ArticleProceedings of the National Academy of Sciences of the United States of America2024
Predecisional information search adaptively reduces three types of uncertainty.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Article
- Competing cognitive pressures on human exploration in the absence of trade-off with exploitation.Nature communications · 2026Article
- Patterns between evidence seeking behaviors, reasoning, and cognitive reflection: A supervised clustering approach.PloS one · 2026Article
- Pre-Decisional Information Search in 2- to 4-Year-Olds: Young Children Select the Relevant Cues When Looking for a Hidden Reward.Developmental science · 2026Article
- Predecisional information search adaptively reduces three types of uncertainty.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
How do people search for information when they are given the opportunity to freely explore their options? Previous research has suggested that people focus on reducing uncertainty before making a decision, but it remains unclear how exactly they do so and whether they do so consistently. We present an analysis of over 1,000,000 information-search decisions made by over 2,500 individuals in a decisions-from-experience setting that cleanly separates information search from choice. Using a data-driven approach supported by a formal measurement framework, we examine how people allocate samples to options and how they decide to terminate search. Three major insights emerge. First, predecisional information search has at least three drivers that can be interpreted as reducing three types of uncertainty: structural, estimation, and computational. Second, the selection of these drivers of information search is adaptive, sequential, and guided by environmental knowledge that integrates prior expectations, task instructions, and personal experiences. Third, predecisional information search exhibits substantial interindividual heterogeneity, with individuals recruiting different drivers of information search. Together, these insights suggest that human information search is complex in ways that cannot be fully explained by monolithic accounts of information search, including proposals focused on estimation uncertainty or cost-benefit analysis. We conclude that broader theories of human information-search behavior are necessary.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.