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The intentional stance as structure learning: a computational perspective on mindreading.

Haris Dindo1, Francesco Donnarumma2, Fabian Chersi3

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Biological Cybernetics
|July 15, 2015
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Summary

Humans use the intentional stance to learn how others act and think. This approach simplifies complex mind-reading by using a basic belief-desire model to bootstrap learning generative models of actions and intentions.

Keywords:
Generative modelIntentional stanceMindreadingOnline learningStructure learning

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Robotics

Background:

  • Current mindreading theories use generative models to understand actions, intentions, and beliefs.
  • Two main approaches are "theory theory" and "simulation theory."
  • A key challenge is understanding how these generative models are learned.

Purpose of the Study:

  • To propose the intentional stance as a solution to the generative model learning problem in mindreading.
  • To demonstrate how the intentional stance facilitates the acquisition of causal models for action and intention recognition.

Main Methods:

  • Characterizing Dennett's "intentional stance" within generative theories.
  • Proposing the intentional stance as a learning bias that simplifies structure learning.
  • Utilizing computational simulations to test the proposed model.

Main Results:

  • The intentional stance acts as a proxy for complex generative structures.
  • It enables continuous refinement of models through interaction and learning.
  • Computational simulations confirmed its effectiveness in solving mindreading problems.

Conclusions:

  • The intentional stance bootstraps the learning of generative models for mindreading.
  • It provides a simplified yet effective framework for understanding others' goal-directed actions.
  • This approach aids in acquiring robust causal models for both self and other-action prediction.