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Several working memory processes have been hypothesized to influence different arithmetic operations. Working memory has been compartmentalized into a number of different sub-processes, such as phonological memory and visuospatial memory that are believed to have unique contributions to the performance of two distinct arithmetic operations: multiplication and subtraction. A previous dual task experiment produced these effects, but subsequent experiments have yielded inconsistent results. Because the reasons for these inconsistencies are not immediately apparent, the current study systematically reviewed these subsequent attempts and attempted to replicate this effect in a within-subjects dual task experiment using tasks developed from prior work across a number of different subsamples. In contrast to the original finding, we observed no differential impact of specific working memory secondary tasks by arithmetic operation in any of our analyses. However, our analyses do not entirely rule out the possibility of differential effects of working memory tasks. Our findings suggest that the working memory facet by arithmetic operation interactions observed in previous work may be idiosyncratic in nature and difficult to predict a priori in subsequent experiments.

Measuring selective attention in a speeded task can provide valuable insight into the concentration ability of an individual, and can inform neuropsychological assessment of attention in aging, traumatic brain injury, and in various psychiatric disorders. There are only a few tools to measure selective attention that are freely available, psychometrically validated, and can be used flexibly both for in-person and remote assessment. To address this gap, we developed a self-administrable, mobile-based test called "UCancellation" (University of California Cancellation), which was designed to assess selective attention and concentration and has two stimulus sets: Letters and Pictures. UCancellation takes less than 7 minutes to complete, is automatically scored, has multiple forms to allow repeated testing, and is compatible with a variety of iOS and Android devices. Here we report the results of a study that examined parallel-test reliability and convergent validity of UCancellation in a sample of 104 college students. UCancellation Letters and Pictures showed adequate parallel test reliability (r = .71-.83, p

Transcranial direct current stimulation (tDCS) is a noninvasive form of electrical brain stimulation popularly used to augment the effects of working memory (WM) training. Although success has been mixed, some studies report enhancements in WM performance persisting days, weeks, or even months that are actually more reminiscent of consolidation effects typically observed in the long-term memory (LTM) domain, rather than WM improvements per se. Although tDCS has been often reported to enhance both WM and LTM, these effects have never been directly compared within the same study. However, given their considerable neural and behavioral overlap, this is a timely comparison to make. This study reports results from a multisession intervention in older adults comparing active and sham tDCS over the left dorsolateral pFC during training on both an n-back WM task and a word learning LTM task. We found strong and robust effects on LTM, but mixed effects on WM that only emerged for those with lower baseline ability. Importantly, mediation analyses showed an indirect effect of tDCS on WM that was mediated by improvements in consolidation. We conclude that tDCS over the left dorsolateral pFC can be used as an effective intervention to foster long-term learning and memory consolidation in aging, which can manifest in performance improvements across multiple memory domains.

Previous cognitive training research with the change-detection paradigm found only sparse effects that went beyond improvements in the training task but stressed an increase in fidelity of internal memory representations. Motivated by the demanding visual processing requirements of change-detection training, we extended this work by focusing on whether training on a change-detection task would improve visual processing skills. Fifty participants were randomly assigned to train on a change-detection task or on a control task for seven sessions. Participants' visual processing skills were assessed before and after the intervention, focusing on visual search, contrast sensitivity, and contour integration. Our results suggest a general improvement in perceptual skills that was primarily driven by a conjunction search task and to a much lesser extent by a complex visual search task and a contrast sensitivity task. The data from the conjunction search task further suggest a causal link between training and improvements of perceptual as opposed to attentional processes. Since the change-detection paradigm is commonly used to assess working memory capacity, future research needs to investigate how much of its variance is explained by memory performance and how much is explained by perceptual processes.

