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Thomas Parr1, Karl J Friston2

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Summary
This summary is machine-generated.

This chapter explains active inference principles, linking short-term memory to beliefs about sensations and actions. It explores active vision, visual exploration, and the computational anatomy of visual neglect.

Keywords:
Active inferenceActive visionMarkov decision processNoveltySaccadesVisual neglect

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Neuroscience

Background:

  • Active inference offers a unified framework for understanding perception and action.
  • Short-term memory mechanisms are crucial for cognitive functions like active vision.

Purpose of the Study:

  • To provide an overview of active inference principles.
  • To explain how short-term memory relates to beliefs about sensations and actions.
  • To motivate an active vision approach based on causal inference and learning.

Main Methods:

  • Formal (mathematical) illustration of active inference principles.
  • Connecting active inference to working memory and synaptic efficacy.
  • Reviewing active inference formulations for visual exploration and its failures.

Main Results:

  • Short-term memory can be formally expressed through beliefs about sensory causes and pursued actions.
  • Active vision can be framed as inferring causes of seen stimuli and predicting future visual input.
  • Failures in active vision, illustrated by visual neglect, have computational anatomical consequences.

Conclusions:

  • Active inference provides a mathematical framework for understanding memory and active vision.
  • The proposed active vision approach integrates perception, learning, and action selection.
  • Computational analysis of visual neglect highlights the importance of active inference in visual processing.