Author Archives: Gabriella Hirsch

It’s no secret that the American education system is saturated with standardized tests.

As of 2016, the average student in America takes a staggering 112 mandatory standardized tests before graduating high school. This averages out to be eight a year according to one 2-year study by the Council of Great City Schools¹.

The tests are “standardized” because all students answer the same questions under comparable conditions and their responses are scored using measured criteria, whether the questions are multiple choice or open-ended².

However, there are numerous reasons to believe that high stakes standardized tests are actually quite damaging to education and have received forceful criticism over the past dozen years as a result. Examples include their propensity to drive out teachers, encouraging teaching “to the test” as well as increasing grade retention and school dropout rates, all of which question the imposition of high quantities of standardized tests throughout a student’s school career².

Furthermore, the disadvantages that standardized tests pose for the many students who take them are substantial. Although these tests were conceptualized to ensure fairness and equity for all, the reality is much more grim. In addition to the problematic application for students with diagnosed (and undiagnosed) learning disabilities and to non-native English speakers, these tests are unfair to countless others due to a host of social, cultural, economic and even biological reasons. One example is the marked disadvantage for students from underprivileged groups, for which there is a proven departure between test scores and actual academic potential³.

In other words, standardized tests can undermine the very abilities they seek to assess.

The goal of this article is to look “under the hood” into some of the research investigating what occurs in the brains and minds of test takers, with an emphasis on studies that employ neurophysiological (e.g. EEG) and functional neuroimaging (e.g. fMRI) methods. These methods allow researchers to look inside the working brain to explore the neural underpinnings of the psychological processes that so many students experience during test taking. The hope is that, by better understanding these mechanisms, we can craft better environments for teaching and evaluating our students.

This article will be limited to discussing neuroscience research on three interrelated psychological processes that occur during standardized test taking, namely i) testing anxiety, ii) “choking” under pressure and iii) stereotype threat. However, it should be noted that this is by no means a comprehensive list and should be viewed as a small glimpse into what happens in the brain during high-pressure standardized testing. 

The consequences of falling into socially constructed stereotypes 

Over the past twenty years, stereotype threat has become one of the most investigated topics in social psychology. It invokes the age-old nature vs nurture debate, as well as questions related to group differences observed in cognitive performance.

Broadly speaking, stereotype threat can be defined as a situation in which individuals believe they are at risk for confirming negative beliefs about their social group. It rose to prominence via a pioneering 1995 study by Steele and Aronson⁴, which found that African American student performance varied depending on the immediate messaging from the environment. Aware of the negative stereotypes regarding their intellectual abilities and thus fearing confirming the stereotype, students exhibited suboptimal performance when asked to display such abilities (in the case of Steele and Aronson, a difficult verbal test) after being reminded of their stereotyped group membership. African American students performed better in a control condition, in which they were not primed with the threat. As a result, the concept is considered highly salient within education.

These findings have been replicated by many others over the years and are generalizable to other stereotyped groups, including (but not limited to) women’s mathematical abilities⁵ and memory recall abilities in elderly populations⁶. Importantly, enthusiasm for this work is bolstered by the notion that these findings help explain the discrepancies observed in academic and cognitive performance among at-risk groups⁷, undermining the assumption that the differences are a result of innate (in)ability.

In an attempt to review the reasons behind impaired performance, one review article⁸ revealed patterns of activation demonstrating that individuals “under threat” have heightened activity in parts of the brain devoted to regulating emotions. Simultaneously, they showed decreased activation in areas typically associated with executive function (such as working memory and attention).

In another study⁹ evaluating a negative stereotype regarding women’s spatial reasoning skills, researchers found that in addition to activation in areas of the brain associated with spatial rotation, participants in the threat condition also had increased activity in regions of the brain associated with emotion regulation and social knowledge (e.g. the rostral-ventral anterior cingulate cortex and right orbital gyrus) synonymous with feelings of anxiety regarding one’s social group.

These results are consistent with a study from 2014 using EEG¹⁰ – a physiological measure favored for its ability to secure very accurate readings of when electrical activity occurs in the brain – which investigated the effect of stereotype threat on women’s performance on a series of math problems. Here, the authors reported that the threat condition negatively affects the anterior cingulate cortex and the dorsolateral prefrontal cortex, which was not found in women placed in the condition in which the stereotype threat was not activated. In other words, the bias caused by the stereotype threat had damaging consequences for attention, memory and general information processing – systems highly likely to play a key role in undermining performance in the math task.

Collectively, what these studies illustrate is that participants under stereotype threat are hypothesized to do worse due to the allocation of important cognitive resources to things like emotion regulation and social knowledge as opposed to concentrating on the task at hand.

Although these studies test abilities within an isolated experimental environment, educational neuroscientists studying the effects of high-stakes testing argue that even when not primed, at-risk groups “underperform relative to their ability merely because they are aware of a negative stereotype about how they should perform”¹¹. An example of these worries can be seen in the female math test-taker who is apprehensive given the stereotype that “guys are better than girls at math”¹¹. 

“Choking” under pressure & testing anxiety

Another dire consequence of high-stakes testing is choking under pressure, a phenomenon associated with poor performance on a cognitive task and one easily relatable to standardized test taking. This is the tendency for some individuals to have their performance hindered by the stress of a given situation. In the case of standardized testing, many controlled laboratory experiments have found evidence of this on a wide range of tasks, most notably math problem solving, category learning, and tests of fluid intelligence.^12,13,14

But what are the mechanisms behind choking under pressure? Researchers have detailed three (not mutually exclusive) explanations for why testers choke under pressure; i) the “distraction account” whereby the pressure of doing well distracts the individual from the task(s) at hand, ii) the “over-monitoring account”, when task performance is worsened due to a hyper vigilance in attending to every required step and finally iii) the “over-arousal account” leaving the person in a heightened, and often stressed, emotional state due to the lingering fear of large losses.^{15, 16}

The Distraction Model vs. The Over-Monitoring Model 

The “distraction” model can be understood in terms of a distraction caused by the pressure to do well on a demanding task (e.g. math problem solving), making it difficult for a person’s working memory to perform optimally. By contrast the “over-monitoring” model argues the opposite of the “distraction” model: instead of being distracted by the pressure to do the task at hand, this theory argues that the extra attention, control and motor operation given to the task actually hinders performance¹⁶. In other words, focusing too much on specific details of a task can take away from the cognitive “horsepower” (e.g. executive function) needed to complete the task successfully.

Although these theories diverge, they are not necessarily mutually exclusive; both can occur simultaneously depending on the nuances and demands of the task as well as the source(s) of motivation provided. Indeed, both theories are supported by fMRI evidence revealing a statistically meaningful relationship between choking and compromised executive control in prefrontal areas of the brain.

Strikingly, further analyses in this area has suggested that individuals with higher working memory capacities are actually even more susceptible to this kind of stress than people with lower working memory capacities, given their reliance on this strength to perform well.¹⁷

The Over Arousal Model 

The third model concerns the neural mechanisms behind reward and motivation. One theory favored by behavioral economists suggests that degraded performance is a result of the flooding of emotion due to high pressure or incentive. You can think of it along the lines of being stressed out by thinking about how big the stakes are. To make matters worse for standardized test takers, many studies have shown that performance is only diminished when the task at hand is one that is complex or not well-learned, such as the ones required during test taking^{18, 19}. By contrast, this phenomenon is not observed during very simple conditioning tasks such as those that require considerable physical effort (e.g. weight lifting), whereby the increased incentive or social pressure can actually enhance performance.²⁰

From a neural perspective, one study found significant deactivation in areas of the brain that are crucial for memory recall as well as hormone and emotion regulation (e.g. the hippocampus, hypothalamus, medial orbital cortex and ACC) in participants subjected to the stress condition. These participants also displayed increased levels of cortisol, which is linked to heightened stress or emotion²¹. Specifically, the extent of deactivation in the parts of the brain implicated in memory was strongly related to the release of cortisol in response to a stress task.

