Even though there is more communication than ever on peer-reviewed brain research, a lot of that communication distorts the science and ends up spreading or creating new neuromyths (Howard-Jones, 2014). What does that distortion look like? I present to you two examples, where something I saw on social media referring to the brain ended up linking back to research with claims that looked quite different.
Example One: "Your Brain Grew"
Yesterday +Joshua Fisher pointed out this tweet:
"when you got the problem right, your brain did nothing; when you got the problem wrong your brain GREW!" Thanks @joboaler #T3IC
— Julie Shouse Riggins (@jrigginsEFHS) March 13, 2015
Being sensitive to neuromyths, I admit I poked a little fun at this tweet-length, out-of-context claim. Rightly, +Paul Hartzer called me out and suggested I search for some context, such as this: |
| http://tvoparents.tvo.org/HH/making-mistakes |
I immediately went for the "growing evidence" link, which took me to this:
 |
| https://www.psychologytoday.com/blog/the-science-willpower/201112/how-mistakes-can-make-you-smarter |
As this was a review of two studies, I dove down to the reference section and tracked down the research. The first, by Moser et al. (2011), had this abstract:
Abstract:
How well people bounce back from mistakes depends on their beliefs about learning and intelligence. For individuals with a growth mind-set, who believe intelligence develops through effort, mistakes are seen as opportunities to learn and improve. For individuals with a fixed mind-set, who believe intelligence is a stable characteristic, mistakes indicate lack of ability. We examined performance-monitoring event-related potentials (ERPs) to probe the neural mechanisms underlying these different reactions to mistakes. Findings revealed that a growth mind-set was associated with enhancement of the error positivity component (Pe), which reflects awareness of and allocation of attention to mistakes. More growth-minded individuals also showed superior accuracy after mistakes compared with individuals endorsing a more fixed mind-set. It is critical to note that Pe amplitude mediated the relationship between mind-set and posterror accuracy. These results suggest that neural mechanisms indexing on-line awareness of and attention to mistakes are intimately involved in growth-minded individuals' ability to rebound from mistakes.This sounds familiar to those who know things about growth vs. fixed mindsets, and shows that growth mindsets are associated with some brain activity that we don't see with fixed mindsets. So maybe brain "growth" doesn't happen to everyone. The second article, by Downar, Bhatt, and Montague (2011), is even more neuroscience-y:
Abstract:
Accurate associative learning is often hindered by confirmation bias and success-chasing, which together can conspire to produce or solidify false beliefs in the decision-maker. We performed functional magnetic resonance imaging in 35 experienced physicians, while they learned to choose between two treatments in a series of virtual patient encounters. We estimated a learning model for each subject based on their observed behavior and this model divided clearly into high performers and low performers. The high performers showed small, but equal learning rates for both successes (positive outcomes) and failures (no response to the drug). In contrast, low performers showed very large and asymmetric learning rates, learning significantly more from successes than failures; a tendency that led to sub-optimal treatment choices. Consistently with these behavioral findings, high performers showed larger, more sustained BOLD responses to failed vs. successful outcomes in the dorsolateral prefrontal cortex and inferior parietal lobule while low performers displayed the opposite response profile. Furthermore, participants' learning asymmetry correlated with anticipatory activation in the nucleus accumbens at trial onset, well before outcome presentation. Subjects with anticipatory activation in the nucleus accumbens showed more success-chasing during learning. These results suggest that high performers' brains achieve better outcomes by attending to informative failures during training, rather than chasing the reward value of successes. The differential brain activations between high and low performers could potentially be developed into biomarkers to identify efficient learners on novel decision tasks, in medical or other contexts.Now we're talking about some brain activity, but the results aren't so simple. Take-away? A group of doctors who performed well on a task had brains that appeared to respond better to failure, while low-performing doctors didn't. Also, don't overlook the last bit: This study is less about finding better teaching than it is about identifying biomarkers that indicate who might be more easily taught. That's an important difference — teachers don't get to scan kids in fMRI machines and only teach the best of the lot.
Example Two: Common Core is Bad for Your Brain
Last year Lane Walker pointed me to this claim in a post on LinkedIn: |
| https://www.linkedin.com/groups/Did-anyone-get-any-interesting-4204066.S.5912659047466680321 |
Curious (and very skeptical), I followed the link to find this:
 |
| https://peter5427.wordpress.com/2014/08/28/stanford-study-common-core-is-bad-for-the-brain/ |
That post was referencing this article on Fox News:
 |
| http://www.foxnews.com/health/2014/08/18/kids-brains-reorganize-when-learning-math-skills/ |
A search for the actual research took me to an article by Qin et al. (2014) with this abstract:
Abstract:
The importance of the hippocampal system for rapid learning and memory is well recognized, but its contributions to a cardinal feature of children's cognitive development—the transition from procedure-based to memory-based problem-solving strategies—are unknown. Here we show that the hippocampal system is pivotal to this strategic transition. Longitudinal functional magnetic resonance imaging (fMRI) in 7–9-year-old children revealed that the transition from use of counting to memory-based retrieval parallels increased hippocampal and decreased prefrontal-parietal engagement during arithmetic problem solving. Longitudinal improvements in retrieval-strategy use were predicted by increased hippocampal-neocortical functional connectivity. Beyond childhood, retrieval-strategy use continued to improve through adolescence into adulthood and was associated with decreased activation but more stable interproblem representations in the hippocampus. Our findings provide insights into the dynamic role of the hippocampus in the maturation of memory-based problem solving and establish a critical link between hippocampal-neocortical reorganization and children's cognitive development.As I suspected, the neuroscience really had nothing to do with Common Core or how to teach math. It just found out which part of the brain became more active as children increase their ability to do things from memory. That should sound exciting if you're a neuroscientist, but pretty useless if you're a teacher.
