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Published: 25 September 2019

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Dr Kerry Hempenstall, Senior Industry Fellow, School of Education, RMIT University, Melbourne, Australia.

Any of my blogs can be downloaded from https://tinyurl.com/y6vat4ut

New Addition - March 2025

You can find the original document at the end of this new section. 

In the description of “cognitive training” above this, there was a varied belief on how valuable CT was in literacy.

Now looking at these new up to date documents – there remains a variable difference.

“Cognitive training can positively impact reading development, particularly for children with dyslexia or those struggling with reading, by improving skills like working memory, attention, and processing speed, which are crucial for reading fluency and comprehension.” AI Overview.

Cognitive Training: A Field in Search of a Phenomenon (2023).

In cognitive training, near transfer occurs when skills learned in one context are applied to similar contexts, while far transfer involves applying those skills to contexts that are quite different. 

“Considerable research has been carried out in the last two decades on the putative benefits of cognitive training on cognitive function and academic achievement. Recent meta-analyses summarizing the extent empirical evidence have resolved the apparent lack of consensus in the field and led to a crystal-clear conclusion:

The overall effect of far transfer is null, and there is little to no true variability between the types of cognitive training. Despite these conclusions, the field has maintained an unrealistic optimism about the cognitive and academic benefits of cognitive training, as exemplified by a recent article (Green et al., 2019).

We demonstrate that this optimism is due to the field neglecting the results of meta-analyses and largely ignoring the statistical explanation that apparent effects are due to a combination of sampling errors and other artifacts. We discuss recommendations for improving cognitive-training research, focusing on making results publicly available, using computer modeling, and understanding participants’ knowledge and strategies.

Given that the available empirical evidence on cognitive training and other fields of research suggests that the likelihood of finding reliable and robust far-transfer effects is low, research efforts should be redirected to near transfer or other methods for improving cognition.

The Broader View

As discussed earlier, our meta-analyses clearly show that cognitive training does not lead to any far transfer in any of the cognitive-training domains that have been studied. In addition, using second-order meta-analysis made it possible to show that the between-meta-analyses true variance is due to second-order sampling error and thus that the lack of far transfer generalizes to different populations and different tasks.

Taking a broader view suggests that our conclusions are not surprising and are consistent with previous research. In fact, they were predictable. Over the years, it has been difficult to document far transfer in experiments (Singley & Anderson, 1989; Thorndike & Woodworth, 1901), industrial psychology (Baldwin & Ford, 1988), education (Gurtner et al., 1990), and research on analogy (Gick & Holyoak, 1983), intelligence (Detterman, 1993), and expertise (Bilalić et al., 2009).

Indeed, theories of expertise emphasize that learning is domain-specific (Ericsson & Charness, 1994; Gobet & Simon, 1996; Simon & Chase, 1973). When putting this substantial set of empirical evidence together, we believe that it is possible to conclude that the lack of training-induced far transfer is an invariant of human cognition (Sala & Gobet, 2019).

Obviously, this conclusion conflicts with the optimism displayed in the field of cognitive training, as exemplified by Green et al.’s (2019) article discussed above. However, it is in line with skepticism recently expressed about cognitive training (Moreau, 2021; Moreau et al., 2019; Simons et al., 2016). It also raises the following critical epistemological question: Given that the overall evidence in the field of cognitive training strongly suggests that the postulated far-transfer effects do not exist, and thus the probability of finding such effects in future research is very low, should one conclude that the reasonable course of action is to stop performing cognitive-training research on far transfer?

We believe that the answer to this question is “yes.” Given the clear-cut empirical evidence, the discussion about methodological concerns is irrelevant, and the issue becomes searching for other cognitive-enhancement methods. However, although the hope of finding far-transfer effects is tenuous, the available evidence clearly supports the presence of near-transfer effects. In many cases, near-transfer effects are useful (e.g., with respect to older adults’ memory), and developing effective methods for improving near transfer is a valuable—and importantly, realistic—avenue for further research.”

Gobet F, Sala G. (2023). Cognitive Training: A Field in Search of a Phenomenon. Perspect Psychol Sci. 2023 Jan;18(1):125-141. doi: 10.1177/17456916221091830. Epub 2022 Aug 8. PMID: 35939827; PMCID: PMC9903001

https://pmc.ncbi.nlm.nih.gov/articles/PMC9903001/#:~:text=The%20question%20of%20%E2%80%9Ctransfer%E2%80%9D%20is,Sala%20&%20Gobet%2C%202020b)

“Theory building in science requires replication and integration of findings regarding a particular research question. Second-order meta-analysis (i.e., a meta-analysis of meta-analyses) offers a powerful tool for achieving this aim, and we use this technique to illuminate the controversial field of cognitive training.

Recent replication attempts and large meta-analytic investigations have shown that the benefits of cognitive-training programs hardly go beyond the trained task and similar tasks. However, it is yet to be established whether the effects differ across cognitive-training programs and populations (children, adults, and older adults).

We addressed this issue by using second-order meta-analysis. In Models 1 (k = 99) and 2 (k = 119), we investigated the impact of working-memory training on near-transfer (i.e., memory) and far-transfer (e.g., reasoning, speed, and language) measures, respectively, and whether it is mediated by the type of population. Model 3 (k = 233) extended Model 2 by adding six meta-analyses assessing the far-transfer effects of other cognitive-training programs (video-games, music, chess, and exergames).

Model 1 showed that working-memory training does induce near transfer, and that the size of this effect is moderated by the type of population. By contrast, Models 2 and 3 highlighted that far Conversely, those theories predicting the transfer effects are small or null.

