1-Back

In 1-back, the task is to correctly answer whether the letter is the same as the previous round, and whether the position is the same as the previous round. It can be both, making 4 possible responses (position, sound, position+sound, & neither).

This stresses working memory since you need to keep in mind 4 things simultaneously: the position and letter of the previous turn, and the position and letter of the current turn (so you can compare the current letter with the old letter and the current position with the old position). Then on the next turn you need to immediately forget the old position & letter (which are now useless) and remember the new position and letter. So you are constantly remembering and forgetting and comparing.

2-Back

But 1-back is pretty easy. The turns come fast enough that you could easily keep the letters in your phonological loop and lighten the load on your working memory. Indeed, after 10 rounds or so of 1-back, I mastered it - I now get 100%, unless I forget for a second that it’s 1-back and not 2-back (or I simply lose my concentration completely). Most people find 1-back very easy to learn, although a bit challenging at first since the pressure is constant (games and tests usually have some slack or rest periods).

The next step up is a doozy: 2-back. In 2-back, you do the same thing as 1-back but as the name suggests, you are instead matching against 2 turns ago. So before you would be looking for repeated letters - ‘AA’ - but now you need to look for separated letters - ‘ABA’. And of course, you can’t forget so quickly, since you still need to match against something like ‘ABABA’.

2-back stresses your working memory even more, as now you are remembering 6 things, not 4: 2 turns ago, the previous turn, and the current turn - all of which have 2 salient features. At 6 items, we’re also in the mid-range of estimates for normal working memory capacity:

Working memory is generally considered to have limited capacity. The earliest quantification of the capacity limit associated with short-term memory was the “magical number seven” introduced by Miller (1956_68ya). He noticed that the memory span of young adults was around seven elements, called chunks, regardless whether the elements were digits, letters, words, or other units. Later research revealed that span does depend on the category of chunks used (eg. span is around seven for digits, around six for letters, and around five for words), and even on features of the chunks within a category….Several other factors also affect a person’s measured span, and therefore it is difficult to pin down the capacity of short-term or working memory to a number of chunks. Nonetheless, Cowan (2001_23ya) has proposed that working memory has a capacity of about four chunks in young adults (and fewer in children and old adults).

And even if there are only a few things to remember, the number of responses you have to choose between go up exponentially with how many ‘modes’ there are, so Triple N-back has not ⅓ more possible responses than Dual N-back, but more than twice as many: if m is the number of modes, then the number of possible responses is 2^m-1 (the -1 is there because one can nothing in every mode, but that’s boring and requires no choice or thought), so DNB has 3 possible responses⁴², while TNB has 7⁴³, Quadruple N-back 15⁴⁴, and Quintuple N-back 31⁴⁵!

Worse, the temporal gap between elements is deeply confusing. It’s particularly bad when there’s repetition involved - if the same square is selected twice with the same letter, you might wind up forgetting both!

So 2-back is where the challenge first really manifests. After about 20 games I started to get the hang of it. (It helped to play a few games focusing only on one of the stimuli, like the letters; this helps you get used to the ‘reaching back’ of 2-back.)

Are Strategies Good or Bad?

People frequently ask and discuss whether they should use some sort of strategy, and if so, what.

A number of N-backers adopt an ‘intuition’ strategy. Rather than explicitly rehearsing sequences of letters (‘f-up, h-middle; f-up, h-middle; g-down, f-up…’), they simply think very hard and wait for a feeling that they should press ‘a’ (audio match), or ‘l’ (location match). Some, like SwedishChef can be quite vociferous about it:

The challenges are in helping people understand that dual-n-back is NOT about remembering n number of visual and auditory stimuli. It’s about developing a new mental process that intuitively recognizes when it has seen or heard a stimuli n times ago.

Initially, most students of dual n-back want to remember n items as fast as they can so they can conquer the dual-n-back hill. They use their own already developed techniques to help them remember. They may try to hold the images in their head mentally and review them every time a new image is added and say the sounds out loud and review the sounds every time a new sound is added. This is NOT what we want. We want the brain to learn a new process that intuitively recognizes if an item and sound was shown 3 back or 4 back. It’s sort of like playing a new type of musical instrument.

I’ve helped some students on the site try to understand this. It’s not about how much you can remember, it’s about learning a new process. In theory, this new process translates into a better working memory, which helps you make connections better and faster.

Other N-backers think that intuition can’t work, or at least doesn’t very well:

I don’t believe that much in the “intuitive” method. I mean, sure, you can intuitively remember you heard the same letter or saw the square at the same position a few times ago, but I fail to see how you can “feel” it was exactly 6 or 7 times ago without some kind of “active” remembering. –Gaël DEEST

I totally agree with Gaël about the intuitive method not holding much water…For me a lot of times the intuitive method can be totally unreliable. You’ll be doing 5-back one game and a few games later your failing miserably at 3-back..your score all over the place. Plus, intuitive-wise, it’s best to play the same n-back level over and over because then you train your intuition…and that doesn’t seem right. –MikeM (same thread)

Few N-backers have systematically tracked intuitive versus strategic playing; DarkAlrx reports on his blog the results of his experiment, and while he considers them positive, others find them inconclusive, or like Pheonexia, even unfavorable for the intuitive approach:

Looking at your graphs and the overall drop in your performance, I think it’s clear that intuitive doesn’t work. On your score sheet, the first picture, using the intuitive method over 38 days of TNB training in 44 days your average n-back increased by less than .25. You were performing much better before. With your neurogenesis experiment, your average n-back actually decreased.

Jaeggi herself was more moderate in ~2008:

I would NOT recommend you [train the visual and auditory task separately] if you want to train the dual-task (the one we used in our study). The reason is that the combination of both modalities is an entirely different task than doing both separately! If you do the task separately, I assume you use some “rehearsal strategies”, eg. you repeat the letters or positions for yourself. In the dual-task version however, these strategies might be more difficult to apply (since you have to do 2 things simultaneously…), and that is exactly what we want… We don’t want to train strategies, we want to train processes. Processes that then might help you in the performance of other, non-trained tasks (and that is our ultimate goal). So, it is not important to reach a 7- or 8-back… It is important to fully focus your attention on the task as well as possible.

I can assure you, it is a very tough training regimen…. You can’t divert your attention even 1 second (I’m sure you have noticed…). But eventually, you will see that you get better at it and maybe you notice that you are better able to concentrate on certain things, to remember things more easily, etc. (hopefully).

(Unfortunately, doubt has been cast on this advice by the apparent effectiveness of single n-back in Jaeggi2010. If single (visual/position) n-back is effective in increasing IQ, then maybe training just audio or just visual is actually a good idea.)

this is a question i am being asked a lot and unfortunately, i don’t really know whether i can help with that. i can only tell you what we tell (or rather not tell) our participants and what they tell us. so, first of all, we don’t tell people at all what strategy to use - it is up to them. thing is, there are some people that tell us what you describe above, i.e. some of them tell us that it works best if they don’t use a strategy at all and just “let the squares/letters flow by”. but of course, many participants also use more conscious strategies like rehearsing or grouping items together. but again - we let people chose their strategies themselves! ref

But it may make no difference. Even if you are engaged in a complex mnemonic-based strategy, you’re still working your memory. Strategies may not work; quoting from Jaeggi’s 2008_16ya paper:

By this account, one reason for having obtained transfer between working memory and measures of g_f is that our training procedure may have facilitated the ability to control attention. This ability would come about because the constant updating of memory representations with the presentation of each new stimulus requires the engagement of mechanisms to shift attention. Also, our training task discourages the development of simple task-specific strategies that can proceed in the absence of controlled allocation of attention.

Even if they do, they may not be a good idea; quoting from Jaeggi2010:

We also proposed that it is important that participants only minimally learn task-specific strategies in order to prevent specific skill acquisition. We think that besides the transfer to matrix reasoning, the improvement in the near transfer measure provides additional evidence that the participants trained on task-underlying processes rather than relying on material-specific strategies.

Hopefully even if a trick lets you jump from 3-back to 5-back, Brain Workshop will just keep escalating the difficulty until you are challenged again. It’s not the level you reach, but the work you do.

Spacing

Penner et al 2012

This study compared a high intensity working memory training (45 minutes, 4 times per week for 4 weeks) with a distributed training (45 minutes, 2 times per week for 8 weeks) in middle-aged, healthy adults…Our results indicate that the distributed training led to increased performance in all cognitive domains when compared to the high intensity training and the control group without training. The most significant differences revealed by interaction contrasts were found for verbal and visual working memory, verbal short-term memory and mental speed.

This is reminiscent of sleep’s involvement in other forms of memory and cognitive change, and Kuriyama2008.

Hardcore

Curtis Warren has noticed that when he underwent a 4-day routine of practicing more than 4 hours a day, he jumped an entire level on even quad N-back⁵⁸:

For example, over the past week I have been trying a new training routine. My goal was to increase my intelligence as quickly as possible. To that end, over the past 4 days I’ve done a total of roughly 360 sessions @ 2 seconds per trial (≈360 minutes of training). I had to rest on Wednesday, and I’m resting again today (I only plan on doing about 40 trials today). But I intend to finish off the week by doing 100 sessions on Saturday and another 100 on Sunday. Or more, if I can manage it.

But he cautions us that besides being a considerable time investment, it may only work for him:

The point is, while I can say without a doubt that this schedule has been effective for me, it might not be effective for you. Are the benefits worth the amount of work needed? Will you even notice an improvement? Is this healthy? These are all factors which depend entirely upon the individual actually doing the training.

Raman started DNB training, and in his first 30 days, he “took breaks every 5 days or so, and was doing about 20-30 session each day and n-back wise I made good gains (2 → 7 touching 9 on the way).”; he kept a journal on the mailing list about the experience with daily updates.

Alas, neither Raman nor Warren took an IQ or digit-span test before starting, so they can only report DNB level increases & subjective assessments.

The research does suggest that diminishing returns does not set in with training regimes of 10 or 15 minutes a day; for example, Nutley2011 trained 4-year-olds in WM exercises, g_f (NVR) exercises, or both:

…These analyses took into account that the groups differed in the amount of training received, full dose for NVR or WM groups or half dose for the CB group (Table 3). Even though the pattern is not consistent across all tests (see Figure 2), this is interpreted as confirmation of the linear dose effect that was expected to be seen. Our results suggest that the amount of transfer to non-trained tasks within the trained construct was roughly proportionate to the amount of training on that construct. A similar finding, with transfer proportional to amount of training, was reported by Jaeggi et al 2008. This has possible implications for the design of future cognitive training paradigms and suggests that the training should be intensive enough to lead to significant transfer and that training more than one construct does not entail any advantages in itself. The training effect presumably reaches asymptote, but where this occurs is for future studies to determine. It is probably important to ensure that participants spend enough time on each task in order to see clinically-significant transfer, which may be difficult when increasing the number of tasks being trained. This may be one of the explanations for the lack of transfer seen in the Owen et al. study (2010_14ya) (training six tasks in 10 minutes).

Jaeggi2005

“Capacity Limitations in Human Cognition: Behavioral and Biological Contributions”; Jaeggi thesis:

…Experiment 6 and 7 finally tackle the issue, whether capacity limitations are trait-like, ie. fixed, or whether it is be possible to extend these limitations with training and whether generalized effects on other domains can be observed. In the last section, all the findings are integrated and discussed, and further issues remaining to be investigated are pointed out.

…In this experiment [6], the effects of a 10-day training of an adaptive version of an n-back dual task were studied. The adaptive version should be very directly depending on the actual performance of the participant: Not being too easy, but also not too difficult; always providing a sense of achievement in the participant in order to keep the motivation high. Comparing pre and post measures, effects on the task itself were evaluated, but also effects on other WM measures, and on a measure of fluid intelligence.

…As stated before, this study [7] was conducted in order to replicate and extend the findings of Experiment 6: I was primarily interested to see whether an asymptotic curve regarding performance would be reached after nearly twice of the training sessions used in Experiment 6, and further, whether generalized and differential effects on various cognitive tasks could be obtained with this training. Therefore, more tasks were included compared to Experiment 6, covering many aspects of WM (ie. verbal tasks, visuospatial tasks), executive functions, as well as control tasks not used in Experiment 6 in order to investigate whether the WM training has a selective effect on tasks which are related to the concept of WM and executive functions with no effect on these control tasks. With respect to fluid intelligence, a more appropriate task than the APM, ie. the ‘Bochumer Matrizentest’ (BOMAT; Hossiep, Turck, & Hasella, 1999_25ya) was used, which has the advantage that full parallel-versions are available and that the task was explicitly developed in order not to yield ceiling effects in student samples. The experiment was carried out together with Martin Buschkuehl and Daniela Blaser; the latter writing her Master thesis on the topic.