Why We Have Theories of Learning
Like a predictable telephone game, you can see how research gets distorted as it morphs its way through news articles, blog posts, and social media posts. You could criticize me for not quite backtracking all the way to the source, as I'm only referring to abstracts and not digging deeply into the research described and cited in the articles themselves. To take that last step, frankly, requires more of a neuroscience background than I possess. I don't expect that of myself, and wouldn't expect a teacher to do that, either. Daniel Willingham wrote about this a few years ago, and acknowledged the role of institutions like schools of education to collectively make sense of such research and make it useful for teachers. There are people like Jo Boaler who are doing this work. I admire her for taking on the challenge of making complex ideas understandable and appealing to a wide audience of educators, and I'm sure every day she thinks hard about what messages she has to craft and how she has to craft them. It's tricky work.
My hope for teachers is this: When you hear claims about the brain and what they mean for your teaching, be skeptical. Avoid the possibility that you'll be fooled by the next big neuromyth. Realize that a lot of neuroscience relies on placing individuals in an fMRI machine and observing their brain activity while they perform a task. Is that cool science? You bet it is. Does this kind of research capture the context and complexity of your classroom? It does not.
Instead, understand and appreciate why education and related fields have theories of learning that don't rely on knowing what the brain does. In general, theories of construcivism don't go into detail about what's happening at the synapse level, nor do they need to. Cognitive theories use schema to theorize what's going on in the head, but no fMRI machines are necessary. Situated and sociocultural theories of learning gain their usefulness not by trying to look inside the learner's head, but rather outward to that learner's environment, the tools they use, the communities they participate in, and how culture and history shape their activity. So teachers, focus on that — focus on the culture of your classroom, how your students participate, and the learning community you support. Focus on how a carefully constructed curriculum, well-enacted, supports a trajectory of student learning. It will get you much further than neuromyths.
My hope for teachers is this: When you hear claims about the brain and what they mean for your teaching, be skeptical. Avoid the possibility that you'll be fooled by the next big neuromyth. Realize that a lot of neuroscience relies on placing individuals in an fMRI machine and observing their brain activity while they perform a task. Is that cool science? You bet it is. Does this kind of research capture the context and complexity of your classroom? It does not.
Instead, understand and appreciate why education and related fields have theories of learning that don't rely on knowing what the brain does. In general, theories of construcivism don't go into detail about what's happening at the synapse level, nor do they need to. Cognitive theories use schema to theorize what's going on in the head, but no fMRI machines are necessary. Situated and sociocultural theories of learning gain their usefulness not by trying to look inside the learner's head, but rather outward to that learner's environment, the tools they use, the communities they participate in, and how culture and history shape their activity. So teachers, focus on that — focus on the culture of your classroom, how your students participate, and the learning community you support. Focus on how a carefully constructed curriculum, well-enacted, supports a trajectory of student learning. It will get you much further than neuromyths.
References
Dekker, S., Lee, N. C., Howard-Jones, P., & Jolles, J. (2012). Neuromyths in education: Prevalence and predictors of misconceptions among teachers. Frontiers in Psychology, (Oct), 1–8. doi:10.3389/fpsyg.2012.00429 Retrieved from http://journal.frontiersin.org/article/10.3389/fpsyg.2012.00429/full
Downar, J., Bhatt, M., & Montague, P. (2011). Neural correlates of effective learning in experienced medical decision-makers. PLoS ONE. doi:10.1371/journal.pone.0027768 Retrieved from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0027768
Howard-Jones, P. A. (2014). Neuroscience and education: Myths and messages. Nature Reviews Neuroscience, 15, 817–824. doi:10.1038/nrn3817 Retrieved from http://www.nature.com/nrn/journal/v15/n12/full/nrn3817.html
Moser, J., Schroder, H., Heeter, C., Moran, T., & Lee, Y. (2011). Mind your errors: Evidence for a neural mechanism linking growth mind-set to adaptive posterror adjustments. Psychological Science, 22(12), 1484-1489. doi:10.1177/0956797611419520 Retrieved from http://pss.sagepub.com/content/22/12/1484
Qin, S., Cho, S., Chen, T., Rosenberg-Lee, M. Geary, D., & Menon, V. (2014) Hippocampal-neocortical functional reorganization underlies children's cognitive development. Nature Neuroscience, 17, 1263-1269. doi:10.1038/nn.3788 Retrieved from http://www.nature.com/neuro/journal/v17/n9/abs/nn.3788.html