Hi, many thanks for sharing this!

I've tested across several device (pc with low quality camera and a recent iphone). On the iphone it works well, but on the pc it works only on Firefox but it doesn't work on Brave (I tried several times). I don't understand why

@gregoryfiorio.bsky.social interesting !

Thanks Tanay !!

📄 Preprint: doi.org/10.31234/osf...
💾 Data, code, materials, and preregistrations: osf.io/aecyr/files?...

Huge thanks to my co-authors, all senior authors with equal contribution: @oliviermorin.bsky.social, @hugoreasoning.bsky.social, @tadegquillien.bsky.social

⚙️ We analyze behavior both at the aggregated level and at the trial level, showing that participants’ accuracy is not merely a wisdom-of-crowds artifact.

🤖 Our Bayesian model outperforms several plausible heuristic models and explains ~90% of the variance at the aggregated level.

More in the paper:

📊 We combine a rich behavioral dataset (N=1,916; 113,400 knowledge-attribution judgments; 2,960 search choices) with a large and diverse stimulus set (N >1200).

📌 The same Bayesian framework explains information search. Participants flexibly queried new information in ways that match an optimal information-theoretic search model.

📌 A Bayesian model best explains these inferences. Participants’ judgments are best described by a Bayesian model that rationally integrates new evidence with prior expectations about others’ competence.

Main findings:

📌 People can accurately predict what others know from very limited information. From a single observed answer, participants could predict the probability that someone would know another trivia question with striking accuracy!

After seeing an individual’s performance on a trivia question, participants had to either:
- predict the individual’s ability to answer other trivia questions from the same theme, or
- select which information would be most diagnostic for inferring an individual’s competence.

In our new preprint (doi.org/10.31234/osf...), we examine how people infer others’ competence from sparse evidence, and the computational principles underlying these judgments.

We tested participants’ inferences on two tasks requiring fine-grained estimations. 👇

Many thanks!

Thanks for sharing.
I now need to debunk the misinformation I shared yesterday 😅

Very cool!

Seems exciting! 👀