Quantum-Inspired Neural Network Model of Optical Illusions
This work addresses the challenge of accurate computational models for ambiguous perception, with potential applications in video games, virtual reality training, and interdisciplinary research, though it appears incremental by combining existing quantum and neural network concepts.
The paper tackled the problem of modeling human perception of ambiguous optical illusions, specifically the Necker cube, by designing a deep neural network with quantum-generated random weights, revealing that perceptual states exist as qubit-like superpositions rather than classical alternatives.
Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate the human's perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results will find applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They will also be useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum-mechanical models of human mind and decision-making.