Research investigating transcranial direct current stimulation (tDCS) to enhance cognitive training augments both our understanding of its long-term effects on cognitive plasticity as well as potential applications to strengthen cognitive interventions. Previous work has demonstrated enhancement of working memory training while applying concurrent tDCS to the dorsolateral prefrontal cortex (DLPFC). However, the optimal stimulation parameters are still unknown. For example, the timing of tDCS delivery has been shown to be an influential variable that can interact with task learning. In the present study, we used tDCS to target the right DLPFC while participants trained on a visuospatial working memory task. We sought to compare the relative efficacy of online stimulation delivered during training to offline stimulation delivered either immediately before or afterwards. We were unable to replicate previously demonstrated benefits of online stimulation; however, we did find evidence that offline stimulation delivered after training can actually be detrimental to training performance relative to sham. We interpret our results in light of evidence suggesting a role of the right DLPFC in promoting memory interference, and conclude that while tDCS may be a promising tool to influence the results of cognitive training, more research and an abundance of caution are needed before fully endorsing its use for cognitive enhancement. This work suggests that effects can vary substantially in magnitude and direction between studies, and may be heavily dependent on a variety of intervention protocol parameters such as the timing and location of stimulation delivery, about which our understanding is still nascent.

Despite being among the fastest growing segments of the student population, English Language Learners (ELLs) have yet to attain the same academic success as their English-proficient peers, particularly in science. In an effort to support the pedagogical needs of this group, educators have been urged to adopt inquiry approaches to science instruction. Whereas inquiry instruction has been shown to improve science outcomes for non-ELLs, systematic evidence in support of its effectiveness with ELLs has yet to be established. The current meta-analysis summarizes the effect of inquiry instruction on the science achievement of ELLs in elementary school. Although an analysis of 26 articles confirmed that inquiry instruction produced significantly greater impacts on measures of science achievement for ELLs compared to direct instruction, there was still a differential learning effect suggesting greater efficacy for non-ELLs compared to ELLs. Contextual factors that moderate these effects are identified and discussed.

Objective: The efficacy of n-back training for children with attention deficit hyperactivity disorder (ADHD) was tested in a randomized controlled trial. Method: 41 children aged 7 to 14 years with ADHD were trained on an n-back task, and their performance was compared with that of an active control group (n = 39) who were trained on a general knowledge and vocabulary task. Results: The experimental group demonstrated transfer of training to a nontrained n-back task as well as to a measure of inhibitory control. These effects were correlated with the magnitude of training gains. Conclusion: Our results suggest that n-back training may be useful in addressing some of the cognitive and behavioral issues associated with ADHD.

BackgroundGenetic factors have been suggested to affect the efficacy of working memory training. However, few studies have attempted to identify the relevant genes.MethodsIn this study, we first performed a randomized controlled trial (RCT) to identify brain regions that were specifically affected by working memory training. Sixty undergraduate students were randomly assigned to either the adaptive training group (N = 30) or the active control group (N = 30). Both groups were trained for 20 sessions during 4 weeks and received fMRI scans before and after the training. Afterward, we combined the data from the 30 participants in the RCT study who received adaptive training with data from 71 additional participants who also received the same adaptive training but were not part of the RCT study (total N = 101) to test the contribution of the COMT Val158/108Met polymorphism to the interindividual difference in the training effect within the identified brain regions.ResultsIn the RCT study, we found that the adaptive training significantly decreased brain activation in the left prefrontal cortex (TFCE-FWE corrected p = .030). In the genetic study, we found that compared with the Val allele homozygotes, the Met allele carriers' brain activation decreased more after the training at the left prefrontal cortex (TFCE-FWE corrected p = .025).ConclusionsThis study provided evidence for the neural effect of a visual-spatial span training and suggested that genetic factors such as the COMT Val158/108Met polymorphism may have to be considered in future studies of such training.

A key need in cognitive training interventions is to personalize task difficulty to each user and to adapt this difficulty to continually apply appropriate challenges as users improve their skill to perform the tasks. Here we examine how Bayesian filtering approaches, such as hidden Markov models and Kalman filters, and deep-learning approaches, such as the long short-term memory (LSTM) model, may be useful methods to estimate user skill level and predict appropriate task challenges. A possible advantage of these models over commonly used adaptive methods, such as staircases or blockwise adjustment methods that are based only upon recent performance, is that Bayesian filtering and deep learning approaches can model the trajectory of user performance across multiple sessions and incorporate data from multiple users to optimize local estimates. As a proof of concept, we fit data from two large cohorts of undergraduate students performing WM training using an N-back task. Results show that all three models predict appropriate challenges for different users. However, the hidden Markov models were most accurate in predicting participants' performances as a function of provided challenges, and thus, they placed participants at appropriate future challenges. These data provide good support for the potential of machine learning approaches as appropriate methods to personalize task performance to users in tasks that require adaptively determined challenges.

