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Subjective randomness as statistical inference.

Thomas L Griffiths1, Dylan Daniels2, Joseph L Austerweil3

  • 1Department of Psychology, University of California, Berkeley, United States.

Cognitive Psychology
|March 11, 2018
PubMed
Summary
This summary is machine-generated.

People

Keywords:
Algorithmic complexityBayesian inferenceRandomness

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Area of Science:

  • Cognitive Science
  • Psychology
  • Computer Science

Background:

  • Human intuition about randomness is often inconsistent.
  • Previous work linked randomness to algorithmic complexity, which is computationally limited.
  • Subjective randomness judgments are crucial for understanding human perception.

Purpose of the Study:

  • To develop a statistical inference model for subjective randomness.
  • To extend models of randomness beyond binary sequences.
  • To quantitatively model human judgments of randomness in complex patterns.

Main Methods:

  • Formulating subjective randomness as statistical inference.
  • Connecting algorithmic complexity to statistical inference.
  • Developing quantitative models for human randomness judgments.
  • Applying models to binary sequences, matrices, and spatial clusters.

Main Results:

  • Subjective randomness judgments can be explained by statistical inference.
  • The proposed models accurately predict human judgments of randomness.
  • Models extend beyond simple binary sequences to more complex data structures.

Conclusions:

  • Human perception of randomness is rooted in statistical inference.
  • This framework offers a more tractable approach than algorithmic complexity.
  • The models provide a robust quantitative understanding of subjective randomness.