How do people search for information to make efficient decisions?

Our new theory, now out in Psychological Review, suggests that an efficient search rule is (at the core of) the answer. And eye-tracking data support our theory.

Check out here (it's open access): psycnet.apa.org/fulltext/202...

Very happy to see our ice-fishing paper on the cover of @science.org this week! 🎣🎉

We tracked large groups of Finnish competitive ice-fishers to study how social foragers use social information when searching for resources. 🐟

Link: www.science.org/doi/10.1126/... (contact me for open access)

People haven't taken up more computational modelling in the ethos of the blue path model in the OP. What I've seen is more equivocation between model & system under study. Removing the ability of models to act as mediators. See also: doi.org/10.1007/s421... doi.org/10.1007/s421... 3/

Plot showing relation between switching rates and choices in line with comparison rules

Empirical data suggests that people select strategies (i.e., combinations of search and comparison rules) that promote expected-value–maximizing choice.

People tend to combine roundwise comparison with infrequent switching, and summary-wise comparison with frequent switching. 4/4

Probability weighting patterns for different sampling strategies

Simulations also show that interplay of search and comparison rules can be a driver of the probability-weighting patterns often seen in decisions from experience. 3/4

• Roundwise comparison → underweighting of rarely sampled events
• Summary-wise comparison → generally more overweighting

Maximization rates for different sampling strategies

We formalizes sampling strategies along three components: a search rule (switching), a comparison rule (roundwise vs. summary), a stopping rule.

Simulations show that roundwise comp. yields more maximizing w/ low switch rates, while summary comp. yields more maximizing w/ high switch rates. 2/4

article cover page and abstract

New article in Cognitive Psychology with @thorstenpachur.bsky.social and Veronika Zilker: “How sampling strategies shape experience-based risky choice.”

We present a computational framework for information search and choice in decisions from experience. 1/4
doi.org/10.1016/j.co...

For all who use Bayesian hierarchical models, have a look at our new preprint, out now together with @linushof.bsky.social @nunobusch.bsky.social and @thorstenpachur.bsky.social

osf.io/preprints/ps...

Congratulations to @linushof.bsky.social from our lab @tum.de for being the runner-up at this year’s #teap2025 poster competition! His research shows that people search adaptively in decisions from experience.