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Can multisensory training aid visual learning? A computational investigation.

Robert A Jacobs1, Chenliang Xu2

  • 1Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA.

Journal of Vision
|September 4, 2019
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Summary
This summary is machine-generated.

Multisensory training, combining visual and haptic signals, can improve artificial neural networks' ability to learn object representations. This suggests perceptual learning may be easier in multisensory environments than previously thought.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Real-world environments are inherently multisensory, yet visual learning is often studied in unisensory (vision-only) settings.
  • Traditional approaches to visual perception research may overlook the benefits of integrating multiple sensory inputs.

Purpose of the Study:

  • To investigate whether multisensory training can enhance visual learning in artificial neural networks.
  • To compare internal representations learned through visual-haptic training versus vision-only training.

Main Methods:

  • Utilized deep artificial neural networks to model object recognition and classification.
  • Trained networks in two conditions: (a) visual and haptic signals, and (b) visual signals only.
  • Analyzed network representations for abstractions and sensitivity to irrelevant parameters like orientation.

Main Results:

  • Networks trained with both visual and haptic signals developed more abstract representations, capturing object categories.
  • Multisensory training resulted in representations less sensitive to irrelevant visual parameters (e.g., viewpoint, orientation).
  • This indicates that multisensory input aids in learning more robust and generalizable visual features.

Conclusions:

  • Multisensory training can simplify perceptual learning problems, contrary to assumptions in vision-only research.
  • Integrating multiple sensory modalities may lead to more efficient and effective learning of visual representations.
  • Future research in perceptual learning should consider the advantages of multisensory integration.