Working memory is a core brain function underlying all higher cognition. There has been a lot of interest in the past decade about the potential to train working memory capacity to improve overall cognitive performance. In this article I provide an introduction to higher order cognitive skills (HOCS), their basis in working memory and the evidence for the effectiveness of different training methods for expanding working memory capacity.[br]
HOCS – Higher Order Cognitive Skills
Our mental abilities or cognitive skills can be divided into Lower-Order Cognitive Skills (LOCS) and Higher-Order Cognitive Skills (HOCS).
LOCS include memorization of content for simple recall – like remembering facts for a history exam. LOCS also include the kind of attention-improving skills that video games may improve with practice. LOCS includes any cognitive skills with specific applications that do not require much understanding, evaluation, or problem solving flexibility.
HOCS involves higher level skills such as application of knowledge in new situations, flexible problem solving, critical thinking, decision making, comprehension, creativity, and the self-management of one’s own thoughts and behaviors to be a better learner – more skilled, more flexible, and better adapted.
HOCs are more important than ever. With the rapid pace of technological and cultural change on a global scale, and the challenges of adapting to this change, student and citizens need to go beyond the building of their knowledge capacity: they need to develop their higher-order thinking skills. HOCS are needed for all purposeful, reasoned, and goal-directed thinking. They are an important to all successful, adaptive behaviour in everyday life as well as educational and professional life.
Working Memory (WM): The Cognitive Basis of HOCS
Working memory (WM) is the core brain function critical for all HOCS. It can be considered as the main control centre of human cognition generally, as illustrated in this BrainScanr graph (Figure 1).
Working memory is necessary for staying focused on a task, blocking out distractions, keeping you updated and aware of what is going on in this process, and applying relevant cognitive strategies to process the information. Working memory impairments result in loss of attentional focus – such as difficultly focusing on reading a text; or memory problems, such as forgetting what to do in the few seconds of walking from one room to the another, or being easily distracted while trying to focus on a task and not being able to finish an activity according to plan.
Working memory can be defined in everyday language as a set of skills that helps us keep information in mind while using that information to complete a task or execute a challenge. Baddeley has defined it as:
a brain system that provides temporary storage and manipulation of the information necessary for such complex cognitive tasks as language comprehension, learning, and reasoning. (Baddeley, 2003, p. 189)
More technically it has been defined as:
a flexible, capacity limited, mental workspace used to store and process information in the service of on-going cognition” (Morrison & Chein, 2011, p. 233)
Limited capacity system that includes a short-term storage of information and the functions of updating and manipulating the storage contents. (Salminen, Strobach & Schubert, 2012, p. 23)
Working memory makes central use of short term memory storage, and attention control. When you comprehend someone’s explanation, make a mental calculation, make a decision or execute a plan you have to hold information into a ‘mental workspace’ long enough to integrate and process it. And you need to selectively attend to what is important and what is merely distracting to perform the task at hand.
A child uses WM when doing math calculations, listening to a story or making something (Figure 2). She has to hold onto the numbers while working with them, she needs to remember the sequence of events and also think of what the story is about, and she needs to hold in mind a plan to enact.
How Does Our Working Memory (WM) System Work? Two Theories
There are two theories about how working memory works. Each of them will be described in the following sections.
Baddely & Hitch Model of Working Memory
According to Baddeley’s (2000; Baddely & Hitch, 1974) model of working memory (Figure 3) there are three subsystems for temporary storage and maintenance of information – the ‘visuo-spatial scratchpad’, the ‘phonological loop’, and the ‘episodic buffer’. There is also a super-system called ‘central executive’ that controls the flow of information into these other systems, filtering out what is unimportant, allowing for higher order processing of the information through reasoning, decision-making, planning and comprehension.
The visuospatial sketchpad is a short term memory store or ‘buffer’ for just visual and spatial information. Information currently active in this WM store is also linked to our ‘visual semantics’ in long-term memory. This is what we know about visual and spatial information in the world, such as directions to work or the layout of the rooms in our home. What is in our long-term memory is outside of our WM system, but the contents of our long term memory are activated by information in WM.
The phonological loop is a short term memory store for verbal information. Items in this store have links with of language and concepts in our long-term memory . In the diagram above, the box diagram would be stored in the visuo-spatial buffer, and the names for the different subsystems would be stored in the phonological loop.
