This post continues our series on SXSW as each fellow shares their highlights and reflections. Read about Jake’s here, Amanda’s here, and Dan’s here.
SXSW was a blast. The schedule was packed with so many interesting sessions, but unfortunately it was impossible to make it to all of them. One of my favorite talks was from Capital One’s Design team where they were gracious enough to share cognitive biases we have when we are testing and validating our ideas. Here is what you should look out for when you are in the midst of user testing:
1. Selective Attention: Please forgive me for the next couple of minutes because I am going to bring your attention to the fact that your nose is always in your field of vision. Do you notice it now? Annoying, I know. Selective attention means we pay attention to what we want to focus on and unconsciously ignore the rest. Watch the video below for a quick example of selective attention.
2. Observer-expectancy Effect: When a researcher expects a certain result and unconsciously influences the result of an experiment based on their beliefs. Unfortunately the observer-expectancy effect doesn't work when you are rooting for your team to win March Madness (Go Terps!).
3. Primacy Effect: Look at this list for 10 seconds and try to remember as many items as you can. What did you remember? The primacy effect makes the argument that you are more likely to remember the first items on the list, than the last items on the list.
4. Frequency Illusion: Have you ever learned a new word and suddenly, after NEVER hearing it before, everyone is saying it? Everyone is not saying it, that’s just your brain playing tricks on you. This phenomenon is called frequency illusion.
5. Availability Heuristics: A shortcut your brain takes that uses the most recent and available information you learn about a topic to help you make decisions. For example if you hear there has been a spike in shark attacks in your area. Are you still going to take that beach trip? Probably not.
6. Pro-Innovation Bias: Is the belief that a certain innovation should be widely adopted by society. How do you know if you are suffering from pro-innovation bias? Three words, “the uber for…”