Weighting positive vs. negative information

I use beans to understand people's biases.

No really, I do.

Imagine the following scenario: You're playing a game and you've learned that one kind of bean is positive (you gain +10 points when you approach it), but you've learned another kind of bean is negative (you lose -10 points when you approach it). What do you do when you see a new bean that equally resembles both the positive and negative one you saw earlier?:

We use an in-lab game, affectionately dubbed BeanFest, to measure this bias. And it turns out that the way people decide whether that novel bean is positive or negative has a big impact on how they live their lives. People who give greater weight to the positive bean when categorizing the novel bean are less impacted by negative word-of-mouth, more likely to try new things, make riskier decisions, less likely to ruminate after a negative experience, less sensitive to the possibility of being rejected by others, and even make more friends over time (e.g., Rocklage and Fazio 2014; Rocklage, Pietri, and Fazio 2017; Fazio, Pietri, Rocklage, and Shook 2015).

Interestingly, people don't seem to be able to report this weighting bias: self-beliefs about our weighting bias don't correspond to the measurement we obtain in the lab.

If you're interested in using the BeanFest paradigm, click here to download the Python version of BeanFest I created, or if you want more information, check out our BeanFest home page here.