While these systems store information in specific modalities, the episodic buffer is a short term store for ‘multi-modal’ information. It’s job is to ‘bind together’ information from the other short term buffers – language information with visuo-spatial information – into unitary representations called ‘episodes’ that make sense to us. An example of integrated information that may be held in the episodic buffer is the full diagram of working memory above, where the different names are organized in a visuo-spatial flow diagram.
The central executive is a goal focused system for selecting, controlling and coordinating the information in the three short term buffers for comprehension, problem solving, decision making, etc. It is essentially an attentional control mechanism, allowing us to focus attention on task-relevant information and ignore distractions.
The central executive of WM involves both the pre-frontal cortex and parietal cortex – in the so-called ‘fronto-parietal axis’ as shown in Figure 4.
Cowan’s Embedded Process Model of Working Memory
Unlike Baddeley’s model, which is concerned with modularity and components of the working memory, Cowan offered a view oriented mostly on underlying cognitive processes. According to his model, the central executive acts as a selective attentional filter to activate task-relevant representations from long-term memory (Figure 5).
Working Memory Capacity: The ‘Mind’s Workspace’
The attentional ‘filter’ of our central executive is capacity limited, typically processing at any one time around 3-4 items of information from the short term buffers. This is our ‘mental workspace’. Another metaphor for WM capacity is a computer’s RAM capacity. A computer’s RAM enables temporary storage and working space for the operating system and applications, The larger the RAM capacity, the more working space it has to process information. The same is true for WM capacity.
WM makes information available for more advanced cognitive processing, WM capacity represents one of the main rate limiting factors for higher-order cognitive functions such as reasoning and decision making. Individuals differ in the size of their WM capacity, and because of this, they differ in their capacity to reason, make decisions, plan and comprehend.
There is also a normal decline in WM capacity with aging, starting around 25-30 years of age, with a decline of about 5-10% per decade.
What Brain Functions Does WM Capacity Predict?
Working memory capacity – our mental workspace – is correlated with a wide range of brain functions and Higher Order Cognitive Skills (HOCS) such as:
- Attentional tasks (Fukuda & Vogel, 2009).
- Resistance to being distracted (Fukuda & Vogel, 2011)
- Sustained attention (without mind wandering) during challenging tasks in daily life (McVay & Kane, 2009).
- Reading comprehension (Daneman & Carpenter, 1980)
- Reasoning, problem solving, and fluid intelligence (Engle et al., 1999; Kane et al. (2005). Fluid intelligence is involved in reasoning and when complex relationships have to be perceived and used to find solutions for new problems.
- Scholastic aptitude (Cowan et al., 2005).
- Academic success (Alloway et al., 2004)
- Language acquisition (Baddeley, 2003).
Skill Learning, Brain Training & Neuroplasticity
Skill learning is defined as an improvement in attention, perception, cognition or motor skills as a result of training and that persists for several weeks or months. Brain training is skill learning for cognitive abilities resulting in long-term neuroplasticity changes in the brain’s neural networks underlying those abilities.
We can improve on virtually any cognitive task with practice. But in general the effects of brain training are highly specific: improvement is observed only in the trained task, with little or no transfer of learning to untrained tasks. In a famous example, Ericsson and Chase (1982) showed a student who did cognitive trained for many hours of practice on a short term number memorization task could successfully recall over 80 randomly ordered digits. But the student was limited to a short term memory of just 7 items when the content to be remembered was not numerical. Extensive practice on computer games is known to result in highly specific attention and motor skills – those needed for better game-performance.
The narrowness (specificity) of the benefits of many classical studies of skill learning have led many to conclude that the benefits of practice on a given task are domain specific – enhancing performance only in the trained task and a small set of closely related tasks that involve the same type of material. Domain general capacities underlying wide ranging cognitive abilities – such as general intelligence or working memory needed for HOCS – have traditionally been understood as more or less ‘hard-wired’ by late childhood – and untrainable.
