The Meriam-Webster defines training as: “the skill, knowledge, or experience acquired by one that trains." In combination with the word “brain” it becomes a redundant and awkward term, similar to brain-based learning, which also makes no sense as all learning is brain-based. The same holds true for brain-training. Nobody would, for instance, use the compound leg-based running.
In Simons’s (Simons et al., 2016) opinion brain-training is an invention of companies selling cognitive software, making it more appealing to the public – kind of a “hard science” backup. Most often the brain-training term is used to describe commercially available programs, such as CogMed, CogniFit, Lumosity, BrainHQ, Fast ForWord, to name just the most known ones. In contrast, interventions used in lab-research are predominantly labeled as cognitive training.
The so-called “controversy” relates to statements signed by two groups of scientists. The first one that was released on October 20th 2014, dubbed A Consensus on the BrainTraining Industry from the Scientific Community, was put forward by the Max Planck Institute for Human Development and the Stanford Center on Longevity signed by 70 researchers in the field. The main message was that there is a discrepancy between the state of the art in scientific research on cognitive training, on the one hand, and the claims made by the industry advertising commercial cognitive-training software, on the other.
The second one was signed by 133 scientists with the following objections to the first one:
“We agree strongly with parts of your statement, agree substantially with other parts, but are compelled to sign this letter to express our concern that many readers of your statement might wrongly conclude that there is no evidence that any cognitive training regimen can improve cognitive function.”
In the introduction to a recent lengthy paper by Simons et al. (2016) the statement – that in our opinion started mainly as an opposition to false claims about benefits of cognitive training programs advertised by software companies, mostly Lumosity – is presented as being just about the question: Can intelligence be increased? NO – the first group, versus YES – the second group.
In this context it is further worth mentioning that Jaeggi was one of the signers of the first consensus statement, which is surprising given that she was one of the authors of the paper suggesting that training on an adaptive dual n-back task can increase fluid intelligence (Jaeggi et al., 2008). The studies that she and her colleagues conducted afterwards provided similar conclusions (Jaeggi et al., 2010; 2011; 2014; Au et al., 2015; 2016; Buschkuehl et al., 2012;2014; Katz et al., 2014). The strongest evidence that the so-called controversy was actually invented later and was initially designed to oppose the claims of software companies, was the recent discussion between two opponents: Melby-Lervåg and Hulme (2016 a) on the NO side and Au, Buschkuehl, Duncan, and Jaeggi (2016) on the YES side. We discuss this issue, along with the problem of biased inclusion/exclusion of studies in meta-analyses, which in turn influence conclusions about the malleability of intelligence, in our forthcoming book Increasing Intelligence (Academic Press, Elsevier, release date: February 2017).
In the book we reviewed 17 meta-analyses about the influence of cognitive training on intelligence. We concluded that far transfer effects on measures of fluid intelligence are small and most often obtained after n-back training or training using multi-factorial interventions targeting multiple cognitive skills.
Let's go back to my suggestion that certain meta-analyses have been characterized by biased study selection. Most meta-analyses implying no far transfer effects of training on intelligence have excluded the COGITO study (Schmiedek et al., 2010; 2013; 2014). In this study, although having a no-contact control group, more than 200 individuals were assigned to the training group. The number of participants contributes to the estimated power a study adds to a meta-analysis. In the most recent meta-analysis by Melby-Lervåg et al. (2016 b), the reason for excluding the COGITO study was: “Did not involve working memory training” (p. 516, Fig.1). The inclusion criteria were further specified: “In cases where an intervention had multiple components, the working memory tasks had to constitute at least 50% of the intervention” (p.517). Schmiedek et al. (2010) included 12 different intervention tasks classified as perceptual speed (6 tasks), working memory (3 tasks) and episodic memory (3 tasks). The 6 memory tasks were adaptive in presentation time which was adjusted in relation to individual performance on trained tasks. In the three episodic memory training tasks the participants had to remember lists of words, pairs of numbers and words, and the position of 12 colored real world objects in a 6 x 6 grid. In our opinion the study would qualify to be included in the meta-analysis by Melby-Lervåg et al. (2016), since 50% of the training tasks actually were WM tasks. It goes without mentioning that the conclusion of the meta-analysis was: “We conclude that there is no evidence that working memory training yields improvements in so-called far-transfer abilities” (p., 526).