Crucially, when placebo effects and publication bias were controlled for, the overall effect size and true variance equaled zero. That is, no impact on far-transfer measures was observed regardless of the type of population and cognitive-training program. The lack of generalization of skills acquired by training is thus an invariant of human cognition.”

Overall, the implications are profound. From the theoretical point of view, those theories of human cognition predicting minimal or no far transfer of skills are corroborated by our findings (e.g., chunking-based theories; for a review, see Gobet, 2016).

Conversely, those theories predicting the generalization of skills acquired by training across multiple domains are refuted (e.g., Bavelier, Green, Pouget, & Schrater, 2012; Jaeggi et al., 2008; Tierney, Krizman, & Kraus, 2015). Regarding practical implications, the obvious conclusion is that, to date, professional and educational curricula should focus on domain-specific knowledge rather than general and allegedly transferable skills.”

Giovanni Sala, N. Deniz Aksayli, K. Semir Tatlidil, Tomoko Tatsumi, Yasuyuki Gondo, Fernand Gobet; Near and Far Transfer in Cognitive Training: A Second-Order Meta-Analysis. Collabra: Psychology 1 January 2019; 5 (1): 18. doi: https://doi.org/10.1525/collabra.203

“In this short opinion piece I first introduce the concepts of near and far transfer, as described in the psychological literature. I then use a second-order meta-analysis on cognitive training to evidence that near transfer may be common and relatively easy to achieve, yet achieving far transfer is far less straightforward.

Nonetheless, many technologies, tools and methods make larger-than-life claims of encouraging far transfer from cognitive or perceptual-cognitive training to sports performance.

In this opinion piece I argument, using evidence from research studies on stroboscopic vision, neurofeedback training and the measurement and development of executive functions, that the claims made for the beneficial effects of these training methods on sports performance, esports performance and football expertise are likely exaggerated.

I conclude by reiterating that these claims of far transfer are not substantiated in the scientific literature, and much greater scrutiny of these claims by researchers is needed in order to assist practitioners to make better-informed decisions about tools, methods and technologies that may aid sports performance.

Fransen, J. (2024). There is no evidence for a far transfer of cognitive training to sport performance. Human Movement Sciences, University Medical Centre Groningen

https://doi.org/10.51224/SRXIV.182

“Considerable research has been carried out in the last two decades on the putative benefits of cognitive training on cognitive function and academic achievement. Recent meta-analyses summarizing the extent empirical evidence have resolved the apparent lack of consensus in the field and led to a crystal-clear conclusion: The overall effect of far transfer is null, and there is little to no true variability between the types of cognitive training. Despite these conclusions, the field has maintained an unrealistic optimism about the cognitive and academic benefits of cognitive training, as exemplified by a recent article (Green et al., 2019). We demonstrate that this optimism is due to the field neglecting the results of meta-analyses and largely ignoring the statistical explanation that apparent effects are due to a combination of sampling errors and other artifacts. We discuss recommendations for improving cognitive-training research, focusing on making results publicly available, using computer modeling, and understanding participants’ knowledge and strategies. Given that the available empirical evidence on cognitive training and other fields of research suggests that the likelihood of finding reliable and robust far-transfer effects is low, research efforts should be redirected to near transfer or other methods for improving cognition.”

As discussed earlier, our meta-analyses clearly show that cognitive training does not lead to any far transfer in any of the cognitive-training domains that have been studied. In addition, using second-order meta-analysis made it possible to show that the between-meta-analyses true variance is due to second-order sampling error and thus that the lack of far transfer generalizes to different populations and different tasks. Taking a broader view suggests that our conclusions are not surprising and are consistent with previous research. In fact, they were predictable. Over the years, it has been difficult to document far transfer in experiments (Singley & Anderson, 1989; Thorndike & Woodworth, 1901), industrial psychology (Baldwin & Ford, 1988), education (Gurtner et al., 1990), and research on analogy (Gick & Holyoak, 1983), intelligence (Detterman, 1993), and expertise (Bilalić et al., 2009). Indeed, theories of expertise emphasize that learning is domain-specific (Ericsson & Charness, 1994; Gobet & Simon, 1996; Simon & Chase, 1973). When putting this substantial set of empirical evidence together, we believe that it is possible to conclude that the lack of training-induced far transfer is an invariant of human cognition (Sala & Gobet, 2019).”

Gobet F, Sala G. Cognitive Training: A Field in Search of a Phenomenon. Perspect Psychol Sci. 2023 Jan;18(1):125-141. doi: 10.1177/17456916221091830. Epub 2022 Aug 8. PMID: 35939827; PMCID: PMC9903001.

https://doi.org/10.31234/osf.io/vxzq9

“At present, numerous studies can be found in which influences and relationships between the principal executive functions, reading comprehension, and academic performance associated with reading are reported. However, there is still a lack of convergence regarding the impact of computerized cognitive training on children’s executive development and its transfer in academic reading performance and comprehension of written texts.

Methods

This study analyzes the effect of implementing a cognitive stimulation program on the performance of reading comprehension and academic performance in the subject of Spanish Language and Literature. To this end, a total sample of 196 children from 23 educational centers received the cognitive intervention for 8 weeks, with three weekly sessions of between 15 and 20 min each occurring on non-consecutive days. Pre-test and post-test measurements were collected and analyzed.

Results

The results demonstrate a significant increase in the reading comprehension scores. In addition, a significant impact of the training on the participants’ academic performance in the subject Spanish Language and Literature was found.”