Higher Order Cognitive Skills (HOCS) Can be Improved by WM Capacity Training
Many recent studies have now shown that brain training can improve one domain-general ability – working memory and its capacity. This kind of training has benefits that extend well beyond the training tasks to a wide variety of cognitive abilities and HOCS. The mind’s workspace can be expanded with training, and the effects are long-lasting (review, Chein & Morrison, 2010).
training can effectively expand the central workspace of the mind…core WM training studies seem to produce more far-reaching transfer effects, likely because they target domain-general mechanisms of WM. (Chein & Morrison, 2010, p. 233)
The results of individual studies encourage optimism regarding the value of WM training as a tool for general cognitive enhancement. …Studies of core training show improvements in a variety of areas of cognition (e.g. cognitive control, reading comprehension), persist even with the use of tightly matched controls, and are consistent with neuroimaging studies demonstrating activation changes in regions associated with domain-general cognitive performance. Core WM training thus represents a favourable approach to achieve broad cognitive enhancement. (Morrison & Chein, 2011, p. 34).
there is a rapidly growing number of studies demonstrating that training-related increases in WM capacity can yield improvements in a range of important cognitive skills (Chein & Morrison, 2010) as well as improved cognitive function in clinical populations with known WM deficiencies. (e.g. Kingberg et al., 2005, p. 72)
The N-Back Training Task
The most widely studied brain training exercise targeting WM capacity is the N-back task. The N-back task involves viewing a continuous stream of items (e.g., letters) and deciding whether each item matches the stimulus presented n stimuli back. The task exercises a number of executive processes including attentional selection, updating, and multi-tasking. It is also adaptive, increasing in difficulty (the n-back interval) as skill in the task improves, ensuring that task performance doesn’t become automatized. In Dual N-back training, a verbal and a visuo-spatial stream of items is presented simultaneously and item matches have to be detected for both modalities (Figure 6). This dual task requires updating items in both the visuospatial sketchpad and the phonological loop WM buffers described above. It is widely considered to be the most effective type of n-back training.
Does Dual N-Back Training Improve Intelligence?
Definitions of Intelligence
Most of the studies that address the issue of whether working memory training improves intelligence use a standard dual n-back working memory training task, and use matrices fluid intelligence tests to measure general intelligence (g). We should first acknowledge that (1) the dual n-back is just one working memory training task, and (2) that matrices (e.g Raven’s) tests are only one type of IQ test.
It is standard for short-term and/or working memory tests to be incorporated in full scale-IQ tests, as one important measure of IQ.
In the well-known WAIS-IV full scale IQ test, matrices tests measure what is labelled the ‘Perceptual Organization Index’. The Working Memory Index is a distinct category in the measurement of general IQ.
In an influential theoretical paper in the field of psychometric IQ testing using factor-analytic approaches, Kevin S. McGrew states:
“the Cattell–Horn Gf–Gc and Carroll Three-Stratum models have emerged as the consensus psychometric-based models for understanding the structure of human intelligence. Although the two models differ in a number of ways, the strong correspondence between the two models has resulted in the increased use of a broad umbrella term for a synthesis of the two models (Cattell–Horn–Carroll theory of cognitive abilities—CHC theory).” McGrew, 2009
This model is shown here:
A well known ‘full scale’ IQ test is the WJ IV, and its subtests map onto the broad IQ factors of CHC theory as follows:
It’s clear that the construct of IQ embraces more than what is measured by matrices tests, and that it includes working memory.
So if working memory training improves working memory, then it improves IQ. We’ll see there is strong evidence that dual n-back training improves working memory. There’s also evidence (although less conclusive) that dual n-back training improves fluid intelligence (Gf) measured by matrices tests.
In what follows we’ll look in more detail at the following:
Meta-analyses of broad cognitive abilities studies
Meta-analyses of working memory studies
Meta-analyses of matrices (Gf) studies
Specific critiques of the Gf studies (e.g. Bayesian)
Fronto-parietal network neuroplasticity studies
1. Meta-analyses of broad cognitive abilities
The most recently published meta-analysis of the working memory training literature by Schwaighofer and colleagues’ (2015) summarizes the training gains in the following table. The asterisks indicate statistical significance. Long-term means 5-12 months after training.
To convert these effect sizes (both short term and long term) to standardized scores such as points in an IQ test, multiply them by 15 – one standard deviation. So for example, the visuospatial working memory gain of 0.63 is equivalent to 0.63 x 15 = 9.5 points on a standardized scale.
In this meta-analysis Schwaighofer and colleagues found that
- Age was not relevant to such gains: they occurred across the full age spectrum.
