- Joel Califa noted "People love shiny new things” and suggested that if people see a new feature, they'll 100% want it and do mind backflips trying to figure out how they'd use it…even if they would've told you, "that doesn't sound very interesting" had you asked at first.
via Wikipedia - Cognitive Bias
What Should We Remember
- We store memories differently based on how they were experience
- We reduce events and lists to their key elements
- We discard specifics to form generalities
- We edit and reinforce some memories after the fact
Too Much Information
- We notice things already primed in memory or repeated often
- Lean UX Research Tips - Joel Califa → On bias regarding people thinking more about a topic after a researcher asks about it - which is why one much ask general questions first, then specific, as not to stir up ideas and direct user feedback unintentionally
- Bizarre, funny, visually striking, or anthropomorphic things stick out more than non-bizarre/unfunny things
- We are drawn to details that confirm our own existing beliefs
- We notice flaws in others more easily than we notice flaws in ourselves
- We notice when something has changed
Need to Act Fast
- To act, we must be confident we can make an impact and feel what we do is important
- We favor simple-looking options and complete information over complex, ambiguous options
- To avoid mistakes, we aim to preserve autonomy and group status, and avoid irreversible decisions
- To get things done, we tend to complete things we've invested time and energy in
- To stay focused, we favor the immediate, relatable thing in front of us
Not Enough Meaning
- We project our current mindset and assumptions onto the past and future
- We think we know what other people are thinking
- We simplify probabilities and numbers to make them easier to think about
- We imagine things and people we're familiar with or fond of as better
- We fill in characteristics from stereotypes, generalities, and prior histories
- We tend to find stories and patterns even when looking at sparse data