Test Anxiety

A similar yet distinct notion common amongst standardized test takers is that of test anxiety, which can come into play during any of the situations discussed previously. Generally speaking, test anxiety refers to an unpleasant emotional and physiological reaction that can include feelings of worry, fear of failure or dread before or during a test. The two key features of test anxiety are thought to be i) emotionality (i.e. feeling anxious) and ii) worry. This is corroborated by neuroimaging evidence showing test anxiety to involve enhanced attention devoted to the threat and poor ability to control the feelings of threat.

Some theorists have conceptualized test anxiety as a dispositional factor, whereby “test trait anxiety” can be considered a trait of a person’s personality that make them predisposed to experience greater-than-average levels of stress or anxiety during a testing situation²². Others have defined it as a genuine phobia, based on the elevated physiological responses linked to heightened emotion and stress²³.

Regardless of its characterization, test anxiety (with math anxiety specifically being the most common) is highly prevalent, with one source claiming it affects an estimated 25% of 4-year college students and up to 80% of community college students in the U.S.²⁴. One such sources comes from outside the U.S., where researchers from the University of Grenada found 6 in 10 Spanish university students to suffer from math anxiety, with greater incidence among women compared to men²⁵.

Crucially, a fascinating 2011 study showed math anxiety doesn’t just occur during math problem solving, but also in anticipation of doing math. Using fMRI, the researchers were able to detect differences in brain activation between the anticipation stage and actually doing the math task²⁶. What they found was that high-anxiety individuals showed increased fronto-parietal network, which is linked with control of negative emotions. In a follow-up study, they revealed that this anticipation stage is also associated with pain networks in the brain (e.g. mid-cingulate cortex and insula).

This suggests that for some people, merely being faced with the prospect of doing math can be psychologically painful²⁷.

To muddy the waters, reports have pointed to the similarities between stereotype threat and test anxiety and how they may overlap¹¹. Although further research is needed to pinpoint interactive effects, recent studies have indicated test anxiety and stereotype threat might share certain mechanisms within the brain, such as their mutual negative impact on working memory. Furthermore, it has been found that stereotype threat perpetuates states of worry, with worry being a key element in test anxiety. However their interaction is a complex one not yet fully fleshed out in the literature²².

Despite this evidence, it stands today that the primary objective of standardized tests is to reliability measure a student’s academic potential. 

So, now what?

So if we shouldn’t use standardized tests, then what should we use?

It is established that standardized tests can be incredibly debilitating, not only for students with disabilities or those from underprivileged backgrounds, but also for many others who suffer from the high-stakes pressure and/or testing anxiety. Yet individual differences along with the complex interplay of social-economic status, culture, social pressure, motivation and reward are all important determinants of performances success or failure. Based on the research that is currently available, it’s hard to dispute the notion that standardized tests are a far from perfect measure of academic ability and assessment, with many students being unfairly penalized and undermined for things that they cannot control.

Many have proposed plausible solutions to the standardized test epidemic. Examples include i) simply reducing the number of standardized tests given, ii) replacing data from assessments with data collected “passively” over long periods of time iii) increasing the prevalence of game-based assessments, or iv) implementing more social and emotional skill surveys. Further details on these proposals can be found in the 2015 book by Anya Kamenetz.

As the field of educational neuroscience continues to expand, it behooves the test prep industry to take into consideration the latest research on how the brain works under test conditions. In addition, given the relatively small number of students with the background and resources necessary to achieve very high scores on standardized tests, the scientific markers discussed in this article should play an active role in redefining ability and educational assessment, in the hopes that one day, educational decisions will never be based solely on a number.

References & Further Reading

1. ­­­­Strauss, V. (2015, October 24). Confirmed: Standardized testing has taken over our schools. But who’s to blame? The Washington Post. [Article]
2. The National Center for Fair & Open Testing. (2007, December 17). The Dangerous Consequences of High-Stakes Standardized Testing.  [Article]
3. Rooks, N. (2012, October). Why It’s Time to Get Rid of Standardized Tests,  [Article]
4. Steele C.M., Aronson J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69:797–811. [Paper]
5. Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype threat and women’s math performance.Journal of Experimental Social Psychology, 35, 4–28. [Paper]
6. Hess, T.M., Auman, C., Colcombe, S.J. Rahhal, T.A. (2003). The impact of stereotype threat on age differences in memory performance. Journals of Gerontology: Psychological Sciences and Social Sciences, 58(B):3–11. [Paper]
7. Schmader, T., Johns, M., & Forbes, C. (2008). An integrated process model of stereotype threat effects on performance. Psychological Review, 115, 336–356. [Paper]
8. Derks, B., Inzlicht, M., & Kang, S. K. (2008). The neuroscience of stigma and stereotype threat. Group Processes & Intergroup Relations, 11, 163–181. [Paper]
9. Wraga, M., Helt, M., Jacobs, E., & Sullivan, K. (2007). Neural basis of stereotype-induced shifts in women’s mental rotation performance. Social Cognitive and Affective Neuroscience, 2, 12–19. [Paper]
10. Forbes, C.E., Leitner, J.B. (2014). Stereotype threat engenders neural attentional bias toward negative feedback to undermine performance. Biological psychology, 102, 98–107. [Paper]
11. Maloney, E.A., Schaeffer, M.W., & Beilock, S.L. (2013). Mathematics anxiety and stereotype threat: shared mechanisms, negative consequences and promising interventions, Research in Mathematics Education, 15:2, 115-128. [Paper]
12. Beilock, S.L., Carr, T.H. (2005). When high-powered people fail: working memory and “choking under pressure” in math. Psychological Science, 16, 101–105. [Paper]
13. Markman, A.B., Maddox, W.T., Worthy, D.A. (2006). Choking and excelling under pressure. Psychological Science, 17, 944–948. [Paper]
14. Gimmig, D., Huguet, P., Caverni, J.P., Cury, F. (2006). Choking under pressure and working memory capacity: when performance pressure reduces fluid intelligence. Psychonomic Bulletin Review, 13, 1005–1010. [Paper]
15. Yu, R. (2015). Choking under pressure: the neuropsychological mechanisms of incentive-induced performance decrements. Frontiers in Behavioral Neuroscience, 9, 19. Doi: http://doi.org/10.3389/fnbeh.2015.00019 [Paper]
16. Lee T. G., Grafton S. T. (2015). Out of control: diminished prefrontal activity coincides with impaired motor performance due to choking under pressure. Neuroimage,105, 145–155. 10.1016/j.neuroimage.2014.10.058 [Paper]
17. Mattarella-Micke A., Mateo J., Kozak M. N., Foster K., Beilock S. L. (2011). Choke or thrive? The relation between salivary cortisol and math performance depends on individual differences in working memory and math-anxiety. Emotion, 11, doi: 1000–1005. 10.1037/a0023224 [Paper]
18. Yerkes RM, Dodson JD (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, doi:18: 459–482.doi:10.1002/cne.920180503 [Paper]
19. Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: The processing efficiency theory. Cognition and Emotion, 6, 409 – 434. [Paper]
20. Strauss, B. (2002). Social facilitation in motor tasks: A review of research and theory. Psychology of Sport and Exercise, 3, 237 – 256. [Paper]
21. Pruessner J. C., Dedovic K., Khalili-Mahani N., Engert V., Pruessner M., Buss C., et al. . (2008). Deactivation of the limbic system during acute psychosocial stress: evidence from positron emission tomography and functional magnetic resonance imaging studies. Biological Psychiatry, 63, 234–240. 10.1016/j.biopsych.2007.04.041 [Paper]
22. Temple, T., & Neumann, R. (2014). Stereotype threat, test anxiety, and mathematics performance. Social Psychology of Education, 17, 491-501 [Paper]
23. Faust, M.W. (1992). Analysis of physiological reactivity in mathematics anxiety. Unpublished doctoral dissertation, Bowling Green State University, Bowling Green, Ohio
24. Adelson, R. (2014). Nervous About Numbers: Brain Patterns Reflect Math Anxiety. Observer: Association for Psychological Science, 27, September. [Article]
25. University of Granada. (2009, April 2). Six Out Of 10 University Students Have Math Anxiety, Spanish Study Finds. ScienceDaily. [Article]
26. Lyons, I. M., & Beilock, S. L. (2011). Mathematics anxiety: Separating the math from the anxiety. Cerebral Cortex, 22(9): 2102-10. [Paper]
27. Lyons, I. M., & Beilock, S. L. (2012). When Math Hurts: Math Anxiety Predicts Pain Network Activation in Anticipation of Doing Math. PLOS ONE.7(10): e48076. doi:10.1371/journal.pone.0048076 [Paper]

