Evidence map›Paper›PMID 33664346›Full record

ArticleScientific reports2021

Efficient Lévy walks in virtual human foraging.

Ketika Garg, Christopher T Kello

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

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.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
  2. FoMo: A unifying theory of visual foraging.PLoS computational biology · 2026
    Article
  3. Review
  4. Socioeconomic Gauging of Brown and Levy Power Motions.Entropy (Basel, Switzerland) · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Brown and Levy Steady-State Motions.Entropy (Basel, Switzerland) · 2025
    Article
  9. Levy Noise Affects Ornstein-Uhlenbeck Memory.Entropy (Basel, Switzerland) · 2025
    Article
  10. Article
  11. Article
  12. Research on re-searching: interrupted foraging is not disrupted foraging.Cognitive research: principles and implications · 2024
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. 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

2 authors.

Ketika GargDepartment of Cognitive and Information Sciences, University of California, Merced, CA, 95343, USA. kgarg@ucmerced.edu.
Christopher T KelloDepartment of Cognitive and Information Sciences, University of California, Merced, CA, 95343, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Efficient foraging depends on decisions that account for the costs and benefits of various activities like movement, perception, and planning. We conducted a virtual foraging experiment set in the foothills of the Himalayas to examine how time and energy are expended to forage efficiently, and how foraging changes when constrained to a home range. Two hundred players foraged the human-scale landscape with simulated energy expenditure in search of naturally distributed resources. Results showed that efficient foragers produced periods of locomotion interleaved with perception and planning that approached theoretical expectations for Lévy walks, regardless of the home-range constraint. Despite this constancy, efficient home-range foraging trajectories were less diffusive by virtue of restricting locomotive search and spending more time instead scanning the environment to plan movement and detect far-away resources. Altogether, results demonstrate that humans can forage efficiently by arranging and adjusting Lévy-distributed search activities in response to environmental and task constraints.

Indexed as

Virtual RealityBehaviorEnergy MetabolismGame TheoryHumansLocomotion

Identifiers

PMID33664346
PMCPMC7933158

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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.