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This study models selective attention using active inference, showing how agents prioritize task-relevant information to reduce uncertainty. This approach may explain atypical exploratory behaviors in conditions like autism and anxiety.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Psychiatry

Background:

  • Information gathering resolves uncertainty by seeking salient sensory consequences.
  • Task relevance is crucial in complex environments, necessitating selective attention.
  • Existing models lack a formal framework for context-dependent information seeking.

Purpose of the Study:

  • To introduce a formal model of selective attention based on active inference and epistemic foraging.
  • To explain goal-directed and task-relevant visual exploration.
  • To investigate the neurobiological underpinnings of attention and its relation to conditions like autism and anxiety.

Main Methods:

  • Developed a formal model integrating active inference and contextual epistemic foraging.
  • Simulated a visual search task emphasizing goal-directed exploration.
  • Modeled attention as a mechanism modulating the precision of sensory-state mappings.

Main Results:

  • Attention selectively samples task-relevant observations, informing hidden states and reducing uncertainty.
  • Task-irrelevant observations yield low expected information gain, thus are not actively sought.
  • Simulations suggest atypical exploratory behaviors may stem from impaired context-specific sensory precision modulation.

Conclusions:

  • The proposed model provides a computational framework for understanding selective attention and information seeking.
  • Dysregulation in sensory precision modulation may underlie specific neurodevelopmental and psychiatric conditions.
  • This work bridges computational modeling with clinical observations in autism and anxiety.