It is difficult to argue that bad air isn’t bad for your health. Unlike many of the polarizing environment and health issues, like global warming, it is commonly agreed upon that ambient air pollution is a public health threat[i] [ii]. In the U.S. alone, more than 100 million people are exposed to varying amounts of particulate matter (PM), lead, sulfur and/or nitrogen dioxide in the air in quantities that exceed the recognized health standards set by the United States Environmental Protection Agency (EPA)[iii] [iv].

 

The danger lies in PM of 2.5 micrometers or less in diameter (or approximately 1/30 of the width of a human hair), which is small enough to penetrate deep into the lungs and other organs of the body. Although trends observed by the EPA have shown that hazardous emissions polluting our air have actually decreased over the course of the past decade, it remains that, as of 2013, over two million deaths a year can be directly linked to air pollution[v].

 

Historically, much of the attention on the risks of air pollution tends to center around cancer and other diseases affecting the respiratory and cardiovascular system[vi]. This makes sense, especially considering the important and obvious links between air quality and lung and heart health. However, recent empirical investigations of the brain have observed concerning evidence about the potential impact of pollution on neurological functioning and wellbeing. In other words, bad air quality has been found to have an unprecedented and insidious impact on our brain.

 

Air Pollution & The Brain

Research suggests air pollution can affect everything from neurodevelopment in-utero to accelerating cognitive decline in older people[vii]. Given the delicate nature of the prenatal environment and its importance for fetal health, it may come as no surprise that toxins found in the air are harmful to healthy brain growth both during pregnancy and throughout the lifespan. Indeed, given the detrimental brain effects on children living in heavily polluted cities, the past few years have witnessed a surging interest in the correlations between ambient air pollution and compromised brain health[viii] 7. For example, one research group using animal models found mice exposed to average metropolitan area levels of pollution performed worse on learning and memory tasks compared to a control group in a container with filtered air. Additionally, in a companion study by the same group, the mice in the polluted air showed more depressive-like and anxiety-like symptoms and behaviors than their filtered-air counterparts[ix].

 

Although these findings are concerning, results from animal studies can’t always be generalized to humans. However, we do know that children’s physiological development is uniquely vulnerable to the exposure to environmental toxins and pollutants compared to adults. Simply from a lung function perspective, children breathe in higher levels of polluted air relative to their weight and also tend to spend greater amounts of time outside, leaving them even more susceptible to the disease and dysfunction caused by pollutants[x].

 

A number of recent investigations conducted between 2012 and 2015 have looked into analyzing brain imaging data belonging to children living in urban areas with the objective of pinpointing some of the dangerous side-effects of living in heavily polluted areas on the brain, particularly in light of poor outcomes on psychometric tests on behalf of children living in these areas[xi]. For instance, upon examining brain structure (i.e. the physical architecture that comprises the brain), results indicate abnormalities in the brain’s white matter, which are often highly correlated with a number of psychological and cognitive diseases and deficits[xii].

 

Unsurprisingly, the structural findings were corroborated by “functional” anomalies (meaning parts of the brain are behaving differently than in a typical population). Examples include compromised senses, including smell and hearing, as well as a number of cognitive deficits, consistent with the poor psychometric assessments noted previously. In fact, many research groups have consistently found that school-aged children located in highly polluted areas perform less well on cognitive and neurological tests, all while controlling for confounding factors such as low-SES, gender, age and mother’s IQ12.

 

Further cases of such studies include a 2010 investigation showing that children with exposure to high levels of nitrogen dioxide scored between 6 and 9 points less on measures of working memory[xiii]. This continues to hold true in 2015, when a research group from the University of Texas found lower grade point averages among El Paso fourth-and-fifth graders exposed to high levels of ambient air pollution[xiv]. Perhaps more alarmingly, evidence shows these effects can start early, whereby children with high levels of prenatal exposure to aromatic hydrocarbons (a group of chemicals that get released upon burning substances such as coal, oil, gasoline and trash) were recorded to have lower than expected IQ scores at the age of 5 compared to children that had not had such exposures in-utero[xv] [xvi].

 

What does this mean for students?

So the bottom line is: how does all this affect how children grow, develop and learn? Despite the body of evidence confirming the negative effects of ambient air pollution on children’s health (as well as the routine air quality monitoring by the EPA), few investigations have been carried out to examine the consequences of the associations between air quality and academic performance. To complicate matters, many groups disproportionately places low-income and ethnic minority communities in areas with high levels of air pollution – segments of the population already associated with lower performance on standardized tests[xvii] 10 Reasons for this may be due to factors such as parental educational disadvantages and school location in urban centers near busy roads, which in turn are populated by greater proportions of at-risk student populations16. However, these links are not found ubiquitously, and many theorists have shown a robust enough relationship between high exposure to air pollutants and compromised academic performance to withstand confounding variables including school size, school location and student demographics10.

 

So what could be the true outcomes of these findings? Based on the sheer number of studies purporting the negative impacts of air pollution on overall health and the brain, it would not be a stretch to imagine the ramifications of even mild exposure to air toxins beyond GPA or IQ – for instance some have anecdotally discussed how newly occurring or exacerbated respiratory problems increase fatigue and attention problems in school, with greater bouts of absenteeism as a result.[xviii]

 

The danger herein lies in the insidious nature of these ill-effects, because it is not likely that common issues such as asthma or attention-deficit disorders be linked with poor air quality; and even if air quality was the primary culprit behind these problems, it would be hard to effectively disentangle it from other possible etiologies. Needless to say, the links between academic performance, air pollution exposure as well as other related health problems remain poorly understood and require further research in order to produce realistic solutions to combat the problem.

 

What can we do about it?