- Training duration most likely makes a difference: the more you training, the greater the gains.
And they suggest that
- More complex activities during working memory brain training – with multiple exercises – may result in more practical advantages after training (what is called ‘wide transfer’).
The average age for this meta-study was relatively young. What about for older adults?
A recent meta-analysis by Karbach and Verhaeghen (Nov 2014) examined the effects of working memory and executive control training (49 studies) in both younger adults and 60+ year olds for a number of general cognitive abilities including attention control, IQ (fluid intelligence), episodic memory (memory for personal experiences), short term and working memory, and processing speed. This study found the following effect sizes with no differences in training-based gains between younger and older adults:
Note that a 0.4 effect size is equivalent to 6.5 points on a standardized scale. These effect sizes are considerable, and include a large IQ (fluid intelligence gain) of ~5.5 IQ points.
For older adults, similar results were found in the meta-analyses of Hindin & Zelinksi, 2012, and Karr et al., 2014. The net gain in overall cognitive ability after (on average) 9 hours of working memory and executive function training is similar in size to the effect of (on average) about five months of regular 45-minute sessions aerobic training (review).
2. Meta-analyses of working memory (Gwm) studies
The latest meta-review of verbal and visuo-spatial short term memory and verbal and visuo-spatial working memory (Schwaighofer et al., 2015) shows there are both short term (within a few days of training) and long-term (6-12 month follow up) training effects, and these effects are considerable (see table below).
So for example, the visuospatial working memory gain of 0.63 is equivalent to 0.63 x 15 = 9.5 points. Long term visuo-spatial working memory gains from training due to neuroplasticity are 6.5 points.
Melby-Lervag and Hulme’s 2013 meta-analysis, also concluded that working memory training resulted in visuo-spatial and verbal working memory gains.
Effect sizes reported by individual studies for visuo-spatial working memory, with short-term gains on the left and long term (up to 1 year) on the right, is shown in this plot.
Effect sizes reported by individual studies for verbal working memory, with short-term gains on the left and long term (up to 1 year) on the right, is shown in this plot.
It’s clear from these plots that working memory training improves working memory. Even at 6-12 month’s after training there is a persisting working memory gain, assessed with standard working memory tests. This demonstrates long-term neuroplasticity change, and this is consistent with the brain science literature (below).
The controversy surrounding studies looking at the effects of working memory training (e.g. the dual n-back) on Gf measured by matrices tests, fails to address these studies looking at the effects of working memory training on working memory capacity/efficiency – another equally valid measure of IQ. Even the fluid intelligence (Gf) gain skeptics Melby-Lervag and Hulme in their 2013 meta-analysis concluded that working memory training resulted in visuo-spatial and verbal working memory gains.
3. Meta-analyses of fluid intelligence (Gf) studies
The 2015 Au et al. meta-analysis found a significant IQ increase in fluid intelligence from working memory training. The authors conclude:
“We urge that future studies move beyond attempts to answer the simple question of whether or not there is transfer [from training to increases in IQ] and, instead, seek to explore the nature and extent of how these improved test scores may reflect true improvements in intelligence that can translate into practical, real-world settings.” Jacky Au and colleagues, University of California, April 2015
Here is the plot of effect sizes reported in relevant studies reviewed in this meta-analysis:
It is the case that many of these studies are underpowered, but the Au et al (2015) estimates are still the best available. (5)
Au and colleagues’ conclusion that working memory training results in IQ gains finds support in the meta-analysis by Karbach and Verhaeghen (Nov 2014) who estimated a 5.5 point IQ increase. And more recently, it is supported by the latest comprehensive 2015 meta-review by Schwaighofer and his colleagues. They found there were significant increases in IQ measured by both non-verbal and verbal ability after training, regardless of control group. In this study they found that these IQ gains did not last without further training when measured between 6 and 12 months later. But in the short term there was a real IQ gain from training, and we can assume these gains could be maintained or improved with continued training.
The effect sizes reported by all studies looking at working memory training’s effect on non-verbal (Gf) IQ are shown in this plot from Schwaighofer and colleagues, 2015:
The effect sizes reported by all studies looking at working memory training’s effect on verbal IQ are shown in this plot:
We can reasonably conclude from these meta-analyses that Gf gains from training are real – for both younger and older adults.