Early exposure to stressful life events is associated with greater risk of chronic diseases and mental health problems, including anxiety. However, there is significant variation in how individuals respond to environmental adversity, perhaps due to individual differences in processing and regulating emotional information. Differences in cognitive control - processes necessary for implementing goal directed behavior - have been linked to both stress exposure and anxiety, but the directionality of these links is unclear. The present study investigated the longitudinal pathway of environmental stress exposure during early adolescence on later adolescent anxiety, and the possible mediating mechanism of cognitive control. Participants were 674 Mexican-origin adolescents (meanage = 10.8 years, 50% male) enrolled in the California Families Project, an ongoing longitudinal study of Mexican-origin families. In the current analysis, we examined self-reports of environmental stressors at age 14 (Time 1), cognitive control at age 16 (Time 2), and anxiety at age 18 (Time 3). Structural equation modeling revealed that environmental stressors (Time 1) had both direct and indirect effects on later anxiety (Time 3) through their effects on cognitive control (Time 2), even when accounting for prior levels of anxiety (Time 2). Cognitive control accounted for 18% of the association between environmental stressors and adolescent anxiety: an increase in stressors decreased cognitive control (β = -0.20, p < 0.001), however, cognitive control buffers against anxiety (β = -0.10, p = 0.004). These findings deepen our understanding of the mechanisms underlying the development of anxiety and highlight the importance of cognitive control as a potential protective factor.

Many cognitive tasks have been adapted for tablet-based testing, but tests to assess nonverbal reasoning ability, as measured by matrix-type problems that are suited to repeated testing, have yet to be adapted for and validated on mobile platforms. Drawing on previous research, we developed the University of California Matrix Reasoning Task (UCMRT)-a short, user-friendly measure of abstract problem solving with three alternate forms that works on tablets and other mobile devices and that is targeted at a high-ability population frequently used in the literature (i.e., college students). To test the psychometric properties of UCMRT, a large sample of healthy young adults completed parallel forms of the test, and a subsample also completed Raven's Advanced Progressive Matrices and a math test; furthermore, we collected college records of academic ability and achievement. These data show that UCMRT is reliable and has adequate convergent and external validity. UCMRT is self-administrable, freely available for researchers, facilitates repeated testing of fluid intelligence, and resolves numerous limitations of existing matrix tests.

Studies have indicated that video gaming is positively associated with cognitive performance in select cognitive domains, but the magnitudes of these associations have been called into question, as they have frequently been based on extreme groups analyses that have compared video gamers with non-gamers. When including the whole range of participants, and not just extreme cases, these effects were observed to reduce markedly (Unsworth et al., 2015). To further study this issue, we compared the associations between video gaming and aspects of working memory (WM) performance in an extreme groups design to those of a design that includes the full range of participants in a large adult sample (n = 503). WM was measured with three composite scores (verbal WM, visuospatial WM, n-back). The extreme groups analyses showed that video gamers performed better than non-gamers on all three WM measures, while the whole sample analyses indicated weak positive associations between the time spent playing video games and visuospatial WM and n-back performance. Thus, study design modulated the effects, but two of the three associations between WM and video gaming were consistent across both analysis techniques. A separate study confirmed that our questionnaire-based estimate of gaming hours was reliable when compared with one-week diaries of videogame playing. While the present cross-sectional results preclude causal inferences, possible mechanisms of WM - videogame playing associations and future research directions are discussed. Overall, our results indicate that cognition - videogame playing relationships, albeit weak, are not solely due to recently discussed methodological artefacts concerning the particular analytical approach and survey reliability.