Similarly, Simons et al. (2016) described their review paper as “…a comprehensive review of the brain-training literature, one that examines both the quantity and the quality of the evidence according to a well-defined set of best practices” (p., 103). However, the authors in support of the NO-effect do not mention data that opposes their viewpoint. In support of their claim that WM training shows only near transfer effects, Simons et al. (2016; p., 111) cite studies on neural plasticity, for instance, that violinists displayed selective neural growth in the right motor cortex, corresponding to the use of their left hand to finger the strings. On the other hand, they fail to notice the extensive review of brain activation patterns during performance on n-back tasks by Owen et al. (2005). The review provided robust evidence for the activation of 7 brain areas in all n-back variants. Moreover, the identified brain activation patterns during n-back performance also correspond with those that are crucial for intelligent behavior identified by the two of the most influential theories of the relationship between brain structure/function and intelligence – the P-FIT (Jung and Haier, 2007) and the multiple-demand system (Duncan, 2010). The study by Kearney-Ramos et al. (2014) replicated the findings described by Owen et al. (2005).
Furthermore, similar changes in brain structure and function in relation to WM training were reported by several recent studies. Román et al. (2016) showed that adaptive n-back training influenced changes in cortical thickness and surface area. In yet another study by Thompson et al. (2016), it was shown that intensive working memory training produced functional changes in large-scale frontoparietal networks which led the authors to conclude that their study “…provides insight into the adaptive neural systems that underlie large gains in working memory capacity through training” (p., 575). A more humorous perspective on the controversy is presented in a book by Dan Hurley, Smarter: The New Science of Building Brain Power (2015, New York: Plume, Penguin Group). I particularly like the reference to Randall Engle’s position, which underscores my standpoint in an amusing way.
Given that researchers are humans there is probably no meta-analysis without bias – the same holds true for our blog. Let me clarify our standpoint in the controversy: We think that intelligence can be increased, but at the moment only in controlled laboratory experiments, for the purpose of verifying or rejecting findings based on correlational studies, thereby widening our understanding of the construct of intelligence. This idea is best summarized in the contributions of authors provided at the International Seminar: Advances on Intelligence Research: What should we expect from the XXI Century? Held in Madrid, April 7-8, 2016 (see the contribution by Santarnecchi).
Au, J., Buschkuehl, M., Duncan, G. J., & Jaeggi, S. M. (2016). There is no convincing evidence that working memory training is NOT effective: A reply to Melby-Lervåg and Hulme (2015). Psychonomic Bulletin & Review, 23(1), 331–337. http://doi.org/10.3758/s13423-015-0967-4
Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuehl, M., & Jaeggi, S. M. (2015). Improving fluid intelligence with training on working memory: a meta-analysis. Psychonomic Bulletin & Review, 22(2), 366–377. http://doi.org/10.3758/s13423-014-0699-x
Buschkuehl, M., Hernandez-Garcia, L., Jaeggi, S. M., Bernard, J. A., & Jonides, J. (2014). Neural effects of short-term training on working memory. Cognitive, Affective, & Behavioral Neuroscience, 14(1), 147–160. http://doi.org/10.3758/s13415-013-0244-9
Buschkuehl, M., Jaeggi, S. M., & Jonides, J. (2012). Neuronal effects following working memory training. Developmental Cognitive Neuroscience, 2, S167–S179.
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172–179. http://doi.org/10.1016/j.tics.2010.01.004
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences, 105(19), 6829–6833.
