What are the best conditions for helping pupils build on what they know?
In this blog we look at updating memory through a process of reconsolidation and what this might mean for teaching pupils.
If our memories are going to serve us well (i.e. make accurate predictions), they need stay relevant. This means they need to update when the environment suggests updating is necessary (Pine et al., 2018).
What are we talking about when we say ‘updating’ memory? Updating is just another way of saying we are changing what we know, i.e., learning.
For our purposes here: memory updating = learning.
Consolidation and reconsolidation
New information begins life as a fragile memory trace. Over time and with repeated access and usage, it becomes less fragile and more stable.
The process of stabilising memory is called consolidation (Dudai, 2004). Consolidation is storing memory in a more permanent form.
But what about updating what we know?
When you reactivate a memory, you may make it fragile again. This means (under some circumstances) you can alter it: strengthen, weaken, build upon, distort.
In other words, you can change your knowledge and understanding. And with time, the memory restabilises again. This is why the process of updating memory is called re-consolidation* (Lee et al., 2017).
I like a plasticine analogy here:
Imagine a piece of plasticine is a schema (network) of consolidated memories. When you roll the plasticine in your palms it becomes soft and malleable, capable of merging with another piece of plasticine. This is like memory when it’s reactivated. Reactivation may destabilise memory again, making it malleable and prone to changes such as adding related knowledge. When reactivation ceases, the memory restabilises – just like when plasticine is left alone, it hardens again.
Conditions for updating memory
However, reactivating memories may not be the only necessary condition for reconsolidation to occur.
Reconsolidation is pretty labour intensive for the brain. If all it took to update memory was reactivation, our brains would update all the time. Logically, our brain only updates memories if the information it is experiencing in the environment is different from what it expected.
Another way of saying this is that our current schema (networks of consolidated knowledge) lead us to make predictions about what will happen. If incoming information mismatches a prediction, we experience a prediction error. A prediction error tells our brain it needs to update.
As a general rule then, reactivation + mismatch may lead to reconsolidation and updating (Ecker, 2015). For example, this could happen when a teacher gives pupils corrective feedback.
You may wonder if there is a point at which memories cease to update. In other words, does reconsolidation ever end? The answer seems to be no. So long as the memories continue to be activated and the environment is not exactly the same as predicted, updating could happen (Dudai, 2012).**
This is good news for teachers: the opportunity is always there to build upon what pupils know.
What might this mean for teachers?
(1) Reactivate and create mismatch
The concept of reconsolidation appears to highlight two necessary criteria: reactivation and mismatch.
First, reactivation. I discuss how we can catalyse learning using what pupils already know in a previous blog.
The most obvious usage of this in the classroom is corrective feedback. Pupils need to see the gap between what they thought was right and what the answer actually is. This mismatch supports updating.
This mismatch is especially pronounced for incorrect answers a pupil had a lot of confidence in (Pine et al., 2019). Why? Because high confidence = greater prediction error when they find out the right answer. Greater prediction error = better updating.
We may also be able to alert pupils to incoming mismatch when we introduce new material too:
Show pupils what they are about to learn is related to, but different (e.g. extends upon) what they already know.
van Kesteren and Meeter (2020) call this operating at an ‘optimal “distance” from the schema’ (p.3). They relate this to the concept of desirable difficulties:
Desirable difficulties describe learning conditions that make it harder for the learner to perform well initially, yet increase learning over the longer term (Bjork, 1994). Retrieval practice is a good example. Initially, it is harder for pupils to retrieve information than to re-read it. Retrieval often leads to decreased performance in the short term. But over the long term, retention is better for retrieved than restudied material (Roediger & Karpicke, 2006).
What’s the desirable difficulty in creating mismatch?
In pointing out the difference between what pupils know and the new material being introduced, pupils need to think to connect their prior knowledge to the new material.
How might this be achieved?
“We’ve learned about aerobic respiration, which we know requires oxygen. How about when we exercise hard and we can’t get enough oxygen? Aerobic respiration can’t occur. Let’s look at what happens then.”
Supporting pupils to understand how what they are being taught relates to what they already know and yet differs, could help create a sense of mismatch.
Furthermore, we tend to be more curious to learn new information if we think we already know something about it (Kang et al., 2009). And curiosity may provide a boost to learning (Gruber et al., 2014).
Therefore, communicating to pupils –
a) you know something about the new information I’m about to teach you, but
b) you don’t know everything about it…
could be a good recipe for piquing curiosity.
(2) Build on sturdy foundations
You can only reliably build upon study foundations. This means building on consolidated knowledge because this knowledge is stable.
What does this mean for teachers?
We can’t introduce too much in one lesson.
For example, if we introduce new information AND try to get pupils to to understand a conflicting idea in the same lesson, this may result in confusion (Richter et al., 2019). The new knowledge may need to consolidate first before we introduce conflicting ideas.
For example, in PE, teaching pupils a new skill AND how to do it differently in another situation might be too confusing. They need time to consolidate the skill first before learning how to use it flexibly.
This reminds me of the stages and characteristics of expertise:
Novice learners need rules (Persky & Robinson, 2017). Novices don’t have the cognitive capacity or knowledge to flex these rules. Think of the new teacher who sticks rigidly to their lesson plan when they ought to change course in response to pupils’ answers.
Only once you know the rules can you learn how to break them.***
Sequence learning to build on consolidated knowledge. Consolidation takes time and usually rest/sleep. Too much in one lesson is likely to lead to confusion.
*Consolidation and reconsolidation are concepts that seem to describe the processes of stabilising and restabilising memory.
** There’s some evidence that older memories continually strengthened over time may be less prone to change but the evidence is mixed (Alberini, 2013).
***Adapted from (what appears to be) Picasso’s quote: “Learn the rules like a pro, so you can break them like an artist.”
Bjork, R.A. (1994). Memory and metamemory considerations in the training of humanbeings. In J. Metcalfe and A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp.185-205). Cambridge, MA: MIT Press. Cited in Bjork, R. A., & Bjork, E. L. (2020). Desirable difficulties in theory and practice. Journal of Applied research in Memory and Cognition, 9 (4), 475-479.
Dudai, Y. (2004). The neurobiology of consolidations, or, how stable is the engram?. Annual Review of Psycholology, 55, 51-86.
Dudai, Y. (2012). The restless engram: consolidations never end. Annual review of neuroscience, 35, 227-247.
Ecker, B. (2015). Memory reconsolidation understood and misunderstood. International journal of neuropsychotherapy, 3(1), 2-46.
Gruber, M. J., Gelman, B. D., & Ranganath, C. (2014). States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron, 84(2), 486-496.
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Nader, K., Hardt, O., Einarrson, E. O., & Finnie, P. S. B. (2013). The Dynamic Nature of Memory. In Alberini, C. M. (Ed.), Memory Reconsolidation (pp. 15-42). Elsevier.
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Pine, A., Sadeh, N., Ben-Yakov, A., Dudai, Y., & Mendelsohn, A. (2018). Knowledge acquisition is governed by striatal prediction errors. Nature communications, 9(1), 1-14.
Richter, F. R., Bays, P. M., Jeyarathnarajah, P., & Simons, J. S. (2019). Flexible updating of dynamic knowledge structures. Scientific Reports, 9(1), 1-15.
Roediger III, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological science, 17(3), 249-255.
van Kesteren, M. T. R., & Meeter, M. (2020). How to optimize knowledge construction in the brain. npj Science of Learning, 5(1), 1-7.
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