Executive function (EF), its importance for scholastic achievement and the question of whether or not EF is malleable, have become a topic of intense interest. Education or schooling is often seen as effective approaches to enhance EF due to the specific school-related requirements as compared to kindergarten or pre-school. However, no study to date has investigated whether targeted training focusing on those domains might be comparable with regular schooling in improving EF and fluid intelligence (Gf). The aim of the present study was to replicate and extend the previously demonstrated schooling effects on EF by using a school-cutoff design, and to further investigate whether a theoretically motivated intervention targeting specific EF, i.e., working memory (WM) or inhibitory control (IC), could achieve comparable effects with schooling in both, WM and IC, as well as Gf. 91 6-year-old kindergarteners and first-graders with similar chronological age participated the study. We compared the performance of a first-grade schooling group with that of two kindergarten training groups as well as a business-as-usual kindergarten control group. Participants were assessed in WM, IC and Gf at baseline, immediately after the intervention (posttest), as well as 3 months after training completion (follow-up). The results showed that the schooling group indeed outperformed the kindergarten groups at baseline in several cognitive tasks. Furthermore, both the WM and IC training showed pronounced gains in the trained tasks, as well as varying degrees of improvement in non-trained outcome measures. Most importantly, both training groups achieved comparable performance with the schooling group, which was especially apparent in Gf at follow-up. Our findings provide further evidence for the malleability of EF demonstrating that both, long-term and short-term interventions can facilitate the acquisition of those important skills, and as such, our work has important implications for educational practice.

A growing body of literature demonstrating the malleability of critical higher-order cognitive functions by means of targeted interventions has incited widespread scientific interest, most notably in the form of cognitive training programs. The results are mixed and a point of contention: It has been argued that gains observed in cognitive training are mainly due to placebo effects. To address this, we examined the effect of participant expectations on one type of cognitive training that has been central to the controversy, namely n-back training, by inducing beliefs about expected outcomes. Participants receiving n-back training showed improvements in non-trained n-back performance regardless of expectations, and furthermore, expectations for positive outcomes did not result in any significant gains in an active control group. Thus, there was no detectable expectancy effect in either direction as a function of the cognitive intervention used, suggesting that training-related improvements are unlikely due solely to a placebo effect.

Sources that contribute to variation in mathematical achievement include both numerical knowledge and general underlying cognitive processing abilities. The current study tested the benefits of tablet-based training games that targeted each of these areas for improving the mathematical knowledge of kindergarten-age children. We hypothesized that playing a number-based game targeting numerical magnitude knowledge would improve children's broader numerical skills. We also hypothesized that the benefits of playing a working memory (WM) game would transfer to children's numerical knowledge given its important underlying role in mathematics achievement. Kindergarteners from diverse backgrounds (n = 148; 52% girls; M[subscript age] = 71.87 months) were randomly assigned to either play a number-based game, a WM game, or a control game on a tablet for 10 sessions. Structural equation modeling was used to model children's learning gains in mathematics and WM across time. Overall, our results suggest that playing the number game improved kindergarten children's numerical knowledge at the latent level, and these improvements remained stable as assessed 1 month later. However, children in the WM group did not improve their numerical knowledge compared to children in the control condition. Playing both the number game and WM game improved children's WM at the latent level. Importantly, the WM group continued to improve their WM for at least a month after playing the games. The results demonstrate that computerized games that target both domain-specific and domain-general skills can benefit a broad range of kindergarten-aged children.

There have been many practical obstacles for teachers to implement evidence-based educational technology, especially in STEM classrooms. By implementing learning principles related to Cognitive Load Theory, we developed an innovative Technology-Assisted Guided Learning (TAGL) approach and its web-based instructional tool, combining expertise from educational research and best teaching practices to enhance guided student-centered learning in chemistry. A total of 185 community college students were randomly assigned to learn stoichiometry through either TAGL or one of two active control interventions, traditional direct instruction and Khan Academy, a widely used web learning platform. We found that the TAGL group significantly outperformed both active control groups immediately after instruction, and furthermore, despite the fact that all groups received additional instruction in stoichiometry, the beneficial effects of TAGL compared to the control groups were maintained a month later. Notably, TAGL was able to eliminate the achievement gap between students with low prior knowledge and students with high prior knowledge. Furthermore, prior-knowledge activation was found to be especially beneficial for students with low prior knowledge. Our work contributes to existing research in learning theories and provides new insight toward the development of more effective and adaptive instructional designs. By translating research into practice, this study demonstrates the great potential of using innovative computer-based technology to improve student learning for all.

