Creating the learning gap

Error detection in the brain

One morning I had a mildly unsettling mundane experience. I was preparing breakfast and had two jars of preserve on the counter. I had already opened one jar and, after spreading the contents on toast, I automatically went to pick up the lid to replace it. I suddenly became aware of feedback from my arm that the lid I had picked up was heavier than expected. This jarred* me into conscious awareness and I realised that I had picked up the other full jar, having mistaken it for the intended lid.

This is a somewhat trivial example of something called predictive coding in action. Predictive coding is a neuroscience theory which proposes that one of the primary functions of the cortical regions of the brain is to minimize ‘prediction error’ — the discrepancy between what we experience and what we expect to experience based on the predictions of our mental models of the situation (Millidge et al., 2022). The ultimate aim is to ensure that our mental models (or meaning schemas as I like to call them) are fit for purpose in helping us understand and navigate the world around us. My brain had anticipated what the experience of picking up the lid would be like and, when the actual experience didn’t match the predictions, the discrepancy set off an alert to focus my conscious awareness on the problem.

Whenever our brains detect this kind of prediction error or discrepancy, we experience varying degrees of cognitive dissonance. When I noticed the unexpected weight of the jar, I experienced feelings of surprise and wrongness which dragged my attention away from what I was doing in order to understand what was happening and to resolve the discrepancy.

The pros and cons of cognitive dissonance

The feelings associated with predictive error detection are very important to the design of reflective learning. Whether it’s surprise, frustration, confusion, worry or just a general sense of wrongness, the emotions generated by cognitive dissonance can be very effective in driving reflection. When we experience this ‘learning gap’ our brains tend to fixate on the issue until the perceived discrepancy is resolved. However, the feeling of discomfort associated with cognitive dissonance can also inhibit deeper reflection. To get rid of the feeling we may settle for quick resolutions rather than seeking more meaningful learning. It may also deter us from engaging with reflective activities in the first place because of the fear of discovering unsettling errors in our meaning schemas.

This is perhaps why journaling, portfolio building and other methods for reviewing our experiences don’t always lead to insightful reflection. Unless you are actively looking for discrepancies between your expectations and your experiences, you run the risk of just rehashing the experience and reinforcing your existing meaning schemas.

Creating unsettling learning

When it comes to designing reflective learning activities, one of the first priorities is to create conditions that increase the likelihood of generating discrepancies and igniting cognitive dissonance in learners. This accords with the ARCS model of learning design (Keller, 2016), in which the first priority is to capture the attention of learners. This might involve introducing content designed to trigger awareness of possible discrepancies, such as providing counterarguments and alternative perspectives, highlighting atypical and counter-intuitive scenario examples or encouraging speculative or counterfactual thinking.

There are other things you can do to increase the likelihood of identifying discrepancies. One technique is to get people to articulate and record their predictions and expectations in advance of a learning experience and to review these predictions after the experience. This can overcome hindsight bias, in which we edit our memories after and event to convince ourselves that we knew all along what would happen (Roese & Vohs, 2012).

The next priority is to create conditions in which learners are encouraged to persist through the emotional discomfort associated with discrepancies and to avoid avoidance behaviours. Active learning design (Bell & Kozlowski, 2008) focuses on creating a training frame in which learners are expected and encouraged to make mistakes (error framing) and are supported in managing the uncomfortable emotions that result from failing (emotion control).

Mind the gap

It's worth thinking about the types of learning gap or discrepancy (performance, mismatch, ambiguity or absence) you want to generate in your learners and how this relates to the kind of activities and assessments you include.

As a rule of thumb, the feelings of discomfort provoked by the discrepancy are likely to increase as you go down the list. So an absence discrepancy may provoke more radical learning efforts than a performance discrepancy but may also induce more avoidance behaviours.

• Activities and assessments where there is a single correct answer or approach are only likely to produce performance or mismatch discrepancies. 

• Activities that allow for multiple possible approaches may produce ambiguity discrepancies, especially if all approaches are equally valid. 

• Activities where there is no agreed best approach (wicked problems) may produce absence discrepancies.

Incorporate the good

It is possible for discrepancies to be positive as well as negative. You might handle things better than you expected to (performance). What you thought was a bad situation might turn out to be a good one (mismatch). You may have more options than expected (ambiguity). You may be encountering a wonderous novel experience (absence).

We tend to give more weight to negative discrepancies as they represent a threat. Learners may need encouragement to engage in reflective learning from positive discrepancies through structured counterfactual thinking.

Bell, B. S., & Kozlowski, S. W. (2008). Active learning: Effects of core training design elements on self-regulatory processes, learning, and adaptability. Journal of Applied Psychology, 93(2), 296. https://doi.org/10.1037/0021-9010.93.2.296

Keller, J. M. (2016). Motivation, Learning, and Technology: Applying the ARCS-V Motivation Model. Participatory Educational Research, 3(2), 1–15. https://doi.org/10.17275/per.16.06.3.2

Millidge, B., Seth, A., & Buckley, C. L. (2022). Predictive coding: A theoretical and experimental review (arXiv:2107.12979). arXiv. https://doi.org/10.48550/arXiv.2107.12979

Roese, N. J., & Vohs, K. D. (2012). Hindsight bias. Perspectives on Psychological Science, 7(5), 411–426. https://doi.org/10.1177/1745691612454303