Qiu2009

“Study on Improving Fluid Intelligence through Cognitive Training System Based on Gabor Stimulus”, 2009_15ya First International Conference on Information Science and Engineering, abstract:

General fluid intelligence (g_f) is a human ability to reason and solve new problems independently of previously acquired knowledge and experience. It is considered one of the most important factors in learning. One of the issues which academic people concentrates on is whether g_f of adults can be improved. According to the Dual N-back working memory theory and the characteristics of visual perceptual learning, this paper put forward cognitive training pattern based on Gabor stimuli. A total of 20 undergraduate students at 24 years old participated in the experiment, with ten training sessions for ten days. Through using Raven’s Standard Progressive Matrices as the evaluation method to get and analyze the experimental results, it was proved that training pattern can improve fluid intelligence of adults. This will promote a wide range of applications in the field of adult intellectual education.

Discussion and criticism of this Chinese⁶⁰⁶¹ paper took place in 2 threads; the SPM was administer in 25 minutes, which while not as fast as Jaeggi 2008_16ya, is still not the normal length. An additional anomaly is that according to the final graph, the control group’s IQ dropped massively in the post-test (driving much of the improvement). As part of my meta-analysis, I tried to contact the 4 authors in May, June, July & September 2012_12ya; they eventually replied with data.

Polar (June 2009_15ya)

A group member, polar, conducted a small experiment at his university where he was a student; his results seemed to show an improvement. As polar would be the first to admit, the attrition in subjects (few to begin with), relatively short time of training and whatnot make the power of his study weak.

Jaeggi2010

“The relationship between n-back performance and matrix reasoning - implications for training and transfer”, Jaeggi et al (coded as Jaeggi2 in meta-analysis); abstract:

…In the first study, we demonstrated that dual and single n-back task performances are approximately equally correlated with performance on two different tasks measuring g_f, whereas the correlation with a task assessing working memory capacity was smaller. Based on these results, the second study was aimed on testing the hypothesis that training on a single n-back task yields the same improvement in g_f as training on a dual n-back task, but that there should be less transfer to working memory capacity. We trained two groups of students for four weeks with either a single or a dual n-back intervention. We investigated transfer effects on working memory capacity and g_f comparing the two training groups’ performance to controls who received no training of any kind. Our results showed that both training groups improved more on g_f than controls, thereby replicating and extending our prior results.

The 2 studies measured g_f using Raven’s APM and the BOMAT. In both studies, the tests were administered speeded to 10 or 15 minutes as in Jaeggi 2008_16ya. The experimental groups saw average gains of 1 or 2 additional correct answers on the BOMAT and APM. It’s worth noting that the Single N-Back was done with a visual modality (and the DNB with the standard visual & audio).

Followup work:

• Schneiders et al 2012 trained audio WM and found no transfer to visual WM tasks; unfortunately, they did not measure any far transfer tasks like RAPM/BOMAT.

• Beavon2012 reports n = 47, experimentals trained on single n-back & controls on “combined verbal tasks Definetime and Who wants to be a millionaire (Millionaire)”; no improvements on “STM span and attention, short term auditory memory span and divided attention, and WM as operationalised through the Woodcock-Johnson III: Tests of cognitive abilities (WJ-III)”.

Stephenson2010

“Does training to increase working memory capacity improve fluid intelligence?”:

The current study was successful in replicating Jaeggi et al.’s (2008_16ya) results. However, the current study also observed improvements in scores on the Raven’s Advanced Progressive Matrices for participants who completed a variation of the dual n-back task or a short-term memory task training program. Participants’ scores improved significantly for only two of the four tests of GJ, which raises the issue of whether the tests measure the construct g_f exclusively, as defined by Cattell (1963_61ya), or whether they may be sensitive to other factors. The concern is whether the training is actually improving g_f or if the training is improving attentional control and/or visuospatial skills, which improves performance on specific tests of g_f. The findings are discussed in terms of implications for conceptualizing and assessing g_f.

136 participants split over 25-28 subjects in experimental groups and the control group. Visual n-back improved more than audio n-back; the control group was a passive control group (they did nothing but served as controls for test-retest effects).

Jaeggi2011

Jaeggi, Buschkuehl, Jonides & Shah 2011_13ya “Short- and long-term benefits of cognitive training” (coded as Jaeggi3 in the meta-analysis); the abstract:

We trained elementary and middle school children by means of a videogame-like working memory task. We found that only children who considerably improved on the training task showed a performance increase on untrained fluid intelligence tasks. This improvement was larger than the improvement of a control group who trained on a knowledge-based task that did not engage working memory; further, this differential pattern remained intact even after a 3-mo hiatus from training. We conclude that cognitive training can be effective and long-lasting, but that there are limiting factors that must be considered to evaluate the effects of this training, one of which is individual differences in training performance. We propose that future research should not investigate whether cognitive training works, but rather should determine what training regimens and what training conditions result in the best transfer effects, investigate the underlying neural and cognitive mechanisms, and finally, investigate for whom cognitive training is most useful.

(This paper is not to be confused with the 2011_13ya poster, “Working Memory Training and Transfer to g_f. Evidence for Domain Specificity?”, Jaeggi et al 2011_13ya.)

It is worth noting that the study used Single N-back (visual). Unlike Jaeggi 2008_16ya, “despite the experimental group’s clear training effect, we observed no significant group × test session interaction on transfer to the measures of g_f. (so perhaps the training was long enough for subjects to hit their ceilings). The group which did n-back could be split, based on final IQ & n-back scores, into 2 groups; interestingly”Inspection of n-back training performance revealed that there were no group differences in the first 3 wk of training; thus, it seems that group differences emerge more clearly over time [first 3 wk: t(30) < 1; P = ns; last week: t(16) = 3.00; P < 0.01] (Fig. 3).” 3 weeks is ~21 days, or >19 days (the longest period in Jaeggi 2008_16ya). It’s also worth noting that Jaeggi 2011_13ya seems to avoid Moody’s most cogent criticism, the speeding of the IQ tests; from the paper’s “Material and Methods” section;

We assessed matrix reasoning with two different tasks, the Test of Nonverbal Intelligence (TONI) (23) and Raven’s Standard Progressive Matrices (SPM) (24). Parallel versions were used for the pre, post-, and follow-up test sessions in counterbalanced order. For the TONI, we used the standard procedure (45 items, five practice items; untimed), whereas for the SPM, we used a shortened version (split into odd and even items; 29 items per version; two practice items; timed to 10 min after completion of the practice items. Note that virtually all of the children completed this task within the given timeframe).

The IQ results were, specifically, the control group averaged 15.33/16.20 (before/after) correct answers on the SPM and 20.87/22.50 on the TONI; the n-back group averaged 15.44/16.94 SPM and 20.41/22.03 TONI. 1.5 more right questions rather than ~1 may not seem like much, but the split groups look quite different - the ‘small training gain’ n-backing group actually fell on its second SPM and improved by <0.2 questions on the TONI, while the ‘large training gain’ increased >3 questions on the SPM and TONI. The difference is not so dramatic in the followup 3 months later: the small group is now 17.43/23.43 (SPM/TONI), and the large group 15.67/24.67. Strangely in the followup, the control group has a higher SPM than the large group (but not the small group), and a higher TONI than either group; the control group has higher IQ scores on both TONI & SPM in the followup than the aggregate n-back group. (The splitting of groups is also unorthodox⁶³.)

UoM produced a video with Jonides; Jaeggi 2011_13ya has also been discussed in mainstream media. From the Wall Street Journal’s “Boot Camp for Boosting IQ”:

…when several dozen elementary- and middle-school kids from the Detroit area used this exercise for 15 minutes a day, many showed significant gains on a widely used intelligence test. Most impressive, perhaps, is that these gains persisted for three months, even though the children had stopped training…these schoolchildren showed gains in fluid intelligence roughly equal to five IQ points after one month of training…There are two important caveats to this research. The first is that not every kid showed such dramatic improvements after training. Initial evidence suggests that children who failed to increase their fluid intelligence found the exercise too difficult or boring and thus didn’t fully engage with the training.

From Discover’s blogs, “Can intelligence be boosted by a simple task? For some…”, come additional details:

She [Jaeggi] recruited 62 children, aged between seven and ten. While half of them simply learned some basic general knowledge questions, the other half trained with a cheerful computerised n-back task. They saw a stream of images where a target object appeared in one of six locations - say, a frog in a lily pond. They had to press a button if the frog was in the same place as it was two images ago, forcing them to store a continuously updated stream of images in their minds. If the children got better at the task, this gap increased so they had to keep more images in their heads. If they struggled, the gap was shortened.

Before and after the training sessions, all the children did two reasoning tests designed to measure their fluid intelligence. At first, the results looked disappointing. On average, the n-back children didn’t become any better at these tests than their peers who studied the knowledge questions. But according to Jaeggi, that’s because some of them didn’t take to the training. When she divided the children according to how much they improved at the n-back task, she saw that those who showed the most progress also improved in fluid intelligence. The others did not. Best of all, these benefits lasted for 3 months after the training. That’s a first for this type of study, although Jaeggi herself says that the effect is “not robust.” Over this time period, all the children showed improvements in their fluid intelligence, “probably [as] a result of the natural course of development”.

…Philip Ackerman, who studies learning and brain training at the University of Illinois, says, “I am concerned about the small sample, especially after splitting the groups on the basis of their performance improvements.” He has a point - the group that showed big improvements in the n-back training only included 18 children….Why did some of the children benefit from the training while others did not? Perhaps they were simply uninterested in the task, no matter how colourfully it was dressed up with storks and vampires. In Jaeggi’s earlier study with adults, every volunteer signed up themselves and were “intrinsically motivated to participate and train.” By contrast, the kids in this latest study were signed up by their parents and teachers, and some might only have continued because they were told to do so.

It’s also possible that the changing difficulty of the game was frustrating for some of the children. Jaeggi says, “The children who did not benefit from the training found the working memory intervention too effortful and difficult, were easily frustrated, and became disengaged. This makes sense when you think of physical training - if you don’t try and really run and just walk instead, you won’t improve your cardiovascular fitness.” Indeed, a recent study on IQ testing which found that they reflect motivation as well as intelligence.

Schweizer Et Al 2011

“Extending Brain-Training to the Affective Domain: Increasing Cognitive and Affective Executive Control through Emotional Working Memory Training”:

This study investigated whether brain-training (working memory [WM] training) improves cognitive functions beyond the training task (transfer effects), especially regarding the control of emotional material since it constitutes much of the information we process daily. Forty-five participants received WM training using either emotional or neutral material, or an undemanding control task. WM training, regardless of training material, led to transfer gains on another WM task and in fluid intelligence. However, only brain-training with emotional material yielded transferable gains to improved control over affective information on an emotional Stroop task. The data support the reality of transferable benefits of demanding WM training and suggest that transferable gains across to affective contexts require training with material congruent to those contexts. These findings constitute preliminary evidence that intensive cognitively demanding brain-training can improve not only our abstract problem-solving capacity, but also ameliorate cognitive control processes (eg. decision-making) in our daily emotive environments.

Notes:

1. There seems to be an IQ increase of around one question on the RPM (but there’s an oddity with the control group which they think they correct for⁶⁴)

2. The RPM does not seem to have been administered speeded⁶⁵

3. The emotional aspect seems to be just replacing the ‘neutral’ existing stimuli like colors or letters or piano keys with more loaded ones⁶⁶, nor does this tweak seem to change the DNB/WM/IQ scores of that group⁶⁷

Their later study “Training the Emotional Brain: Improving Affective Control through Emotional Working Memory Training” did not use any measure of fluid intelligence.

Kundu Et Al 2011

“Relating individual differences in short-term memory-derived EEG to cognitive training effects” (coded as Kundu1 in the meta-analysis); 3 controls (Tetris), 3 experimentals (Brain Workshop) for 1000_1,024ya minutes. RAPM showed a slight increase. Extremely small experimental size, which may form part of the data for Kundu et al 2012.

Zhong2011

“The Effect Of Training Working Memory And Attention On Pupils’ Fluid Intelligence” (abstract), Zhong 2011_13ya; original encrypted file (8M), screenshots of all pages in thesis (20M); discussion

Appears to have found IQ gains, but no dose-response effect, using a no-contact control group. Difficult to understand: translation assistance from Chinese speakers would be appreciated.

Jausovec2012

“Working memory training: Improving intelligence - Changing brain activity”, Jaušovec 2012_12ya:

The main objectives of the study were: to investigate whether training on working memory (WM) could improve fluid intelligence, and to investigate the effects WM training had on neuroelectric (electroencephalography - EEG) and hemodynamic (near-infrared spectroscopy - NIRS) patterns of brain activity. In a parallel group experimental design, respondents of the working memory group after 30 h of training significantly increased performance on all tests of fluid intelligence. By contrast, respondents of the active control group (participating in a 30-h communication training course) showed no improvements in performance. The influence of WM training on patterns of neuroelectric brain activity was most pronounced in the theta and alpha bands. Theta and lower-1 alpha band synchronization was accompanied by increased lower-2 and upper alpha desynchronization. The hemodynamic patterns of brain activity after the training changed from higher right hemispheric activation to a balanced activity of both frontal areas. The neuroelectric as well as hemodynamic patterns of brain activity suggest that the training influenced WM maintenance functions as well as processes directed by the central executive. The changes in upper alpha band desynchronization could further indicate that processes related to long term memory were also influenced.

