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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Machine Learning

Background:

  • Intrinsically motivated learning (IML) is crucial for efficient coding in active perception.
  • Agents use internal reinforcement signals to optimize sensory data encoding through actions.

Purpose of the Study:

  • To propose a novel hypothesis for imitation learning development rooted in intrinsic motivations.
  • To explore how IML mechanisms can explain the emergence of imitation beyond sensory-motor control.

Main Methods:

  • A theoretical framework is presented linking IML to imitation.
  • The model involves a learner observing a tutor, building a sensory model, and generating actions.
  • Reinforcement is based on how well the learner's sensory consequences are encoded by the model.

Main Results:

  • Actions mimicking the tutor yield better sensory encoding and higher internal reinforcement.
  • This process progressively tunes the learner's behavior to match the tutor's sensory model.
  • The mechanism provides an account for mirror neuron development and offers testable predictions.

Conclusions:

  • The proposed IML mechanism supports imitation learning by optimizing sensory prediction.
  • This framework connects efficient coding, intrinsic motivation, and imitation learning.
  • It offers insights into natural learning processes like language acquisition and song learning.