“Recent studies have highlighted the potential of computerized cognitive training for the development of children’s executive functions and many related components associated with academic achievement (Conesa and Duñabeitia, 2021) and decision-making (Sánchez-Castañeda et al., 2021).

The results reported in this study demonstrate that children increase their reading comprehension performance after completing a cognitive stimulation program, suggesting that the implementation of gamified activities as part of a computerized cognitive training is a valid tool to improve children’s reading skills.

Interestingly enough, these results align with previous research using the same platform for training certain executive functions in adults (Horowitz-Kraus and Breznitz, 2009; Shiran and Breznitz, 2010) and adolescents (Horowitz-Kraus and Breznitz, 2014), also demonstrating its impact in reading comprehension development.

An important aspect of the current results is the mediating role of the age of the participants, showing that the implementation of a gamified program for the cognitive stimulation of the executive functions related to working memory and inhibitory control results in a larger impact on the performance demonstrated in reading comprehension in younger than in older children.

The evaluated participants in an age range between 9 and 10 years old showed a more significant increase in their reading competence after performing the intervention than the participants between 11 and 12 years old.”

Reina-Reina C, Conesa PJ, Duñabeitia JA. (2023). Impact of a cognitive stimulation program on the reading comprehension of children in primary education. Front Psychol. 2023 Jan 6;13:985790. doi: 10.3389/fpsyg.2022.985790. PMID: 36687904; PMCID: PMC9853897.

https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.985790/full

“This paper used the Early Childhood Longitudinal Study-Birth Cohort (N = 1,258) to examine the influence of hilevels of cognitive stimulation from mothers, fathers, and childcare providers at 24 months and children’s pre-academic skills at 48 and 60 months in two parent families.

Results from path analysis showed direct positive effects of fathers’ early cognitive stimulation on early reading and math skills at 48 and 60 months. There were also two moderated effects: The effects of high levels of maternal stimulation at 24 months on early math and reading skills at 48 months were largest for children also receiving high levels of cognitive stimulation from their childcare providers.

Implications for including fathers in studies of the home cognitive stimulation and strengthening the parent-childcare connection are discussed. … They emphasize the unique role that mothers, fathers, and child-care providers play in getting children ready for school:

Father effects seem to be direct and long-lasting, mother effects seem to be mostly interactive (with child-care providers). Our findings also suggest that child-care centers and other programs for young children should include both mothers and fathers into their early education programs, as these are more likely to pay dividends compared to programs where only mothers are included.”

Cabrera NJ, Jeong Moon U, Fagan J, West J, Aldoney D. Cognitive Stimulation at Home and in Child Care and Children's Preacademic Skills in Two-Parent Families. Child Dev. 2020 Sep;91(5):1709-1717. doi: 10.1111/cdev.13380. Epub 2020 Jul 25. PMID: 32712964.

https://pubmed.ncbi.nlm.nih.gov/32712964/

“This paper used longitudinal data from five studies conducted in Bangladesh, Bhutan, Cambodia, Ethiopia, and Rwanda to examine the links between family stimulation and early childhood development outcomes (N = 4904; M _age = 51.5; 49% girls). Results from random‐effects and more conservative child‐fixed effects models indicate that across these studies, family stimulation, measured by caregivers’ engagement in nine activities (e.g., reading, playing, singing), predicted increments in children's early numeracy, literacy, social‐emotional, motor, and executive function skills (standardized associations ranged from 0.05 to 0.11 SD). Study‐specific models showed variability in the estimates, with null associations in two out of the five studies. These findings indicate the need for additional research on culturally specific ways in which caregivers may support early development and highlight the importance of promoting family stimulation to catalyze positive developmental trajectories in global contexts.”

Cuartas J, McCoy D, Sánchez J, Behrman J, Cappa C, Donati G, Heymann J, Lu C, Raikes A, Rao N, Richter L, Stein A, Yoshikawa H. Family play, reading, and other stimulation and early childhood development in five low-and-middle-income countries. Dev Sci. 2023 Nov;26(6):e13404. doi: 10.1111/desc.13404. Epub 2023 Apr 28. PMID: 37114644; PMCID: PMC11475363.

https://pmc.ncbi.nlm.nih.gov/articles/PMC11475363/

This next segment is the original, broader document and includes earlier periods.

Process weaknesses and literacy

From time to time, a program is developed claiming that it holds the key that unlocks the problems of learning afflicting one or more cohorts of learners. The developers often claimed that there is a weakness in some underlying cognitive process that is impeding these students from attaining a skill, such as reading. The new program boosts this process, removing the blockage without the need for any instructional focus on reading, and the student, now unleashed, is able to progress rapidly and catch up with his peers. The process may or may not appear related to the task of learning to read. Often, the selected process does bear a relationship with reading, and hence may have an intuitive appeal. Other such approaches appear speculative at best.

When a program is developed that, it is argued, underpins literacy, then techniques designed to enhance that specific process must also produce a positive impact on literacy. For example, there has been recent interest in working memory training (WM) programs. It is well understood that students with strong working memory are able to hold information whilst performing operations with that information. In contrast to those with lesser working memory, they do so without their mind wandering and losing track of the task. Working memory is also known to be associated with numerous academic skills. Thus, it is tempting to target working memory, in particular for those students who struggle with academic tasks. But if you can show improvement in some WM abilities, will it necessarily be reflected in performance in, say, reading? For more on working memory research, see Brain training programs, such as working memory training. Do they aid reading development?

