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.