14 experimental & 15 controls; the testing was a little unusual:

Respondents solved four test-batteries, for which the procedure was the same during pre- and post-testing. The same test-batteries were used on pre- and post-testing. The digit span subtest (WAIS-R) was administered separately, according to the directions in the test manual (Wechsler, 1981_43ya). The other three tests (RAPM, verbal analogies and spatial rotation) were administered while the respondents’ EEG and NIRS measures were recorded.

The RAPM was based on a modified version of Raven’s progressive matrices (Raven, 1990_34ya), a widely used and well established test of fluid intelligence (Sternberg, Ferrari, Clinkenbeard, & Grigorenko, 1996_28ya). The correlation between this modified version of RAPM and WAIS-R was r = .56, (p < .05, n = 97). Similar correlations of the order of 0.40-0.75, were also reported for the standard version of RAPM (Court & Raven, 1995_29ya). Therefore it can be concluded that the modified application of the RAPM did not significantly alter its metric characteristics. Used were 50 test items - 25 easy (Advanced Progressive Matrices Set I - 12 items and the B Set of the Colored Progressive Matrices), and 25 difficult items (Advanced Progressive Matrices Set II, items 12-36). Participants saw a figural matrix with the lower right entry missing. They had to determine which of the four options fitted into the missing space. The tasks were presented on a computer screen (positioned about 80-100 cm in front of the respondent), at fixed 10 or 14 s interstimulus intervals. They were exposed for 6 s (easy) or 10 s (difficult) following a 2-s interval, when a cross was presented. During this time the participants were instructed to press a button on a response pad (1-4) which indicated their answer.

At 25 hard questions, and <14s a question, that implies the RAPM was administered in <5.8 minutes. They comment:

To further investigate possible influences of task difficulty on the observed performance gains on the RAPM a GLM for repeated measures test/retest  easy/difficult-items  group (WM, AC) was conducted. The analysis showed only a significant interaction effect for the test/retest condition and type of training used in the two groups (F(1, 27) = 4.47; p < .05; partial eta2 = .15). A GLM conducted for the WM group showed only a significant test/retest effect (F(1, 13) = 30.11; p < .05; partial eta2 = .70), but no interaction between the test/retest conditions and the difficulty level (F(1, 13) = 1.79; p = .17 not-significant; partial eta2 = .12). As can be seen in Fig. 4 after WM training an about equal increase in respondents’ performance for the easy and difficult test items was observed. On the other hand, no increases in performance, neither for the easy nor for the difficult test items, in respondents of the active control group were observed (F(1, 14) = .47; p = .50 not- significant; partial eta2 = .03).

(Even on the “easy” questions, no group performed better than 76% accuracy.)

Clouter2013

“The Effects of Dual n-back Training on the Components of Working Memory and Fluid Intelligence: An Individual Differences Approach”, Clouter 2013_11ya:

A number of recent studies have provided evidence that training working memory can lead to improvements in fluid intelligence, working memory span, and performance on other untrained tasks. However, in addition to a number of mixed results, many of these studies suffer from design limitations. The aim of the present study was to experimentally investigate the effects of a dual n-back working memory training task on a variety of measures of fluid intelligence, reasoning, working memory span, and attentional control. The present study compared a training group with an active control group (a placebo group), using appropriate methods that overcame the limitations of previous studies. The dual n-back training group improved more than the active control group on some, but not all outcome measures. Differential improvement for the training group was observed on fluid intelligence, working memory capacity, and response times on conflict trials in the Stroop task. In addition, individual differences in pre-training fluid intelligence scores and initial performance on the training task explain some of the variance in outcome measure improvements. We discuss these results in the context of previous studies, and suggest that additional work is needed in order to further understand the variables responsible for transfer from training.

Jaeggi Et Al 2013

“The role of individual differences in cognitive training and transfer”:

Working memory (WM) training has recently become a topic of intense interest and controversy. Although several recent studies have reported near- and far-transfer effects as a result of training WM-related skills, others have failed to show far transfer, suggesting that generalization effects are elusive. Also, many of the earlier intervention attempts have been criticized on methodological grounds. The present study resolves some of the methodological limitations of previous studies and also considers individual differences as potential explanations for the differing transfer effects across studies. We recruited intrinsically motivated participants and assessed their need for cognition (NFC; Cacioppo & Petty Journal of Personality and Social Psychology 42:116-131, 1982_42ya) and their implicit theories of intelligence (Dweck, 1999_25ya) prior to training. We assessed the efficacy of two interventions by comparing participants’ improvements on a battery of fluid intelligence tests against those of an active control group. We observed that transfer to a composite measure of fluid reasoning resulted from both WM interventions. In addition, we uncovered factors that contributed to training success, including motivation, need for cognition, preexisting ability, and implicit theories about intelligence.

This is quite a complex study, with a lot of analysis I don’t think I entirely understand. The quick summary is table 2 on pg10: the DNB group fell on APM, rose on BOMAT (neither statistically-significant); the SNB group increased on APM & BOMAT (but only BOMAT was statistically-significant).

Michael J. Kane has written some critical comments on the results.

Savage2013

“Near and Far Transfer of Working Memory Training Related Gains in Healthy Adults”, Savage 2013_11ya:

Enhancing intelligence through working memory training is an attractive concept, particularly for middle-aged adults. However, investigations of working memory training benefits are limited to younger or older adults, and results are inconsistent. This study investigates working memory training in middle age-range adults. Fifty healthy adults, aged 30-60, completed measures of working memory, processing speed, and fluid intelligence before and after a 5-week web-based working memory (experimental) or processing speed (active control) training program. Baseline intelligence and personality were measured as potential individual characteristics associated with change. Improved performance on working memory and processing speed tasks were experienced by both groups; however, only the working memory training group improved in fluid intelligence. Agreeableness emerged as a personality factor associated with working memory training related change. Albeit limited by power, findings suggest that dual n-back working memory training not only enhances working memory but also fluid intelligence in middle-aged healthy adults.

The personality correlations seem to differ with Studer-Luethi.

Stepankova Et Al 2013

“The Malleability of Working Memory and Visuospatial Skills: A Randomized Controlled Study in Older Adults”, Stepankova et al 2013_11ya:

There is accumulating evidence that training on working memory (WM) generalizes to other nontrained domains, and there are reports of transfer effects extending as far as to measures of fluid intelligence. Although there have been several demonstrations of such transfer effects in young adults and children, they have been difficult to demonstrate in older adults. In this study, we investigated the generalizing effects of an adaptive WM intervention on nontrained measures of WM and visuospatial skills. We randomly assigned healthy older adults to train on a verbal n-back task over the course of a month for either 10 or 20 sessions. Their performance change was compared with that of a control group. Our results revealed reliable group effects in nontrained standard clinical measures of WM and visuospatial skills in that both training groups outperformed the control group. We also observed a dose-response effect, that is, a positive relationship between training frequency and the gain in visuospatial skills; this finding was further confirmed by a positive correlation between training improvement and transfer. The improvements in visuospatial skills emerged even though the intervention was restricted to the verbal domain. Our work has important implications in that our data provide further evidence for plasticity of cognitive functions in old age.

Horvat2014

“The effect of working memory training on cognitive abilities”, Horvat2014; Slovenian, English abstract:

In the last few years, there is a growing evidence in psychological literature indicating that working memory training could serve as a useful tool to improve performance on non-trained tasks that measure higher cognitive abilities; however, results of different studies remain inconsistent. The aim of the present master thesis was to discover whether working memory training could improve short-term memory capacity and increase test scores on test of fluid intelligence in normal developing children.

Final sample consisted of 29 participants, between 13 to 15 years old; 14 of them were in experimental group, 15 was controls. Experimental group completed series of ten working memory trainings, based on adaptive dual n-back task. Control group was passive and did not do any training in the meantime.

Results of our study showed that all participants in experimental group improved their performance on trained task. There was no statistically-significant effect of experimental group on measures of digit span and visuospatial memory span before and after training, when comparing with performance of control group. However, experimental group improved more on measure of fluid intelligence compared with control group.

Finding of our study suggest the importance of investigating factors associated with effectiveness of working memory training in future research.

Heinzel Et Al 2016

“Neural correlates of training and transfer effects in working memory in older adults”, Heinzel et al 2016:

As indicated by previous research, aging is associated with a decline in working memory (WM) functioning, related to alterations in fronto-parietal neural activations. At the same time, previous studies showed that WM training in older adults may improve the performance in the trained task (training effect), and more importantly, also in untrained WM tasks (transfer effects). However, neural correlates of these transfer effects that would improve understanding of its underlying mechanisms, have not been shown in older participants as yet. In this study, we investigated blood-oxygen-level-dependent (BOLD) signal changes during n-back performance and an untrained delayed recognition (Sternberg) task following 12 sessions (45 minutes each) of adaptive n-back training in older adults. The Sternberg task used in this study allowed to test for neural training effects independent of specific task affordances of the trained task and to separate maintenance from updating processes. Thirty-two healthy older participants (60-75 years) were assigned either to an n-back training or a no-contact control group. Before (t1) and after (t2) training/waiting period, both the n-back task and the Sternberg task were conducted while BOLD signal was measured using functional Magnetic Resonance Imaging (fMRI) in all participants. In addition, neuropsychological tests were performed outside the scanner. WM performance improved with training and behavioral transfer to tests measuring executive functions, processing speed, and fluid intelligence was found. In the training group, BOLD signal in right lateral middle frontal gyrus/ caudal superior frontal sulcus (Brodmann area, BA 6/8) decreased in both the trained n-back and the updating condition of the untrained Sternberg task at t2, compared to the control group. FMRI findings indicate a training-related increase in processing efficiency of WM networks, potentially related to the process of WM updating. Performance gains in untrained tasks suggest that transfer to other cognitive tasks remains possible in aging.

Moody 2009_15ya (re: Jaeggi 2008_16ya)

Jaeggi2008, you may remember, showed that training on N-back improved working memory, but it also boosted scores on tests of g_f. The latter would be a major result - indeed, unique - and is one of the main research results encouraging people to do N-back in a non-research setting. People want to believe that N-back is efficacious and particularly that it will do more than boost working memory. So we need to be wary of confirmation bias (for those of you who read too much fantasy, you’ll know this as Wizard’s First Rule).

Fortunately, we can discuss at length the work of one David E. Moody who has published a criticism of how the odd methodology of Jaeggi 2008_16ya undermines this result. He’s worth quoting at length, since besides being important to understanding Jaeggi’s study, it’s an interesting example of how subtle issues can be important in psychology:

“The subjects were divided into four groups, differing in the number of days of training they received on the task of working memory. The group that received the least training (8 days) was tested on Raven’s Advanced Progressive Matrices (Raven, 1990_34ya), a widely used and well-established test of fluid intelligence. This group, however, demonstrated negligible improvement between pre- and post-test performance.

The other three groups were not tested using Raven’s Matrices, but rather on an alternative test of much more recent origin. The Bochumer Matrices Test (BOMAT) (Hossiep, Turck, & Hasella, 1999_25ya) is similar to Raven’s in that it consists of visual analogies. In both tests, a series of geometric and other figures is presented in a matrix format and the subject is required to infer a pattern in order to predict the next figure in the series. The authors provide no reason for switching from Raven’s to the BOMAT.

The BOMAT differs from Raven’s in some important respects, but is similar in one crucial attribute: both tests are progressive in nature, which means that test items are sequentially arranged in order of increasing difficulty. A high score on the test, therefore, is predicated on subjects’ ability to solve the more difficult items.

However, this progressive feature of the test was effectively eliminated by the manner in which Jaeggi et al. administered it. The BOMAT is a 29-item test which subjects are supposed to be allowed 45 min to complete. Remarkably, however, Jaeggi et al. reduced the allotted time from 45 min to 10. The effect of this restriction was to make it impossible for subjects to proceed to the more difficult items on the test. The large majority of the subjects-regardless of the number of days of training they received-answered less than 14 test items correctly.
By virtue of the manner in which they administered the BOMAT, Jaeggi et al. transformed it from a test of fluid intelligence into a speed test of ability to solve the easier visual analogies. The time restriction not only made it impossible for subjects to proceed to the more difficult items, it also limited the opportunity to learn about the test-and so improve performance-in the process of taking it. This factor cannot be neglected because test performance does improve with practice, as demonstrated by the control groups in the Jaeggi study, whose improvement from pre- to post-test was about half that of the experimental groups. The same learning process that occurs from one administration of the test to the next may also operate within a given administration of the test-provided subjects are allowed sufficient time to complete it.

Since the whole weight of their conclusion rests upon the validity of their measure of fluid intelligence, one might assume the authors would present a careful defense of the manner in which they administered the BOMAT. Instead they do not even mention that subjects are normally allowed 45 min to complete the test. Nor do they mention that the test has 29 items, of which most of their subjects completed less than half.

