The lesson is that patterns drive perception more so than the integrity of the data that create the patterns. We perceive our way through life; we don’t think our way through it. Thinking is what we do after we realize that our perception has failed us somehow. But because pattern recognition is so powerfully efficient, it’s our default state.
To account for the role of noise within the essential skill of pattern recognition, we need to remind ourselves how complex seemingly simple tasks really are. Whenever people are faced with complexity (i.e., all day, every day), they use pattern recognition to identify, decipher, and understand what’s going on instantly, instead of examining each component individually. The catch is that all of the valuable consumer thought processes we want to address -understanding, passion, persuasion, the decision to act – are complex.
However, the research we use to help us design for these situations usually tries to dismantle this complexity. It also assumes a user who is actually paying attention, undistracted, in a clean and quiet environment (such as a market research facility), and cares deeply about the topic. Then we “clean” the data we collect, in an attempt to remove the noise. And getting rid of noise destroys the patterns that enable people to navigate those complex functions. So we wind up relying on an approach that does a poor job of modeling the system we’re trying to influence.
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