Why does retrieval practice benefit memory? How about elaboration?
Using teaching strategies without understanding the landscape in which they operate prevents teachers using them optimally.
Teachers need background knowledge of how memory works. Armed with this, teachers know more about why, when, and how strategies, like retrieval practice, spaced practice and others, work. They can make better decisions about how to teach.
In this blog we’ll start learning this background knowledge.
But first, some demolition work…
Tearing down the library
When I first started teaching, I thought memory functioned like a library. I thought pupils would all take away the same understanding from my explanations because information would slot into their minds. When I asked them questions, I assumed they would extract answers and reinsert them unchanged. Next lesson, I expected them to return with the information intact. Just like books on shelves.
But memory is not like a library.
After my explanations, my pupils asked questions demonstrating different levels of understanding. They returned having forgotten stuff, confused, or sometimes with an even better understanding!
If we are to understand how memory works, we must tear down the library and replace it with a better analogy:
Memory is like an ecosystem
In ecosystems, life exists in interconnected networks. Change to one part of the habitat affects life connected to it.
In ecosystems, when the equilibrium is disturbed, organisms adapt or die out.
In ecosystems, life exists in a state of dynamic equilibrium: everything is constantly developing within a wider state of balance.
Memory too is –
- In networks.
Memory exists in networks
Schemas are networks of related memories (Ghosh & Gilboa, 2014). They contain memories for facts and concepts (Badre & Wagner, 2002).
For example: from lessons on World War I, pupils form concepts like ‘patriotism’ and learn facts like ‘WWI took place 1914-1918’. This is called semantic memory. Semantic memory is formed when the brain extracts common things from lots of experiences to store in networks.
Schemas are extremely powerful. We use them to make sense of everything we encounter. Think of them as ‘mental templates’ fitting new information to what we already know (Gilboa & Marlatte, 2017). They dictate what we notice and how we learn.
Everyone’s schemas are different (in terms of knowledge and connections between knowledge). This means it was impossible for my pupils to take the same understanding from explanations: what they understood depended upon the knowledge they already had!
Schemas are interconnected networks of knowledge. Just like the ecosystem, a change to the network affects other memories. Memories can’t be accessed in isolation like pulling a book from a shelf. When we ask pupils to recall information, they activate related knowledge, altering the connections between memories and changing how they think (Ritvo et al., 2019).
Memory is adaptive
One theory is that memory adapts so we make more accurate predictions about what we will encounter (Kroes & Fernández, 2012). This makes sense from an evolutionary perspective: our ancestors needed to update their memories with new threats in order to survive.
In modern times, our brains also stay one step ahead.
Weakening access to memories we rarely use (forgetting) and updating memory when our predictions are not quite right (learning) keeps memory current.
Updating our networks of knowledge (schemas) = learning.
In future blogs we will explore how our memory updates and how teachers can improve learning. For now, let’s focus on one interesting thing:
Learning can be optimised under certain conditions.
Information that fits with what we already know, but is also different, is most attractive for the brain to learn.
To understand this, imagine a friendship group. They’re a supportive network of friends who have built their friendships over time.
Then someone new comes along. Should they let them into the group?
To make this decision they’ll want to ensure this person will fit in well. They don’t want a toxic influence.
But they also want this person to bring something new to the group. Perhaps an interesting hobby.
In short, this person needs to fit in but be different enough to bring something new.
This is the same for the brain. It doesn’t want to ruin a carefully curated network of knowledge by admitting information that obliterates what we already know (McClelland, 2013). It wants knowledge that fits in with the network. At the same time, the information must be different enough to warrant the energy expenditure to update the network.
This means when we teach pupils new information, it should be at an ‘optimal distance’ (van Kesteren et al., 2020, p.3) from what they already know. Pupils should see that it is linked to what they know but different enough so they have to work to update their memory.
Memory is dynamic
Unlike books in a library, the contents of our memories are constantly changing. In fact, rest and sleep are when our brains do some of their best work.
Memories start out fragile, prone to forgetting/interference. Only when they stabilise are they stored for the long term (learned). This process of stabilisation, especially for more complex learning (Quentin et al., 2021), takes place during rest and sleep (Dudai et al., 2015). Teachers are unlikely to see ‘learning’ during lessons.
A mechanism for changing our memories is activation. We activate our memories whenever we think, i.e., all the time! Therefore, nothing is ‘learned’ in a static sense, like a book being placed on a shelf. It is constantly subject to revision (Dudai, 2012).
This means that pupils will return to my lesson with varying memories of what I taught them: some will find it hard to retrieve, others may have done the homework and strengthened what they know or return confused!
All sorts can happen. But one thing’s certain: unlike a library, memory never shuts. Neural processes beaver away in the ecosystem of our minds.
In the next blog we are going to delve into memory storage. Memory storage is learning. This will give us some useful background and concepts to use when teaching pupils.
Badre, D., & Wagner, A. D. (2002). Semantic retrieval, mnemonic control, and prefrontal cortex. Behavioral and cognitive neuroscience reviews, 1(3), 206-218.
Dudai, Y. (2012). The restless engram: consolidations never end. Annual review of neuroscience, 35, 227-247.
Dudai, Y., Karni, A., & Born, J. (2015). The consolidation and transformation of memory. Neuron, 88(1), 20-32.
Ghosh, V. E., & Gilboa, A. (2014). What is a memory schema? A historical perspective on current neuroscience literature. Neuropsychologia, 53, 104-114.
Gilboa, A., & Marlatte, H. (2017). Neurobiology of schemas and schema-mediated memory. Trends in cognitive sciences, 21(8), 618-631.
Kroes, M. C., & Fernández, G. (2012). Dynamic neural systems enable adaptive, flexible memories. Neuroscience & Biobehavioral Reviews, 36(7), 1646-1666.
McClelland, J. L. (2013). Incorporating rapid neocortical learning of new schema-consistent information into complementary learning systems theory. Journal of Experimental Psychology: General, 142(4), 1190.
Quentin, R., Fanuel, L., Kiss, M., Vernet, M., Vékony, T., Janacsek, K., Cohen., L. G., & Nemeth, D. (2021). Statistical learning occurs during practice while high-order rule learning during rest period. NPJ Science of learning, 6(1), 1-8.
Ritvo, V. J., Turk-Browne, N. B., & Norman, K. A. (2019). Nonmonotonic plasticity: how memory retrieval drives learning. Trends in Cognitive Sciences, 23(9), 726-742.