- Estimates of the overall training benefit for both non-verbal (Gf) and verbal IQ range between 2.0 – 5.5 standardized points (1,2,3 )
- An estimate of the overall training benefit for IQ when we only look at experiments in which the comparison/control group is ‘passive’ and does no computer activity is ~7 IQ points (1)
4. Specific Critiques of the Gf Studies
Melby-Lervag and Hulme critique
The conclusion of this meta-analysis has been challenged by Melby-Lervåg and Hulme in a reanalysis of the data. While they do not doubt that there is a Gf gain after brain training of around 7-8 IQ standardized points, they argue that this gain is essentially a placebo effect since when the comparison (‘control’) groups are active (i.e. do other computer tasks such as a simple attention exercises), the effect size is greatly reduced.
But this Oct 2015 response to Melby-Lervag and Hulme counters their arguments and supports Au et al.’s original conclusion that the working memory training does indeed improve IQ:
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. (Au et al., Oct, 2015)
Au et al (2015) point out that
“…the present direction of effects actually suggests that passive control groups could end up outperforming active control groups which runs opposite to the direction suggested by the idea that Hawthorne or expectancy effects drive improvements in both active control and treatment groups.”
Dougherty and colleagues (2015) Bayesian critique
“We find that studies using a noncontact (passive) control group strongly favor the alternative hypothesis that training leads to transfer but that studies using active-control groups show modest evidence in favor of the null. We discuss these findings in the context of placebo effects.”
To counter their critique, the first obvious point is that the 7.7 : 1 probability in favor of the null hypothesis in active control studies is not very reassuring. This compares to a 13,241 : 1 factor in favor of the alternative hypothesis in passive control studies – a level of evidence that is certainly convincing. 7.7:1 is one step above ‘weak’ and well below ‘decisive’ in terms of their ‘points of reference’ categories for how to interpret degrees of evidence using the Bayes factor.
Second, cultural differences may be largely driving the hypothesized active vs passive difference. Here is Figure 5 from the Dougherty et al study.
It is clear that it is exclusively the US x active control studies that support the null. The active control studies in Europe have fairly high g effect sizes (even if the CI crosses zero), and there are a couple of US studies with passive controls that do not support the null hypothesis.
Dougherty and colleagues argue that what is explanatory here is the active vs passive control contrast – not the cultural contrast. But they admit that cultural differences may be the driver: “this leaves open the possibility that cultural differences are driving the difference between the active and passive studies.
There is evidence that in the US particularly over the last 25 years the difference in effectiveness between real drugs and placebo ones has narrowed considerably, suggesting that Americans are particularly susceptible to the placebo effect more generally (5, 6). Now in the US even many well-established medical drugs would not pass placebo control trials and this is a major concern for medical research.
Since the ‘susceptibility to placebo’ is a plausible alternative explanation to Dougherty and colleagues’ it needs to be directly addressed in future studies. What is needed before placebo criticisms can be regarded as a serious challenge is direct evidence for placebo effects in the form of experiments where expectations are systematically varied, or adding a third group to the controlled trial set-up, which takes an existing intervention that is known to work – if both that group and the group given the effective intervention fail to beat the placebo, researchers know that their trial design is flawed.
5. Fronto-parietal network neuroplasticity studies
Studies that look a cortical network and synaptic neuroplasticity effects from WM and CC training are consistent with wide IQ transfer interpretations of the behavioral meta-analyses
There are now many neuroimaging studies showing consistent fronto-parietal network (FPN) neuroplasticity effects from working memory (e.g. dual n-back) brain training (e.g. Thompson et al., 2016; Metzler-Baddeley et al. 2016; Kundu et al., 2015). There is extensive evidence for the recruitment of the fronto-parietal network (FPN) – relative to other cortical networks – in tasks requiring fluid intelligence (IQ) (e.g. Preusse et al., 2011).
FPN neuroplasticity effects are clearly not ‘placebo’ effects, and they help explain the consistent WM training gains that are seen in meta-analyses, as well as the consistent emotion-regulation effects that are observed.
