Evidence map›Paper›PMID 38739672›Full record

ArticlePLoS computational biology2024

Foraging in a non-foraging task: Fitness maximization explains human risk preference dynamics under changing environment.

Yasuhiro Mochizuki, Norihiro Harasawa, Mayank Aggarwal, Chong Chen, Haruaki Fukuda

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Evolving choice hysteresis in reinforcement learning: Comparing the adaptive value of positivity bias and gradual perseveration.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yasuhiro MochizukiCenter for Data Science, Waseda University, Shinjuku-ku, Tokyo, Japan.ORCID 0000-0001-9326-8687
Norihiro HarasawaRIKEN Center for Brain Science, Wako City, Saitama, Japan.
Mayank AggarwalRIKEN Center for Brain Science, Wako City, Saitama, Japan.
Chong ChenDivision of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Yamaguchi, Japan.ORCID 0000-0002-3189-7397
Haruaki FukudaGraduate School of Business Administration, Hitotsubashi University, Kunitachi, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Changes in risk preference have been reported when making a series of independent risky choices or non-foraging economic decisions. Behavioral economics has put forward various explanations for specific changes in risk preference in non-foraging tasks, but a consensus regarding the general principle underlying these effects has not been reached. In contrast, recent studies have investigated human economic risky choices using tasks adapted from foraging theory, which require consideration of past choices and future opportunities to make optimal decisions. In these foraging tasks, human economic risky choices are explained by the ethological principle of fitness maximization, which naturally leads to dynamic risk preference. Here, we conducted two online experiments to investigate whether the principle of fitness maximization can explain risk preference dynamics in a non-foraging task. Participants were asked to make a series of independent risky economic decisions while the environmental richness changed. We found that participants' risk preferences were influenced by the current and past environments, making them more risk-averse during and after the rich environment compared to the poor environment. These changes in risk preference align with fitness maximization. Our findings suggest that the ethological principle of fitness maximization might serve as a generalizable principle for explaining dynamic preferences, including risk preference, in human economic decision-making.

Indexed as

Choice BehaviorDecision MakingRisk-TakingAdultComputational BiologyEconomics, BehavioralEnvironmentFemaleHumansMaleYoung Adult

Identifiers

PMID38739672
PMCPMC11115364

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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.