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Updated: Aug 9, 2025

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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Inconsistent illusory motion in predictive coding deep neural networks.

O R Kirubeswaran1, Katherine R Storrs2

  • 1Indian Institute of Science Education and Research Pune, India.

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|February 21, 2023
PubMed
Summary
This summary is machine-generated.

Deep neural networks (DNNs) like PredNet can mimic human perception of illusory motion in static images. However, inconsistencies reveal that predictive coding may not fully explain human-like illusory motion.

Keywords:
Deep neural networksMotion perceptionPeripheral drift illusionPredictive codingVisual illusions

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Area of Science:

  • Neuroscience
  • Computer Vision
  • Computational Neuroscience

Background:

  • Illusory motion in static images is a complex phenomenon with several proposed explanations, including eye movements and neural processing.
  • Recurrent deep neural networks (DNNs) based on predictive coding principles, such as PredNet, have recently shown promise in replicating visual illusions like 'Rotating Snakes'.

Purpose of the Study:

  • To investigate the consistency of PredNet's behavior with human psychophysics and non-human primate electrophysiology regarding illusory motion.
  • To determine if predictive coding, as implemented in PredNet, reliably explains human-like perception of illusory motion.

Main Methods:

  • Replication of PredNet's ability to reproduce the 'Rotating Snakes' illusion.
  • In silico psychophysics and electrophysiology experiments comparing PredNet's internal unit responses to human and primate data.
  • Analysis of PredNet's performance on variations of the illusion, including greyscale and simplified patterns, across multiple network instances.

Main Results:

  • PredNet replicated illusory motion for 'Rotating Snakes' components, aligning with human perception.
  • Internal unit response delays in PredNet did not match electrophysiological data.
  • PredNet's motion detection relied more on luminance contrast than human perception, and showed significant variability across network instances.

Conclusions:

  • While DNNs can reproduce certain aspects of human visual illusions, detailed comparisons reveal critical inconsistencies.
  • Predictive coding, as modeled by PredNet, does not consistently yield human-like illusory motion perception.
  • Variability across DNN instances highlights the need for caution when interpreting their ability to model human cognition.