Barbey et al. (2012) investigated the neural substrates of the general factor of intelligence (g) and executive function in 182 patients with focal brain damage using MRI brain imaging. They concluded:
Impaired performance on these measures in the WAIS-IV and Delis-Kaplan Executive Function test were associated with damage to a distributed network of left lateralized brain areas, including regions of frontal and parietal cortex and white matter association tracts, which bind these areas into a coordinated system. The observed findings support an integrative framework for understanding the architecture of general intelligence and executive function, supporting their reliance upon a shared fronto-parietal network for the integration and control of cognitive representations.
Kundu and colleagues (2015) have recently proposed that the transfer of WM training to other cognitive abilities is supported by changes in connectivity in frontoparietal and parieto-occipital networks – active in both the trained and transfer tasks. The frontoparietal network is part of the Cognitive Control Network (9) involved in attention control and goal maintenance.
Consistent with this, in a recent MIT, Harvard and Stanford neuroimaging study (Jan, 2016), Thompson and colleagues, found:
[Dual n-back] training differentially affected activations in two large-scale frontoparietal networks thought to underlie working memory: the executive control network and the dorsal attention network. …Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. These results provide insight into the adaptive neural systems that underlie large gains in working memory capacity through training.
And in their recent paper Task complexity and location specific changes of cortical thickness in executive and salience networks after working memory training, Metzler-Baddeley and colleagues (2016) found that working memory training resulted in increases of cortical thickness in right parieto-frontal cortex. (They also found that training led to a reduction of thickness in the right insula and that these changes were related to changes in working memory span.)
There is an important overlap in brain circuitry between interference control, working memory capacity and IQ (Gf). Brain imaging studies reveal that neural mechanisms of interference control underlie the relationship between fluid intelligence and working memory span.
Greenwood and Parasuraman (2015) hypothesize that training-related increases in control of attention in the frontoparietal control network and circuits underlying interference control underlie ‘far transfer’ of cognitive training to untrained abilities, notably to fluid intelligence.
Interference control allows us to suppress distractions. There is compelling evidence that distraction suppression (evident in behavior, neuronal firing, scalp electroencephalography, and hemodynamic change) is important for protecting target processing during perception and holding information in working memory. Consistent with this evidence, forms of cognitive training that increase the ability to ignore distractions (e.g., working memory training and perceptual training) not only affect the frontoparietal control network but also affect transfer to fluid intelligence.
Working memory training games that incorporate systematic interference control and distraction shielding may be expected to enhance IQ gains.
This brief review of the 2014-2015 scientific meta-analysis literature supports the conclusion that working memory and executive control training increases general cognitive performance – whether IQ (verbal or non-verbal ability), short-term memory or working memory. This training benefit is mediated in part by neuroplastic changes in the frontoparietal network.
Based on the available evidence, we can conclude that there are not sufficient grounds to discredit the claim that working memory training is an effective and efficient strategy for improving IQ.
Au, J., Buschkuehl, M., Duncan, G. J., & Jaeggi, S. M. (2015). There is no convincing evidence that working memory training is NOT effective: A reply to Melby-Lervåg and Hulme. Psychonomic Bulletin & Review. Oct, 2015. Abstract
Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuehl, M., & Jaeggi, S. M. (2015). Improving fluid intelligence with training on working memory: a meta-analysis. Psychonomic Bulletin & Review, 22(2), 366-377. Abstract
Barbey, A. K., Colom, R., Paul, E. J., Grafman, J. (2013). Architecture of fluid intelligence and working memory revealed by lesion mapping. Brain Structure and Function, 219, 2. 485-94. Article.
Barbey, A. K., Colom, R., Solomon, J., Krueger, F., Forbes, C., & Grafman, J. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain, 135(4), 1154–1164. http://doi.org/10.1093/brain/aws021
Bogg, T., & Lasecki, L. (2014). Reliable gains? Evidence for substantially underpowered designs in studies of working memory training transfer to fluid intelligence. Frontiers in Psychology, 5, 1589. Abstract.
Greenwood, P. M., & Parasuraman, R. (2015). The Mechanisms of Far Transfer From Cognitive Training: Review and Hypothesis.Neuropsychology. [Ahead of print].
Hindin S.B., Zelinski E.M. Extended Practice and Aerobic Exercise Interventions Benefit Untrained Cognitive Outcomes in Older Adults: A Meta-Analysis. Journal of the American Geriatrics Society.2012;60(1):136–141. [Article]
Karbach, J., & Verhaeghen, P. (2014). Making working memory work: A meta-analysis of executive-control and working memory training in older adults. Psychological Science, 25, 2027–2037. Abstract.