Near Transfer

The concepts of near and far transfer are useful in addressing this issue. Near transfer involves instruction that is successful in producing attainment of the actual skills being directly taught to the learner. For example, my Olfactory Reading Program© is designed to improve reading by enhancing students’ sense of smell (scam alert!). I claim to have tested hundreds of children, and noticed that skilled readers are usually olfactorily superior; whereas, struggling readers tend to be olfactorily challenged. Also, neuroscience has shown that thousands of new olfactory bulb neurons are produced every day in the rodent brain (true). There, I’ve even addressed the new essential program component in education – neuroplasticity! Therefore, I figured, if I could improve struggling readers’ sense of smell, then, voilà! their reading will quickly progress. Sure enough, my training group improved on my smell test by following my program. Thus, their reading must have progressed. I have some cool anecdotes from satisfied customers. Just look at my website under the heading –Research. Like my program? Just send me money.

So, that was an example of successful near transfer. You build your program on working memory or olfactory expertise or whatever other process you consider to be related to reading progress. If training it enhances the learner’s performance in tasks similar to your trained examples, then near transfer has occurred.

Far transfer occurs when there is transfer of learner knowledge and skills from the taught context to another dissimilar context.

Far Transfer

Let’s assume you’ve shown that the students have now improved their knowledge/skill in the process being addressed. Can they do things beyond that which you taught them? Does improvement in this capacity of itself produce improvement in the targeted literacy domain? If so, this represents far transfer. It is something of a holy grail in education.

“It is these underlying basic processes, insights, and modes of cognizing that are reputed to have enduring applicability beyond the specific lessons in which they are taught (see Sewell, Hauser, & Featherman, 1976; Walberg, 1982; Wiley, 1976). Hence, it is a presupposition of educators that a student taught to permute a set of items in school will transfer this skill to sets of items confronted outside of school; a student taught arithmetic will transfer this knowledge to calculate a bowling average; a student taught to organize items hierarchically will transfer this skill to answering questions on IQ tests (Ceci, 1991). The transferability of learning is of prime importance in evaluating these educational claims … If the skills developed by such efforts do not transfer beyond the training context, much of the investment may be considered wasted, as noted in a National Research Council report on enhancing human performance (Druckman & Bjork, 1994). Nonetheless, this is precisely the criticism that a number of scholars have made about the failure of transfer studies to document that training in one context or on one type of problem generalizes to related problems in different contexts. Consider these examples:

“Transfer has been one of the most actively studied phenomena in psychology. ….Reviewers are in almost total agreement that little transfer occurs.” (Detterman, 1993, p. 5, 8).

“The question for which we do have some empirical answers has to do with how generalizable cognitive training is from one subject area to another. As of now, the answer is not very much (Schooler, 1989, p. 11).” (Barnett & Ceci, 2002, 613-614)

So, the research-based perspective is that far transfer in education doesn’t usually occur – leading to one possible conclusion exemplified by the statement: What you teach is what you get, and where you teach it is where you get it (anon). In other words, directly teaching the requisite academic skills has produced superior outcomes to process training. That doesn’t imply impossibility for cognitive process training to enhance academics, but strong evidence will be required before a research consensus about its value is likely to be obtained.

Examples of processes

The history of education has been littered with claims of resolving educational problems by improving underlying processes presumed to be important. For example, visual perception, vergence, strabismus, scotopic sensitivity, balance, primitive reflexes, auditory processing speed, executive function, perceptual motor skill, brain patterns, balance, working memory, and so on. Generally speaking, these endeavours have not been rewarded (Arter & Jenkins, 1979). In some cases, the interventions didn’t improve the underlying processes; in some, they did improve the underlying processes but had no impact on reading skill. In others, there were difficulties in accurately assessing the processes and/or teaching them effectively. Many have followed this mantra - remove the obstacle to learning and attainment will then occur.

Empirical research has demonstrated that the underlying process approach so far represents an educational cul de sac. There were intractable problems in some or all of: ascertaining precisely what these fundamental processes were, reliably and validly assessing them, and then achieving far transfer.

“Process training has always made the phoenix look like a bedraggled sparrow. You cannot kill it. It simply bides its time in exile after being dislodged by one of history's periodic attacks upon it and then returns, wearing disguises or carrying new noms de plum, as it were, but consisting of the same old ideas, doing business much in the same old ways” (Mann, 1979, p. 539).

For example, the perceptual-motor deficit theory was very strong in the 1960’s and 1970’s, and an industry of intervention programs erupted. Though some children became adept at, say, drawing lines accurately within parallel boundaries (near transfer), there was no reliable impact on reading progress (Arter & Jenkins, 1979). In fact, the meta-analysis performed by Kavale and Forness (1985) produced an overall effect size for perceptual-motor training of 0.08, which is considered a small effect (Cohen, 1988). What was not readily apparent at that time was that being explicitly taught to read was the most effective way to master many of those skills. Hence valuable instructional time was better spent on the target task.

“If the goal is for children to learn a particular skill (like reading), it is more efficient to teach it directly than to expect it to transfer from other learning” (Singer & Balow, 1981, p. 107).

Kavale’s (1990) summary of research into direct instruction concluded that the direct instruction is five to ten times more effective for struggling students than are practices aimed at altering unobservable learning processes such as perception.

More recently, Fuchs, Hale, and Kearns (2011) reviewed the evidence generally for such cognitively focussed aptitude-treatment interactions, asking the question: “Among low-performing students, do cognitively focused interventions promote greater academic growth than business-as-usual instruction?”(p.101). Their conclusions?

