I'm very grateful to my co-authors, Maëlle Gueguen, Sophie Bavard @sophiebavard.bsky.social, Dirk Wulff @dirkwulff.bsky.social, Julien Bastin @julienbastin.bsky.social, and my supervisor, Stefano Palminteri @stepalminteri.bsky.social, for their thoughtful feedback and support across the project.

🎲 #2: People learned gains and losses equally well, but their risk preferences differed: more risk in gains, more caution in losses. In lotteries, this pattern is reversed, revealing a strong gap between experience and description, and in contrast with the Prospect Theory.

📊 #1: Complete feedback increased risk-taking more than it improved EV maximization. It helped only when the risky option was optimal, indicating that richer feedback shifts risk attitude rather than accuracy.

🧩 We focused on two key factors:
Feedback richness: Do people see only the outcome of their choice, or also what would have happened if they chose differently?
Valence: Are they dealing with gains or with losses?

Here are the two main results:

💡Why this matters:
Risky decisions are everywhere: investing, health choices, and everyday trade-offs.
But people don’t always choose the “better” option, and their behavior changes depending on whether they are hoping to win or trying to avoid losing.

Congrats Charley! 🎇