Mirjam Neelen & Paul A. Kirschner
Neuroscience has been pillaged and plundered by often well-intentioned people who – this is a Dutch saying translated into English – have heard the clock chime/strike, but don’t know where the clapper/tongue is [people who think that they understand something, but fail to grasp the full story, real essence, or meaning].
What follows is based on a long conversation between Mirjam and Daniel Ansari, professor of Psychology and Education at the Department of Psychology and the Faculty of Education at the University of Western Ontario in London, Ontario. The full interview has been published in our book Evidence-Informed Learning Design.
While five to ten years ago, ‘neuroscience’ was a seldom used term in the learning field, we now can’t open a newspaper or read a blog where someone isn’t making claims about learning based on ‘neuroscience’ or ‘neuroscientific evidence’. Whether we’re actually talking about evidence from neuroscience or other sciences that are part of the learning sciences, such as cognitive science, educational psychology, or behavioural science, there’s a tendency to use the label ‘neuroscience’. Apparently, it’s sexy and has the power to convince.
Maybe this is because ‘neuroscience’ sounds cooler than, for example ‘cognitive science’. Maybe it’s because the images look sexy. Whatever it is, people seem to find anything related to the brain intriguing and therefore, ‘misuse’ neuroscience to pique people’s interest and those with, often, good intentions use it to convince others that we should use scientific evidence to inform design decisions in the learning field.
This enthusiasm for neuroscience can, of course, be used as a vehicle to bring more solid evidence to the field. If people start to apply evidence from cognitive science, such as spaced learning and retrieval practice because they’re so enthusiastic about neuroscience, then that in itself is (relatively) fine. It can also be used in the other direction to try to see what happens in our heads when we use these techniques, just as eye-tracking can be used to try to see what differences in study materials do with how we look at and (possibly) process it.
After all, every single cognitive scientist and cognitive psychologist is always studying the brain in a way. Reaction time, for example, is a measurement of processing speed and that processing occurs in the brain. So, from that perspective it’s not completely scientifically wrong to use ‘neuroscience’ to help describe what’s going on. After all, the brain is the organ of learning (see our ‘brain-based bullocks’ blog) and, when we don’t use neuro imaging, we always study the brain implicitly.
However, a warning is in order. Or at least, a ‘reality check’.
The non-existing practical implications of neuroscience
Neuroscientists are sometimes interested in brain mechanisms in and of themselves. They ask fundamental scientific questions and, in that case, often use functional magnetic resonance imaging (fMRI).
According to Wikipedia, “functional magnetic resonance imaging (fMRI) relies on the paramagnetic properties of oxygenated and deoxygenated hemoglobin to see images of changing blood flow in the brain associated with neural activity. This allows images to be generated that reflect which brain structures are activated (and how) during the performance of different tasks or at resting state. According to the oxygenation hypothesis, changes in oxygen usage in regional cerebral blood flow during cognitive or behavioral activity can be associated with the regional neurons as being directly related to the cognitive or behavioral tasks being attended.”
In other words, if the question is neurobiological in nature and blood flow has something to do with it, researchers can use imaging to try to understand things around neuro mechanisms.
Now here’s where the warning comes in: In general, brain imaging techniques in and of themselves don’t have any real practical implications. At best, for learning, it can be used in combination with behavioural research to try to understand processes underlying learning. Neuroscience is just one discipline contributing to the general science of learning, and its toolkit (e.g., fMRI or brain-imaging, computed axial tomography, positron emission tomography, etc.) may not be the strongest. Add to this that neuroimaging is still in its early days of development, and you can understand why it’s premature at best and foolish at worst to use this technique to ‘explain’ learning let alone make any claims as to how to teach!
When interested in behaviours and in better understanding how certain processes give rise to those behaviours, brain imaging can best be seen as complementary. It can sometimes help to explain certain phenomena and why people behave in a certain way.
There are plenty of examples in the learning field where fMRI images are shared and interpreted in completely the wrong way. What follows is a VERY illustrative example of how things become problematic when learning people start interpreting and publishing findings from (cognitive) neuroscience. Here goes.
Recently, Klein-Flügge, Wittmann, Shpektor , Jensen and Rushworth (2019) published an article in Nature, in which they demonstrated, using neuroimaging, that “representations of task knowledge are derived via multiple learning process operating at different time scales that are associated with partially overlapping and partially specialized anatomical regions”. In other words, they studied the associative structures formed through reinforcement (rewards) and incidental learning mechanisms, and they describe two neural circuits with distinct coding systems.