“There was no evidence for the notion that when a treatment is matched to a cognitive deficit it produces better effects. … Scientific evidence does not justify practitioners’ use of cognitively focused instruction to accelerate the academic progress of low-performing children with or without apparent cognitive deficits and an SLD label. At the same time, research does not support “shutting the door” on the possibility that cognitively focused interventions may eventually prove useful to chronically nonresponsive students in rigorous efficacy trials” (p.101-102).

As Fuchs et al. note, the history of failure of underlying processing approaches doesn’t mean that the next big thing won’t work. It simply means that an array of strong empirical, independent evidence is necessary - because to back yet another lame horse has serious implications for struggling students. Even if the interventions are non-harmful, there is an opportunity-cost for students (and often a financial cost to parents), and a residue of negative emotion for both parents and child when the approach has no discernible effect.

How is it determined whether approaches to reading are effective?

There are two major areas of interest in judging whether an approach has merit. The first is to consider whether the theoretical constructs behind the approach are consistent with what is known about a given educational issue. In technical terms, is there face validity? This is not a perfect pass-fail test, as occasionally a new paradigm makes earlier theories redundant or in need of modification. However, that is rare. In the case of the various approaches that implicate vision problems as the cause of reading problems, one would acknowledge that they sound plausible (to a greater or lesser degree) as an intervention focus. In fact, the early history of reading research emphasised visual over language-based causation of reading problems.

The second criterion goes beyond the theoretical relevance, and is the issue of whether addressing the reading problem by intervening at the visual level has a positive impact on reading (far transfer). So, it becomes then an empirical issue, rather than a purely theoretical one.

Reading and vision as an example

The reading process requires some quite tricky eye movements. For example, rather than the eyes moving smoothly across a line of print, they travel in little staccato-like jerks called saccades (covering about 8 letter spaces usually), followed by brief fixations (250 ms) during which we gain visual information. These figures are not invariant, and may vary markedly with differences in text difficulty. It’s easy to envisage problems occurring for some students in this complex visuo-perceptual coordination task, not to mention the impact of the complexity involved in fluent orthographic processing.

“Saccades challenge the visual system by producing abrupt changes in the retinal stimulus as the visual field image moves over the retina. Our brain ignores the retinal motion and compensates for the repositioning of gaze, generating perceptual constancy. Psychophysical studies in humans and electrophysiological data in primates indicate that, although not perceived, visual stimulation during saccades continues to be processed in the visual system, influencing processes at refixation (Ibbotson & Cloherty, 2009). The extent to which retinal motion modulates word processing in reading remains unknown. In addition to such visual effects, central mechanisms mediated by brain regions that control eye movements and attention alter visual processing after saccades. In primates, thalamic recordings typically reveal transsaccadic suppression followed by enhancement (Reppas et al., 2002; Royal et al., 2006). This pattern has been identified in a number of cortical visual areas (Ibbotson and Krekelberg, 2011), although results remain variable at the single-neuron level and controversial (Wurtz, 1969; DiCarlo and Maunsell, 2000; Gawne and Martin, 2002; Ibbotson et al., 2008; MacEvoy et al., 2008) Central suppression, reported from 100 ms before onset to 50 ms after the end of saccades, is thought to decrease the sensation of image motion in active vision (Burr et al., 1994; Ross et al., 2001). Postsaccadic facilitation lasting 200–400 ms presumably amplifies visual sensitivity at fixation (Ibbotson and Cloherty, 2009). In the absence of behavioral measures it is not known, however, if and how these opposite neural effects, individually or together, alter perception. Further, there is as yet no evidence that central postsaccadic mechanisms modulate word processing” (Temereanca et al., 2012, p.4482).

So, it’s quite a challenge to learn to read. Making it tougher is that, as opposed to the relative ease with which oral language develops, evolution hasn’t provided us with a dedicated brain module for reading.

“ … rather (it is) the result of a neuronal recycling from an area of the brain that evolution has dedicated to the recognition of certain forms, notably intersections of straight lines or curves’ (Quercia, Feiss, & Michel, 2013, p., 873).

“Reading is certainly the most complex oculomotor activity that modern humans use daily. The processing involved is classically separated into lower and higher levels. The first corresponds to the different steps involved in the ocular capture of the word’s image, which is the start of cerebral analysis in the occipital cortex. The second represents the different cognitive phenomena that permit the identification of and then represent and make sense of the word just read. The constant interdependence between these phenomena, notably during the oculomotor phase of reading, makes this separation artificial” (Quercia, Feiss, & Michel, 2013, p. 869)

Research has noted that there are numerous visual skills in which a proportion of students diagnosed with dyslexia have been shown to be deficient. For example, Quercia, Feiss, and Michel (2013) list a number of them:

“Numerous scientific studies have also documented the presence of eye movement anomalies and deficits of perception of low contrast, low spatial frequency, and high frequency temporal visual information in dyslexics. Anomalies of visual attention with short visual attention spans have also been demonstrated in a large number of cases. Spatial orientation is also affected in dyslexics who manifest a preference for spatial attention to the right. This asymmetry may be so pronounced that it leads to a veritable neglect of space on the left side. The evaluation of treatments proposed to dyslexics whether speech or oriented towards the visual anomalies remains fragmentary” (Quercia, Feiss, & Michel, 2013, p.869).

