ArticleeLife2020
Efficient sampling and noisy decisions.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Article
- Fast efficient coding and sensory adaptation in gain-adaptive recurrent networks.Nature communications · 2026Article
- Endogenous precision of the number sense.eLife · 2026Article
- Article
- Economic Consequences of Numerical Adaptation.Psychological science · 2025Article
- Efficient numerosity estimation under limited time.PLoS computational biology · 2025Article
- The parieto-occipital cortex is a candidate neural substrate for the human ability to approximate Bayesian inference.Communications biology · 2024Article
- Individual risk attitudes arise from noise in neurocognitive magnitude representations.Nature human behaviour · 2023Article
- The effects of base rate neglect on sequential belief updating and real-world beliefs.PLoS computational biology · 2022Article
- Efficient coding of numbers explains decision bias and noise.Nature human behaviour · 2022Article
- Efficiently irrational: deciphering the riddle of human choice.Trends in cognitive sciences · 2022Review
- Rethinking delusions: A selective review of delusion research through a computational lens.Schizophrenia research · 2022Review
- Rational inattention in mice.Science advances · 2022Article
- Sources of confidence in value-based choice.Nature communications · 2021Article
- Optimal utility and probability functions for agents with finite computational precision.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Human decisions are based on finite information, which makes them inherently imprecise. But what determines the degree of such imprecision? Here, we develop an efficient coding framework for higher-level cognitive processes in which information is represented by a finite number of discrete samples. We characterize the sampling process that maximizes perceptual accuracy or fitness under the often-adopted assumption that full adaptation to an environmental distribution is possible, and show how the optimal process differs when detailed information about the current contextual distribution is costly. We tested this theory on a numerosity discrimination task, and found that humans efficiently adapt to contextual distributions, but in the way predicted by the model in which people must economize on environmental information. Thus, understanding decision behavior requires that we account for biological restrictions on information coding, challenging the often-adopted assumption of precise prior knowledge in higher-level decision systems.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.