Next, Oxford University published a press release on the research, titled ‘How our brains remember things depends on how we learn them’.
The press release explains that the study used MRI to “observe changes in parts of the brain associated with learning and learned experiences while volunteers completed tasks that involved a reward”. The changes seen in the participants’ neural pathways associated with learning were different depending upon how each person had learned the new skill. Miriam Klein-Flügge explains in the release that we know that humans can learn in different ways (e.g., through observation or trial and error) and that their research shows that we have multiple networks in the brain that help us store learnt knowledge or associations. She adds that some of this knowledge is very persistent, and the brain does not forget about it even when it becomes irrelevant.
Long story short, an interesting study!
Fast forward to a post on LinkedIn  in which the poster references an article in Neuroscience News, which discusses the Oxford press release (which, to be clear, explained the research very well). The poster states that “Our brains remember differently if learning is from observation or practice. Former is more malleable, latter is retained better.” But really, nowhere in the Neuroscience News article (nor in the press release, nor in the original article in Nature) it says that practice is retained better than observation! It’s simply NOT what the study is about.
This is only one example of the danger of incorrectly interpreting neuroscience findings and then making all kinds of false claims about practical implications.
Another popular one is claims around dopamine. For example, a statement like “Dopamine improves motivation, increases learners’ attention spans, and makes learning addictive.”
Nope. Dopamine is a neurotransmitter that we really don’t fully understand. We know that it’s strongly involved in our fine motor coordination and our ability to maintain muscle tone. The idea that lots of dopamine will make people more motivated is simply wrong. We need to ask how it even helps to talk about dopamine when we talk about motivation or learning in the workplace or in schools. This is one of the cases where we really don’t need the metaphor at all. And what’s worrisome is that people talk about dopamine and learning as if it’s a fact. It’s basically saying everything and nothing at the same time and throwing in neurotransmitters.
It’s also a false overgeneralization. It’s an example where people use dopamine to make something sound scientific, when it’s actually not scientific at all. This is where neuroscience is being abused. We can’t make such general claims. Dopamine is just a neurotransmitter that regulates the system. It’s value free, meaning that’s it’s neither positive nor negative.
You might see why this is a problem. People become convinced they’re doing the right thing when they’re really not. It’s actually the opposite. They back up false claims with sexy-sounding ‘evidence’ (although they might not be aware). And the worst is when its use is neither heartfelt or ideological, but rather when it’s commercially driven, using neuroscience to make claims that products are underpinned by evidence and taking advantage of the uninformed enthusiasm of instructors or other learning professionals.
How neuroscience can be valuable for learning
At the moment, there’s very little that we can take from neuroscience and can then implement for education or training. Neuroscience might be able to provide us with one level of explanation of part of a process but given the complexity of the situation within which people interact in the workplace or learn in schools, it’s presently impossible to bring this up or down to the level of an experiment on how people can better learn.
No, we’re not saying that neuroscience has no place in our toolkit as researchers, learning designers, or teachers trying to help people learn and design effective, efficient, and enjoyable learning experiences. Of course, the biological level of explanations can be useful. It can be informative. It can help us figure out what brain regions are involved when trying to answer certain ‘learning’ questions, what they intersect with, what they’re similar and dissimilar to and that can then feed back into behavioural studies and help to, for example, better understand the relationship between individual behaviours or context. We can use it to formulate hypotheses which we can then test in learning situations. We can use it in conjunction with other types of research to study the biological plausibility of theories. That’s how neuroscience can contribute.
We need to follow developments in this space. When measurement precision increases, it may be able to support the diagnosis of learning disorders or track learner progress over time. It’s likely that there will be progress made, however, no matter what progress we make, neuroscience will remain informative to the learning sciences and not directly and practically applicable.
Let’s stop the neuroscience abuse for learning and give neuroscience the respect it deserves.
Klein-Flügge, M. C., Wittmann, M. K., Shpektor, A., Jensen, D. E., & Rushworth, M. F. (2019). Multiple associative structures created by reinforcement and incidental statistical learning mechanisms. Nature communications, 10(1), 1-15. Retrieved from https://www.nature.com/articles/s41467-019-12557-z.pdf
 We’re not providing the link as it’s not about naming and shaming, it’s to provide an illustrative example