Vellutino and Fletcher (2005) also described some low level visual deficits, in particular involving the magnocellular system:

“Difficulties in learning to read have also been attributed to low-level visual deficits, in particular, visual tracking problems caused by oculomotor deficiencies (Getman, 1985); visual masking effects caused by a hypothesized deficit in the “transient visual system” (Badcock & Lovegrove, 1981; Breitmeyer, 1989; Lovegrove, Martin, & Slaghuis, 1986; Stein, 2001); and abnormalities in visual motion perception (Eden et al., 1996). Moreover, transient system and motion perception deficits have both been linked to dysfunction in the magnocellular visual subsystem. The magnocellular subsystem is one of two parallel components of the visual system, the other being the parvocellular system. The magnocellular system consists of large neurons that are sensitive to movement and rapid changes in the visual field. It is often called the “transient system,” insofar as it is presumed to be responsible for suppressing the visual trace that normally persists for a short duration (250 milliseconds) after a visual stimulus has disappeared. The parvocellular system consists of densely packed, small neurons that are sensitive to color and fine spatial details. In reading, the parvocellular system is believed to be operative during eye fixations and the magnocellular (transient) system is believed to be operative during saccadic movements of the eyes” (p.366).

Among other deficits that have been proposed as important are visual attention span (Bosse, Tainturier, & Valdois, 2007), and sluggish attentional shifting (Lallier, Donnadieu, Berger, & Valdois, 2010).

Correlation and causation

Such a list of apparent deficits sounds compelling; however, just because they co-occur doesn’t demonstrate that one causes the other. So, a demonstrated relationship between two events may not be a causal one, or it may be causal but not in the direction expected. Additionally, it is possible that both the correlated variables are actually caused by a third variable. There has been an analogous finding in medicine in relation to back pain. Imaging techniques (MRI, CT, X-ray, etc.) are often recommended by those endeavouring to treat back pain, a condition that most people experience to some degree at some time in their life. The images will often show problems with the structure of the back, and various therapies may be recommended to treat the apparent site of the back problem. However, the empirical evidence has found that treating the apparent problem is not superior to no treatment at all. So, intrusive operations can be performed with no positive impact on back pain, but often reduced mobility is a consequence. Most (not all) back conditions resolve themselves eventually anyway, whether the patient rests or simply remains active. Why should this be so? It seems counter-intuitive. When studies were performed in which people with no back pain were provided with imaging, the results were surprising. There were also wonky looking backs among the symptom-free population. This suggests that the structural problems shown in the images were incidental to the back pain, not central or causal. They appeared to be a likely source of the distress, but weren’t, because resolving the structural issue didn’t fix the problem.

In a similar manner, the presence of a range of visual problems among struggling readers does not of itself mean that they are causes of reading problems. Although significant visual differences have been found between individuals with dyslexia and normally developing readers, only about 30% of dyslexics are so affected (Ramus et al., 2003). As with the back pain analogy, one can find visual processing deficits in skilled readers too, which indicates that a visual processing deficit is not a defining characteristic of dyslexia The identified oculomotor anomalies are considered by the majority of researchers to be secondary to difficulties of cognitive analysis of language (Quercia, Feiss, & Michel, 2013).

"Although it may be accurate that many students with LD have underlying neurological and/or processing disorders, researchers and educators have been singularly unsuccessful at reliably identifying these difficulties and designing specific treatments to remediate them. … However, it is important to note that despite lack of support for process identification and treatment models, they continue to persist." (Vaughn & Linan-Thompson, 2003, p. 141).

Another finding regarding dyslexic readers’ eye movements was that they were subject to a higher rate of regressive eye movements than good readers. That is, their focus frequently shifted back to the left along the line instead of the right. It accompanies, therefore it causes. "Reading relies on vision; this child has a visual problem; therefore, we should fix the visual problem in order to resolve the reading problem." The product of this faulty logic was that a lot of children wasted potential instructional time with eye exercises. The trouble was that regressive eye movements do not cause reading problems - they are a consequence of reading problems. If you fix the reading problem through effectively teaching the alphabetic concept and provide adequate opportunities for practice, lo and behold, the regressive eye problems become normalised.

On the other hand, even if such processing skills are not a significant cause of reading problems, there could be a role for visual skill assessment as early predictors of reading problems. If it can be shown that these skills can be assessed in quite young children (prior to the introduction of reading instruction), it may be possible to discover and intervene earlier than occurs at present – after reading failure has occurred (Facoetti et al., 2010). The visual attention task used in one study employed tests that asked children to pick out specific symbols in the presence of visual distractions (Franceschini, Gori, Ruffino, Pedrolli, & Facoetti, 2012). It may also eventuate that this assessment will produce too many false positives. That is, it may identify students with visual problems, and some of them may be at-risk for reading problems. However, it may also identify those students with issues with their vision who would have no subsequent reading difficulties. Obviously, more research is required before this early prediction via visual processing assessment can be considered as worthwhile.

A final comment on vision and reading

“It is important in children with learning difficulties to exclude visual acuity problems. Few professionals would argue against the notion that every child with difficulty should have either an accurate and valid visual acuity screening test performed by a competent professional or, alternatively, should be assessed by an ophthalmologist or an optometrist. Where there are problems with visual acuity, it is appropriate to recommend corrective lenses. In the rare circumstance where other significant visual pathology is detected, then treatment should be directed appropriately.” (National Health & Medical Research Council of Australia, 2009).

So, when we read of cognitive processes underlying language and literacy problems, it is important to ask how strong is the evidence for far transfer when attempts are made to ameliorate the process. Of course, if instruction in language and literacy is provided along with the process training, the cause of any positive outcomes becomes extremely difficult to disentangle, and it may appear that the whiz-bang new therapy was the cause of the improvement (apparent far transfer).

