Tom Friedman recently interviewed the head of HR for Google, Lazlow Bock, about hiring approaches at Google. What Tom reports (and the very good cognitive scientist Daniel Cunningham comments on) gives a good example of how our instincts about expertise and learning can be both right and wrong. Just as our learning environments might benefit from a deeper exposure to learning science to remove some of the misconceptions there, it may well be that our HR practices can benefit, too.
Where things get stickier, as Willingham points out, is on the matter of expertise. Friedman reports that at Google, "[t]he least important attribute they look for is 'expertise.' . . . Most of the time the nonexpert will come up with the same answer, added Bock, 'because most of the time it’s not that hard.'"
For most organizations at scale, and for most roles, cognitive science is very clear that this is not how expertise works. What actually happens, as Doug Lemov relates in a WSJ editorial about the importance of practice (and Rick Hess and I relate in our book), is that lots of deliberate practice with feedback, often slow and error filled at the start, leads to more and more parts of a complex task becoming automated, embedded in fast, fluent, non-verbal long-term memory.
This makes expert solutions to standard problems increasingly fast and error-free, which is great, where efficiency matters. (Google is "special," though, around efficiency - remember the free food?) Perhaps more important when an area is rapidly changing, this internal automation also frees up our working memory. Working memory is the slower, narrower, flexible, verbal part of our minds that tackles new problems and the hardest problems (including learning new things). By automating more parts of tasks, these no longer tie up working memory, allowing working memory to act at a higher level, on bigger "chunks" of problems, and to try out more types of patterns and solutions because of what's immediately available from long term memory.
So for most organizations, the idea that you can ignore what expertise a person already has on-board for any task of any complexity, and just go for a pure novice who knows how to learn, is a recipe for inefficiency and challenges. Do you really want a "fast learner" trying to diagnose your chest pains? ("Give me a second - working Google now. . . Heart trouble - heart trouble - country western songs - wait for it, almost there. . .") As Lemov says in his editorial, even for new problems, "[r]ote learning and conceptual thinking often feed synergistically on each other, freeing our brain capacity for those tasks that require the maximum amount of attention and creativity."
Obviously, Google is a unique place, with an amazing pool of talent to draw from, and has done so successfully for many years: it would be churlish to suggest "think what they could be if only. . ." What's helpful, though, is to filter some of the comments of Mr. Bock by what we know applies to most minds out there, as we seek to understand how to make things work better elsewhere.
Paying a lot more attention to getting the right repeated levels of practice and feedback, the right motivation, and the right evidence-based design of instruction, can systematically build out the kind of expertise that is both efficient and creative, whether at work or at school.