Karr J.E., Areshenkoff C.N, Rast P, Garcia-Barrera M.A. (2014). An Empirical Comparison of the Therapeutic Benefits of Physical Exercise and Cognitive Training on the Executive Functions of Older Adults: A Meta-Analysis of Controlled Trials. Neuropsychology, 28(6):829-45. [Article]
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(19), 6829-6833. Abstract / Article
Metzler-Baddeley, C., Caeyenberghs, K., Foley, S., & Jones, D. K. (n.d.). Task complexity and location specific changes of cortical thickness in executive and salience networks after working memory training.NeuroImage. http://doi.org/10.1016/j.neuroimage.2016.01.007
McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1–10. http://doi.org/10.1016/j.intell.2008.08.004
Melby-Lervag, M., & Hulme, C. (2013). Is working-memory training effective? A meta-analytic review. Developmental Psychology, 49, 270– 291. Abstract.
Preusse, F., Elke, van der M., Deshpande, G., Krueger, F., & Wartenburger, I. (2011). Fluid Intelligence Allows Flexible Recruitment of the Parieto-Frontal Network in Analogical Reasoning. Frontiers in Human Neuroscience, 5. http://doi.org/Abstract
Schwaighofer, M., Fischer, F., Buhner, M. (2015) Does Working Memory Training Transfer? A Meta-Analysis Including Training Conditions as Moderators. Educational Psychologist 50, 2. Abstract, Article.
Some useful definitions
For those of you without training in experimental design, here are some useful definitions that will equip you to understood some of the more technical content of this review, and help you evaluate it for yourself.
Experiments / Randomized Control Trials involve randomly assigning participants in the study to receive one of a number of cognitive interventions. One of these interventions is the computerized cognitive training (brain training) program. One of these interventions is the standard of comparison or control. The control may be an active control (e.g. playing a simple game or doing cross-words for the same duration as the brain training), or a passive control where there is no intervention at all.
Peer-reviewed journal articles. These are published articles of randomized control trials (studies) on brain training that have been submitted to the scrutiny of experts in the same field, and judged to acceptable for publication.
Meta-analyses systematically assess all peer-reviewed studies meeting adequate standards of experimental design and relevance criteria for a particular type of brain training. A meta-analysis uses a statistical approach to combine the results from multiple trials to improve estimates of the size of the effect and resolve uncertainty when reports disagree – for example when one study concludes there is an effect and another study does not. It can also correct for publication bias – the tendency to only publish reports when there is a positive result. A meta-analysis, compared to a single peer-reviewed journal article, enables us to draw much stronger conclusions about the effectiveness of brain training interventions.
If there is statistical significance in a brain training study, it means that the difference in tested outcomes such as average IQ score between training group and the control group is very unlikely (p < 0.05) to have occurred by chance. If the study is well-designed, this gives us confidence that the difference in IQ scores between the brain training and placebo group is due to the training itself, and not some fluke.
The effect size is a measure of the magnitude of the outcome difference between the two groups, which can be measured in standardized scores. Effect size is typically measured in ‘standard deviation’ units (g). When SD = 1.0, this is equivalent to 15 points in a standardized IQ test. If SD = 0.5 this would be 7.5 points. And so on. As a reference, antidepressant drugs typically have an effect size (compared to placebo) of 0.3 – 0.5 – i.e. 4.5 – 7.5 points.
Working Memory and Executive Control Training Apps
The company CogMed also provides working memory training tools with a scientific basis.
My own working memory and executive functioning brain training software implements a combination of working memory training (e.g. n-back) and executive function training. (e.g. interference control and task-switching for cognitive flexibility). This can be found at this website. I am happy to provide this software free of charge for any research or education related projects.
A free app resource for dual n-back is Brain Workshop. This provides variations of the dual n-back.
