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A stochastic world model on gravity for stability inference.

Taicheng Huang1, Jia Liu1

  • 1Department of Psychological and Cognitive Sciences & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China.

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Summary
This summary is machine-generated.

Humans infer object stability using a mental world model of gravity. This model represents gravity stochastically, not perfectly, explaining stability illusions and aiding flexible environmental interaction.

Keywords:
gravityhumanintuitionneurosciencereinforcement learningsimulationstability inferenceworld model

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Physics

Background:

  • Humans intuitively understand object stability and gravity.
  • Previous research suggests a 'world model' aids in mental simulations of physical phenomena.
  • The precise nature of this internal world model for gravity remains unclear.

Purpose of the Study:

  • To investigate the internal world model of gravity in humans.
  • To determine how this model represents gravitational forces.
  • To link the model's characteristics to subjective perceptions of object stability.

Main Methods:

  • Measured participants' sensitivity to gravity.
  • Developed a computational model incorporating reinforcement learning.
  • Compared model predictions with observed human behavior and subjective reports.

Main Results:

  • The human world model of gravity is not a perfect replica of physical laws.
  • It encodes gravity's vertical direction as a Gaussian distribution (stochastic representation).
  • This stochastic model explains subjective stability judgments and the illusion that taller objects are more likely to fall.

Conclusions:

  • The stochastic nature of the gravity world model likely arises from experience-dependent learning.
  • This representation balances accuracy and speed for efficient stability inference.
  • The findings illustrate how a priori knowledge is implemented in the mind for flexible environmental navigation.