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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Related Experiment Video

Updated: May 8, 2025

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
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Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

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Learning dynamic cognitive map with autonomous navigation.

Daria de Tinguy1, Tim Verbelen2, Bart Dhoedt1

  • 1Department of Engineering and Architecture, Ghent University/IMEC, Ghent, Belgium.

Frontiers in Computational Neuroscience
|December 26, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a novel computational model for navigation and mapping, inspired by animal strategies. The model efficiently explores environments using a dynamically expanding cognitive map and active inference, outperforming existing methods.

Keywords:
active inferenceautonomous navigationcognitive mapdynamic mappingknowledge learningstructure learning

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Robotics

Background:

  • Animals navigate complex environments using memory and strategic decision-making.
  • Existing computational models often lack adaptability to novel situations.

Purpose of the Study:

  • To develop a novel computational model for space navigation and mapping.
  • To replicate animal-like navigation using biologically inspired principles and active inference.

Main Methods:

  • Incorporating a dynamically expanding cognitive map over predicted poses.
  • Utilizing an active inference framework for enhanced generative model plasticity.
  • Implementing structure learning and active inference for exploration and exploitation.

Main Results:

  • The model demonstrates efficient exploration and exploitation in mini-grid environments.
  • It dynamically expands model capacity for novel locations and updates maps with new evidence.
  • Achieves rapid environmental structure learning within a single episode with minimal overlap.

Conclusions:

  • The proposed model shows robustness and efficacy in navigating intricate environments.
  • It learns environmental structures without prior knowledge of observation or world dimensions.
  • Offers a promising approach for AI navigation and cognitive mapping.