Alloway, T. P., Gathercole, S. E., Willis, C., & Adams, A.-M. (2004). A structural analysis of working memory and related cognitive skills in young children. Journal of Experimental Child Psychology, 87(2), 85–106. doi:10.1016/j.jecp.2003.10.002
Baddeley, A. (2000). The episodic buffer: a new component of working memory? Trends in Cognitive Sciences, 4(11), 417–423. doi:10.1016/S1364-6613(00)01538-2
Baddeley, A. (2003). Working memory and language: an overview. ASHA 2002, 36(3), 189–208. doi:10.1016/S0021-9924(03)00019-4
Baddeley, A., & Hitch, G. (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:Academic Press.
Chein, J., & Morrison, A. (2010). Expanding the mind’s workspace: Training and transfer effects with a complex working memory span task. Psychonomic Bulletin & Review, 17(2), 193–199. doi:10.3758/PBR.17.2.193
Cowan, N., Elliott, E., Scott Saults, J., Morey, C., Mattox, S., Hismjatullina, A., & Conway, A. (2005). On the capacity of attention: its estimation and its role in working memory and cognitive aptitudes. Cognitive psychology, 51(1), 42–100.
Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19(4), 450–466. doi:10.1016/S0022-5371(80)90312-6=
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. A. (1999). Working memory, short-term memory, and general fluid intelligence: A latent-variable approach. Journal of Experimental Psychology: General, 128(3), 309–331. doi:10.1037/0096-3418.104.22.1689
Ericsson, K. A., & Chase, W. G. (1982). Exceptional memory. American Scientist, 70, 607-615.
Fukuda, K., & Vogel, E. K. (2009). Human Variation in Overriding Attentional Capture. The Journal ofNeuroscience, 29(27), 8726–8733. doi:10.1523/JNEUROSCI.2145-09.2009
Fukuda, K., & Vogel, E. K. (2011). Individual Differences in Recovery Time From Attentional Capture. Psychological Science, 22(3), 361–368. doi:10.1177/0956797611398493
Gray, J.R., Chabris, C.F., and 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(4), 692–701. doi:10.1037/a0014345
Kane, M. J., Hambrick, D. Z., Tuholski, S. W., Wilhelm, O., Payne, T. W., & Engle, R. W. (2004). The Generality of Working Memory Capacity: A Latent-Variable Approach to Verbal and Visuospatial Memory Span and Reasoning. Journal of Experimental Psychology: General, 133(2), 189–217. doi:10.1037/0096-3422.214.171.124
Klingberg, T., Fernell, E., Olesen, P. J., Johnson, M., Gustafsson, P., Dahlström, K., Gillberg, C. G., et al. (2005). Computerized Training of Working Memory in Children With ADHD-A Randomized, Controlled Trial. Journal of the American Academy of Child & Adolescent Psychiatry, 44(2), 177–186. doi:10.1097/00004583-200502000-00010
McNab, F., & Klingberg, T. (2008). Prefrontal cortex and basal ganglia control access to working memory. Nat Neurosci, 11(1), 103–107. doi:10.1038/nn2024
McVay, J. C., & Kane, M. J. (2009). Conducting the train of thought: Working memory capacity, goal neglect, and mind wandering in an executive-control task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(1), 196–204. doi:10.1037/a0014104
Morrison, A. B., & Chein, J. M. (2011). Does working memory training work? The promise and challenges of enhancing cognition by training working memory. Psychonomic Bulletin & Review, 18(1), 46–60. doi:10.3758/s13423-010-0034-0
Salminen, T., Strobach, T., & Schubert, T. (2012). On the impacts of working memory training on executive functioning. Frontiers in Human Neuroscience, 6(166). doi:10.3389/fnhum.2012.00166
Smith, E. E., & Jonides, J. (1998). Neuroimaging analyses of human working memory. Proceedings of the National Academy of Sciences, 95(20), 12061–12068. doi:10.1073/pnas.95.20.12061
Tsuchida, Y., Katayama, J., & Murohashi, H. (2012). Working memory capacity affects the interference control of distractors at auditory gating. Neuroscience Letters, 516(1), 62–66. doi:10.1016/j.neulet.2012.03.057
Turley-Ames, K. J., & Whitfield, M. M. (2003). Strategy training and working memory task performance. Journal of Memory and Language, 49(4), 446–468. doi:10.1016/S0749-596X(03)00095-0
Wager, T.D., & Smith, E. E. (2003). Neuroimaging studies of working memory: A meta analysis. Cognitive and Affective Behavioral Neuroscience, 3, 255–274.