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011). Short- and long-term benefits of cognitive training. Proceedings of the National Academy of Sciences, 108(25), 10081–10086. http://doi.org/10.1073/pnas.1103228108
Jaeggi, S. M., Buschkuehl, M., Shah, P., & Jonides, J. (2014). The role of individual differences in cognitive training and transfer. Memory & Cognition, 42(3), 464–480. http://doi.org/10.3758/s13421-013-0364-z
Jaeggi, S. M., Studer-Luethi, B., Buschkuehl, M., Su, Y.-F., Jonides, J., & Perrig, W. J. (2010). The relationship between n-back performance and matrix reasoning — implications for training and transfer. Intelligence, 38(6), 625–635. http://doi.org/10.1016/j.intell.2010.09.001
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(02), 135. http://doi.org/10.1017/S0140525X07001185
Katz, B., Jaeggi, S., Buschkuehl, M., Stegman, A., & Shah, P. (2014). Differential effect of motivational features on training improvements in school-based cognitive training. Frontiers in Human Neuroscience, 8. http://doi.org/10.3389/fnhum.2014.00242
Kearney-Ramos, T. E., Fausett, J. S., Gess, J. L., Reno, A., Peraza, J., Kilts, C. D., & James, G. A. (2014). Merging Clinical Neuropsychology and Functional Neuroimaging to Evaluate the Construct Validity and Neural Network Engagement of the n-Back Task. Journal of the International Neuropsychological Society, 20(7), 736–750. https://doi.org/10.1017/S135561771400054X
Melby-Lervåg, M., & Hulme, C. (2016 a). There is no convincing evidence that working memory training is effective: A reply to Au et al. (2014) and Karbach and Verhaeghen (2014). Psychonomic Bulletin & Review, 23(1), 324–330. http://doi.org/10.3758/s13423-015-0862-z
Melby-Lervag, M., Redick, T. S., & Hulme, C. (2016 b). Working Memory Training Does Not Improve Performance on Measures of Intelligence or Other Measures of “Far Transfer”: Evidence From a Meta-Analytic Review. Perspectives on Psychological Science, 11(4), 512–534. https://doi.org/10.1177/1745691616635612
Owen, A. M., McMillan, K. M., Laird, A. R., & Bullmore, E. (2005). N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human Brain Mapping, 25(1), 46–59. https://doi.org/10.1002/hbm.20131
Román, F. J., Lewis, L. B., Chen, C.-H., Karama, S., Burgaleta, M., Martínez, K., … Colom, R. (2016). Gray matter responsiveness to adaptive working memory training: a surface-based morphometry study. Brain Structure and Function, 221(9), 4369–4382. https://doi.org/10.1007/s00429-015-1168-7
Schmiedek, F., Lövden., M., & Lindenberger., U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: findings from the COGITO study. Frontiers in Aging Neuroscience. http://doi.org/10.3389/fnagi.2010.00027
Schmiedek, F., Lovden, M., & Lindenberger, U. (2013). Keeping It Steady: Older Adults Perform More Consistently on Cognitive Tasks Than Younger Adults. Psychological Science, 24(9), 1747–1754. http://doi.org/10.1177/0956797613479611
Schmiedek, F., Lövdén, M., & Lindenberger, U. (2014). Younger adults show long-term effects of cognitive training on broad cognitive abilities over 2 years. Developmental Psychology, 50(9), 2304–2310. http://doi.org/10.1037/a0037388
Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do “Brain-Training” Programs Work? Psychological Science in the Public Interest, 17(3), 103–186. https://doi.org/10.1177/1529100616661983
Thompson, T. W., Waskom, M. L., & Gabrieli, J. D. E. (2016). Intensive Working Memory Training Produces Functional Changes in Large-scale Frontoparietal Networks. Journal of Cognitive Neuroscience, 28(4), 575–588. https://doi.org/10.1162/jocn_a_00916