Given the complex nature of pediatric air pollution research, extensive interdisciplinary collaboration between fields like neuroscience, radiology and epidemiology (to name a few) is necessary in order to create greater awareness and build efforts on behalf of schools and educational facilities to improve indoor environment quality. This, coupled with a more comprehensive understanding of the damage of air pollution on the brain will hopefully facilitate more effective interventions to compensate for the ill-effects of bad air quality on future generations8.

 

From a survey of the current literature, a number of research groups have initiated studies on the effects of poor air quality; however relatively few have posed any concrete solutions to the problem. As of October 2015, select federal agencies (such as the EPA) along with public health officials have acknowledged some of the research discussed in this article and have responded by organizing online events and workshops to work with schools and educational institutes to better monitor indoor air quality in schools:

 

1. Creating Healthy Indoor Air Quality (IAQ) in Schools: http://www.epa.gov/iaq-schools

 

2. Webinars hosted by the EPA on improving air quality in schools: http://www.epa.gov/schools/schools-webinars
3. A list of organizations working to combat air pollution:

http://www.inspirationgreen.com/organizations-air.html#AirPollutionOrganizations

REFERENCES

 

 

1. Katsouyanni, K. (2003). Ambient Air Pollution and Health, British Medical Bulletin, 68, 143-156.
2. World Health Organization (WHO). (2014). Fact sheet N°313 Ambient (outdoor) air quality and health.
3. Lelieveld J., Evans J.S., Fnais M., Giannadaki D., Pozzer A. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525(7569):367-371.
4. World Health Organization. (2003). Health aspects of air pollution with particulate matter, ozone and nitrogen dioxide, report, 98 pp., Bonn, Germany.
5. United States Environmental Protection Agency. (2015, September). FAQ. Retrieved From the Environmental Protection Agency website: http://www3.epa.gov/pmdesignations/faq.htm
6. Silva, R. A. et al. (2013). Global premature mortality due to anthropogenic outdoor air pollution and the contribution of past climate change. Environmental Research Letters, 8, doi: 10.1088/1748-9326/8/3/034005
7. Chen, J.C., Wang, X., Wellenius, G.A., Serre, M.L., Driscoll, I., Casanova, R., McArdle, J.J., Manson, J.E., Chui, H.C., Espeland, M.A. (2015). Ambient air pollution and neurotoxicity on brain structure: evidence from women’s health initiative memory study. Annals of Neurology. 78, 466–476.
8. Calderón-Garcidueñas, L., Torres-Jardón, R., Kulesza, R. J., Park, S.-B., & D’Angiulli, A. (2014). Air pollution and detrimental effects on children’s brain. The need for a multidisciplinary approach to the issue complexity and challenges. Frontiers in Human Neuroscience, 8, 613. http://doi.org/10.3389/fnhum.2014.00613
9. Fonken, L.K., Xu, X., Weil, Z.M., Chen G., Sun Q., Rajagopalan S., Nelson, R.J. (2011). Air pollution impairs cognition, provokes depressive-like behaviors and alters hippocampal cytokine expression and morphology. Molecular Psychiatry, doi: 0.1038/mp.2011.76
10. Mohai P., Kweon B.S., Lee S., Ard K. (2011). Air pollution around schools is linked to poorer student health and academic performance. Health Affairs, 30 (5):852–62.
11. Calderón-Garcidueñas, L., Solt, A.C., Henriquez-Roldan, C., Torres-Jardón, R., Nuse, B., Herritt, L., Villareal-Calderón, R., Osnaya, N., Stone, I., Garcia, R., Brooks, D.M., et al. (2008). Long-term air pollution exposure is associated with neuroinflammation, an altered innate immune response, disruption of the blood–brain barrier, ultrafine particulate deposition, and accumulation of amyloid beta-42 and alpha-synuclein in children and young adults. Toxicologic Pathology. 36, 289–310.
12. Calderón-Garcidueñas, L., Cross, J.V., Franco-Lira, M., Aragon-flores, M., Kavanaugh, M., Torres-Jardón, , et al. (2013). Brain immune interactions and air pollution: macrophage inhibitory factor (MIF), prion cellular protein (PrPC ), interleukin-6 (IL-6), interleukin 1 receptor antagonist (IL-1Ra), and serum interleukin-2 (IL-2) in cerebrospinal fluid and MIF in serum differentiate urban children exposed to severe vs. low air pollution. Frontiers in Neuroscience, 7, 183.
13. Freire C., Ramos R., Puertas R., Lopez-Espinosa M.J., Julvez J., Aguilera I., Cruz F., Fernandez M.F., Sunyer J., Olea N. (2010). Association of traffic-related air pollution with cognitive development in children. Journal of Epidemiological & Community Health, 64:223–228.
14. Clark-Reyna, S., Grineski, S.E., Collins, T.W. (2015). Residential exposure to air toxics is linked to lower grade point averages among school children in El Paso, Texas, USA. Population & Environment, 1-22. doi: 10.1007/s11111-015-0241-8.
15. Edwards, S.C., Jedrychowski, W., Butscher, M., Camann, D., Kieltyka, A., Mroz, E., et al. (2010). Prenatal exposure to airborne polycyclic aromatic hydrocarbons and children’s intelligence at 5 years of age in a prospective cohort study in Poland. Environmental Health Perspectives, 118, 1326–1331.

1. Suglia F., Gryparis A., Wright R.O., Schwartz J.,Wright R.J. (2008). Association of black carbon with cognition among children in a prospective birth cohort study. American Journal of Epidemiology. 167(3):280–6.
2. Pastor M., Morello-Frosch R., Sadd J. (2006). Breathless: pollution, schools, and environmental justice in California. Policy Studies Journal, 34(3):337–62.
3. Miller, S., Vela, M. (2013). The Effects of Air Pollution on Educational Outcomes: Evidence from Chile (Working Paper No. IDB-WP-468). Retrieved from Inter-American Development Bank website: http://www.iadb.org/en/research-and-data/publication-details,3169.html?pub_id=IDB-WP-468

 

[xviii]

It is difficult to argue that bad air isn’t bad for your health. Unlike many of the polarizing environment and health issues, like global warming, it is commonly agreed upon that ambient air pollution is a public health threat^1,2. In the U.S. alone, more than 100 million people are exposed to varying amounts of particulate matter (PM), lead, sulfur and/or nitrogen dioxide in the air in quantities that exceed the recognized health standards set by the United States Environmental Protection Agency (EPA)^3,4.

The danger lies in PM of 2.5 micrometers or less in diameter (or approximately 1/30 of the width of a human hair), which is small enough to penetrate deep into the lungs and other organs of the body. Although trends observed by the EPA have shown that hazardous emissions polluting our air have actually decreased over the course of the past decade, it remains that, as of 2013, over two million deaths a year can be directly linked to air pollution⁵.

Historically, much of the attention on the risks of air pollution tends to center around cancer and other diseases affecting the respiratory and cardiovascular system⁶. This makes sense, especially considering the important and obvious links between air quality and lung and heart health. However, recent empirical investigations of the brain have observed concerning evidence about the potential impact of pollution on neurological functioning and wellbeing. In other words, bad air quality has been found to have an unprecedented and insidious impact on our brain.

