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

Observational Learning01:12

Observational Learning

470
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
470
Associative Learning01:27

Associative Learning

760
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
760
Steps in the Modeling Process01:14

Steps in the Modeling Process

410
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
410
Reinforcement Schedules01:24

Reinforcement Schedules

288
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,...
288
Social Exchange Theory01:26

Social Exchange Theory

80
As formulated by John Thibaut and Harold Kelley, Social Exchange Theory explains human relationships as economic-like exchanges that maximize rewards and minimize costs. This theory suggests that individuals engage in relationships to gain benefits and reduce burdens, similar to economic transactions. It has been widely applied to various types of relationships, including romantic, professional, and social interactions.Rewards and Costs in RelationshipsRelationship rewards include emotional...
80
Social Exchange Theory02:06

Social Exchange Theory

37.2K
We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).
37.2K

You might also read

Related Articles

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

Sort by
Same author

Eliminating synchronization of coupled neurons adaptively by using feedback coupling with heterogeneous delays.

Chaos (Woodbury, N.Y.)·2021
Same author

Introduction to Focus Issue: When machine learning meets complex systems: Networks, chaos, and nonlinear dynamics.

Chaos (Woodbury, N.Y.)·2020
Same author

Detecting unstable periodic orbits based only on time series: When adaptive delayed feedback control meets reservoir computing.

Chaos (Woodbury, N.Y.)·2019
Same author

Effects of dynamical and structural modifications on synchronization.

Chaos (Woodbury, N.Y.)·2019
Same author

Emergent dynamics of coordinated cells with time delays in a tissue.

Chaos (Woodbury, N.Y.)·2019
Same author

Achieving control and synchronization merely through a stochastically adaptive feedback coupling.

Chaos (Woodbury, N.Y.)·2017
Same journal

Gap junction architecture and synchronization clusters in the thalamic reticular nuclei.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exact computation of Lyapunov exponents via system parameters in multi-triangle chaotic maps: Bifurcation analysis and circuit realization.

Chaos (Woodbury, N.Y.)·2026
Same journal

Integrating score-based generative modeling and neural ODEs for accurate representation of multiscale chaotic dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A data-driven tuberculosis model with behavioral changes and saturated treatment: Optimal control and cost-effectiveness study.

Chaos (Woodbury, N.Y.)·2026
Same journal

Breathers, rational solutions, and their exact physical spectra in F = 1 spinor Bose-Einstein condensates.

Chaos (Woodbury, N.Y.)·2026
Same journal

Finite invariant sets with bridging points in logistic IFS.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Oct 29, 2025

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.8K

Non-Bayesian social learning model with periodically switching structures.

Yuankai Ha1, Yao Guo2, Wei Lin1

  • 1School of Mathematical Sciences, Fudan University, Shanghai 200433, China.

Chaos (Woodbury, N.Y.)
|July 12, 2021
PubMed
Summary
This summary is machine-generated.

This study explores a non-Bayesian social learning model with changing structures. We show that even with relaxed network conditions, the model converges to the true state, offering insights into real-world social dynamics.

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.8K

Related Experiment Videos

Last Updated: Oct 29, 2025

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.8K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.8K

Area of Science:

  • Social learning dynamics
  • Network theory
  • Probability theory

Background:

  • Non-Bayesian social learning models often require strongly connected networks for convergence.
  • Existing models lack flexibility in handling dynamic network structures.

Purpose of the Study:

  • To investigate the convergence properties of a non-Bayesian social learning model with periodically switching structures.
  • To relax the stringent connectivity requirements typically imposed on such models.
  • To provide convergence rate estimations for successful social learning.

Main Methods:

  • Mathematical and rigorous validation of model dynamics.
  • Analysis of convergence under relaxed network configurations.
  • Numerical demonstrations using representative examples with switching structures.

Main Results:

  • The model converges to a true state of social learning even with non-strongly connected networks during switching periods.
  • Convergence rate estimations for successful social learning were established.
  • Analytical conditions and estimations were numerically validated.

Conclusions:

  • The proposed model offers a more flexible framework for studying social learning dynamics.
  • Results suggest applicability to understanding complex real-world social activities.
  • Relaxed network conditions do not impede convergence in this non-Bayesian social learning model.