Summary: Humans have a strong capacity for pattern recognition. This is beneficial in many circumstances, aiding in our survival and helping us safely navigate our environment. However, the same cognitive mechanisms can also cause us to make incorrect associations between cause and effect. One example is a sports fan believing that a ritual like wearing “lucky” socks during an event will increase the chances of success for their favorite team. In statistics this over-interpretation of random events and correlations is known as a Type I error. The same type of behavior can be seen in animal studies, where pigeons will repeat an action that they have incorrectly assumed is the cause of food being delivered to them, when the timing was actually random.
Conceptual Design: When designing a new product are we utilizing our users’ strength for pattern recognition? Often humans can tease complex patterns from noisy data far more effectively than computers. Can users effectively see the link between using our product and having the positive outcomes that they desire? If our product has benefits, we would definitely like our users to associate them properly. What can we do to make our product as indispensable as “lucky” socks?
Interaction Design: Pattern recognition makes it possible for users to quickly learn how to operate and interact with a product. The experience should be consistent across different components of the product, allowing users to recognize the patterns of use and to streamline their experience. The product should leverage users’ existing knowledge of similar products so that users can begin working successfully sooner.
Interface Design: To facilitate accurate pattern recognition among users, interfaces should be designed to provide clear and consistent feedback. Feedback should be provided in a timely manner, and there should be no ambiguity about which actions provide which responses. Users are clearly able to see the results of their actions they are far less likely to come up with their own explanations of why things are happening, thereby reducing the prevalence of Type I errors.