Air Pollution & The Brain

Research suggests air pollution can affect everything from neurodevelopment in-utero to accelerating cognitive decline in older people⁷. Given the delicate nature of the prenatal environment and its importance for fetal health, it may come as no surprise that toxins found in the air are harmful to healthy brain growth both during pregnancy and throughout the lifespan. Indeed, given the detrimental brain effects on children living in heavily polluted cities, the past few years have witnessed a surging interest in the correlations between ambient air pollution and compromised brain health^8,7. For example, one research group using animal models found mice exposed to average metropolitan area levels of pollution performed worse on learning and memory tasks compared to a control group in a container with filtered air. Additionally, in a companion study by the same group, the mice in the polluted air showed more depressive-like and anxiety-like symptoms and behaviors than their filtered-air counterparts⁹.

Although these findings are concerning, results from animal studies can’t always be generalized to humans. However, we do know that children’s physiological development is uniquely vulnerable to the exposure to environmental toxins and pollutants compared to adults. Simply from a lung function perspective, children breathe in higher levels of polluted air relative to their weight and also tend to spend greater amounts of time outside, leaving them even more susceptible to the disease and dysfunction caused by pollutants¹⁰.

A number of recent investigations conducted between 2012 and 2015 have looked into analyzing brain imaging data belonging to children living in urban areas with the objective of pinpointing some of the dangerous side-effects of living in heavily polluted areas on the brain, particularly in light of poor outcomes on psychometric tests on behalf of children living in these areas¹¹. For instance, upon examining brain structure (i.e. the physical architecture that comprises the brain), results indicate abnormalities in the brain’s white matter, which are often highly correlated with a number of psychological and cognitive diseases and deficits¹².

Unsurprisingly, the structural findings were corroborated by “functional” anomalies (meaning parts of the brain are behaving differently than in a typical population). Examples include compromised senses, including smell and hearing, as well as a number of cognitive deficits, consistent with the poor psychometric assessments noted previously. In fact, many research groups have consistently found that school-aged children located in highly polluted areas perform less well on cognitive and neurological tests, all while controlling for confounding factors such as low-SES, gender, age and mother’s IQ¹².

Further cases of such studies include a 2010 investigation showing that children with exposure to high levels of nitrogen dioxide scored between 6 and 9 points less on measures of working memory¹³. This continues to hold true in 2015, when a research group from the University of Texas found lower grade point averages among El Paso fourth-and-fifth graders exposed to high levels of ambient air pollution¹⁴. Perhaps more alarmingly, evidence shows these effects can start early, whereby children with high levels of prenatal exposure to aromatic hydrocarbons (a group of chemicals that get released upon burning substances such as coal, oil, gasoline and trash) were recorded to have lower than expected IQ scores at the age of 5 compared to children that had not had such exposures in-utero^15,16.

What does this mean for students?

So the bottom line is: how does all this affect how children grow, develop and learn? Despite the body of evidence confirming the negative effects of ambient air pollution on children’s health (as well as the routine air quality monitoring by the EPA), few investigations have been carried out to examine the consequences of the associations between air quality and academic performance. To complicate matters, many groups disproportionately place low-income and ethnic minority communities in areas with high levels of air pollution – segments of the population already associated with lower performance on standardized tests^17,10. Reasons for this may be due to factors such as parental educational disadvantages and school location in urban centers near busy roads, which in turn are populated by greater proportions of at-risk student populations16. However, these links are not found ubiquitously, and many theorists have shown a robust enough relationship between high exposure to air pollutants and compromised academic performance to withstand confounding variables including school size, school location and student demographics¹⁰.

So what could be the true outcomes of these findings? Based on the sheer number of studies purporting the negative impacts of air pollution on overall health and the brain, it would not be a stretch to imagine the ramifications of even mild exposure to air toxins beyond GPA or IQ – for instance, some have anecdotally discussed how newly occurring or exacerbated respiratory problems increase fatigue and attention problems in school, with greater bouts of absenteeism as a result ¹⁸.

The danger herein lies in the insidious nature of these ill-effects, because it is not likely that common issues such as asthma or attention-deficit disorders be linked with poor air quality; and even if air quality was the primary culprit behind these problems, it would be hard to effectively disentangle it from other possible etiologies. Needless to say, the links between academic performance, air pollution exposure as well as other related health problems remain poorly understood and require further research in order to produce realistic solutions to combat the problem. 

What can we do about it?

From a survey of the current literature, a number of research groups have initiated studies on the effects of poor air quality; however relatively few have posed any concrete solutions to the problem. As of October 2015, select federal agencies (such as the EPA) along with public health officials have acknowledged the research discussed in this article and have responded by organizing online events and workshops to work with schools and educational institutes to better monitor indoor air quality in schools:

 

1. Creating Healthy Indoor Air Quality (IAQ) in Schools: http://www.epa.gov/iaq-schools
2. Webinars hosted by the EPA on improving air quality in schools: http://www.epa.gov/schools/schools-webinars
3. A list of organizations working to manage with air pollution: http://www.inspirationgreen.com/organizations-air.html#AirPollutionOrganizations

Clearly, given the complex nature of pediatric air pollution research, extensive interdisciplinary collaboration between fields like neuroscience, radiology and epidemiology (to name a few) is necessary in order to create greater awareness and build efforts on behalf of schools and educational facilities to improve indoor environment quality. This, coupled with a more comprehensive understanding of the damage of air pollution on the brain will hopefully facilitate more effective interventions to compensate for the ill-effects of bad air quality on future generations⁸.

 

