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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...
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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Prediction-for-CompAction: navigation in social environments using generalized cognitive maps.

Jose A Villacorta-Atienza1, Carlos Calvo, Valeri A Makarov

  • 1Department of Applied Mathematics, Universidad Complutense de Madrid, Avda Complutense s/n, 28040, Madrid, Spain.

Biological Cybernetics
|February 14, 2015
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Summary

Human-like robots can navigate human environments more efficiently and safely through cooperative collision avoidance. This approach leverages recursive cognition, allowing robots to anticipate and adapt to human actions, benefiting everyone.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Mobile robot navigation in human environments is challenging.
  • Robot perception of humans influences navigation strategies.
  • Cooperative collision avoidance requires advanced cognitive abilities.

Purpose of the Study:

  • To propose a novel neural network architecture for human-robot cooperative navigation.
  • To enable robots to exhibit human-like social compliance and efficiency.
  • To enhance navigation safety and efficiency in cluttered environments.

Main Methods:

  • Developed a Prediction-for-CompAction neural network paradigm.
  • Implemented recursive cognition for human-agent decision-making interdependence.
  • Utilized a compact cognitive map as a dynamic GPS for planning.

Main Results:

  • Cooperative navigation significantly improves efficiency in cluttered pedestrian flows.
  • Human-like agents choose shorter paths compared to functional machines.
  • Navigation safety and collision avoidance are enhanced under cooperation.
  • The strategy imposes no additional societal effort.

Conclusions:

  • Treating robots as human-like agents unlocks cooperative collision avoidance benefits.
  • The proposed method enhances navigation efficiency, safety, and social compliance.
  • Humanoid agents can integrate seamlessly into human environments as "one of us".