The authors’ entire rationale for reducing the allotted time to 10 min is confined to a footnote. That footnote reads as follows:

Although this procedure differs from the standardized procedure, there is evidence that this timed procedure has little influence on relative standing in these tests, in that the correlation of speeded and non-speeded versions is very high (r = 0.95; ref. 37).

The reference given in the footnote is to a 1988_36ya study (Frearson & Eysenck 1986) that is not in fact designed to support the conclusion stated by Jaeggi et al. The 1988_36ya study merely contains a footnote of its own, which refers in turn to unpublished research conducted forty years earlier. That research involved Raven’s matrices, not the BOMAT, and entailed a reduction in time of at most 50%, not more than 75%, as in the Jaeggi study.

So instead of offering a reasoned defense of their procedure, Jaeggi et al. provide merely a footnote which refers in turn to a footnote in another study. The second footnote describes unpublished results, evidently recalled by memory over a span of 40 years, involving a different test and a much less severe reduction in time.

In this context it bears repeating that the group that was tested on Raven’s matrices (with presumably the same time restriction) showed virtually no improvement in test performance, in spite of eight days’ training on working memory. Performance gains only appeared for the groups administered the BOMAT. But the BOMAT differs in one important respect from Raven’s. Raven’s matrices are presented in a 3 × 3 format, whereas the BOMAT consists of a 5 × 3 matrix configuration.

With 15 visual figures to keep track of in each test item instead of 9, the BOMAT puts added emphasis on subjects’ ability to hold details of the figures in working memory, especially under the condition of a severe time constraint. Therefore it is not surprising that extensive training on a task of working memory would facilitate performance on the early and easiest BOMAT test items-those that present less of a challenge to fluid intelligence.

This interpretation acquires added plausibility from the nature of one of the two working-memory tasks administered to the experimental groups. The authors maintain that those tasks were “entirely different” from the test of fluid intelligence. One of the tasks merits that description: it was a sequence of letters presented auditorily through headphones.

But the other working-memory task involved recall of the location of a small square in one of several positions in a visual matrix pattern. It represents in simplified form precisely the kind of detail required to solve visual analogies. Rather than being “entirely different” from the test items on the BOMAT, this task seems well-designed to facilitate performance on that test.”

Sternberg reviewed Jaeggi2008

Email from Jaeggi to Pontus about the timelimit; visual problems bias RPM against women who have a slightly lower average visuospatial performance.

Nutley2011 discusses why one test may be insufficient when an experimental intervention is done:

Since the definition of g_f itself stems from factor analytical methods, using the shared variance of several tests to define the g_f factor, a similar method should be used to measure gains in g_f. Another issue raised by Sternberg (2008_16ya) is that the use of only one single training task makes it difficult to infer if the training effect was due to some specific aspect of the task rather than the general effect of training a construct.

Shipstead, Redick, & Engle 2012_12ya elaborate on how, while matrix-style IQ tests are considered gold standards, they are not perfect measures of IQ such that an increase in performance must reflect an increase in underlying intelligence:

…far transfer tasks are not perfect measures of ability. In many training studies, Raven’s Progressive Matrices (Ravens; Raven, 1990_34ya, 1995_29ya, 1998_26ya) serves as the sole indicator of g_f. This “matrix reasoning” task presents test takers with a series of abstract pictures that are arranged in a grid. One piece of the grid is missing, and the test taker must choose an option (from among several) that completes the sequence. Jensen1998 estimates that 64% of the variance in Ravens performance can be explained by g_f. Similarly, Figure 3 indicates that in the study of Kane et al 2004_20ya, 58% of the Ravens variance was explained by g_f. It is clear that Ravens is strongly related to g_f. However, 30%-40% of the variance in Ravens is attributable to other influences. Thus, when Ravens (or any other task) serves as the sole indicator of far transfer, performance improvements can be explained without assuming that a general ability has changed. Instead, it can be parsimoniously concluded that training has influenced something that is specific to performing Ravens, but not necessarily applicable to other reasoning contexts (Carroll, 1993_31ya; Jensen, 1998_26ya; Moody, 2009_15ya; Schmiedek et al 2010_14ya; te Nijenhuis, van Vianen, & van der Flier, 2007_17ya).

…Preemption of criticisms such as Moody’s (2009_15ya) is, however, readily accomplished through demonstration of transfer to several measures of an ability. Unfortunately, the practice of equating posttest improvement on one task with change to cognitive abilities is prevalent within the WM training literature (cf. Jaeggi et al 2008_16ya; Klingberg, 2010_14ya). This is partially driven by the time and monetary costs associated with conducting multisession, multiweek studies. Regardless, training studies can greatly improve the persuasiveness of their results by measuring transfer via several tasks that differ in peripheral aspects but converge on an ability of interest (eg. a verbal, g_f. and spatial task from Figure 3). If a training effect is robust, it should be apparent in all tasks.

Explicit attempts at measuring speeding:

• Chuderski 2013_11ya, “When are fluid intelligence and working memory isomorphic and when are they not?”

• Colom et al 2015, “Fluid intelligence and working memory capacity: Is the time for working on intelligence problems relevant for explaining their large relationship?”

• Chuderski2015, “The broad factor of working memory is virtually isomorphic to fluid intelligence tested under time pressure”

Seidler2010

“Cognitive Training As An Intervention To Improve Driving Ability In The Older Adult”, a technical report by a group which includes Susanne Jaeggi, studied the effect of DNB on the driving ability of younger/older adults. As part of the before/after test battery, a Raven’s was administered:

Type 2 tests included Raven’s matrices (Raven et al 1990_34ya), which is a standardized test of fluid intelligence, and the BOMAT and verbal analogies tests of intelligence (Hossiep et al 1995_29ya). We have previously shown that working memory training transfers to performance on this task (Jaeggi et al 2008_16ya), and we included it here for the sake of replication.

They found the null result:

There were no significant group by test session interactions for the intelligence measures or complex motor tasks for the young adults, although one of the intelligence measures exhibited a trend for transfer effects that scaled with training task gains.

…Unlike in our previous work (Jaeggi et al 2008_16ya) we did not observe transfer to measures of intelligence. This may have been a by-product of the rather extensive pre and post test battery of assessments that we performed, particularly given that one of the intelligence measures was always performed last in the sequence of tests. Given this, participants may have been too fatigued and / or unmotivated to perform these tests well.

Jonasson2011

“Investigating training and transfer in complex tasks with dual n-back”, bachelor degree thesis:

No clear consensus exists in the scientific community of what constitutes efficient dual-tasking abilities. Moreover, the training of executive components has been given increased attention in the literature in recent years. Investigating transferability of cognitive training in a complex task setting, thirty subjects practiced for five days on a Name-Tag task (controls) or a Dual n-Back task (experimental), subsequently being tested on two transfer tasks; the Automated Operation Span and a dual task (Trail Making task + Mathematical Addition task). Dual n-Back training previously transferred to unrelated intelligence tests and in this study is assumed to rely primarily on executive attention. Executive attention, functioning to resolve interference and maintaining task-relevant information in working memory, has previously been linked to fluid intelligence and to dual-tasking. However, no transfer effects were revealed. The length of training may have been too short to reveal any such effects. However, the three complex tasks correlated significantly, suggesting common resources, and therefore having potentials as transfer tasks. Notably, subjects with the highest task-specific improvements performed worse on the transfer tasks than subjects improving less, suggesting that task-specific gains do not directly correlate with any transfer effect. At present, if transfer exists in these settings, data implies that five days of training is insufficient for a transfer to occur. Important questions for future research relates to the necessary conditions for transfer to occur, such as the amount of training, neural correlates, attention, and motivation.

Caveats for this study:

1. It did not attempt to measure any form of g_f

2. It used 30 total subjects, or 15 in each group

3. Training was over 5-6 days, 16-20 minutes each day (although the DNB subjects did increase their scores), which may not be enough; although Jonasson comments (pg 44-45):

   Nevertheless, training for five days or less has also led to significant improvements in performance on transfer tasks (Damos & Wickens, 1980; Kramer et al 1995; Rueda et al 2005). However, the study by Kramer et al 1995_29ya may have transferred a strategy rather than training a specific component, and the study by Rueda et al 2005_19ya found transfer in children between ages four and six, the children possibly being more susceptible to training than adults.

4. Jonasson suggests that subjects were unmotivated, perhaps by the training being done at home on Lumosity.com; only one did the full 6 days of training, and incentives often increase performance on IQ and other tests.

Chooi2011

“Improving Intelligence by Increasing Working Memory Capacity”, PhD thesis:

…The current study aimed to replicate and extend the original study conducted by Jaeggi et al 2008_16ya in a well-controlled experiment that could explain the cause or causes of such transfer if indeed the case. There were a total of 93 participants who completed the study, and they were randomly assigned to one of three groups - a passive control group, active control group and experimental group. Half of the participants were randomly assigned to the 8-day condition and the other half to the 20-day condition. All participants completed a battery of tests at pre- and post-tests that consisted of short timed tests, a complex working memory span and a [untimed] matrix reasoning task. Participants in the active control group practiced for either 8 days or 20 days on the same task as the one used in the experimental group, the dual n-back, but at the easiest level to control for Hawthorne effect. Results from the current study did not suggest any significant improvement in the mental abilities tested, especially fluid intelligence and working memory capacity, after training for 8 days or 20 days. This leads to the conclusion that increasing one’s working memory capacity by training and practice did not transfer to improvement on fluid intelligence as asserted by Jaeggi and her colleagues (2008_16ya, 2010_14ya).

Jonathan Toomim points out a concern about statistical power: the multiple control groups means that the number of subjects doing actual n-backing is small and the null result is only trustworthy if one expects a dramatic effect from n-backing, a huge effect size taken from Jaeggi 2010_14ya (but not Jaeggi2008’s smaller effect size). He comments: “the effect size for DNB training is probably less than 0.98. (Of course, that’s what I believed anyway before I saw this.) The effect size could quite reasonably still be as high as 0.75.” Chooi 2011_13ya seems to have been summarized as Chooi & Thompson2012, which discusses the power issue further:

A major limitation of the study was the small sample size and possibly sample characteristic, which may have lowered the power of analyses conducted. When Jaeggi et al 2010_14ya repeated the study with 25 students who trained on the Raven’s Advanced Progressive Matrices (RAPM) for 20 days, they obtained an effect size (Cohen’s d) of 0.98. Additionally, participants in the Jaeggi et al 2010_14ya study were culturally different from the participants in the current study. Participants from the former study were undergraduates from an university in Taiwan (mean age=19.4), while those from the current study were mostly American students attending a Midwestern university. The current study was designed according to the claims put forth by Jaeggi et al 2008_16ya as a study of replication and extension. In that study, participants were healthy, young adults who were slightly older (mean age=25.6 years) than the current sample (mean age= 20.0), and they were recruited from an university in Bern, Switzerland. Effect sizes obtained from our study for RAPM were not as high as reported by Jaeggi et al (2008_16ya, 2010_14ya) - d = 0.65 and d = 0.98 respectively. With such large effect sizes, the analysis of paired t-test could achieve a power of 0.80 with 10- 12 participants. Referring to Table 4, the highest RAPM effect size (d = 0.50) was from the 8-day passive control group that had 22 participants and this achieved a power of 0.83. The 20-day training group (n = 13) had an effect size of 0.06 in RAPM, and to achieve a power of 0.80 this group would need more than 1700_324ya participants. On the other hand, the effect size from the 20-day active control group with 11 participants was 0.40, and power could be improved by increasing the number of participants to 34. These observations led us to believe that the lack of improvements in the test variables was probably due to a combination of low sample size and differences in sample characteristics, of which participants in our study had restriction of range in intellectual ability.

Preece 2011_13ya / Palmer2011

“The Effect of Working Memory (n-back) Training on Fluid Intelligence”, David Preece 2011_13ya:

The present study replicated and extended these results by testing the fluid intelligence construct using a different type of fluid intelligence test, and employing an ‘active’ rather than ‘no-contact’ control group to account for motivational effects on intelligence test performance. 58 participants were involved and their fluid intelligence was assessed pre-training using the Figure Weights subtest from the Wechsler Adult Intelligence Scale - Fourth Edition (WAIS-IV). Participants were randomly assigned to two groups (experimental or active control), and both groups did a training task on their home computer for 20 days, for 20 minutes a day. The experimental group trained using a single n-back task whilst the control group completed general knowledge and vocabulary questions. After training, participants were retested using the Figure Weights subtest. Participants’ Figure Weights scores were analysed using an analysis of covariance (ANCOVA). The results of this analysis revealed no significant difference between the training groups in terms of performance on the Figure Weights subtest, suggesting that the n-back task was not effective in increasing fluid reasoning ability. These findings were in contrast to those of Jaeggi et al 2008_16ya and Jaeggi et al 2010_14ya and suggested that differences between the working memory group and control group found in these studies were likely the result of placebo/motivational effects rather than the properties of the n-back task itself.

