Neuroscience Of Innovation (CONT’D FROM PAGE 20)
ation, more focus, more conscious thought, and liter- ally uses more energy. So, we subconsciously avoid it whenever we can. If you avoid (or limit) System 2 thinking when it’s need- ed in your innovation process, you will, at the least, miss out on really good ideas—and at the worst, make some bad judgment calls that you might have avoided if you had effectively used System 2. One of the phases where people frequently try to avoid System 2 thinking is immediately after idea generation, when it’s time to select the best ideas. The brainstorming is usually lots of fun. It’s fast, and our brains are making sub-conscious and intuitive connections. Then comes the time we have to be focused and deliberate to narrow to a manageable set of ideas. Suddenly, it all becomes…a Lot. Less. Fun. Know that your team will try hard to avoid System 2 thinking, and you need to be prepared to counter the ob- jections, and ensure that the needed deliberate thinking will happen. For example, people will say, “It takes too long to review all the ideas. We don’t have time.” or, “Let’s just
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have everyone champion a few ideas instead of reviewing all of them. The ones we remember are probably the best ones anyway.” (Which isn’t true, but that’s another topic.) Another all-too-common scenario -- the team has got- ten together and spent several hours generating ideas. Then, everyone gets five sticky dots to vote for top ideas. Most people will do this in five minutes and immediately dash out the door. They weren’t forced to engage System 2 thinking, so they won’t. Their decisions will rely on Sys- tem 1, with all its concurrent biases, shortcuts, and mistak- en intuition. There will never be the deliberate, conscious, effortful thinking that’s needed at this stage. If this is the typical process in your innovation sessions, you need to make some significant changes. 3. The brain is a “Bayesian inference machine.” Bayes- ian logic is a very specific, formulaic method that provides a disciplined way of combining new evidence with prior models. So, the reference to our brains being a Bayesian inference machine is obviously a metaphor, although a very apt one.
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22 December 13, 2021
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