Despite being among the fastest growing segments of the student population, English Language Learners (ELLs) have yet to attain the same academic success as their English-proficient peers, particularly in science. In an effort to support the pedagogical needs of this group, educators have been urged to adopt inquiry approaches to science instruction. Whereas inquiry instruction has been shown to improve science outcomes for non-ELLs, systematic evidence in support of its effectiveness with ELLs has yet to be established. The current meta-analysis summarizes the effect of inquiry instruction on the science achievement of ELLs in elementary school. Although an analysis of 26 articles confirmed that inquiry instruction produced significantly greater impacts on measures of science achievement for ELLs compared to direct instruction, there was still a differential learning effect suggesting greater efficacy for non-ELLs compared to ELLs. Contextual factors that moderate these effects are identified and discussed.

Individual differences in Executive Function (EF) are well established to be related to overall mathematics achievement, yet the mechanisms by which this occurs are not well understood. Comparing representations (problems, solutions, concepts) is central to mathematical thinking, and relational reasoning is known to rely upon EF resources. The current manuscript explored whether individual differences in EF predicted learning from a conceptually demanding mathematics lesson that required relational reasoning. Analyses revealed that variations in EF predicted learning when measured at a delay, controlling for pretest scores. Thus, EF capacity may impact students' overall mathematics achievement by constraining their resources available to learn from cognitively demanding reasoning opportunities in everyday lessons. To assess the ecological validity of this interpretation, we report follow-up interviews with mathematics teachers who raised similar concerns that cognitively demanding activities such as comparing multiple representations in mathematics may differentially benefit their high versus struggling learners. Broader implications for ensuring that all students have access to, and benefit from, conceptually rich mathematics lessons are discussed. We also highlight the utility of integrating methods in Science of Learning (SL) research.

The approximate number system (ANS) is an innate cognitive template that allows for the mental representation of approximate magnitude, and has been controversially linked to symbolic number knowledge and math ability. A series of recent studies found that an approximate arithmetic training (AAT) task that draws upon the ANS can improve math skills, which not only supports the existence of this link, but suggests it may be causal. However, no direct transfer effects to any measure of the ANS have yet been reported, calling into question the mechanisms by which math improvements may emerge. The present study investigated the effects of a 7-day AAT and successfully replicated previously reported transfer effects to math. Furthermore, our exploratory analyses provide preliminary evidence that certain ANS-related skills may also be susceptible to training. We conclude that AAT has reproducible effects on math performance, and provide avenues for future studies to further explore underlying mechanisms - specifically, the link between improvements in math and improvements in ANS skills.

There is growing interest in the contributions of neuroscience to educational practice; however, to date, neuroscience seems to have had little impact on education. Nonetheless, neuroscience has potential value for education on several fronts, as illustrated by the articles in this Special Topic. These articles provide excellent examples for how neuroscientific approaches can complement behavioral work, and they demonstrate how understanding the neural level can help researchers develop richer models of learning and development. These articles further show that, ideally, research efforts in neuroscience and education should be reciprocal. Specifically, education should encourage psychology and neuroscience to develop learning theories that are relevant in the real world and further improve our understanding of how specific instructional practices affect learning and achievement; in turn, psychology and neuroscience can provide insights into underlying neural and cognitive mechanisms of learning, with the overall goal to maximize human potential and learning for all.

This symposium integrates findings across studiesconducted in both laboratory and classroom contexts todraw attention to the relationships between ExecutiveFunctions (EFs) and feelings of anxiety in a context witheducational consequences: Mathematics. EFs, the cognitiveresources including working memory and inhibitory controlthat enable attentional control, manipulation of mentalrepresentations, and task switching (Miyake et al, 2000),powerfully predict mathematics achievement (Bull & Lee,2014). Mathematics is also a domain in which anxiety andperformance pressure are often heightened, which can resultin worry ideation and load to EF resources (Foley et al,2017; Schmader & Beilock, 2012). However, despite theserelationships, mathematics cognition under pressure remainsunder-considered.

Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on "resting-state" networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults.

In the current literature, there are a number of cognitive training studies that use N-back tasks as their training vehicle; however, the interventions are often bland, and many studies suffer from considerable attrition rates. An increasingly common approach to increase participant engagement has been the implementation of motivational features in training tasks; yet, the effects of such "gamification" on learning have been inconsistent. To shed more light on those issues, here, we report the results of a training study conducted at two Universities in Southern California. A total of 115 participants completed 4 weeks (20 sessions) of N-back training in the laboratory. We varied the amount of "gamification" and the motivational features that might make the training more engaging and, potentially, more effective. Thus, 47 participants trained on a basic color/identity N-back version with no motivational features, whereas 68 participants trained on a gamified version that translated the basic mechanics of the N-back task into an engaging 3D space-themed "collection" game (Deveau et al. Frontiers in Systems Neuroscience, 8, 243, 2015). Both versions used similar adaptive algorithms to increase the difficulty level as participants became more proficient. Participants' self-reports indicated that the group who trained on the gamified version enjoyed the intervention more than the group who trained on the non-gamified version. Furthermore, the participants who trained on the gamified version exerted more effort and also improved more during training. However, despite the differential training effects, there were no significant group differences in any of the outcome measures at post-test, suggesting that the inclusion of motivational features neither substantially benefited nor hurt broader learning. Overall, our findings provide guidelines for task implementation to optimally target participants' interest and engagement to promote learning, which may lead to broader adoption and adherence of cognitive training.

A great deal of interest surrounds the use of transcranial direct current stimulation (tDCS) to augment cognitive training. However, effects are inconsistent across studies, and meta-analytic evidence is mixed, especially for healthy, young adults. One major source of this inconsistency is individual differences among the participants, but these differences are rarely examined in the context of combined training/stimulation studies. In addition, it is unclear how long the effects of stimulation last, even in successful interventions. Some studies make use of follow-up assessments, but very few have measured performance more than a few months after an intervention. Here, we utilized data from a previous study of tDCS and cognitive training [Au, J., Katz, B., Buschkuehl, M., Bunarjo, K., Senger, T., Zabel, C., et al. Enhancing working memory training with transcranial direct current stimulation. Journal of Cognitive Neuroscience, 28, 1419-1432, 2016] in which participants trained on a working memory task over 7 days while receiving active or sham tDCS. A new, longer-term follow-up to assess later performance was conducted, and additional participants were added so that the sham condition was better powered. We assessed baseline cognitive ability, gender, training site, and motivation level and found significant interactions between both baseline ability and motivation with condition (active or sham) in models predicting training gain. In addition, the improvements in the active condition versus sham condition appear to be stable even as long as a year after the original intervention.

Regions within the left inferior frontal gyrus (LIFG) have simultaneously been implicated in syntactic processing and cognitive control. Accounts attempting to unify LIFG's function hypothesize that, during comprehension, cognitive control resolves conflict between incompatible representations of sentence meaning. Some studies demonstrate co-localized activity within LIFG for syntactic and non-syntactic conflict resolution, suggesting domain-generality, but others show non-overlapping activity, suggesting domain-specific cognitive control and/or regions that respond uniquely to syntax. We propose however that examining exclusive activation sites for certain contrasts creates a false dichotomy: both domain-general and domain-specific neural machinery must coordinate to facilitate conflict resolution across domains. Here, subjects completed four diverse tasks involving conflict -one syntactic, three non-syntactic- while undergoing fMRI. Though LIFG consistently activated within individuals during conflict processing, functional connectivity analyses revealed task-specific coordination with distinct brain networks. Thus, LIFG may function as a conflict-resolution "hub" that cooperates with specialized neural systems according to information content.