Subjects were also tested on the RAPM pre/post, but that was reported in a separate thesis, Vaughan Palmer’s “Improving fluid intelligence (g_f) though training”, which is not available. I have emailed the supervising professor for more information.

2 closely related theses are “Improving Memory Using N-Back Training”, Beavon 2012_12ya (short-term & working memory); and “Visual Memory Improvement in Recognition”, Prandl 2012_12ya.

Kundu Et Al 2012

“Behavioral and EEG Effects of Working Memory Training” (RAPM supplement); 13 controls and 13 experimentals trained for 1000_1,024ya minutes on dual n-back (Brain Workshop) or Tetris. “Training does not appear to transfer to g_f [RAPM] or complex span [OSPAN].” This is not a published study but a conference poster, so details such as RAPM scores are not included. It may be related to Kundu et al 2011.

Salminen2012

“On the impacts of working memory training on executive functioning”, Salminen & Strobach & Schubert, Frontiers in Human Neuroscience:

Recent studies have reported improvements in a variety of cognitive functions following sole working memory (WM) training. In spite of the emergence of several successful training paradigms, the scope of transfer effects has remained mixed. This is most likely due to the heterogeneity of cognitive functions that have been measured and tasks that have been applied. In the present study, we approached this issue systematically by investigating transfer effects from WM training to different aspects of executive functioning. Our training task was a demanding WM task that requires simultaneous performance of a visual and an auditory n-back task, while the transfer tasks tapped WM updating, coordination of the performance of multiple simultaneous tasks (ie. dual-tasks) and sequential tasks (ie. task switching), and the temporal distribution of attentional processing. Additionally, we examined whether WM training improves reasoning abilities; a hypothesis that has so far gained mixed support. Following training, participants showed improvements in the trained task as well as in the transfer WM updating task. As for the other executive functions, trained participants improved in a task switching situation and in attentional processing. There was no transfer to the dual-task situation or to reasoning skills. These results, therefore, confirm previous findings that WM can be trained, and additionally, they show that the training effects can generalize to various other tasks tapping on executive functions.

Passive control group; unspeeded RAPM test.

Redick Et Al 2012

“No evidence of transfer after working memory training: A controlled, randomized study” (supplement), Redick et al 2012_12ya; abstract:

Numerous recent studies seem to provide evidence for the general intellectual benefits of working memory training. In reviews of the training literature, Shipstead, Redick, and Engle (2010_14ya, in press) argued that the field should treat recent results with a critical eye. Many published working memory training studies suffer from design limitations (no-contact control groups, single measures of cognitive constructs), mixed results (transfer of training gains to some tasks but not others, inconsistent transfer to the same tasks across studies), and lack of theoretical grounding (identifying the mechanisms responsible for observed transfer). The current study compared young adults who received 20 sessions of practice on an adaptive dual n-back program (working memory training group) or an adaptive visual search program (active placebo-control group) with a no-contact control group that received no practice. In addition, all subjects completed pre-test, mid-test, and post-test sessions, comprising multiple measures of fluid intelligence, multitasking, working memory capacity, crystallized intelligence, and perceptual speed. Despite improvements on both the dual n-back and visual search tasks with practice, and despite a high level of statistical power, there was no positive transfer to any of the cognitive ability tests. We discuss these results in the context of previous working memory training research, and address issues for future working memory training studies.

75 subjects; RAPM was speeded.

Rudebeck2012

“A Potential Spatial Working Memory Training Task to Improve Both Episodic Memory and Fluid Intelligence”, Rudebeck et al 2012_12ya:

One current challenge in cognitive training is to create a training regime that benefits multiple cognitive domains, including episodic memory, without relying on a large battery of tasks, which can be time-consuming and difficult to learn. By giving careful consideration to the neural correlates underlying episodic and working memory, we devised a computerized working memory training task in which neurologically healthy participants were required to monitor and detect repetitions in two streams of spatial information (spatial location and scene identity) presented simultaneously (ie. a dual n-back paradigm). Participants’ episodic memory abilities were assessed before and after training using two object and scene recognition memory tasks incorporating memory confidence judgments. Furthermore, to determine the generalizability of the effects of training, we also assessed fluid intelligence using a matrix reasoning task. By examining the difference between pre- and post-training performance (ie. gain scores), we found that the trainers, compared to non-trainers, exhibited a significant improvement in fluid intelligence after 20 days. Interestingly, pre-training fluid intelligence performance, but not training task improvement, was a significant predictor of post-training fluid intelligence improvement, with lower pre-training fluid intelligence associated with greater post-training gain. Crucially, trainers who improved the most on the training task also showed an improvement in recognition memory as captured by d-prime scores and estimates of recollection and familiarity memory. Training task improvement was a significant predictor of gains in recognition and familiarity memory performance, with greater training improvement leading to more marked gains. In contrast, lower pre-training recollection memory scores, and not training task improvement, led to greater recollection memory performance after training. Our findings demonstrate that practice on a single working memory task can potentially improve aspects of both episodic memory and fluid intelligence, and that an extensive training regime with multiple tasks may not be necessary.

Speeded BOMAT (“Due to time restrictions and the possibility of ceiling effects associated with some g_f tests, participants were given 10 minutes to complete as many patterns as they could in each assessment session (for a similar procedure see Jaeggi et al 2008_16ya).”); 55 subjects total, experimentals trained for 400 minutes, passive control group. The improvement predictor sounds like a post hoc analysis and may be something like regression to the mean.

Heinzel Et Al 2013

“Working memory training improvements and gains in non-trained cognitive tasks in young and older adults”, Heinzel et al 2013_11ya:

Previous studies on working memory training have indicated that transfer to non-trained tasks of other cognitive domains may be possible. The aim of this study is to compare working memory training and transfer effects between younger and older adults (n = 60). A novel approach to adaptive n-back training (12 sessions) was implemented by varying the working memory load and the presentation speed. All participants completed a neuropsychological battery of tests before and after the training. On average, younger training participants achieved difficulty level 12 after training, while older training participants only reached difficulty level 5. In younger participants, transfer to Verbal Fluency and Digit Symbol Substitution test was found. In older participants, we observed a transfer to Digit Span Forward, CERAD Delayed Recall, and Digit Symbol Substitution test. Results suggest that working memory training may be a beneficial intervention for maintaining and improving cognitive functioning in old age.

Single n-back; passive control group; no transfer in young or old training group to “Raven’s Standard Progressive Matrices (Raven’s SPM) and the Figural Relations subtest of a German intelligence test (Leistungspruefsystem, LPS, Horn, 1983_41ya)” (increased but sample size is too small to reach statistical-significance in the young group); RPM speeded (7.5 minutes). See pg19 for graphs of the IQ test performance.

Thompson Et Al 2013

“Failure of Working Memory Training to Enhance Cognition or Intelligence”:

…The current study attempted to replicate and expand those results by administering a broad assessment of cognitive abilities and personality traits to young adults who underwent 20 sessions of an adaptive dual n-back working memory training program and comparing their post-training performance on those tests to a matched set of young adults who underwent 20 sessions of an adaptive attentional tracking program. Pre- and post-training measurements of fluid intelligence, standardized intelligence tests, speed of processing, reading skills, and other tests of working memory were assessed. Both training groups exhibited substantial and specific improvements on the trained tasks that persisted for at least 6 months post-training, but no transfer of improvement was observed to any of the non-trained measurements when compared to a third untrained group serving as a passive control. These findings fail to support the idea that adaptive working memory training in healthy young adults enhances working memory capacity in non-trained tasks, fluid intelligence, or other measures of cognitive abilities.

Covariate details:

…Two groups of young adults, stratified so as to be equated on initial fluid IQ scores, were randomly assigned to two conditions (a randomized controlled trial or RCT). The experimental group performed the dual n-back task (as in the original Jaeggi et al 2008 study [6]) for approximately 40 minutes per day, 5 days per week for 4 weeks (20 sessions of 30 blocks per session, exceeding the maximum of 19 sessions of 20 blocks per day in the original study). An active control group performed a visuospatial skill learning task, multiple object tracking (or MOT), on an identical training schedule. We also tested a no-contact group equated for initial fluid IQ in case both kinds of training enhanced cognitive abilities…Participants were given 25 minutes to complete each half of the RAPM…Participants in the training groups were paid $20 per training session, with a $20 bonus per week for completing all five training sessions in that week. All participants were paid $20 per hour for behavioral testing, and $30 per hour for imaging sessions (data from imaging sessions are reported separately)….After recruitment, participants underwent approximately six hours of behavioral testing spread across three days and two hours of structural and functional magnetic resonance imaging. [Thompson says there were additional payments for imaging not mentioned, so the true expected-value of participation was $740.]

In line with my meta-analysis’s null on a dose-response effect:

One method of assessing whether the amount of training improvement affects the degree of transfer is to measure the correlation between training and transfer gains. For both the n-back and MOT groups, a positive correlation was observed between the amount of improvement during training and the amount of improvement on the trained task between the pre- and post-assessment (n-back r = .85, p<.0001; MOT r = .77, p<.0001). However, the amount of training gain did not significantly predict improvement on any transfer task; participants who improved to a greater extent on the training tasks did not improve more or less on potential transfer tasks than did participants who improved to a lesser extent (all n-back r values <.33, all p’s >.15; all MOT r values <.38, all p’s >.11). Figure S2 depicts the absence of a relation between improvement on trained tasks and the post-training changes in the RAPM and the combined span tasks.

Note also that the post hoc split of children into ‘improvers’ and not in that Jaeggi paper does not replicate here either:

Another analysis that has previously revealed a difference in transfer between participants who exhibited larger or smaller training gains has been a division of participants into groups based on training gains above or below the group median (median split) [15]. Such a median split of participants in the present study who performed the n-back training yielded no significant differences in transfer between groups (all n-back t-ratios <1.78, all p’s >.09). The only transfer measure that approached significance (at p = .09) was on the RAPM test, in which the participants who improved less on the trained n-back task had higher scores on the post-training behavioral testing. Similarly, when separating the MOT participants into two groups based on median MOT improvement, the two groups showed no significant differences in transfer performance (all MOT t-ratios <1.74, all p’s >.10).

The personality correlates from Studer-Luethi 2012_12ya also don’t work:

We also examined whether personality assessments were associated with different training or transfer outcomes. Neither the Dweck measure of attitude toward intelligence (a “growth mindset”) nor measures of conscientiousness or grit correlated significantly with training gains on either training task, although there was a trend toward a significant negative correlation between the growth mindset and improvement on the n-back training task (r = −.44, p = .051), such that participants who viewed intelligence as more malleable had less improvement across their n-back training. A greater growth mindset score was positively correlated, however, with improvement on the Ravens Advanced Progressive Matrices in the n-back group (r = .53, p = .017) and in the passive control group (r = .51, p = .027), but not in the MOT control group (r = .031, p>.9). No other transfer measures were significantly predicted by growth mindset scores.

Although the conscientiousness scores and “grit” scores were highly correlated in each of the three treatment groups (n-back r = .75, p<.001; MOT r = .70, p<.001; passive r = .76, p<.001), the two measures differed in their correlations with the behavioral outcome measures. A higher “grit” score predicted less improvement on the RAPM for the n-back group (r = −.45, p = .049) and the MOT group (r = −.58, p = .009), such that participants who viewed themselves as having more “grit” improved less on the RAPM after training, although this relationship did not hold for the No-Contact group (r = .17, p = .5). Similarly, a higher score on the conscientiousness measure predicted less improvement on the RAPM for the MOT group (r = −.57, p = .01), such that participants who saw themselves as more conscientious improved less on the RAPM after training, although this was not observed in either of the other two groups (n-back r = −.21, p = .37; no-contact r = −.04, p = .85). Finally, a high conscientiousness score predicted a lower Pair Cancellation improvement within the MOT group (r = −.47, p = .04), but not in the n-back or no-contact control groups (n-back r = −.07, p = .77; no-contact r = −.13, p = .58). No other transfer measures were significantly predicted by either conscientiousness or grit scores.

Smith Et Al 2013

“Exploring the effectiveness of commercial and custom-built games for cognitive training”, Smith et al 2013

There is increasing interest in quantifying the effectiveness of computer games in non-entertainment domains. We have explored general intelligence improvements for participants using either a commercial-off-the-shelf (COTS) game [Brain Age], a custom do-it-yourself (DIY) training system for a working memory task [DNB] or an online strategy game to a control group (without training). Forty university level participants were divided into four groups (COTS, DIY, Gaming, [Passive] Control) and were evaluated three times (pre-intervention, post-intervention, 1-week follow-up) with three weeks of training. In general intelligence tests both cognitive training systems (COTS and DIY groups) failed to produce [statistically-]significant improvements in comparison to a control group or a gaming group. Also neither cognitive training system produced [statistically-]significant improvements over the intervention or follow-up periods.

Dual n-back; RAPM (10 minutes each test); 1 passive control group, 2 actives; 340 minutes training; minimal compensation (course credit & entry into “a prize draw”, which was worth 100£). Very small sample sizes (~10 in each of the 4 groups).

