While our cognitive architecture has incredible capabilities (how else could we come up with advances such as Mystery Science Theater 3000?), it also has limitations. The same adaptive capabilities that let us cope with information overload in both familiar and new ways also lead to some systematic flaws. And it led me to think about the ways in which we support these limitations, as they have implications for designing solutions for our organizations.
The first limit is at the sensory level. Our mind actually processes pretty much all the visual and auditory sensory data that arrives, but it disappears pretty quickly (within milliseconds) except for what we attend to. Basically, your brain fills in the rest (which leaves open the opportunity to make mistakes). What do we do? We’ve created tools that allow us to capture things accurately: cameras and microphones with audio recording. This allows us to capture the context exactly, not as our memory reconstructs it.
A second limitation is our ‘working’ memory. We can’t hold too much in mind at one time. We ‘chunk’ information together as we learn it, and can then hold more total information at one time. Also, the format of working memory largely is ‘verbal’. Consequently, using tools like diagramming, outlines, or mindmaps add structure to our knowledge and support our ability to work on it.
Another limitation to our working memory is that it doesn’t support complex calculations, with many intermediate steps. Consequently we need ways to deal with this. External representations (as above), such as recording intermediate steps, works, but we can also build tools that offload that process, such as calculators. Wizards, or interactive dialog tools, are another form of a calculator.
Processing information in short term memory can lead to it being retained in long term memory. Here the storage is almost unlimited in time and scope, but it is hard to get in there, and isn’t remembered exactly, but instead by meaning. Consequently, models are a better learning strategy than rote learning. But external sources like the ability to look up or search for information is far better than trying to get it in the head.
Similarly, external support for when we do have to do things by rote is a good idea. So, support for process is useful and the reason why checklists have been a ubiquitous and useful way to get more accurate execution.
In execution, we have a few flaws too. We’re heavily biased to solve new problems in the ways we’ve solved previous problems (even if that’s not the best approach. We’re also likely to use tools in familiar ways and miss new ways to use tools to solve problems. There are ways to prompt lateral thinking at appropriate times, and we can both make access to such support available, and even trigger same if we’ve contextual clues.
We’re also biased to prematurely converge on an answer (intuition) rather than seek to challenge our findings. Access to data and support for capturing and invoking alternative ways of thinking are more likely to prevent such mistakes.
Overall, our use of more formal logical thinking fatigues quickly. Scaffolding help like the above decreases the likelihood of a mistake and increases the likelihood of an optimal outcome.
When you look at performance gaps, you should look to such approaches first, and look to putting information in the head last. This more closely aligns our support efforts with how our brains really think, work, and learn. This isn’t a complete list, I’m sure, but it’s a useful beginning.
We have such a bias for things stored in the head. Is there any swifter cognitive putdown than “He had to look it up?”
That explains the popularity of Jeopardy, but this bias is also part of individual or organizational resistance toward “putting information in the head last.” When someone’s well versed in a field (or when he thinks he is), he doesn’t readily see the difference between task A and task B. My example at Amtrak was checking train schedules: you can check train schedules without knowing the three-letter city codes if I give you the codes — and checking train schedules is a richer-looking task to the newcomer than looking up a city code is.
What that means in practice is that if you’re learning to be a reservation agent or a ticket clerk, I can on the one hand just supply the codes (so you can practice various schedule scenarios), and I can show you how to look up codes (so you know how to find them when you need to). My own opinion is that requiring you as a novice to look up codes every single time gets in the way of your learning the higher-value skill.
Dave, I admittedly wasn’t considering the relative perceptions of value of skills. And sure, sequence the skills however makes sense (presumably, looking up codes is not only lower value, but lower learning; even I have been able to do it on flights and trains with web interfaces ;). Which doesn’t, to me, mean put the codes in the head regardless.