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Designing Distractions

While the brain can seem almost boundless in its potential, it has limitations, such as processing speed, attentional limitations, working memory limitations, and sensitivity to interference, which can be both internal and external. You’ve all been there – that annoying, wicked challenge that you just can’t seem to get your mind to process. Frustration brews. It all goes bad.

But occasionally you might have a ‘Eureka Moment’ where the solution seems to miraculously present itself to you.

The eureka effect refers to a common human experience of suddenly understanding a previously incomprehensible problem or concept… Some research describes the eureka effect as a memory advantage, but conflicting results exist as to where exactly it occurs in the brain, and it is difficult to predict under what circumstances one can predict a eureka effect.

Eureka can be conceptualized as a two phase process;

  • The first phase requires the problem solver to come upon an impasse, where they become stuck and even though they may seemingly have explored all the possibilities, are still unable to retrieve or generate a solution.
  • The second phase occurs suddenly and unexpectedly. After a break in mental fixation or re-evaluating the problem, the answer is usually retrieved.

The Eureka Theories

There are also currently two theories for how people arrive at the solution during a eureka moment.

  • The first is the Progress Monitoring Theory (when you hear me referring to PMT in the office, please be aware that I have a slightly different definition than most people!). A person will analyze the distance from their current state to the goal state. Once a person realizes that they cannot solve the problem while on their current path, they will seek alternative solutions. In complex problems this usually occurs late in the challenge.
  • The second way that people attempt to solve these puzzles is the Representational Change Theory. The problem solver initially has a low probability for success because they use inappropriate knowledge as they set unnecessary constraints on the problem. Once the person relaxes his or her constraints, they can bring previously unavailable knowledge into working memory to solve the problem.

Currently both theories have support, with the progress monitoring theory being more suited to multiple step problems, and the representational change theory more suited to single step problems.

FMRi (Functional magnetic resonance imaging) and EEG (Electro Encephalogram) studies have found that problem solving requiring insight (non-linear problems) involves increased activity in the right cerebral hemisphere as compared with problem solving not requiring insight (linear problems). In particular, increased activity was found in the right hemisphere anterior superior temporal gyrus.

I’ve been interested in moments of ‘eureka’ for a while now because along with the moment itself is a real sense of achievement, euphoria and self-worth. We know that a quiet mind allows the weak connections of non-conscious processing to rise to awareness.

Basically that the act of coming back fresh to a problem is valuable in itself.

New research by Neuroscientist David Creswell from Carnegie Mellon sheds some more light on this phenomenon.

Creswell wanted to explore what happens in the brain when people tackle problems that are too big for their conscious mind to solve. He had people think about purchasing an imaginary car, based on multiple wants and needs. One group had to choose immediately. These people didn’t do great at optimizing their decision. A second group had time to try to consciously solve the problem. Their choices weren’t much better. A third group were given the problem, then given a distracter task – something that lightly held their conscious attention but allowed their non-conscious to keep working. This group did significantly better than the other groups at selecting the optimum car for their overall needs.

FMRI scans showed something interesting happening with the third group. The brain regions that were active during the initial learning of the decision information continued to be active (we call this unconscious neural reactivation) even while the brain was distracted with another task. This reactivation was predictive of how good participants were at making a better decision — more reactivation was associated with better decisions.

To put it plainly – people who were distracted did better on a complex problem-solving task than people who put in conscious effort. This isn’t so surprising – the problem-solving resources of the non-conscious are millions if not billions of times larger than that of the conscious. What’s surprising is how fast this effect kicked in – the third group were distracted for only a few minutes. This wasn’t the ‘sleep on it’ effect, or about quieting the mind. It was something more accessible to all of us every day, in many small ways.

Deliberate Distractions

So now to the bit that I’m driving towards – Designing complex flows, patterns, challenges, data-capture and encouraged behaviours.

What we’ve been doing for decades now is try to simplify complex tasks – I agree this should be encouraged. Of course we want to make complex things simpler. But what if we’re missing the trick of building in other mechanisms for helping audiences solve complex challenges… like telling them to “Stop”.

Some classic examples;

  • Filling out application forms online – Big pain drain.
  • Managing an investment portfolio – Oh boy that one hurts the novice investor.
  • Writing an article (like this one!) to submit to the masses of people who demand something punchy – The pressure!

We have a lot of analytics running within our digital experiences that track everything an audience does now. From where they click, to how long they dwell… we even know where they came from to arrive at the challenge. So we can pretty much derive if they’re slick or if they’re thick. It’s how we learn about their behaviour and make our systems smarter, our messages more potent and our support programmes better.

So why wouldn’t we turn that same machine learning back on the audience? Literally tell them when they’re in a bad spot. Let’s say we spot erratic, confused or behaviour that implies someone is over-thinking or struggling to solve a problem, why not just prompt the user to take a break?

Pete, go play Angry Birds for 5 minutes buddy, come back when you’ve cleared your mind.

We’re letting people go about a cognitive challenge the wrong way – by allowing them to continue pushing at a problem consciously when we should be allowing them to go off and ignore the problem.

If we do this you’ll genuinely find that you’re audiences gets better outcomes and faster, with less effort.

There are so many things we’ve got wrong with design because we haven’t stopped to look at the brain. As we begin to develop the tools to understand the brains quirks better, I suspect that many more surprising discoveries will emerge.

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