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Uncertainty, epistemics and active inference.

Thomas Parr1, Karl J Friston2

  • 1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK thomas.parr.12@ucl.ac.uk.

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|November 24, 2017
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Summary
This summary is machine-generated.

Biological systems navigate uncertainty using generative models of their own uncertainty. This research shows how uncertainty impacts optimal visual foraging strategies in simulated noisy and volatile environments.

Keywords:
Bayesianacetylcholineactive inferenceneuromodulationnoradrenalineuncertainty

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Area of Science:

  • Computational neuroscience
  • Cognitive science
  • Decision theory

Background:

  • Biological systems must make decisions despite sensory noise and environmental volatility.
  • Maintaining integrity requires optimal decision-making under uncertainty.
  • Existing models explore uncertainty representation but lack direct links to foraging behavior.

Purpose of the Study:

  • To propose a generative model for an agent's beliefs about its own uncertainty.
  • To investigate how uncertainty influences optimal epistemic (visual) foraging in simulated environments.
  • To establish a correspondence between uncertainty representation and neuromodulatory systems.

Main Methods:

  • Developed a generative model of agent uncertainty.
  • Simulated a noisy and volatile environment.
  • Analyzed the impact of uncertainty on visual foraging behavior, including saccade deployment and inhibition of return.

Main Results:

  • Simulations showed reduced saccades to low-sensory precision regions.
  • Increased environmental volatility led to shorter inhibition of return.
  • Demonstrated how uncertainty influences optimal visual foraging strategies.

Conclusions:

  • The proposed generative model provides a principled explanation for visual foraging behaviors.
  • Findings suggest a link between uncertainty representation and ascending neuromodulatory systems.
  • This work complements existing theories on uncertainty and decision-making in biological systems.