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Maddie Cusimano1, Luke B Hewitt1, Josh H McDermott2

  • 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, United States of America.

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Summary
This summary is machine-generated.

This study introduces a computational generative model for auditory perception, explaining how the brain models sound. The model successfully accounts for auditory illusions and real-world sound organization.

Keywords:
Auditory scene analysisBayesian inferenceGroupingIllusionsNatural soundsPerceptionPerceptual organizationProbabilistic programWorld model

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Auditory Perception

Background:

  • Perception theories often propose internal world models but lack testability.
  • Previous models struggled with the complexity of explaining sensory signals.

Purpose of the Study:

  • To develop and test a computational generative model for auditory perception.
  • To investigate the role of internal models in explaining auditory illusions and real-world sounds.

Main Methods:

  • Utilized contemporary computational tools to infer sound explanations using an internal generative model.
  • Employed ecologically inspired audio synthesizers as a candidate model of the auditory world.
  • Applied 'rich falsification' by combining stimulus-computability and interpretable model structure.

Main Results:

  • The generative model successfully accounted for classic auditory illusions.
  • Model inferences demonstrated human-like perceptual organization for real-world sound mixtures.
  • Identified necessary assumptions about sound generation for accurate perception.

Conclusions:

  • Generative models offer a testable framework for understanding auditory perception.
  • These models can explain both auditory illusions and everyday sound perception.
  • Highlight opportunities and challenges in integrating generative models into perception theories.