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Generative Feedback Explains Distinct Brain Activity Codes for Seen and Mental Images.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • The relationship between mental imagery and visual perception remains unclear.
  • It is unknown if distinct neural codes exist for seen versus mentally imagined images.
  • Hierarchical generative networks offer a model for synthesizing images via feedback.

Purpose of the Study:

  • To investigate whether neural activity during mental imagery differs from visual perception.
  • To model mental imagery as feedback in a hierarchical generative network.
  • To test the prediction that low-level visual areas encode mental images with less precision than seen images.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) experiment with subjects imagining and viewing stimuli.
  • Development of novel imagery-encoding models to analyze brain responses.
  • Comparison of voxel tuning to seen and imagined spatial frequencies and receptive field properties.

Main Results:

  • Imagery-encoding models accurately predicted brain responses to imagined stimuli.
  • Decoding of imagined stimulus position and content was successful.
  • Low-level visual areas showed reduced spatial frequency tuning and larger receptive fields for imagined stimuli compared to seen stimuli.

Conclusions:

  • Distinct neural codes are employed for seen and mentally imagined images.
  • Mental imagery processing is linked to the computational principles of generative networks.
  • Findings provide insights into the neural mechanisms underlying visual perception and imagination.