Nussbaumer Et Al 2013

“Limitations and chances of working memory training”, Nussbaumer et al 2013_11ya:

Recent studies show controversial results on the trainability of working memory (WM) capacity being a limiting factor of human cognition. In order to contribute to this open question we investigated if participants improve in trained tasks and whether gains generalize to untrained WM tasks, mathematical problem solving and intelligence tests.

83 adults trained over a three week period (7.5 hours total) in one of the following conditions: A high, a medium or a low WM load group. The present findings show that task specific characteristics could be learned but that there was no transfer between trained and untrained tasks which had no common elements. Positive transfer occurred between two tasks focusing on inhibitory processes. It might be possible to enhance this specific component of WM but not WM capacity as such. A possible enhancement in a learning test is of high educational interest and worthwhile to be investigated further.

One of the two IQ tests was the RAPM; this was dual n-back, but it was adaptive only in the “high” experimental group (so the “medium” and “low” groups are largely irrelevant). Paper does not provide RAPM score details, so I emailed the lead author.

Oelhafen Et Al 2013

“Increased parietal activity after training of interference control”,

…In the current study, we examined whether training on two variants of the adaptive dual n-back task would affect untrained task performance and the corresponding electrophysiological event-related potentials (ERPs). 43 healthy young adults trained for three weeks with a high or low interference training variant of the dual n-back task, or they were assigned to a passive control group. While n-back training with high interference led to partial improvements in the Attention Network Test (ANT), we did not find transfer to measures of working memory and fluid intelligence. ERP analysis in the n-back task and the ANT indicated overlapping processes in the P3 time range. Moreover, in the ANT, we detected increased parietal activity for the interference training group alone. In contrast, we did not find electrophysiological differences between the low interference training and the control group. These findings suggest that training on an interference control task leads to higher electrophysiological activity in the parietal cortex, which may be related to improvements in processing speed, attentional control, or both.

Sprenger Et Al 2013

“Training working memory: Limits of transfer”, Sprenger et al 2013_11ya; abstract:

In two experiments (totaling 253 adult participants), we examined the extent to which intensive working memory training led to improvements on untrained measures of cognitive ability. Although participants showed improvement on the trained task and on tasks that either shared task characteristics or stimuli, we found no evidence that training led to general improvements in working memory. Using Bayes Factor analysis, we show that the data generally support the hypothesis that working memory training was ineffective at improving general cognitive ability. This conclusion held even after controlling for a number of individual differences, including need for cognition, beliefs in the malleability of intelligence, and age.

Colom Et Al 2013

“Adaptive n-back training does not improve fluid intelligence at the construct level; gains on individual tests suggest training may enhance visuospatial processing”, Colom et al 2013_11ya:

Short-term adaptive cognitive training based on the n-back task is reported to increase scores on individual ability tests, but the key question of whether such increases generalize to the intelligence construct is not clear. Here we evaluate fluid/abstract intelligence (g_f), crystallized/verbal intelligence (Gc), working memory capacity (WMC), and attention control (ATT) using diverse measures, with equivalent versions, for estimating any changes at the construct level after training. Beginning with a sample of 169 participants, two groups of twenty-eight women each were selected and matched for their general cognitive ability scores and demographic variables. Under strict supervision in the laboratory, the training group completed an intensive adaptive training program based on the n-back task (visual, auditory, and dual versions) across twenty-four sessions distributed over twelve weeks. Results showed this group had the expected systematic improvements in n-back performance over time; this performance systematically correlated across sessions with g_f. Gc, and WMC, but not with ATT. However, the main finding showed no significant changes in the assessed psychological constructs for the training group as compared with the control group. Nevertheless, post-hoc analyses suggested that specific tests and tasks tapping visuospatial processing might be sensitive to training.

<– One hundred and sixty nine psychology undergraduates completed a battery of twelve intelligence tests and cognitive tasks measuring fluid-abstract and crystallized-verbal intelligence, working memory capacity, and attention control. After computing a general index from the six intelligence tests, two groups of twenty-eight females were recruited for the study. They were paid for their participation€. Members of each group were carefully matched for their general intelligence index, so they were perfectly overlapped and represented a wide range of scores. All participants were right handed, as assessed by the Edinburgh Test (Oldfield, 1971). They also completed a set of questions asking for medical or psychiatric disorders, as well as substance intake. The recruitment process followed the Helsinki guidelines (World Medical Association, 2008) and the local ethics committee approved the study. Descriptive statistics for the demographic variables and performance on the cognitive measures for the two groups of participants (training and control) can be seen in the Appendix (Table A.1.). [200€ if assigned to the training group and 100€ if assigned to the control group.] [150 euros in dollars is $204]

The collective psychological assessment for the pretest stage was done from September 19 to October 14 2011_13ya. Participants were assessed in groups not greater than twenty-five. The data obtained for the complete group (n = 169) were analyzed for recruiting the training (n = 28) and control (n = 28) groups based on the general index computed from the measures of fluid and crystallized intelligence (Table A.1.). The adaptive cognitive training program began in November 14 2011_13ya, remained active until 2012-02-17, and lasted for twelve weeks (with a break from December 24 2011_13ya to January 9 2012_12ya). The psychological assessment for the posttest was done individually from February 20 to March 09 (intelligence tests) and from March 12 to March 30 (cognitive tasks) 2012_12ya.

Intelligence and cognitive constructs were assessed by three measures each. As noted above, fluid intelligence (g_f) requires abstract problem solving abilities, whereas crystallized intelligence (Gc) involves the mental manipulation of cultural knowledge. g_f was measured by screening versions (odd numbered items and even numbered items for the pretest and posttest evaluations, respectively) of the Raven Advanced Progressive Matrices Test (RAPM), the abstract reasoning subtest from the Differential Aptitude Test (DAT-AR), and the inductive reasoning subtest from the Primary Mental Abilities Battery (PMA-R). Gc was measured by screening versions (odd numbered items and even numbered items for the pretest and posttest evaluations, respectively) of the verbal reasoning subtest from the DAT (DAT-VR), the numerical reasoning subtest from the DAT (DAT-NR), and the vocabulary subtest from the PMA (PMA-V). g_f and Gc were measured by tests with (PMA subtests) and without (RAPM and DAT subtests) highly speeded constraints.

The framework for the cognitive training program followed the guidelines reported by Jaeggi et al 2008_16ya but it was re-programmed for Visual Basic (2008_16ya Version). Nevertheless, there were some differences: (a) the training began with four sessions (weeks 1 and 2) with a visual adaptive n-back version and four sessions (weeks 3 and 4) with an auditory adaptive n-back version before facing the sixteen sessions of the adaptive n-back dual program (weeks 5 to 12), and (b) while the training program is usually completed in one month, here we extended the training period to three months (12 weeks). There were two training sessions per week lasting around 30 min each and they took place under strict supervision in the laboratory. Participants worked within individual cabins and the experimenter was always available for attending any request they might have. Data were analyzed every week for checking their progress at both the individual and the group level. Participants received systematic feedback regarding their performance. Furthermore, every two weeks participants completed a motivation questionnaire asking for their (a) involvement with the task, (b) perceived difficulty level, (c) perceived challenging of the task levels, and (d) expectations for future achievement. At the end of the training period participants were asked with respect to their general evaluation of the program. Using a rating scale 0–10, average values were (a) 8.1 (range 8.0 to 8.2 across sessions), (b) 7.9 (range 7.4 to 8.5 across sessions), (c) 8.0 (range 7.8 to 8.2 across sessions), and (d) 7 (range 6.5 to 7.7 across sessions). [12 weeks * 2 sessions * 30 minutes = 720 minutes]

The control group was passive. After the recruitment process, members of this no-contact control group were invited to follow their normal life as university students. As reasoned in some of our previous research reports addressing the potential effect of cognitive training, and according to the main theoretical framework, we were not interested in comparing different types of training, but in the comparison between a specific cognitive training and doing nothing beyond regular life.

Four out of six intelligence tests were applied without severe time constraints. For the RAPM there was more than one minute per item (20 minutes for 18 items). For DAT-AR DAT-NR and DAT-VR, there were approximately 30 seconds per item (10 minutes for 20 items). For the speeded tests (PMA-R and PMA-V) there were between 5 and 12 seconds per item (PMA-R: 3 minutes for 15 items and PMA-V: 2 min for 25 items.)

posttest: training, n=28 RAPM: 37.25 (6.23) control n=28 RAPM: 35.46 (8.26)

DAT-AR PMA-R

Mean differences between the odd and even items were significant (p < 0.001 for all the tests, excluding the DAT-VR) which implies that pretest (odd) and posttest (even) scores must not be directly compared. –>

Burki Et Al 2014

“Individual differences in cognitive plasticity: an investigation of training curves in younger and older adults”, Burki et al 2014

To date, cognitive intervention research has provided mixed but nevertheless promising evidence with respect to the effects of cognitive training on untrained tasks (transfer). However, the mechanisms behind learning, training effects and their predictors are not fully understood. Moreover, individual differences, which may constitute an important factor impacting training outcome, are usually neglected. We suggest investigating individual training performance across training sessions in order to gain finer-grained knowledge of training gains, on the one hand, and assessing the potential impact of predictors such as age and fluid intelligence on learning rate, on the other hand. To this aim, we propose to model individual learning curves to examine the intra-individual change in training as well as inter-individual differences in intra-individual change. We recommend introducing a latent growth curve model (LGCM) analysis, a method frequently applied to learning data but rarely used in cognitive training research. Such advanced analyses of the training phase allow identifying factors to be respected when designing effective tailor-made training interventions. To illustrate the proposed approach, a LGCM analysis using data of a 10-day working memory training study in younger and older adults is reported.

Republication of a thesis.

Pugin Et Al 2014

“Working memory training shows immediate and long-term effects on cognitive performance in children and adolescents”, Pugin et al 2014:

Working memory is important for mental reasoning and learning processes. Several studies in adults and school-age children have shown performance improvement in cognitive tests after working memory training. Our aim was to examine not only immediate but also long-term effects of intensive working memory training on cognitive performance tests in children and adolescents. Fourteen healthy male subjects between 10 and 16 years trained a visuospatial n-back task over 3 weeks (30 min daily), while 15 individuals of the same age range served as a passive control group. Significant differences in immediate (after 3 weeks of training) and long-term effects (after 2-6 months) in an auditory n-back task were observed compared to controls (2.5 fold immediate and 4.7 fold long-term increase in the training group compared to the controls). The improvement was more pronounced in subjects who improved their performance during the training. Other cognitive functions (matrices test and Stroop task) did not change when comparing the training group to the control group. We conclude that spatial working memory training in children and adolescents boosts performance in similar memory tasks such as the auditory n-back task. The sustained performance improvement several months after the training supports the effectiveness of the training.

Heffernan2014

“The Generalizability of Dual n-Back Training in Younger Adults”, Heffernan2014 (Halifax, Nova Scotia; Canada):

Introduction: The popularity of cognitive training has increased in recent years. Accumulating evidence shows that training can sometimes improve trained and non-trained cognitive functions, and these improvements may be related to individual differences in initial capacity and performance on the training task. The current study assessed the effectiveness of a custom-designed n-back task (the N-IGMA) versus an active control task (Blockmaster) at improving various forms of working memory capacity, attention, and fluid intelligence. Three measures of working memory capacity were considered: verbal, visuospatial and observed action. Methods: Outcome measures were assessed pre- and post-training. Nineteen healthy young adults (19-30 years of age) trained at-home for 30 minutes per day, five days a week for three weeks with either the N-IGMA (n=9) or Blockmaster (n=10) at-home games. Results: Pre-post changes were observed for some outcome measures and these were equal for the N-IGMA and active control group. Outcome improvements could be due to simple test/re-test benefits or alternatively the N-IGMA and Blockmaster tasks may produce equivalent training effects. Improvements in the training tasks did not correlate with the changes in the outcome measures, suggesting improvements in the outcome measures might not be attributable to transfer of learning. For verbal working memory only, participants with higher (versus lower) initial fluid intelligence demonstrated larger improvements on the outcome measures suggesting that in future research training tasks might need to be tailored to the individual participant. Pre-assessment but not change scores were related for observed action and visuospatial working memory, consistent with some overlap between content domains. Conclusion: Despite specifically targeting working memory, the N-IGMA was not better than a visuospatial control game at improving a variety of cognitive outcome measures in this small sample. Results suggest that the individual’s initial cognitive capacity might need to be considered in future training studies. Caution should be used in extrapolating the results of this study to other populations of interest (eg. older adults or individuals with cognitive deficits) since the present investigation included relatively high functioning individuals.

