. Yet, the challenge of synthesizing paradigm differences continues to be worthwhile. For instance, understanding the dynamics of a maturing neural system has implications for educational research—particularly to those investigating critical periods in brain development where FCF training might be most beneficial (Ansari et al., 2012; Jolles & Crone, 2012). Nomenclature. An additional complication of the diversity of domains investigating FCFs is that it is not always clear that a function in one study is the same as in another study, even if the function has the exact same name. As a rather trivial example, WM defined in animal models (e.g., as measured by the Morris water–maze problem) is necessarily quite different from WM conceptualized in humans (e.g., as measured by the n-back task). Such comparative differences are well acknowledged, accepted, and arguably, in and of themselves, uninteresting. However, there are sufficient differences across domains for nontrivial concern. Consider three prototypical FCFs—fluid intelligence (Gf), working memory (WM), and executive functions (EFs). Gf is empirically defined in differential psychology as the latent trait extracted from the factor analysis of various tests thought to tap Gf. WM theory, on the other hand, was
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Figure 3.1. Domains in Which Fluid Cognitive Functions Have Been Investigated, and Exemplar Fluid Cognitive Functions
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developed within the cognitive-experimental paradigm (Baddeley & Hitch, 1974), mostly using dual-task methodologies to dissociate various storage and processing systems. Recent research has more explicitly brought WM into the differential psychology hierarchy (Ackerman, Beier, & Boyle, 2005), with some theorists suggesting that the statistical commonality between WM and Gf reflects a controlled-attention capacity (Birney & Bowman, 2009; Kane, Bleckley, Conway, & Engle, 2001). Finally, the conceptualization of executive functions has a more recent history largely stemming from cognitive neuropsychology and developmental cognitive psychology. The tasks used in these related but distinct research programs have been developed with different purposes in mind. For instance, differential psychology uses complex-span tasks to assess WM (Redick et al., 2012). Neuropsychology often uses the n-back task to assess a function with the same name (Gray, Chabris, & Braver, 2003), but which is different in many other respects (see Figure 3.2). Birney et al. (2006) have argued that it is not always clear how to differentiate cases where different theoretical accounts and operationalizations of processes referred to by different paradigms overlap. This is important to understand because one of the critical hypotheses underlying training and expectations of transfer is that training and transfer tap common processes (Shipstead, Hicks, & Engle, 2012) localized to common (or at least contiguous) brain regions (Perrig, Hollenstein, & Oelhafen, 2009). The task of mapping domain similarities and differences is necessarily prior to the task of identifying and investigating common processes across domains (Birney et al., 2006). As a case in point, the complex-span and n-back WM tasks referred to previously do not correlate highly with each other (rs = −.08 to .22), despite them both being considered WM tasks (Kane, Conway, Miura, & Colflesh, 2007). The authors argued that this is presumably because they tap different processes, although this account is far from simple with different methodological issues (e.g., systematic sample and task modality effects) at play (Redick & Lindsey, 2013). As another case in point, the reverse digit span task is a common standardized clinical assessment of WM used in several cognitive ability batteries. The task requires participants to recall a list of digits in the reverse order to show how they were presented. However, the status of the reverse digit span task as an indicator of WM is not uncontroversial. For instance, Engle, Tuholski, Laughlin, and Conway (1999) present evidence that in adults simple span performance reflects short-term memory and not WM, regardless of whether it is presented in a forward or backward format. Although we know of no research that has investigated analogous trends in child development, if it is the case that the same task assesses WM at one age and short-term memory at another, this needs to be reflected more clearly in assessment batteries and developmental theories. Outcome Expectations. Not all findings are reported at the same level of analysis, nor with equal focus and clarity, which makes understanding the diversity of effects associated with cognitive training at times NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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Figure 3.2. Example Working Memory Tasks
Notes: (A) spatial n-back task (where n = 2) and (B) complex span task.
