“I chose this option, therefore its features are the best”

We have a tendency to remember our choices as being better than they actually were. We over-attribute positive features to the options we’ve chosen. On the other hand, we do the opposite for options that we did not choose: we attribute negative features to the non-chosen options.

choice supportive bias

Scientific research example

Imagine researchers ask you several times to choose between two classic cars, each with a couple of distinctive features. After you finished, the researcher thank you and ask you to come back in 7 days.

One week later you come back. The researchers (Henkel & Mather) show your choices again, together with the list of features. For each feature you have to indicate whether it belongs to the classic car you chose, to the rejected old-timer, or if it is a new feature (Henkel & Mather (2007) actually tested with ‘used cars’, but I like classics better…).

Do you think you’d remember the features correctly, or maybe randomly? Or has your memory predicatively been mis-attributing features to the chosen and not-chosen old-timers? It turns out that the latter will happen. Your memory has a choice-supportive bias in remembering which features belong to which classic car. Henkel & Mather proved you associate more positive features with the classics you choose, and associate negative features with the rejected ones…

In other words: your memory is your best friend: it tries to help you feel good about your choices.

Online Persuasion tips:

In order to get your customers to attribute positive features to you, and negative ones to others:
[checklist]

  • Test by asking your users why they visit your website or use your app.
  • Ask them why they bought and use your product.
  • Show previously visited pages and bought items!

[/checklist]

Further reading on autonomy:

  • Henkel, L.A.; Mather, M., Journal of Memory and Language 57 (2): 163–176 (2007). “Memory attributions for choices: How beliefs shape our memories”.
  • Choice-supportive bias on Wikipedia