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Related Concept Videos

Social Foundations of Self IV: Self in Digital Communication01:30

Social Foundations of Self IV: Self in Digital Communication

Since the early 2000s, computer-mediated communication (CMC) has grown rapidly, playing a crucial role in self-development. A key distinction between CMC and real-life interactions is the lack of a physically present partner. This absence makes non-verbal cues such as facial expressions, body language, and paralinguistic signals unavailable in CMC platforms like email, instant messaging, or social media. The lack of these cues can create ambiguity and complicate how feedback is interpreted.The...
Neuronal Communication01:28

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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
Social Exchange Theory02:06

Social Exchange Theory

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).
Social Exchange Theory01:26

Social Exchange Theory

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...
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Communication

Sharing information, concepts, and emotions to foster mutual understanding is communication. The sender, recipient, and transaction must be considered in this manner. The sender is the person who shares the message, the recipient is the person who receives and understands the message, and the transaction is the method used to deliver the message and the variables that affect the communication's context and surroundings. The nurse-client connection is built on therapeutic communication.
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Communication01:03

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Communication between two animals occurs when one animal transmits an information signal that causes a change in the animal that receives the information. Organisms communicate with one another in a host of different ways. Signals can be auditory, chemical, visual, tactile, or a combination of these. Communication is a critical behavioral adaptation that promotes survival, growth, and reproduction.

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Related Experiment Video

Updated: Jun 1, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Communicability across evolving networks.

Peter Grindrod1, Mark C Parsons, Desmond J Higham

  • 1Department of Mathematics, University of Reading, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|May 24, 2011
PubMed
Summary
This summary is machine-generated.

New centrality measures extend static network analysis to dynamic systems. These methods efficiently compute communicability indices for evolving networks, aiding in real-time monitoring and prediction.

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

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Last Updated: Jun 1, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Area of Science:

  • Network Science
  • Complex Systems
  • Data Science

Background:

  • Dynamic networks, such as communication or contact tracing data, present unique analytical challenges compared to static networks.
  • Traditional network analysis methods often fail to capture the temporal dependencies crucial for understanding information or disease spread.
  • Aggregate network summaries can obscure critical directional information flow.

Purpose of the Study:

  • To extend static network centrality measures to dynamic, time-ordered network sequences.
  • To develop computationally efficient methods for analyzing evolving networks.
  • To introduce communicability indices for dynamic networks to assess information broadcasting and reception capabilities.

Main Methods:

  • Defined a natural walk on evolving networks to account for temporal order.
  • Extended classic static centrality measures to the dynamic setting.
  • Utilized linear algebra, specifically non-commutative matrix-matrix multiplication, to capture temporal asymmetry.
  • Computed communicability indices for nodes in dynamic networks.

Main Results:

  • Demonstrated that static centrality measures can be effectively extended to dynamic networks.
  • Introduced computationally convenient methods for calculating these extended measures.
  • Showcased the ability of communicability indices to summarize node-level information flow in time-evolving networks.
  • Validated the approach with synthetic and real-world communication datasets.

Conclusions:

  • Classic network centrality concepts are adaptable to dynamic network structures.
  • The proposed methods provide a robust framework for analyzing temporal network data.
  • These new centrality measures offer valuable tools for real-time network monitoring and predictive analysis.