confusing. Take for instance Cogmed, a commercially available cognitive training intervention for children advertised on the publisher’s website as “ . . . a computer-based solution for attention problems caused by poor working memory.”1 In discussing the effects of cognitive training, Diamond (2012) presented what seems to be a very optimistic outlook on the evidence for cognitive training generally, and Cogmed in particular. Diamond NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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states that executive functions can be improved and that Cogmed computerized training has been found to be successful in this regard. This is in stark contrast to what other researchers, such as Shipstead et al. (2012), have reported. Different fields may be more or less prepared to advocate support of cognitive training not (necessarily) because of differences in the understanding of what scientific rigor means, although this is always a possibility. Differences across domains can be a function of the outcomes most valued in that domain. Chacko et al. (2013) have related concerns, albeit ones more directly targeted at the use of Cogmed to treat ADHD symptoms. They argue, as have others (e.g., Green & Bavelier, 2008; Shipstead et al., 2012), that interventions are often confounded to an unknown extent with therapist and parent effects. Placebo- and active-control groups that do not take such effects into consideration are likely to overestimate intervention effects. This is particularly important because the broader advantages children gain from collaborative problem solving (for instance), or even the mere interaction with an adult interested in their welfare, can be difficult to estimate and control experimentally (Chacko et al., 2013; Diamond, 2012). Changes in mood, level of motivation, or desire to please can lead to apparent shortterm improvements that without careful experimental designs can easily be mistaken for true transfer (Green & Bavelier, 2008). Problems arise when intervention effectiveness is subjectively assessed using self-reports. Although self-report outcomes might be considered meaningful at one level of analysis (e.g., educational practitioners and parents), they are notoriously sensitive to biases and thus considered meaningless to researchers who have different expectations about what constitutes an appropriate outcome assessment (e.g., calibrated tests contrasted against theoretically aligned active-control groups in a randomized trial).
What Is Being Trained? A Tale of Two FCFs Two exemplar FCFs that have recently been the focus of training are working memory (WM) and spatial ability. Each is considered in turn. Working Memory. The most broadly understood model of WM is that derived from the early Baddeley and Hitch (1974) account of a simultaneous processing and storage cognitive architecture. WM is an encompassing, multifaceted concept that has facilitated a successful line of experimental research premised on relatively fixed cognitive capacity limits (Apter, 2012). The original model specified three systems, two that focus on dissociable phonological and visuospatial stores, and a common central executive system that “acts” upon and integrates information from these stores. Within the individual differences literature, these distinctions are conceptualized as verbal WM and spatial WM. Verbal WM is the storage and processing of verbal information, whereas spatial WM is the storage and processing of spatial information (Hegarty & Waller, 2005). Melby-Lerv˚ag and Hulme’s NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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(2013) meta-analytic review suggested that although WM training resulted in reliable short-term improvements in verbal WM and more persistent spatial WM improvements (both of which are examples of near transfer2 ), the utility of WM training programs as methods to improve more distal cognitive function (i.e., far transfer2 ) in healthy children and adults remains questionable.3 Apter (2012) argues that to conceptualize change, the subcomponents of the WM model need to be more completely understood and that the cross-modality, simultaneous processing, and storage features need to be more highly specified than they currently are. Apter’s views are that classic WM models operate more as theoretical metaphors (of assessment) than dynamic theories capable of conceptualizing change. To understand processes underlying change, accounts of WM need to go beyond models of assessment. Given WM is multifaceted, considerable overlap in the FCFs targeted by a given assessment is likely to exist. When such assessments are used for training, this overlap might obscure identification of the “active ingredient.” As mentioned previously, complex-span and n-back tasks both tend to correlate with Gf but are poorly correlated with each other (Kane et al., 2007; Redick & Lindsey, 2013). This is in spite of the fact that they both assess WM. This overlap between FCF measures is not yet completely understood, but attempts to do so will likely be important for targeting cognitive training. Spatial Abilities. Unlike the largely inconsistent results of WM training (Melby-Lerv˚ag & Hulme, 2013), spatial skills training has been shown to produce very robust near and far transfer gains (Uttal et al., 2013). Unpacking this dissociation will be important for the FCF malleability debate. The extent to which spatial skills training gains transfer to general WM processes requires particular attention. From an individual differences perspective, the general spatial ability factor has been shown to include spatial-orientation and visualization subfactors (Hegarty & Waller, 2005). Uttal et al. (2013) however based their meta-analysis into the malleability of spatial abilities on a 2 × 2 framework of spatial skills: intrinsic versus extrinsic and static versus dynamic. Intrinsic spatial activities require distinguishing between characteristics of a single object, such as hidden figures, mental rotation, and paper folding (Figure 3.3A–C, respectively). Extrinsic activities require participants to distinguish between the spatial relations among objects in a group, such as in navigating with a map. In static activities, the object of focus remains stationary throughout the task (e.g., hidden figures). In dynamic activities, the object of focus moves either physically or mentally in the mind of the participant (e.g., mental rotation). Tasks can be cross-classified. For instance, the mental rotation task would be considered an intrinsic-dynamic task, as would be the spatial n-back task (Figure 3.2A). Map reading would be considered an extrinsic-static task. NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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Figure 3.3. Example Spatial Ability Tasks
Notes: (A) hidden figures, (B) mental rotation, and (C) paper folding.
