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Contextualizing predictive minds.

Martin V Butz1, Maximilian Mittenbühler1, Sarah Schwöbel2

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Summary
This summary is machine-generated.

Human memory

Keywords:
AbstractionActive inferenceBehaviorCognitive modelingContext inferenceDeep learningEventsFree energyLearningPrediction

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Human memory supports prediction, planning, and behavior.
  • A tripartite memory structure (concepts, events, contexts) forms a mental world model.

Purpose of the Study:

  • To propose a tripartite memory structure for efficient cognition.
  • To highlight the role of contextualization in adaptivity and flexibility.
  • To address the frame problem using cognitive contextualization.

Main Methods:

  • Drawing evidence from developmental psychology and neuroscience.
  • Utilizing computational cognitive modeling research.
  • Examining recent deep-learning architectures.

Main Results:

  • Contextualization enhances adaptivity and flexibility in cognitive processes.
  • Cognitive contextualization provides a solution to the frame problem.
  • Context sensitivity is critical for maximizing cognitive efficiency.

Conclusions:

  • A tripartite memory structure with contextualization optimizes human cognition.
  • Context-sensitive memory can emerge in self-organized learning systems.
  • This model integrates findings from psychology, neuroscience, and AI.