References & Further Reading

1. Katsouyanni, K. (2003). Ambient Air Pollution and Health, British Medical Bulletin, 68, 143-156.
2. World Health Organization (WHO). (2014). Fact sheet N°313 Ambient (outdoor) air quality and health.
3. Lelieveld J., Evans J.S., Fnais M., Giannadaki D., Pozzer A. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525(7569):367-371.
4. World Health Organization. (2003). Health aspects of air pollution with particulate matter, ozone and nitrogen dioxide, report, 98 pp., Bonn, Germany.
5. United States Environmental Protection Agency. (2015, September). FAQ. Retrieved From the Environmental Protection Agency website: http://www3.epa.gov/pmdesignations/faq.htm
6. Silva, R. A. et al. (2013). Global premature mortality due to anthropogenic outdoor air pollution and the contribution of past climate change. Environmental Research Letters, 8, doi: 10.1088/1748-9326/8/3/034005
7. Chen, J.C., Wang, X., Wellenius, G.A., Serre, M.L., Driscoll, I., Casanova, R., McArdle, J.J., Manson, J.E., Chui, H.C., Espeland, M.A. (2015). Ambient air pollution and neurotoxicity on brain structure: evidence from women’s health initiative memory study. Annals of Neurology. 78, 466–476.
8. Calderón-Garcidueñas, L., Torres-Jardón, R., Kulesza, R. J., Park, S.-B., & D’Angiulli, A. (2014). Air pollution and detrimental effects on children’s brain. The need for a multidisciplinary approach to the issue complexity and challenges. Frontiers in Human Neuroscience, 8, 613. http://doi.org/10.3389/fnhum.2014.00613
9. Fonken, L.K., Xu, X., Weil, Z.M., Chen G., Sun Q., Rajagopalan S., Nelson, R.J. (2011). Air pollution impairs cognition, provokes depressive-like behaviors and alters hippocampal cytokine expression and morphology. Molecular Psychiatry, doi: 0.1038/mp.2011.76
10. Mohai P., Kweon B.S., Lee S., Ard K. (2011). Air pollution around schools is linked to poorer student health and academic performance. Health Affairs, 30 (5):852–62.
11. Calderón-Garcidueñas, L., Solt, A.C., Henriquez-Roldan, C., Torres-Jardón, R., Nuse, B., Herritt, L., Villareal-Calderón, R., Osnaya, N., Stone, I., Garcia, R., Brooks, D.M., et al. (2008). Long-term air pollution exposure is associated with neuroinflammation, an altered innate immune response, disruption of the blood–brain barrier, ultrafine particulate deposition, and accumulation of amyloid beta-42 and alpha-synuclein in children and young adults. Toxicologic Pathology. 36, 289–310.
12. Calderón-Garcidueñas, L., Cross, J.V., Franco-Lira, M., Aragon-flores, M., Kavanaugh, M., Torres-Jardón, R., et al. (2013). Brain immune interactions and air pollution: macrophage inhibitory factor (MIF), prion cellular protein (PrPC ), interleukin-6 (IL-6), interleukin 1 receptor antagonist (IL-1Ra), and serum interleukin-2 (IL-2) in cerebrospinal fluid and MIF in serum differentiate urban children exposed to severe vs. low air pollution. Frontiers in Neuroscience, 7, 183.
13. Freire C., Ramos R., Puertas R., Lopez-Espinosa M.J., Julvez J., Aguilera I., Cruz F., Fernandez M.F., Sunyer J., Olea N. (2010). Association of traffic-related air pollution with cognitive development in children. Journal of Epidemiological & Community Health, 64:223–228.
14. Clark-Reyna, S., Grineski, S.E., Collins, T.W. (2015). Residential exposure to air toxics is linked to lower grade point averages among school children in El Paso, Texas, USA. Population & Environment, 1-22. doi: 10.1007/s11111-015-0241-8.
15. Edwards, S.C., Jedrychowski, W., Butscher, M., Camann, D., Kieltyka, A., Mroz, E., et al. (2010). Prenatal exposure to airborne polycyclic aromatic hydrocarbons and children’s intelligence at 5 years of age in a prospective cohort study in Poland. Environmental Health Perspectives, 118, 1326–1331.
16. Suglia F., Gryparis A., Wright R.O., Schwartz J.,Wright R.J. (2008). Association of black carbon with cognition among children in a prospective birth cohort study. American Journal of Epidemiology. 167(3):280–6.
17. Pastor M., Morello-Frosch R., Sadd J. (2006). Breathless: pollution, schools, and environmental justice in California. Policy Studies Journal, 34(3):337–62.
18. Miller, S., Vela, M. (2013). The Effects of Air Pollution on Educational Outcomes: Evidence from Chile

The Increase in Preterm Survival Rates

Preterm birth is on the rise. According the World Health Organization (WHO)¹, preterm birth is defined as any birth occurring prior to 37 weeks of pregnancy, or fewer than 259 days since the mother’s last menstrual cycle². The youngest premature babies have been reported to survive around 22 weeks gestation with the youngest ever recorded born at just 21 weeks or 5.5 months³. Strikingly, complications arising from preterm birth are responsible for approximately 35% of all neonatal deaths that take place in a given year, roughly 3.1 million globally².

There are numerous reasons why a pregnancy might end prematurely, including a wide variety of complex social and environmental factors, as well as genetic and epigenetic influences that affect conception and circumstances of birth. For example, contributors such as advanced maternal age are being increasingly linked to rises in numbers of preterm babies, and couples in western societies seeking assistance conceiving a child are much more likely to carry multiple pregnancies (such as twins and triplets), which are 10 times more likely to result in a preterm birth compared to mothers carrying a single child². Other notable factors include complications related to obesity, such as high blood pressure and chronic conditions like diabetes, however many preterm births occur due to reasons are that unknown or unclear¹.

Regardless of the reason behind these increased rates, the number of premature babies continues to rise AND survive at younger and younger gestational ages. In 1960, the survival rate for an infant weighing 3.3 lbs or less was just 28%. Today, surviving premature infants can be the length of a standard 5-inch pen and weigh as little as 1 lb. Advancements in medical and neonatal care technology are in part to thank for these startling survival rates, with many hospitals across the United States building sophisticated newborn facilities (called NICUs), equipped with anything that might be needed by surgeons and specialists to care for the most vulnerable of babies. In fact, it has been estimated that every decade, the age of viability for premature infants goes down by 1 week⁴.

The New Challenges That Come with Progress

The power and impact of these medical technologies is remarkable, however the increasingly high survival rates of younger and younger babies come at a price. For one, the hospital stay for a child born prior to 32 weeks ran up an average medical bill of over $280,811 in 2014. More importantly, both physicians and parents are forced to make unbearably difficult ethical decisions, because even if the child defies the odds in the first few hours, days or weeks of life, the chances of survival with a severe lifelong disability are significant⁵. For example, anywhere from 17% to 48% of babies born preterm will have some kind of neuromotor abnormality, whereby displaying signs of having neurological issues and/or problematic motor control; this includes conditions like cerebral palsy (CP) which can lead to a life of severe intellectual and physical disability⁵.

Studies across a number of scientific fields have attempted to determine whether cognitive and social capacities are in some way impaired in premature children born without complications. Many premature babies will still pass initial newborn screening tests, which typically include an assessment of sensory (such as hearing) and basic motor reflexes as well as blood tests. However, even in cases where the child appears typically developing during the first few months of life, preterm children, on average, have been found to struggle in school compared to their full-term counterparts, regardless of race, ethnicity and socio-economic background⁶.

One 2013 study tested over 1300 8-year-old children (ranging from full term to severely premature) on a number of cognitive tests with the objective of investigating high cognitive load in preterm children. The results showed that a “higher” workload (requiring the brain to simultaneously coordinate many pieces of information) brought out more pronounced cognitive deficits in children born preterm compared to participants born full term. In other words, the shorter the pregnancy, the more severe the deficit during high “cognitive load” tasks⁷.

These findings are corroborated by a 2015 Nature study which found preterm children to be more vulnerable in tasks requiring mathematical reasoning and visuospatial processing, which in turn was corroborated by deficits found in tasks investigating working memory and processing speed⁸. Furthermore, there is evidence to suggest that these differences are reflected in observational studies exploring social outcomes of adults born prematurely. Two of the larger longitudinal investigations were conducted by the National Child Development Study (NCDS), beginning in 1958 and the British Cohort Study (BCS), beginning in 1970 which found that, by age 42, people who had been born prematurely were significantly more likely to experience lower overall wealth, with greater rates of unemployment and reports of financial hardship compared to people born full-term⁹. This of course is not true for all preterm babies, but the frequency with which additional challenges are faced raise new questions about how to help.

Addressing the Challenges in Education

The questions now are (i) whether anything can be done to prevent the consequences of preterm birth, particularly as it relates to motor and cognitive disabilities, and (ii) whether educational policy should take the unique challenges of preterm babies into account (and if so, what that would look like).

Currently, many programs in the form of post-discharge early intervention programs are being implemented, with the aim of preventing or lessening the effects of preterm birth via multi-modal sensory stimulation during the first few weeks of life. This involves a combination of tactile, visual, vestibular and auditory stimulation with the objectives of improving motor, physiological and eventually cognitive and social functioning later in life¹⁰. However, some have expressed skepticism on how effective these measures are in the long term. A recent meta-analysis of 21 studies (including over 3000 randomized children) investigated the effectiveness of early developmental intervention methods on infants born prematurely (prior to 37 weeks). The effect found was significant but relatively small. To complicate matters further, it is difficult to say whether it is the timing of the delivery of these programs or the content of the intervention themselves that do not stand the test of time.