Working memory (WM) is a key cognitive system that is strongly related to other cognitive domains and relevant for everyday life. However, the structure of WM is yet to be determined. A number of WM models have been put forth especially by factor analytical studies. In broad terms, these models vary by their emphasis on WM contents (e.g., visuospatial, verbal) vs. WM processes (e.g., maintenance, updating) as critical, dissociable elements. Here we conducted confirmatory and exploratory factor analyses on a broad set of WM tasks, half of them numerical-verbal and half of them visuospatial, representing four commonly used task paradigms: simple span, complex span, running memory, and n-back. The tasks were selected to allow the detection of both content-based (visuospatial, numerical-verbal) and process-based (maintenance, updating) divisions. The data were collected online which allowed the recruitment of a large and demographically diverse sample of adults (n = 711). Both factor analytical methods pointed to a clear division according to task content for all paradigms except n-back, while there was no indication for a process-based division. Besides the content-based division, confirmatory factor analyses supported a model that also included a general WM factor. The n-back tasks had the highest loadings on the general factor, suggesting that this factor reflected high-level cognitive resources such as executive functioning and fluid intelligence that are engaged with all WM tasks, and possibly even more so with the n-back. Together with earlier findings that indicate high variability of process-based WM divisions, we conclude that the most robust division of WM is along its contents (visuospatial vs. numerical-verbal), rather than along its hypothetical subprocesses.

Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on "resting-state" networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults.

Here we analyze gray matter indices before and after completing a challenging adaptive cognitive training program based on the n-back task. The considered gray matter indices were cortical thickness (CT) and cortical surface area (CSA). Twenty-eight young women (age range 17-22 years) completed 24 training sessions over the course of 3 months (12 weeks, 24 sessions), showing expected performance improvements. CT and CSA values for the training group were compared with those of a matched control group. Statistical analyses were computed using a ROI framework defined by brain areas distinguished by their genetic underpinning. The interaction between group and time was analyzed. Middle temporal, ventral frontal, inferior parietal cortices, and pars opercularis were the regions where the training group showed conservation of gray matter with respect to the control group. These regions support working memory, resistance to interference, and inhibition. Furthermore, an interaction with baseline intelligence differences showed that the expected decreasing trend at the biological level for individuals showing relatively low intelligence levels at baseline was attenuated by the completed training.

Tensor-Based Morphometry (TBM) allows the automatic mapping of brain changes across time building 3D deformation maps. This technique has been applied for tracking brain degeneration in Alzheimer's and other neurodegenerative diseases with high sensitivity and reliability. Here we applied TBM to quantify changes in brain structure after completing a challenging adaptive cognitive training program based on the n-back task. Twenty-six young women completed twenty-four training sessions across twelve weeks and they showed, on average, large cognitive improvements. High-resolution MRI scans were obtained before and after training. The computed longitudinal deformation maps were analyzed for answering three questions: (a) Are there differential brain structural changes in the training group as compared with a matched control group? (b) Are these changes related to performance differences in the training program? (c) Are standardized changes in a set of psychological factors (fluid and crystallized intelligence, working memory, and attention control) measured before and after training, related to structural changes in the brain? Results showed (a) greater structural changes for the training group in the temporal lobe, (b) a negative correlation between these changes and performance across training sessions (the greater the structural change, the lower the cognitive performance improvements), and (c) negligible effects regarding the psychological factors measured before and after training.

Our recent meta-analysis concluded that training on working memory can improve performance on tests of fluid intelligence (Au et al., Psychon Bull Rev, 22(2), 366-377, 2015). Melby-Lervåg and Hulme (Psychon Bull Rev, doi: 10.3758/s13423-015-0862-z ) challenge this conclusion on the grounds that it did not take into consideration baseline differences on a by-study level and that the effects were primarily driven by purportedly less rigorous studies that did not include active control groups. Their re-analysis shows that accounting for baseline differences produces a statistically significant, but considerably smaller, overall effect size (g = 0.13 vs g = 0.24 in Au et al.), which loses significance after excluding studies without active controls. The present report demonstrates that evidence of impact variation by the active/passive nature of control groups is ambiguous and also reveals important discrepancies between Melby-Lervåg and Hulme's analysis and our original meta-analysis in terms of the coding and organization of data that account for the discrepant effect sizes. We demonstrate that there is in fact no evidence that the type of control group per se moderates the effects of working memory training on measures of fluid intelligence and reaffirm the original conclusions in Au et al., which are robust to multiple methods of calculating effect size, including the one proposed by Melby-Lervåg and Hulme.