# Motion Illusions Generated Using Predictive Neural Networks Also Fool Humans

arXiv: https://arxiv.org/abs/2112.13243 · HTML: https://mlsanity.com/paper/2112.13243/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 25 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning](https://mlsanity.com/paper/1605.08104/) (2016)
- [Predictive Coding: a Theoretical and Experimental Review](https://mlsanity.com/paper/2107.12979/) (2021)
- [Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure](https://mlsanity.com/paper/1903.01069/) (2019)
- [Synthesizing Visual Illusions Using Generative Adversarial Networks](https://mlsanity.com/paper/1911.09599/) (2019)
- [Perspective: Purposeful Failure in Artificial Life and Artificial Intelligence](https://mlsanity.com/paper/2102.12076/) (2021)

## Built on by (most cited first, 1 of 1)

- [Hybrid Life: Integrating Biological, Artificial, and Cognitive Systems](https://mlsanity.com/paper/2212.00285/) (2022)

## Abstract (arXiv)

Why do we sometimes perceive static images as if they were moving? Visual motion illusions enjoy a sustained popularity, yet there is no definitive answer to the question of why they work. Here we present evidence in favor of the hypothesis that illusory motion is a side effect of the predictive abilities of the brain. We present a generative model, the Evolutionary Illusion GENerator (EIGen), that creates new visual motion illusions based on a video predictive neural network. We confirm that the constructed illusions are effective on human participants through a psychometric survey. Our results support the hypothesis that illusory motion might be the consequence of perceiving the brain's own predictions rather than perceiving raw visual input from the eyes. The philosophical motivation of this paper is to call attention to the untapped potential of "motivated failures", ways for artificial systems to fail as biological systems fail, as a worthy outlet for Artificial Intelligence and Artificial Life research.