Although severe cognitive disabilities due to premature birth are still relatively uncommon, 15 million babies a year are born preterm worldwide¹ and even small increases in cognitive impairment due to rising prematurity may have considerable effects on society in general, and education in particular. Yet as the number of preterm children increase, so do the demands placed on the education system. Some have suggested the aggressive implementation of accessible educational interventions, particularly targeting student achievement in mathematics, which many research groups argue to be a common point of concern^11,12. The idea is to design educational interventions in the form of computerized training programs for school-aged children, built to deliver information in a slower, more sequential manner so as not to overwhelm struggling children by presenting lots of forms of information simultaneously⁷.

The implementation of such educational tools to curb learning difficulties in struggling children is a decisive nod towards the ongoing controversial debate surrounding the practice of classroom “grouping” or “tracking” based on academic ability. Divvying up classrooms based on ability — regardless of biological age — has been a subject of contention as far back as the 1930s, whereby education researchers and specialists have since disputed over the efficacy of ability grouping with the objective of catering for the needs of each child. Generally speaking, the most common forms of grouping are (i) within-class ability grouping and (ii) between-class grouping. In the former, individual teachers place children into smaller sub-groups within the same class while the latter is done at a more systematic level involving formal allocation of children into separate classes or curricular tracks based on achievement¹⁴. One pioneering study found that overall, between-class grouping did very little for student achievement, if only partially benefitting higher-achieving students¹³. To make matters worse, it has been argued that students placed in lower tracks can suffer lower motivation towards school compared to their advanced-track peers¹⁴. That being said, some success has been observed for within-class grouping, particularly if implemented for one or two select core subjects (such as mathematics and reading), while remaining within their respective heterogeneous classes for other courses¹³.

Clearly, future research is needed to address these concerns; both in terms of the research on the neurodevelopment of premature children as well as the best way to educate kids that might be struggling in school. Unfortunately, the situation is exacerbated by the lack of suitable training and information given to teachers about what to potential learning issues to expect and how to deal with them. Despite the eagerness of teachers to understand the best way to address issues children face in school, more often than not schools do not or cannot provide the necessary training and guidance teachers need⁹.

As the survival rates for preterm babies continue to rise, we must remain cognizant of how our advancements in technology and medical care are impacting how we understand and think about education’s ability to address the needs of all children.

 

References & Further Reading

1. World Health Organization (WHO). (2015). Fact sheet N°363 Preterm Birth. [Report]
2. Blencowe, H., Cousens, S., Chou, D., Oestergaard, M., Say, L., Moller, A.-B., … Lawn, J. (2013). Born Too Soon: The global epidemiology of 15 million preterm births. Reproductive Health, 10(Suppl 1), S2. [Paper]
3. Bird, C. (December 2014). World’s Smallest Preemies. [Report]
4. Kluger, J. (2014, June 2). Saving Preemies. Time, 183 (21), 26-31. [Article]
5. Abbott, A. (2015). Neuroscience: The brain, interrupted. Nature, 7537, 24–26. [Article]
6. Baker, L. (October 2000). Children Born Prematurely Remain at Risk for Educational Underachievement at Age 10. University at Buffalo, SUNY News Center. [News Release]
7. Jaekel, J., Baumann, N., Wolke, D. (2013). Effects of Gestational Age at Birth on Cognitive Performance: A Function of Cognitive Workload Demands. PLoS ONE 8(5): e65219. [Paper]
8. Simms, V., Gilmore, C., Cragg, L., Marlow, N., Wolke, D., & Johnson, S. (2012). Mathematics difficulties in extremely preterm children: evidence of a specific deficit in basic mathematics processing. Pediatric research, 2, 236–244. [Paper]
9. Wolke, D. (2015, September 3). Premature birth linked with lower wealth: how education could help bridge the gap. The Conversation. [Article]
10. Feldman, R. (2002). Intervention programs for premature infants: considering potential mechanisms for change, Newsletter of the World Association for Infant Mental Health, 10 (3-4). [Newsletter]
11. Basten, M., Jaekel, J., Johnson, S., Gilmore, C., & Wolke, D. (2015). Preterm Birth and Adult Wealth: Mathematics Skills Count. Psychological science, 10, 1608–1619. [Paper]
12. Jaekel, J., & Wolke, D. (2014). Preterm birth and dyscalculia. The Journal of pediatrics, 6, 1327–1332. [Paper]
13. Slavin, R. E. (1987). Ability Grouping and Student Achievement in Elementary Schools: A Best-Evidence Synthesis. Review of Educational Research, 57(3), 293–336. [Review]
14. Collins, C.A., & Gan, Li. (2013). Does Sorting Students Improve Scores? An Analysis of Class Composition, NBER Working Paper Series (Cambridge, MA: National Bureau of Economic Research)[Report]

 

From the moment a child is born (and in some cases even before), their environment and experiences will have an impact on his or her brain. Equipped with our many senses and associated sensory organs, our dynamic perceptual systems help to shape and direct the constant changes that take place in our brains. From the molecular to the electrophysiological to the cortical, limited only by our developmental constraints, the human brain is constantly refining to better suit its context. “Neuroplasticity” is the umbrella term that refers to this incredible capacity to reorganize and adapt in response to our experiences. You may also have heard the term “plasticity”, which basically refers to the ability to change. Once thought to occur primarily during early childhood, we now know that this is an inherent quality of the brain that is maintained throughout the lifespan¹.

The Rise of Neuroplasticity

In the past decade, neuroplasticity has been a hallmark of an insurgence of neuro-franchises, engulfing the brain-training and “brain education” market with remarkable tenacity. Everywhere we turn we are assured that our so-called “self-directed” plasticity will make us smarter, happier, better. Sometimes referred to as “neuroessentialism”, this strategic adoption of neuroscientific concepts to enhance psychological or sociological claims has flooded the educational market².

The good news is that the power of these frameworks has effectively combatted once-held conceptualizations of the brain as a static, unchanging machine. Albeit an old concept dating as far back as the late 1800s, the notion of a plastic brain has only recently permeated the public sphere (reassuring those in an endless battle to learn Mandarin or “boost their IQ” that their efforts may not be in vain). Perhaps unsurprisingly, informing students of their ability to “change their own brain” can be a source of empowerment for disillusioned pupils with less-than-desirable test scores. However, to avoid misconceptions and exploitation, this type of information must be communicated carefully — and in a manner that is compliant with current scientific literature.

The Difference between “Science-Sounding” and Science

Over the past several years, teachers around the world have been on the receiving end of a number of brain-based learning packages that all too often contain startling levels of misinformation. What on the surface appear to be credible evidence-driven solutions are often comprised of sensationalized, distorted, or entirely fabricated concepts spun to sound like neuroscience. Pertinent examples include detailed diagrams on the categorization of students into “left-brain” or “right-brain” learners, as well as reports of encouragement for classroom instructors to teach skills in sync with their respective “periods of synaptogenesis” in order to effectively alter neural networks³. In both cases, the ideas sound like science, with one major problem – they aren’t.

These frameworks are a misattribution of funds at best, and damaging at worst. If a young girl is labeled “right-brained”, but develops a love for mathematics, who’s to say her trajectory won’t be shaped by the fact that she believes she has the wrong sort of brain for numbers? And how does the blanket claim that students need to sit in silence to form a new connection affect the child with ADD who already struggled to stay still through class? Without any real scientific basis for their effectiveness, such products were packaged to sell, not to serve.