Some relevant quotes on various cognitive dimensions:

“Researchers are yet to reach an agreement about the actual possibility of obtaining far transfer of skills. Some authors have suggested, directly or indirectly, that the lack of far transfer is a fundamental characteristic in human cognition (e.g., Chase & Ericsson, 1982; Detterman, 1993; Sala & Gobet, 2017a; Simons et al., 2016). According to them, domain-specific skills acquired by training exert an impact on the relevant domain but hardly generalize to other domains. Moreover, even transfer of skills from one particular field of expertise to one of its sub-domains appears to lead to significant decrease in performance (e.g., Bilalić, McLeod, & Gobet, 2009; Rikers, Schmidt, & Boshuizen, 2002). This line of research is not inconsistent with the fact that some people manage to excel in more than one domain. However, it does not offer an explanation based on transfer. Rather, people with superior cognitive ability are more likely to excel in several domains because they acquire knowledge and process information better and faster than the general population (e.g., Burgoyne et al., 2016; Campitelli & Gobet, 2011; Chassy & Gobet, 2010; Detterman, 2014; Schmidt, 2017).” (p.9-10)

Sala,G., Aksayli, N.D., Tatlidil, K.S., Tomoko Tatsumi, T., Gondo, Y., & Gobet, F. (2019). Near and far transfer in cognitive training: A second-order metaanalysis. Collabra Psychology, 5(1), 18, https://doi.org/10.1525/collabra.203

“In the present meta-analysis we examined the near- and far-transfer effects of training components of children’s executive functions skills: working memory, inhibitory control, and cognitive flexibility. We found a significant near-transfer effect (g 0.44, k 43, p < .001) showing that the interventions in the primary studies were successful in training the targeted components. However, we found no convincing evidence of far-transfer (g 0.11, k 17, p> .11). That is, training a component did not have a significant effect on the untrained components. By showing the absence of benefits that generalize beyond the trained components, we question the practical relevance of training specific executive function skills in isolation. Furthermore, the present results might explain the absence of far-transfer effects of working memory training on academic skills (Melby-Lervag & Hulme, 2013; Sala & Gobet, 2017).” (p.165)

Kassai, R., Futo, J., Demetrovics, Z., & Takacs, Z.K. (2019). A meta-analysis of the experimental evidence on the near- and far-transfer effects among children’s executive function skills. Psychological Bulletin, 145(2), 165–188.

“As we have seen, cognitive training tends to produce only ‘near transfer’, that is, performance improvements to the task trained on or similar tasks, rather than more broadly across cognition (Sala & Gobet, 2017). Nevertheless, there remains a desire within educational neuroscience to find elusive activities that bring more widespread benefits to cognition. There is enthusiastic engagement from teachers/ public/media when such possibilities are raised, enthusiasm which is sometimes in advance of the actual science. This trend for seeking general benefits can also be found within the commercial sector in so-called ‘brain training’ products – though to date, commercial products have not yet achieved the advertised far transfer (Simons et al., 2016). Within educational neuroscience, a number of possibilities are currently under investigation, among them executive function training (Diamond & Ling, 2016), mindfulness training (see Felver et al., 2016), playing chess (Sala & Gobet, 2016), action video game playing (Bediou et al., 2018), learning a musical instrument or a second language (Moreno, Lee, Janus, & Bialystok, 2015), sleep (Sharman, Illingworth, & Harvey, in press) and aerobic fitness training (see Ruiz-Ariza et al., 2017). The jury is still out on many of these activities. Research is rendered more difficult on the one hand by the frequent lack of random allocation of participants to conditions, allowing for the possibility of confounds (e.g. that in some populations, bilinguals might also have higher education or SES levels; that those who successfully learn musical instruments may be more intelligent or dedicated to practise; that those who persistently play action video games may tend to have faster sensorimotor responses); and on the other hand, by the challenge of designing intervention studies to achieve random allocation. One recent review of the cognitive benefits of action video game playing suggested that intervention studies with random allocation produced effect sizes of around a third of the size of those observed in correlational studies without random allocation (Altarelli, Green, & Bavelier, in press). This suggests both possible effects of the training, and also that there are pre-existing differences between the sorts of young adults who frequently play action video games compared to those who do not. After promises of broad benefits, detailed research has sometimes revealed that transfer is not as far as first anticipated [e.g. for action video games, the main transfer is to selective visual and auditory attention (Altarelli et al., in press); working memory training improves performance on other working memory tasks but not other components of executive functioning, such as cognitive flexibility or inhibition (Diamond & Ling, 2016; Melby-Lervag, Redick, & Hulme, 2016)]. In any event, for each of these putative generally beneficial activities, it is desirable for investigators to propose and evaluate the cognitive and brain structures that mediate the transfer from training task to other cognitive skills. The less plausible the underlying mechanistic basis for the transfer, the more critically the published evidence in favour of the transfer must be examined. (p.485-486)

Thomas, M.S.C., Ansari, D., & Knowland, V.C.P. (2019). Annual research review: Educational neuroscience: Progress and prospects. Journal of Child Psychology and Psychiatry, 60(4), 477-492.