Hancock2013

“Processing speed and working memory training in Multiple Sclerosis: a blinded randomized controlled trial”, Hancock2013

Between 40-65% of patients with multiple sclerosis (MS) experience cognitive deficits associated with the disease. The two most common areas affected are information processing speed and working memory. Information processing speed has been posited as a core cognitive deficit in MS, and working memory has been shown to impact performance on a wide variety of domains for MS patients. Currently, clinicians have few reliable options for addressing cognitive deficits in MS. The current study aimed to investigate the effect of computerized, home-based cognitive training focused specifically on improving information processing speed and working memory for MS patients. Participants were recruited and randomized into either the Active Training or Sham Training group, tested with a neurocognitive battery at baseline, completed six weeks of training, and then were again tested with a neurocognitive battery at follow-up. After correcting for multiple comparisons, results indicated that the Active Training group scored higher on the Paced Auditory Serial Addition Test (a test of information processing speed and attention) following cognitive training, and data trended toward significance on the Controlled Oral Word Associations Task (a test of executive functioning), Letter Number Sequencing (a test of working memory), Brief Visuospatial Memory Test (a test of visual memory), and the Conners’ Continuous Performance Test (a test of attention). Results provide preliminary evidence that cognitive training with MS patients may produce moderate improvement in select areas of cognitive functioning. Follow-up studies with larger samples should be conducted to determine whether these results can be replicated, and also to determine the functional outcome of improvements on neurocognitive tests.

Waris Et Al 2015

“Transfer after Working Memory Updating Training”, Waris et al 2015

During the past decade, working memory training has attracted much interest. However, the training outcomes have varied between studies and methodological problems have hampered the interpretation of results. The current study examined transfer after working memory updating training by employing an extensive battery of pre-post cognitive measures with a focus on near transfer. Thirty-one healthy Finnish young adults were randomized into either a working memory training group or an active control group. The working memory training group practiced with three working memory tasks, while the control group trained with three commercial computer games with a low working memory load. The participants trained thrice a week for five weeks, with one training session lasting about 45 minutes. Compared to the control group, the working memory training group showed strongest transfer to an n-back task, followed by working memory updating, which in turn was followed by active working memory capacity. Our results support the view that working memory training produces near transfer effects, and that the degree of transfer depends on the cognitive overlap between the training and transfer measures.

Baniqued Et Al 2015

“Working Memory, Reasoning, and Task Switching Training: Transfer Effects, Limitations, and Great Expectations?”, Baniqued et al 2015:

Although some studies have shown that cognitive training can produce improvements to untrained cognitive domains (far transfer), many others fail to show these effects, especially when it comes to improving fluid intelligence. The current study was designed to overcome several limitations of previous training studies by incorporating training expectancy assessments, an active control group, and “Mind Frontiers,” a video game-based mobile program comprised of six adaptive, cognitively demanding training tasks that have been found to lead to increased scores in fluid intelligence (g_f) tests. We hypothesize that such integrated training may lead to broad improvements in cognitive abilities by targeting aspects of working memory, executive function, reasoning, and problem solving. Ninety participants completed 20 hour-and-a-half long training sessions over four to five weeks, 45 of whom played Mind Frontiers and 45 of whom completed visual search and change detection tasks (active control). After training, the Mind Frontiers group improved in working memory n-back tests, a composite measure of perceptual speed, and a composite measure of reaction time in reasoning tests. No training-related improvements were found in reasoning accuracy or other working memory tests, nor in composite measures of episodic memory, selective attention, divided attention, and multi-tasking. Perceived self-improvement in the tested abilities did not differ between groups. A general expectancy difference in problem-solving was observed between groups, but this perceived benefit did not correlate with training-related improvement. In summary, although these findings provide modest evidence regarding the efficacy of an integrated cognitive training program, more research is needed to determine the utility of Mind Frontiers as a cognitive training tool.

Kuper & Karbach2015

“Increased training complexity reduces the effectiveness of brief working memory training: evidence from short-term single and dual n-back training interventions”, Kuper & Karbach2015:

N-back training has recently come under intense scientific scrutiny due to reports of training-related improvements in general fluid intelligence. As of yet, relatively little is known about the effects of short-term n-back training interventions, however. In a pretest-training-posttest design, we compared brief dual and single n-back training regimen in terms of training gains and transfer effects relative to a passive control group. Transfer effects indicated that, in the short-term, single n-back training may be the more effective training task: At the short training duration we employed, training group showed far transfer to specific task switch costs, Stroop inhibition or matrix reasoning indexing fluid intelligence. Yet, both types of training resulted in a reduction of general task switch costs indicating improved cognitive control during the sustained maintenance of competing task sets. Single but not dual n-back training additionally yielded near transfer to an untrained working memory updating task.

Lindeløv Et Al 2016

“Training and transfer effects of N-back training for brain-injured and healthy subjects”, Lindeløv et al 2016:

Working memory impairments are prevalent among patients with acquired brain injury (ABI). Computerised training targeting working memory has been researched extensively using samples from healthy populations but this field remains isolated from similar research in ABI patients. We report the results of an actively controlled randomized controlled trial in which 17 patients and 18 healthy subjects completed training on an N-back task. The healthy group had superior improvements on both training tasks (SMD = 6.1 and 3.3) whereas the ABI group improved much less (SMD = 0.5 and 1.1). Neither group demonstrated transfer to untrained tasks. We conclude that computerised training facilitates improvement of specific skills rather than high-level cognition in healthy and ABI subjects alike. The acquisition of these specific skills seems to be impaired by brain injury. The most effective use of computer-based cognitive training may be to make the task resemble the targeted behavior(s) closely in order to exploit the stimulus-specificity of learning.

Schwarb Et Al 2015

“Working memory training improves visual short-term memory capacity”, Schwarb et al 2015

Lawlor-Savage & Goghari2016

“Dual n-Back Working Memory Training in Healthy Adults: A Randomized Comparison to Processing Speed Training”, Lawlor-Savage & Goghari2016:

Enhancing cognitive ability is an attractive concept, particularly for middle-aged adults interested in maintaining cognitive functioning and preventing age-related declines. Computerized working memory training has been investigated as a safe method of cognitive enhancement in younger and older adults, although few studies have considered the potential impact of working memory training on middle-aged adults. This study investigated dual n-back working memory training in healthy adults aged 30-60. Fifty-seven adults completed measures of working memory, processing speed, and fluid intelligence before and after a 5-week web-based dual n-back or active control (processing speed) training program. Results: Repeated measures multivariate analysis of variance failed to identify improvements across the three cognitive composites, working memory, processing speed, and fluid intelligence, after training. Follow-up Bayesian analyses supported null findings for training effects for each individual composite. Findings suggest that dual n-back working memory training may not benefit working memory or fluid intelligence in healthy adults. Further investigation is necessary to clarify if other forms of working memory training may be beneficial, and what factors impact training-related benefits, should they occur, in this population.

Studer-Luethi Et Al 2015

“Working memory training in children: Effectiveness depends on temperament”, Studer-Luethi et al 2015:

Studies revealing transfer effects of working memory (WM) training on non-trained cognitive performance of children hold promising implications for scholastic learning. However, the results of existing training studies are not consistent and provoke debates about the potential and limitations of cognitive enhancement. To examine the influence of individual differences on training outcomes is a promising approach for finding causes for such inconsistencies. In this study, we implemented WM training in an elementary school setting. The aim was to investigate near and far transfer effects on cognitive abilities and academic achievement and to examine the moderating effects of a dispositional and a regulative temperament factor, neuroticism and effortful control. Ninety-nine second-graders were randomly assigned to 20 sessions of computer-based adaptive WM training, computer-based reading training, or a no-contact control group. For the WM training group, our analyses reveal near transfer on a visual WM task, far transfer on a vocabulary task as a proxy for crystallized intelligence, and increased academic achievement in reading and math by trend. Considering individual differences in temperament, we found that effortful control predicts larger training mean and gain scores and that there is a moderation effect of both temperament factors on post-training improvement: WM training condition predicted higher post-training gains compared to both control conditions only in children with high effortful control or low neuroticism. Our results suggest that a short but intensive WM training program can enhance cognitive abilities in children, but that sufficient self-regulative abilities and emotional stability are necessary for WM training to be effective….We found no significant training group interaction on the performance in the Raven’s Progressive Matrices (F(2,92) = 1.57, p = .22, η_p² = .004)…We found no significant long-term effects in the variables memory span, cognitive control, g_f. Gc, and scholastic tests (all T < 1.4).

Minear Et Al 2016

“A simultaneous examination of two forms of working memory training: Evidence for near transfer only”, Minear et al 2016

The efficacy of working-memory training is a topic of considerable debate, with some studies showing transfer to measures such as fluid intelligence while others have not. We report the results of a study designed to examine two forms of working-memory training, one using a spatial n-back and the other a verbal complex span. Thirty-one undergraduates completed 4 weeks of n-back training and 32 completed 4 weeks of verbal complex span training. We also included two active control groups. One group trained on a non-adaptive version of n-back and the other trained on a real-time strategy video game. All participants completed pre- and post-training measures of a large battery of transfer tasks used to create composite measures of short-term and working memory in both verbal and visuospatial domains as well as verbal reasoning and fluid intelligence. We only found clear evidence for near transfer from the spatial n-back training to new forms of n-back, and this was the case for both adaptive and non-adaptive n-back.

Studer-Luethi Et Al 2016

“Working memory training in children: Effectiveness depends on temperament”, Studer-Luethi et al 2016:

Studies revealing transfer effects of working memory (WM) training on non-trained cognitive performance of children hold promising implications for scholastic learning. However, the results of existing training studies are not consistent and provoke debates about the potential and limitations of cognitive enhancement. To examine the influence of individual differences on training outcomes is a promising approach for finding causes for such inconsistencies. In this study, we implemented WM training in an elementary school setting. The aim was to investigate near and far transfer effects on cognitive abilities and academic achievement and to examine the moderating effects of a dispositional and a regulative temperament factor, neuroticism and effortful control. Ninety-nine second-graders were randomly assigned to 20 sessions of computer-based adaptive WM training, computer-based reading training, or a no-contact control group. For the WM training group, our analyses reveal near transfer on a visual WM task, far transfer on a vocabulary task as a proxy for crystallized intelligence, and increased academic achievement in reading and math by trend. Considering individual differences in temperament, we found that effortful control predicts larger training mean and gain scores and that there is a moderation effect of both temperament factors on post-training improvement: WM training condition predicted higher post-training gains compared to both control conditions only in children with high effortful control or low neuroticism. Our results suggest that a short but intensive WM training program can enhance cognitive abilities in children, but that sufficient self-regulative abilities and emotional stability are necessary for WM training to be effective.

…Fluid intelligence was assessed using either the even or odd items of Raven’s Progressive Matrices in counterbalanced order (RPM, 30 items; Raven, 1998_26ya). After two practice trials, children were allowed to work for 10 min and cross the right solution for each task. The number of correct solutions provided in this time limit was used as the dependent variable.

…We found no significant training group interaction on the performance in the Raven’s Progressive Matrices (F(2,92) = 1.57, p = .22, η_p² = .004).

N-Back Improves Working Memory

There are quite a few studies showing significant increases in working memory: WM is something that can be trained. See for example “Changes in cortical activity after training of working memory - a single-subject analysis.” or “Increased prefrontal and parietal activity after training of working memory”.

There are a few studies showing that DNB training enhances g_f; see the support section. There is also a study showing that WM training (not DNB) enhances Gc⁶⁸.

IQ Tests

Chein2010

“Expanding the mind’s workspace: Training and transfer effects with a complex working memory span task” (FLOSS implementation); from the introduction:

In the present study, a novel working memory (WM) training paradigm was used to test the malleability of WM capacity and to determine the extent to which the benefits of this training could be transferred to other cognitive skills. Training involved verbal and spatial versions of a complex WM span task designed to emphasize simultaneous storage and processing requirements. Participants who completed 4 weeks of WM training demonstrated significant improvements on measures of temporary memory. These WM training benefits generalized to performance on the Stroop task and, in a novel finding, promoted significant increases in reading comprehension. The results are discussed in relation to the hypothesis that WM training affects domain-general attention control mechanisms and can thereby elicit far-reaching cognitive benefits. Implications include the use of WM training as a general tool for enhancing important cognitive skills.

While WM training yielded many valuable benefits such as increased reading comprehension, it did not improve IQ as measured by an unspeeded Advanced Progressive Matrices (APM) IQ test;

However, such power limitations do not readily account for our failure to replicate a transfer of WM training benefits to measures of fluid intelligence (as was observed by Jaeggi et al 2008_16ya), since we did not find even a trend for improvement in trained participants on Raven’s APM. Beyond statistical explanations, differences in the training paradigms used for the two studies may explain the differences in transfer effects. The training program used by Jaeggi et al 2008_16ya involved 400 trials per training session, with a dual n-back training paradigm designed to emphasize binding processes and task management. Conversely, our training paradigm included only 32 trials per session and more heavily emphasized maintenance in the face of distraction. Finally, the seemingly conflicting results may be due to differences in intelligence test administration. As was pointed out in a recent critique (Moody, 2009_15ya), Jaeggi et al 2008_16ya used atypical speeded procedures in administering their tests of fluid intelligence, and these alterations may have confounded the apparent effect of WM training on intelligence.