These products can also obscure how important it is for good teaching practice to be responsive to factors such as attention deficits, learning disabilities and testing anxiety. Many children are affected by developmental and learning disabilities, which many brain training programs claim to “make smarter”. Such programs claim to “identify and attack the root problems of disability”, using mental exercises to train “cognitive skills”. For example, one training task from a popular program was designed to improve visual perception and alter neural growth by requiring students to match patterns under timed conditions⁴. The reality is that we don’t know much about what these tasks are doing, if anything. One reason is simply because the exact mechanisms of neuroplasticity and neural reorganization are still the subject of intense investigation. The other is that, in all likelihood, it’s not going to be the same for every student. Making unsupported promises to struggling students doesn’t always end well: they may not see progress, and may attribute such failure to themselves rather than the program.

Neuroplasticity is just one example of a concept from neuroscience that has been irresponsibly translated by unregulated organizations for financial gain. It’s equally important to note that not all brain-based programs lack evidence. Some are excellent applications of carefully researched phenomena. The problem is, of course, figuring out how to tell the difference. There’s no easy answer or foolproof method to this, but a good starting point is getting to know how the concept in question (in this case neuroplasticity) does work, so that we’re less susceptible to products that pitch us on ways that it doesn’t.

So, What Do We Know? Neuroplasticity through the Lens of Sensory Impairment

Neuroplasticity has been studied in many ways. One of the most interesting and fruitful is through the lens of sensory deprivation or impairment (e.g. blindness, deafness). Indeed, children born without one or more senses have historically been viewed as “impoverished” by developmental theory. The focus has often been on the devastating effects that their disabilities may have on their learning, academic performance and quality of life.

Fortunately, considerable research from the past decade has shown that is possible for children living with sensory impairment to adapt remarkably well, often superseding learners with intact sensory function. In fact, behavioral work stretching back to the early 1990s has revealed blind participants outperforming their normally sighted counterparts on a wide range of tasks including (but not limited to) tactile identification and discrimination, sound localization and identification, as well as enhanced olfactory abilities⁵. Such enhancements include executive functions (e.g. memory) and navigational skills. For example, one study that used a route-learning wayfinding task to compare blind (including congenital blindness as well as individuals who went blind later in life) and normally sighted participants found that blind participants made fewer errors in following a pre-memorized path compared to the normally sighted wayfinders⁶. This suggests that increased memory use and navigational skill are employed to compensate for their lack of sight, not only in the case of those who are blind at birth, but also for those who lose their vision later in life.

Thanks to functional neuroimaging methodologies, we have learned about the occurrence of crossmodal plasticity, which means that one or more physical brain structures are recruited to perform the function of a different sense. When we say “recruit”, we mean that there is an area or network in the brain that tends to be involved with that behavior. In the blind, evidence of crossmodal reorganization is evident during tactile (tasks that involve touch) or auditory tasks. Structures of the brain that are typically responsible for vision and visual processing are instead recruited to relay the necessary and accessible sensory information from the tactile or auditory stimuli^{7, 8}. In other words, no areas of the brain go to waste – they are just used in different ways to make sensory processing as efficient as possible for that individual. This kind of evidence makes it apparent that the brain’s ability to reorganize itself is not only striking from a research perspective, but can also provide remarkable benefits to atypically developing brains.

Sensory impairment research has also shed light on the “dark side” of neuroplasticity; namely the maladaptive consequences of neuroplastic changes. For example, adult deaf patients with newly implanted cochlear devices struggle to use their newfound hearing to learn and use language, often choosing to continue with sophisticated communication skills such as ASL or lip-reading instead⁹. Similar struggles have been observed in blind participants in vision rehabilitation programs whose diagnoses were due to curable conditions (e.g. congenital cataracts). Furthermore, it has been suggested that –in some cases — areas of the brain most susceptible to neuroplastic changes may be the same areas considered most vulnerable in individuals at risk for developmental or learning disabilities. For example, one study comparing the ability of deaf and dyslexic individuals to process motion showed deaf participants were indeed better at processing motion than their dyslexic counterparts. In other words, brain processes that are most modifiable by experience may be most vulnerable in developmental disorders and the most compensatory enhancement following sensory deprivation¹⁰.

What this tells us about Science-Sounding Products

These types of findings highlight the need to not take neuroscientific concepts like neuroplasticity at face value. Not only can it be misleading, but given the great deal of scientific inquiry still to be done, the implementation of such concepts into a training or education paradigm is often meaningless without a strong scientific basis for their effectiveness within the context of the product in question. Indeed, according to the “synaptogenesis” theory mentioned previously by a popular training program, those who go blind later in life would be unlikely to regain necessary behavioral skills in light of them having surpassed the so-called “window of opportunity”. Finally, the chorus that neuroplasticity is an invariably positive manifestation is inaccurate and misrepresentative.

To say these findings are but the tip of the iceberg is an understatement. Not only in terms of our somewhat limited understanding of the underlying mechanisms of the brain’s plasticity, but also for the potential implications for teaching, education research and rehabilitation. The surplus of neuroscience-sounding misinformation dominating education is not only dangerous and nonsensical, but also takes away from the much more consequential strides actually made by cognitive neuroscience. The exact mechanisms of plasticity remain a source of ongoing investigation, but the current evidence in both normal and atypically developing brains is a crucial starting point for evaluating the merits of neuroplasticity-wrapped educational products.

Unfortunately, these products are unlikely to go away any time soon, and any attempts to regulate them are not yet reliable. It’s impossible to know everything, but by arming ourselves with knowledge of the science, we can begin to vet product quality and promote appropriate application within classroom settings; and perhaps most importantly, take steps towards eradicating the misuse of neuroscientific concepts and perpetuation of neuromyths inside and outside of the classroom.

 

References & Further Reading

1. Greenwood, P.M. (2007). Functional plasticity in cognitive aging: review and hypothesis. Neuropsychology. 16, 657-673. [Paper]

2. D Hanson. (2014, January 23). Neuroessentialism: The “Dark Side” of Focus on Brain Plasticity? [Web blog].

3. Goswami, U. (2006). Neuroscience and education: From research to practice? Nature Reviews Neuroscience Nature Reviews Neuroscience, 406-413. [Paper]

4. Schultz, M. (2015, July 5th). Brain Training center treats learning disabilities. WUFT.org. [Web Article]

5. Hirsch, G.V., Bauer C.M. and Merabet, L.B. (2015). Using structural and functional brain imaging to uncover how the brain adapts to blindness. Annals of Neuroscience and Psychology, 2, 5. [Paper]

6. Fortin, M., et al. (2008). Wayfinding in the blind: larger hippocampal volume and supranormal spatial navigation. Brain, 131(11), 2995-3005. [Paper]

7. Sadato N., et al. (1996). Activation of the primary visual cortex by Braille reading in blind subjects. Nature. 380, 526–528. [Paper]

8. Merabet, L. B. and Pascual-Leone, A. (2010). Neural reorganization following sensory loss: the opportunity of change. Nature Reviews Neuroscience ,11, 44-52. [Paper]

9. Giraud A.L., Lee H.J. (2007). Predicting cochlear implant outcome from brain organization in the deaf. Restorative Neurology and Neuroscience, 25, 381–390. [Paper]

10. Stevens, C., Neville, H. (2006). Neuroplasticity as a double-edged sword: deaf enhancements and dyslexic deficits in motion processing. Journal of Cognitive Neuroscience, 18(5), 701-14. [Paper]