Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Optimal Foraging00:48

Optimal Foraging

13.8K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
13.8K
Reinforcement01:23

Reinforcement

917
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
917
Muscles that Move the Arm01:31

Muscles that Move the Arm

4.8K
Nine muscles are involved in arm movements. Two of these, the pectoralis major and latissimus dorsi, originate from the axial skeleton and are called axial muscles. The other seven originate from the scapula and are called the scapular muscles.
The pectoralis major has two origins. Its clavicular head originates on the medial half of the clavicle. In contrast, the sternocostal head originates on the costal cartilages of ribs 1-6, the sternum, and the aponeurosis of the external oblique of the...
4.8K
Corrosion of Reinforcement01:27

Corrosion of Reinforcement

576
The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
However, over time and under certain conditions like carbonation, chloride ingress, and cracking this protective state can be compromised. Steel has areas with...
576
Reinforcement Schedules01:24

Reinforcement Schedules

497
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
497
Reinforcements in Concrete01:25

Reinforcements in Concrete

466
Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
466

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mating-dependent lifespan cost of sterol depletion in male <i>Drosophila melanogaster</i>.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Short-Term Puzzle Feeder Enrichment Increases Food Engagement but Not Stress-Related Behaviour in Captive Golden-Headed Lion Tamarins.

Ecology and evolution·2026
Same author

Population structure and reproductive aspects of flatfishes in the Guaratuba Bay estuary, a protected subtropical estuary in Brazil.

Anais da Academia Brasileira de Ciencias·2026
Same author

Role of visual and non-visual opsins in blue light-induced neurodegeneration in <i>Drosophila melanogaster</i>.

Frontiers in public health·2025
Same author

Amino Acid Frequency in the Proteome Is Not Associated With Realised Thermal Limit nor Dietary Niche Breadth in 35 Lepidoptera Species.

Ecology and evolution·2025
Same author

The Developmental Environment Mediates Adult Seminal Proteome Allocation in Male Drosophila melanogaster.

Molecular ecology·2025

Related Experiment Video

Updated: Jan 29, 2026

A Single-fly Assay for Foraging Behavior in Drosophila
13:01

A Single-fly Assay for Foraging Behavior in Drosophila

Published on: November 4, 2013

13.7K

Foraging decisions as multi-armed bandit problems: Applying reinforcement learning algorithms to foraging data.

Juliano Morimoto1

  • 1Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia; Programa de Pós-Graduação em Ecologia e Conservação, Federal University of Paraná, Curitiba, Brazil, 19031, CEP, 81531-990.

Journal of Theoretical Biology
|February 9, 2019
PubMed
Summary
This summary is machine-generated.

Animal foraging decisions involve balancing exploration and exploitation. This study applies reinforcement learning algorithms, Upper-Confidence-Bound (UCB) and Thompson Sampling (TS), to model these choices, finding UCB more accurate for fruit fly larvae data.

More Related Videos

Foraging Path-length Protocol for Drosophila melanogaster Larvae
07:26

Foraging Path-length Protocol for Drosophila melanogaster Larvae

Published on: April 23, 2016

9.9K
Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

6.5K

Related Experiment Videos

Last Updated: Jan 29, 2026

A Single-fly Assay for Foraging Behavior in Drosophila
13:01

A Single-fly Assay for Foraging Behavior in Drosophila

Published on: November 4, 2013

13.7K
Foraging Path-length Protocol for Drosophila melanogaster Larvae
07:26

Foraging Path-length Protocol for Drosophila melanogaster Larvae

Published on: April 23, 2016

9.9K
Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

6.5K

Area of Science:

  • Behavioral Ecology
  • Computational Biology
  • Reinforcement Learning

Background:

  • Animal survival and reproduction depend on resource acquisition, yet the decision-making processes for exploring new versus exploiting known resources are poorly understood.
  • The exploration-exploitation trade-off theory posits that animals must balance efforts between staying with a known resource and exploring for new ones.
  • Developing flexible and tractable statistical models for this trade-off has been a significant challenge, limiting our understanding of foraging decisions.

Purpose of the Study:

  • To model animal foraging decisions using multi-armed bandit problems and reinforcement learning algorithms.
  • To generate testable predictions for foraging behavior using deterministic (Upper-Confidence-Bound or UCB) and Bayesian (Thompson Sampling or TS) algorithms.
  • To provide a quantitative framework for analyzing empirical foraging data from tephritid fruit fly larvae (Bactrocera tryoni).

Main Methods:

  • Applied deterministic UCB and Bayesian TS algorithms to simulated data to derive a priori predictions.
  • Analyzed empirical foraging data from Bactrocera tryoni larvae using both UCB and TS algorithms.
  • Conducted qualitative and quantitative analyses to compare the performance of UCB and TS in predicting foraging patterns.

Main Results:

  • Qualitative analysis showed TS exhibited shorter exploration periods than UCB, though both yielded similar qualitative outcomes.
  • Quantitative analysis indicated UCB was more accurate than TS in predicting observed foraging patterns.
  • Both algorithms struggled to quantitatively estimate foraging patterns in high-density groups (50-100 larvae), likely due to intraspecific competition.

Conclusions:

  • Reinforcement learning algorithms, specifically UCB and TS, offer a viable framework for modeling animal foraging decisions.
  • UCB demonstrates higher predictive accuracy than TS for the studied fruit fly larvae, but both have limitations in high-competition scenarios.
  • The proposed framework facilitates a deeper understanding and quantification of animal foraging behavior, highlighting the influence of social factors.