July 21, 2026

FRICtion Factor 3: Inertia - Part Two:

Rewiring the Brain

By Ruth Tank

As any parent who has had to help a child with primary school maths will tell you, we forget most of what we know over time.

The Ebbinghaus Forgetting Curve (Ebbinghaus, 1913) was an early exploration of what happens when we don’t properly reinforce new learning. It shows that we forget most of what we learn within the first few days, which is why traditional classroom training is rarely effective on its own. 

We might engage with the content in the moment, but the brain cannot transfer all new information into long-term memory. It needs a signal that the information is important enough to both store and retain – and that it is more important than all the other competing information our brains will receive each day.

Embedding new knowledge and skills requires:

  1. Selection of information to transfer from working memory to long-term memory
  2. Subsequent need to retrieve that information or practice that skill.

Selection

We ignore vast amounts of data each day. We couldn’t function if this weren’t the case, so how does the brain decide that something is worth retaining?

Emotions

Evidence shows that information that provokes an emotional response is more likely to be remembered (Tyng et al., 2017). Kensinger (2009) further elaborates that negative emotions have a stronger effect than positive ones. In terms of application within a change context, this explains why the Conner’s ‘burning platform’ for change is such an enduring metaphor as it focuses on the dangers of the current state (Conner, 1993).

Having said that, there are also credible studies (and lived common sense) to suggest that a constant state of moving away from danger can lead directly to stress, and its many familiar negative associations. So in a change context this isn't about being binary: combining a real sense of urgency (move away) with a vivid positive vision of the future (move toward) taps into different points on the emotion spectrum.

Storytelling

Stories are an important evolutionary mechanism by which we encode and transfer information between generations. Research (Green Brock, 2000; Isberner & Richter, 2018; Vaccaro et al., 2021) confirms that we remember information much more effectively when it is structured as a story, so this can be an especially powerful way of enhancing the stickiness of communications and training content. 

Examples might be showcasing specific examples of pain points that people are experiencing with how the proposed change will make life better (and publishing success stories when it does), or explaining training content using specific real-life use cases rather than generic examples. It can also be helpful to consider the change as a ‘hero’s journey’, making those impacted the protagonists, change practitioners as the guide, and the current way of working as the ‘villain’ that must be overcome to achieve the desired future state.

Retrieval or practice

As the saying goes, “use it or lose it!”. The more often we retrieve information, the more embedded those neural pathways become. We can support this process during change projects by finding ways to include retrieval within our training strategy. This starts right from the first training intervention: short quizzes interspersed within chunks of training content can significantly improve retention, even months later (Roediger & Karpicke, 2006), so building in appropriate, low stakes tests are more effective that simply consuming the training content alone.

But given how plastic the brain is – constantly optimising its storage capacity to align with the current needs of the environment and filtering out so much – we cannot expect knowledge that is important at a particular point in time to be retained indefinitely. This concept that learning must be reinforced is not news, but precisely how to apply this in practice may be less so.

Cepeda et al. (2008) have studied the optimal period between learning events and found that there is wide variation in knowledge retention depending on this ‘spacing effect’. Test subjects were given two training sessions spaced between zero days and three months apart. They were then tested on how much they remembered from seven days up to a year later. 

The differences in retention were dramatic – a 64% difference between the worst and best gaps. The results were non-linear, but the most relevant finding for our purposes was that:

  1. If people need to remember information a week after training, they should have (at least) two training events spaced a day or two apart (but no closer).
  2. If they will not need to remember that information a month after training, then a gap of seven to ten days is ideal.
  3. If you don’t have this flexibility, increasing the gap between training sessions is less damaging than decreasing it – shorter gaps had a much more dramatic negative effect than longer ones (Cepeda et. Al, 2008).

So, how much time does it take for a new skill to become a habit? As you’d expect, this very much depends on many factors, such as complexity of task, individual motivation and level of proficiency required, but Lally et al (2010) showed that with daily practice, new behaviours became automatic at around two months (66 days). A meta-analysis conducted by Singh et al (2024) backed up this figure of roughly two months for habit formation. This has a couple of implications for change programmes:

  1. Ensure that training support continues for at least two months after the ‘go live’ of any new changes (if they need to use the knowledge regularly).
  2. It could be worthwhile communicating this statistic to those impacted, both to manage expectations and to reinforce the importance of daily practice [1].
  3. Gamify engagement with the learning content  - offer rewards, recognition or publish a leaderboard to encourage regular (ideally daily) practice.

[1] The variation was large, however – ranging from 18 to 254 days (Lally et al, 2010).

Practical example

One client I worked with had a very decentralised workforce of around 40,000, spread across multiple depots and regional offices. Many were frontline workers, with an average reading age of 10, and there was a huge variation in levels of digital skills and confidence. We did a series of roadshows with in-person presentations and a team of floor walkers with iPads to show people what to do in real time. We then did train-the-trainer sessions, equipping them to run follow up sessions. For those who were office based, we provided a ‘single-source-of-truth’ intranet page with all the training collateral: guides, videos, top tips.

This article forms part of our FRICtion Factors series.

Our whitepaper, The FRICtion Factors - what's stopping you change?, explores four factors that cause resistance, helping you to understand what’s really going on and providing practical advice on what to do about it. Click here to download the whitepaper.

BIBLIOGRAPHY

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