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

Relationship Formation02:12

Relationship Formation

40.2K
What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
40.2K
Protein Networks02:26

Protein Networks

4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

70
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
70
Social Exchange Theory02:06

Social Exchange Theory

34.6K
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).
34.6K
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

8.4K
The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
8.4K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
88

You might also read

Related Articles

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

Sort by
Same author

Detecting changepoints in dynamical systems: Modeling time-varying transmission of seasonal influenza.

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

Automated influencers: Unravelling the dynamics of social influence by bots (automated agents) with various strategies in online interactions.

Acta psychologica·2026
Same author

Acromegaly presenting with normal insulin-like growth factor-1 levels in a patient with advanced liver cirrhosis.

BMJ case reports·2026
Same author

Anchoring race: improving the construction of race dimensions in word embeddings.

Journal of computational social science·2026
Same author

The news in black and white: word embeddings quantify racism in South African news.

EPJ data science·2025
Same author

A replication of Triplett's 'social facilitation experiment'.

Scientific reports·2025
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Aug 4, 2025

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions
10:45

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

Published on: July 6, 2011

11.7K

Agent-based null models for examining experimental social interaction networks.

Susan C Fennell1, James P Gleeson1, Michael Quayle2,3

  • 1MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland.

Scientific Reports
|March 31, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing online social experiment data, revealing ingroup favoritism and reciprocity in interactions. These social behaviors were observed to strengthen over time on the Virtual Interaction APPLication (VIAPPL).

More Related Videos

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
09:40

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

13.9K
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

7.7K

Related Experiment Videos

Last Updated: Aug 4, 2025

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions
10:45

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

Published on: July 6, 2011

11.7K
Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
09:40

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

13.9K
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

7.7K

Area of Science:

  • Social network analysis
  • Computational social science
  • Behavioral economics

Background:

  • Analyzing temporal data from online social experiments is challenging due to violated independence assumptions.
  • Classical statistical methods may not adequately capture the complexities of interactive social behaviors.
  • Understanding social dynamics requires methods that account for non-independent observations.

Purpose of the Study:

  • To develop a novel approach for analyzing temporal data from interactive social experiments.
  • To compare observed social interaction structures with a null model of random interactions.
  • To identify ingroup favoritism, reciprocity, and outlier behaviors in online social platforms.

Main Methods:

  • Proposed a method comparing fitted linear models from observed data against an agent-based null model.
  • Utilized network visualizations to identify social patterns and individual behaviors.
  • Applied the methodology to experimental data from the Virtual Interaction APPLication (VIAPPL).

Main Results:

  • The analysis revealed significant ingroup favoritism and reciprocity in social interactions.
  • These social behaviors were found to strengthen over the duration of the experiment.
  • Identified specific individuals exhibiting behaviors that deviated from the norm.

Conclusions:

  • The developed methodology effectively analyzes complex temporal social interaction data.
  • Ingroup favoritism and reciprocity are prevalent and dynamic features of online social interactions.
  • The approach is applicable beyond VIAPPL to various social interaction datasets.