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The Uttal et al. (2013) meta-analysis was of nonclinical studies and excluded research that focused on rehabilitation of high-risk or at-risk populations. The studies considered were predominately educationally focused, and because of this, it may be the case that training improved far transfer performance by simply encouraging the use of strategies without inducing any structural process changes in the brain that increase capacity directly (Jolles & Crone, 2012). Strategy training aims to provide individuals with the strategies necessary to better exploit existing cognitive resources. Process training aims to make structural changes to underlying neural mechanisms. In practice, the two approaches are often intermixed to varying extents meaning that causes of any improvement in performance may not always be clear (e.g., Cogmed entails process training but often also includes one-on-one coaching). However, it may the case that even if brain changes are observed, these changes may be just as meaningfully associated with strategy learning and increased metacognitive awareness as with increases in capacity (Apter, 2012). With these conceptual issues in mind, it remains the case that the most remarkable finding from the Uttal et al. (2013) research was the pervasiveness of improvements observed, even after controlling for study design, type of control group, age, gender, and starting level of spatial skills. That is, the evidence presented suggests that a wide range of training interventions improve spatial skills. This is a particularly important finding in relation to acquisition and application of strategies or rules, particularly for STEM4 related domains and related policy, as the authors point out. However, there are also implications for the trainability of basic cognitive functions (e.g., WM and attention) via a spatial training route that are yet to be fully unpacked. This is particularly so if training-related gains in spatial ability have occurred through “basic cognitive pathways, such as improved attention and memory,” as Uttal et al. (2013, p. 368) propose.
Summary and Recommendations To summarize, the objective of this article was to draw attention to general challenges of interdisciplinary investigations and to consider how these challenges present themselves when attempting to consolidate cognitive training intervention outcomes. Challenges associated with differences in levels of analysis, nomenclature, and expectations of training outcomes were identified. Researchers will need multilingual competencies to be able to synthesize the intricacies of different investigative paradigms. Understanding these challenges will open up opportunities for new ways to conceptualize cognitive training. Two implications are particularly salient. First, as suggested by Apter (2012), a focus on training singlefacet functions, such as attention/inhibition, will help mitigate complexities of training multifaceted functions like WM (a proposal consistent with Harrison et al., 2013). However, while single-facet training may be NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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experimentally more tractable in that components are highly specified, a reductionist approach runs the risk of overlooking benefits brought about by the synergy of functionally complex tasks—that is, complexity per se (Birney & Bowman, 2009). A balanced but well-informed cross-domain approach, particularly focusing on spatial abilities, is recommended. Second, there is a need for further work to separate strategy training and process training and to consider the possibility that spontaneously acquired strategies lead to apparent changes during or as a consequence of process-only training. This is particularly the case given the success of predominately strategy-based spatial ability training. Future investigations that address these issues will continue to inform and hopefully further clarify the FCF malleability debate.
Notes 1. Extracted from http://www.cogmed.com/program, September 22, 2014. 2. Near transfer entails concomitant gains on tasks similar to the training task; far transfer is in relation to gains on substantially different tasks. 3. A variety of tasks have been used in cognitive training. Cogmed for instance uses short-term memory tasks, like the digit-span task; others have used tasks as represented in Figures 3.2 and 3.3. 4. STEM = science, technology, engineering, and mathematics.
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DAMIAN PATRICK BIRNEY is a senior lecturer in the School of Psychology at the University of Sydney, Australia. NEW DIRECTIONS FOR CHILD AND ADOLESCENT DEVELOPMENT • DOI: 10.1002/cad
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