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Summary

Surprise occurs when events undermine our predictive models, indicating a need for new causal explanations. This research defines surprise as randomness deficiency, supported by empirical evidence of its role in belief updating.

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Algorithmic information theoryBayesian reasoningData compressionInterestingnessRandomness deficiencyRepresentational updatingStochastic modelSurprise

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Area of Science:

  • Cognitive Science
  • Psychology
  • Computational Theory

Background:

  • Surprise is a common cognitive process, often defined by improbability or violated expectations.
  • Existing definitions fail to fully capture the essence and function of surprise.
  • A new perspective is needed to understand how surprise influences learning and belief revision.

Purpose of the Study:

  • To propose a novel computational theory of surprise.
  • To define surprise not as improbability but as a model's breakdown.
  • To investigate the role of randomness deficiency in experiencing and utilizing surprise.

Main Methods:

  • Formalizing surprise as randomness deficiency using algorithmic information theory.
  • Presenting empirical evidence of human response to randomness deficiency.
  • Analyzing how randomness deficiency impacts belief adjustment regarding causal origins.

Main Results:

  • Surprise arises when observed patterns contradict a model predicting random noise.
  • People demonstrably respond to randomness deficiency in their environment.
  • This response leads to adjustments in understanding the causal factors behind events.

Conclusions:

  • Surprise is fundamentally linked to detecting patterns that violate existing predictive models.
  • Randomness deficiency serves as a key heuristic for identifying surprising events.
  • This framework offers new insights into learning, interestingness, and cognitive model updating.