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Related Concept Videos

Barriers to Effective Communication I01:30

Barriers to Effective Communication I

A communication barrier is any distortion or interruption during a conversation, resulting in miscommunication of the message. A good communicator should know these barriers and continuously check for the listener's understanding by obtaining feedback.
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Related Experiment Video

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Computer-Generated Animal Model Stimuli
26:43

Computer-Generated Animal Model Stimuli

Published on: July 29, 2007

Intentional communication: computationally easy or difficult?

Iris van Rooij1, Johan Kwisthout, Mark Blokpoel

  • 1Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Nijmegen, Netherlands.

Frontiers in Human Neuroscience
|July 13, 2011
PubMed
Summary
This summary is machine-generated.

The computational complexity of human communication varies with situational factors, not constant. This study introduces a new methodology to analyze communication complexity, aiding cognitive neuroscience research.

Keywords:
Bayesian modelingcommunicationcomputational complexitycomputational modelinggoal inferenceintractability

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Area of Science:

  • Cognitive Neuroscience
  • Computational Linguistics
  • Psychology

Background:

  • Human intentional communication is flexible and context-dependent.
  • Understanding the computational power of brain mechanisms is crucial for explaining communication.
  • The computational complexity of communication is currently unknown and debated.

Purpose of the Study:

  • To determine if communication complexity is constant or situational.
  • To propose a methodology for studying communication complexity under varying constraints.
  • To identify parameters controlling cognitive processes in communication.

Main Methods:

  • Defending the position that communication complexity is a function of situational factors.
  • Presenting a methodology to study and characterize communication complexity.
  • Illustrating the methodology with a model of sender-receiver problems.

Main Results:

  • Communication complexity is not constant but varies with situational factors.
  • A novel methodology for assessing communication complexity has been developed.
  • The methodology allows for principled identification of parameters controlling cognitive processes.

Conclusions:

  • The proposed methodology provides a framework for understanding the computational demands of communication.
  • This research opens new avenues for investigating the cognitive underpinnings of intentional communication.
  • Future work can utilize this approach to explore specific situational influences on communication.