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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
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Reclaiming saliency: Rhythmic precision-modulated action and perception.

Ajith Anil Meera1, Filip Novicky2, Thomas Parr3

  • 1Department of Cognitive Robotics, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, Netherlands.

Frontiers in Neurorobotics
|August 15, 2022
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Summary
This summary is machine-generated.

This study redefines salience as an active inference process for artificial intelligence and robotics, focusing on uncertainty minimization and rhythmic scheduling for better perception-action loops. It enhances visual attention models for improved robotic capabilities.

Keywords:
active inferenceattentionbrain-inspired roboticscognitive roboticsfree-energy principleprecisionsaliency

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Robotics

Background:

  • Current computational models of visual attention use saliency maps, focusing on information-current cause relationships.
  • These models neglect the crucial perception-action feedback loop, failing to determine optimal next sensory sampling points.
  • The concept of circular causality between perception and action is not adequately addressed in existing models.

Purpose of the Study:

  • To reframe salience as an active inference process integrating uncertainty minimization and rhythmic scheduling.
  • To differentiate between attention (precision control) and salience (uncertainty minimization for future data selection).
  • To propose a novel account of attention based on rhythmic precision-modulation for AI and robotics.

Main Methods:

  • Distinguishing between attention as precision control and salience as uncertainty minimization.
  • Developing an active inference framework for salience.
  • Introducing rhythmic precision-modulation as a new model for attention.

Main Results:

  • The proposed model offers a new perspective on attention and salience in computational systems.
  • Numerical experiments demonstrate advantages in state and noise estimation for robotic systems.
  • The approach shows benefits for system identification and informative path planning in robotics.

Conclusions:

  • Reclaiming salience as an active inference process provides a more complete model of visual attention.
  • Rhythmic precision-modulation offers a promising new mechanism for attention in artificial systems.
  • The framework has significant potential applications in advancing robotic perception and action selection.