“'Brain training', or the goal of improved cognitive function through the regular use of computerized tests, is a multimillion-pound industry, yet in our view scientific evidence to support its efficacy is lacking. Modest effects have been reported in some studies of older individuals and preschool children, and video-game players outperform non-players on some tests of visual attention. However, the widely held belief that commercially available computerized brain-training programs improve general cognitive function in the wider population in our opinion lacks empirical support. The central question is not whether performance on cognitive tests can be improved by training, but rather, whether those benefits transfer to other untrained tasks or lead to any general improvement in the level of cognitive functioning. Here we report the results of a six-week online study in which 11,430 participants trained several times each week on cognitive tasks designed to improve reasoning, memory, planning, visuospatial skills and attention. Although improvements were observed in every one of the cognitive tasks that were trained, no evidence was found for transfer effects to untrained tasks, even when those tasks were cognitively closely related.”

Owen, A.M., Hampshire, A., Grahn, J.A., Stenton, R., Dajani, S., Burns, A.S., Howard, R.J., & Ballard, C.G. (2010). Putting brain training to the test. Nature, 465(7299), 775-8.

"The results of our study, in combination with many meta-analyses (Melby-Lervåg et al., 2016, Sala and Gobet, 2017, Soveri et al., 2017) demonstrates that cognitive training fails to provide strong evidence for far-transfer effects.” (p.13)

Landis, T.D., Hart, K.C., & Graziano, P.A. (2018). Targeting selfregulation and academic functioning among preschoolers with behavior problems: Are there incremental benefits to including cognitive training as part of a classroom curriculum? Child Neuropsychology, 1-17. DOI: 10.1080/09297049.2018.1526271

“To conclude, the current results show that individuals already higher in WM or Gf are the ones that show greater WM training gains. This result is particularly concerning for those who wish to train lower performing students up to the level of their higher performing peers, under the assumption that individuals who show greater WM training gains should subsequently exhibit more transfer.” (p.183)

Wiemers, E.A., Redick, T.S., & Morrison, A.B. (2019). The influence of individual differences in cognitive ability on working memory training gains. Journal of Cognitive Enhancement, 3, 174–185.

“The prediction of generalized transfer from improvement in perceptual tasks to complex linguistic tasks, implicitly assumes a bottom-up pattern of improvement and transfer. For example, Tallal et al. (1996) assumed that sluggish sensory processing underlies the deficits of individuals with language and reading disabilities in both simple pitch discrimination tasks (e.g., Tallal, 1980) and in acquiring expert linguistic skills. The underlying hypothesis was that training with simple tasks will “upgrade” sensory processing, and consequently improve the representations of more complex phonetic and then phonological representations. The expected outcome was improved phonological memory and phonological awareness. A different conceptualization is proposed by the Reverse Hierarchy Theory of perceptual learning (RHT; Ahissar & Hochstein, 2004; Ahissar et al., 2009). It proposes that learning progresses in a top– down manner. Improvement is driven by gradually gaining access to informative lower-level representations, by applying a top– down driven search for reliable inputs. Thus, training with two-tone discrimination yields improvement because of better task-specific access rather than because of improved sensory tuning at lower processing stages. Therefore, no transfer to tasks that do not benefit directly from this implicitly trained strategy is expected. It is important to note that this later claim is not in contradiction to the claim that training induces brain plasticity. Indeed, our shared assumption is that changes in behavior after training do reflect modifications in the brain; nonetheless, brain plasticity does not indicate transfer.” (p.15

“Sensitivity to various dimensions of an auditory signal, specifically to frequency discrimination (FD) has been shown to be related to higher linguistic abilities. For example, reading skills are correlated with discrimination thresholds, both in the general population (Ahissar, Protopapas, Reid, & Merzenich, 2000) and in individuals with language impairments (Specific Language Impairment: McArthur & Bishop, 2004; Mengler, Hogben, Michie, & Bishop, 2005; Dyslexia: Ahissar, Lubin, Putter-Katz, & Banai, 2006; Halliday & Bishop, 2006). More important, poor FD abilities at infancy predict subsequent linguistic difficulties at toddlerhood (Cantiani et al., 2016). … Training WM. WM training typically involves training with a single, or a combined dual, task rather than with several tasks. A common protocol involves training with a single (or dual) n-back task (reviewed in Morrison & Chein, 2011). It was claimed that both WM abilities and a range of other cognitive abilities are enhanced by these training regimes (e.g., Foy & Mann, 2014; Jaeggi et al., 2008, 2011; Söderqvist & Bergman Nutley, 2015). However, all the studies that reported improvement in mildly far tasks lack control for rewarding challenges, enthusiastic experimenters, and participants, as noted in a series of critical review studies (Dougherty, Hamovitz, & Tidwell, 2016; Jacoby & Ahissar, 2013, 2015; Redick et al., 2013; Shipstead, Redick, & Engle, 2010; Shipstead, Hicks, & Engle, 2012), and meta-analyses (Melby-Lervåg & Hulme, 2013; Redick & Lindsey, 2013; Soveri, Antfolk, Karlsson, Salo, & Laine, 2017). Indeed, when a similarly challenging task was trained by an active control group, there was no evidence of relative benefit to the group trained with a WM n-back task (e.g., Hitchcock & Westwell, 2017; Metzler-Baddeley, Caeyenberghs, Foley, & Jones, 2016; Redick et al., 2013; Thorell, Lindqvist, Bergman Nutley, Bohlin, & Klingberg, 2009; Vermeij, Claassen, Dautzenberg, & Kessels, 2016; Wayne et al., 2016).” (p.2, 9)

Jakoby, H., Raviv, O., Jaffe-Dax, S., Lieder, I., & Ahissar, M. (2019). Auditory frequency discrimination is correlated with linguistic skills, but its training does not improve them or other pitch discrimination tasks. Journal of Experimental Psychology. General. PMID 30843719 DOI: 10.1037/xge0000573

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