Colom2010

“Improvement in working memory is not related to increased intelligence scores” trained 173 students on WM tasks (such as the reading span task) with randomized difficulties, and found no linked IQ improvement; the IQ tests were “the Advanced Progressive Matrices Test (APM) along with the abstract reasoning (DAT-AR), verbal reasoning (DAT-VR), and spatial relations (DAT-SR) subtests from the Differential Aptitude Test Battery”. None were speeded as in Jaeggi 2008_16ya. Abstract:

The acknowledged high relationship between working memory and intelligence suggests common underlying cognitive mechanisms and, perhaps, shared biological substrates. If this is the case, improvement in working memory by repeated exposure to challenging span tasks might be reflected in increased intelligence scores. Here we report a study in which 288 university undergraduates completed the odd numbered items of four intelligence tests on time 1 and the even numbered items of the same tests one month later (time 2). In between, 173 participants completed three sessions, separated by exactly one week, comprising verbal, numerical, and spatial short-term memory (STM) and working memory (WMC) tasks imposing high processing demands (STM-WMC group). 115 participants also completed three sessions, separated by exactly one week, but comprising verbal, numerical, and spatial simple speed tasks (processing speed, PS, and attention, ATT) with very low processing demands (PS-ATT group). The main finding reveals increased scores from the pre-test to the post-test intelligence session (more than half a standard deviation on average). However, there was no differential improvement on intelligence between the STM-WMC and PS-ATT groups.

Commentators on the ML discussion criticized the study for:

1. Not using DNB itself

2. apparently little training time on the WM tasks (3 sessions over weeks, each of unclear duration)

3. the randomization of difficulty (as opposed to DNB’s adaptiveness)

4. the large increase in scores on the WM tasks over the 3 sessions (suggesting growing familiarity than real challenge & growth)

5. and the statistical observation that if IQ gains were linear with training and started small then 173 participants is not enough to observe with confidence any improvements.

Loosli Et Al 2011

“Working memory training improves reading processes in typically developing children”, Loosli, Buschkuehl, Perrig, and Jaeggi:

The goal of this study was to investigate whether a brief cognitive training intervention results in a specific performance increase in the trained task, and whether there are transfer effects to other nontrained measures. A computerized, adaptive working memory intervention was conducted with 9- to 11-year-old typically developing children. The children considerably improved their performance in the trained working memory task. Additionally, compared to a matched control group, the experimental group significantly enhanced their reading performance after training, providing further evidence for shared processes between working memory and reading.

This is showing connection to useful tasks, but not for showing any gain to IQ. The difference in score improvement between groups was small, half a point, and the training period fairly short; the authors write:

Due to the short training time, we did not expect large effects on g_f (cf. Jaeggi et al 2008_16ya), also since two other studies that trained ADHD children observed transfer effects on g_f only after 5 weeks involving sessions of 40 minutes each (Klingberg et al 2002_22ya, 2005_19ya).

In addition, the same group failed to show transfer on g_f with a shorter training (Thorell et al 2008_16ya). Thus, considering that our training intervention was merely 10 sessions long, our lack of transfer to g_f is hardly surprising; although there is now recent evidence that transfer to g_f is possible with very little training time (Karbach & Kray2009; poster). Our results, however, are comparable to those of Chein & Morrison 2010_14ya, who also trained their participants on a complex WM task and found no transfer to g_f.

(Similar studies have also found improvement in reading skills after WM training, eg. Dahlin2011 and Shiran & Breznitz2011, but I do not believe others used n-back or looked for possible IQ gains.)

Nutley2011

“Gains in fluid intelligence after training non-verbal reasoning in 4-year-old children: a controlled, randomized study”, Sissela Bergman Nutley et al:

Fluid intelligence (g_f) predicts performance on a wide range of cognitive activities, and children with impaired g_f often experience academic difficulties. Previous attempts to improve g_f have been hampered by poor control conditions and single outcome measures⁷⁷. It is thus still an open question whether g_f can be improved by training. This study included 4-year-old children (n = 101) who performed computerized training (15 min/day for 25 days) of either non-verbal reasoning, working memory, a combination of both, or a placebo version of the combined training. Compared to the placebo group, the non-verbal reasoning training group improved significantly on g_f when analysed as a latent variable of several reasoning tasks. Smaller gains on problem solving tests were seen in the combination training group. The group training working memory improved on measures of working memory, but not on problem solving tests. This study shows that it is possible to improve g_f with training, which could have implications for early interventions in children.

Points:

1. The WM tasks were not n-back:

   “The WM training was the same as described in Thorell et al 2009 developed by Cogmed Systems Inc. There were seven different versions of visuo-spatial WM tasks, out of which three were trained every day on a rotating schedule. Briefly, the tasks all consisted of a number of animated figures presented in different settings (eg. swimming in a pool, riding on a rollercoaster). Some of the figures (starting with two figures and then increasing in number depending on the child’s performance) made a sound and changed colour during a short time period. The task then consisted of remembering which figures had changed colour and in what order this had occurred.”

2. The magnitude of g_f increase was not suspiciously large:

   “The NVR training group showed transfer both when this was estimated with single tests, as well as when g_f was measured as a latent variable. The magnitude of this improvement was approximately 8% (compared to the placebo group) which is comparable with previously reported gains of g_f of 5-13.5% (Hamers et al 1998; Jaeggi et al 2008; Klauer & Willmes, 2002; Stankov, 1986).”

3. There are some possible counter-arguments to generalizing the lack of g_f gains in the WM-only group, mostly related to the young age:

   “This could mean that WM is not a limiting factor for 4-year-old children solving reasoning problems such as Raven’s CPM and Block Design. The moderate correlations between the Grid Task and the reasoning tests (between 0.3 and 0.6, see Table 1) point to the somewhat counterintuitive conclusion that correlation between two underlying abilities is not a sufficient predictor to determine amount of transfer of training effects between these abilities. A similar conclusion was drawn after the lack of training effects on WM after training inhibitory functions (Thorell et al 2009_15ya). In that study WM capacity correlated with performance on the inhibitory tasks at baseline (R = 0.3). An imaging study also showed that performance on a WM grid task and inhibitory tasks activate overlapping parts of the cortex (McNab, Leroux, Strand, Thorell, Bergman & Klingberg, 2008). Inhibitory training improved performance on the trained tasks, yet there was no transfer seen on WM tasks. The principles governing the type of cognitive training that will transfer are still unclear and pose an important question for future studies.

   One way to find these principles may be through understanding the neural mechanisms of training. For example, WM training in 4-year-olds might have a more pronounced effect on the parietal lobe, compared to the less mature frontal lobe. If the transfer to g_f is dependent on prefrontal functions, it may explain the lack of transfer from WM training to g_f in 4-year-olds. In other words, transfer effects may differ with the progression of development.”

Zhao Et Al 2011

“Effect of updating training on fluid intelligence in children”, Chinese Science Bulletin:

Recent studies have indicated that working memory (WM) training can improve fluid intelligence. However, these earlier studies confused the impact of WM storage and central executive function on the effects of training. The current study used the running memory task to train the updating ability of [33] 9-11 year-old children using a double-blind controlled design. The results revealed that children’s fluid intelligence was significantly improved by memory-updating training. Overall, our findings suggest that the increase in fluid intelligence achieved with WM training is related to improving central executive function.

Roughan & Hadwin2011

“The impact of working memory training in young people with social, emotional and behavioral difficulties”, Laura Roughan & Julie A. Hadwin 2011_13ya:

This study examined the impact of a working memory (WM) training programme on measures of WM, IQ, behavioral inhibition, self-report test and trait anxiety and teacher reported emotional and behavioral difficulties and attentional control before and after WM training and at a 3 month follow-up. The WM training group (N=7) showed significantly better post-training on measures of IQ, inhibition, test anxiety and teacher-reported behavior, attention and emotional symptoms, compared with a non-intervention passive control group (N=8). Group differences in WM were also evident at follow-up. The results indicated that WM training has some potential to be used to reduce the development of school related difficulties and associated mental health problems in young people. Further research using larger sample sizes and monitoring over a longer time period is needed to replicate and extend these results.

The WM training was done using Cogmed; it’s unclear whether the Cogmed tasks use DNB or not (they seem to have similar tasks available in it, at least), but the study did find IQ gains:

Considering T1 T2 IQ difference scores, the analysis revealed a significant group effect with a large ES (F(1,14) = 10.37, p<.01, n = 0.44); the intervention group showed increased IQ difference scores (N = 7, mean=5.36, SD = 6.52, range= -2.5 to 17.5) compared with the control group (N = 7, mean=-6.35, SD = 7.21, range = -15 to 5). T1 T3 analyses indicated that the T1 T3 difference was not significant (see Fig. 1).

Note the means as compared with the standard deviation; these are very troubled young people.

Brehmer Et Al 2012

“Working-memory training in younger and older adults: training gains, transfer, and maintenance”:

Working memory (WM), a key determinant of many higher-order cognitive functions, declines in old age. Current research attempts to develop process-specific WM training procedures, which may lead to general cognitive improvement. Adaptivity of the training as well as the comparison of training gains to performance changes of an active control group are key factors in evaluating the effectiveness of a specific training program. In the present study, 55 younger adults (20-30 years of age) and 45 older adults (60-70 years of age) received 5 weeks of computerized training on various spatial and verbal WM tasks. Half of the sample received adaptive training (ie. individually adjusted task difficulty), whereas the other half-worked on the same task material but on a low task difficulty level (active controls). Performance was assessed using criterion, near-transfer, and far-transfer tasks before training, after 5 weeks of intervention, as well as after a 3-month follow-up interval. Results indicate that (a) adaptive training generally led to larger training gains t/han low-level practice, (b) training and transfer gains were somewhat greater for younger than for older adults in some tasks, but comparable across age groups in other tasks, (c) far-transfer was observed to a test on sustained attention and for a self-rating scale on cognitive functioning in daily life for both young and old, and (d) training gains and transfer effects were maintained across the 3-month follow-up interval across age.

Used Cogmed, which Jaeggi says is not dual n-back.

Self-Experiment

Brain Workshop now has logging of saccading implemented; this was added at my request to make experimenting with saccading easier, since you can’t compare scores unless you know when you were saccading or not. After this was added (thanks Jonathan etc), I began to randomize each day to saccading or not-saccading before rounds with a coin flip. Blinding is impossible, so I did nothing about that. After 158 rounds over roughly 35 days between 10 September and 2012-11-05, the result is: no difference. Not even close. So apparently though I am strongly right-handed as the original study’s memory effect required, saccading makes no difference to my n-back performance.

Android

See also Lucas Charles’s August 2011_13ya review of 6 Android DNB apps.

Free:

• Brain N Back (Quazar)

• Brain N-Back (Phuc Nguyen)

• Dual n-Back (Polytech Marseille)

• IQ Boost (the free version)

• N-Back (Piotr Wieczorek)

• Single N-back (Martin Ankerl; note the title)

Paid:

• IQ Boost

• N-Back Maestro (Urbian)

• NBack (waterdev)

• Brainturk (?)

IPhone

Free:

• BetterBrain Lite

• “Dual n-Back”, by Mikko Tyrskeranta

Paid:

• BetterBrain

• IQboost

• Double Dynamo (video gameplay demonstration)

• Brainturk Brain Trainer

Piracetam

A useful pharmaceutical is piracetam; TheQ17 mentions that “Personally, I have found piracetam be quite useful in helping me stay alert and focused during long study hours or doing redundant tasks.” Other members also swear by piracetam+choline.

The author of this FAQ reports that piracetam and choline helped reduced mental fatigue and gave a small (~10%) increase in his D4B score.

Huperzine

Reece writes

I’ve tried huperzine [a chemical extracted from an herb] (actually been using it for about a year now) and it is quite effective for both lucid dreaming and increasing dream recall if taken shortly before bed, not to mention the other benefits you’d expect from a potent acetylcholinesterase inhibitor. I haven’t had anything in the way of negative side effects when I’ve stuck to a 5 day/week dosage of 200mcg.

I’ve never tried piracetam, however oxiracetam felt like a placebo when compared to the benefits I’ve received from huperzine A. At larger doses, I’ve found huperzine A to be far more powerful than any nootropic I’ve ever tried (haven’t tried any prescription meds such as deprenyl), however the side effects such as blurry vision and light-headedness weren’t something I could tolerate.

He further compared their effects:

I found Oxiracetam to have a somewhat “speedy” effect—you would certainly know you took something if someone slipped that in your drink! As for effects, Oxiracetam seemed to help most with verbal fluency (auditory working memory?) and creativity. Huperzine helped more with working memory although it didn’t have some of the interesting effects Oxiracetam had on creativity, nor the speedy rush that sometimes seemed like a powerful motivator to get work done.

(Reece did not take the oxiracetam with any choline supplements, which is usually recommended.)

Creatine

In the realm of unusual supplements to n-backing, we can include creatine possibly increasing intelligence, but the evidence is too weak to say much.