# Quantum-Inspired Neural Network Model of Optical Illusions
## 4 Discussion
### Potential Applications in Artificial Intelligence and Virtual Reality Systems  
The proposed neural network algorithm can be used as a model of optical illusions in filmmaking, architecture design and game development [4; 109; 110]. For example, the video game _Superluminal_ uses forced perspective techniques that manipulate human visual perception to make an object appear larger or smaller than it actually is [111]. Typically, the design of such games requires an input from psychologists and experts in vision science, yet relying on subjective feedback provided by testers and professional video games players. The application of the model proposed in this work can help reduce the impact of subjectivity from game design procedures. The models proposed in this work can be used in an advanced machine vision system intended to simulate the human perception and decision making. In particular, the so-designed machine vision system may be tasked to play a video game such as _Superluminal_ and its actions can be compared with the actions of a human operator, providing a valuable feedback for engineers, neuroscientists and psychologists. Intriguingly, a number of recent research works have demonstrated that the psychological perception of game scenarios can also be modelled using the laws of physics [112]. Subsequently, the outcomes if this work can help improve deep learning models developed to play video games like a human [113; 114]. The neural network model of optical illusions can be used to study the impact of weightlessness on the ability of astronauts to undertake complex tasks during and after spaceflights. On Earth, the majority of observers of ambiguous figures such as the Necker cube perceive one interpretation more often than the other. However, in weightlessness, this asymmetry gradually disappeared and, after spending several months in orbit, both interpretations have the same occurrence [16; 17]. At present, such studies rely on experimental data that are often obtained in microgravity conditions or provided by individuals who experiences the effect of microgravity [115]. It is plausible that a virtual reality system that simulate the effect of microgravity on the ability of humans to perceive optical illusions will improve the fidelity of experimental results by reducing the effect of subjectivity. Such virtual reality systems can also enable anyone to experience the world thought the eyes of an astronaut. The operation of unmanned aerial vehicles (UAVs), commonly known as drones, is another area where models of optical illusions may help extend the abilities of both humans and AI. For example, at present, the skills of human race drone operators significantly exceed the performance of the most advanced machine vision algorithms [116]. A better understanding of the ability of a human pilot to select appropriate motor commands from highly dynamic visual information may provide key insights for solving current challenges in vision-based autonomous navigation. Yet, the neural network model of optical illusions can be used to validate certain neuroscience and psychological perception theories that are complimentary to the quantum mind hypothesis [117; 118]. According to some mainstream theories, our subjective perception of the world is unitary coherent [119]. Here, unitary means that we perceive one interpretation at a time (e.g., one of the two possible states of the Necker cube) rather than a blur of the possible interpretations (i.e., we never see the two possible states of the cube together). In turn, coherent means that we perceive scenes that do not contain contradictory parts (e.g., we do not see a part of one cube and a part of another one at the same time). However, such an intuitive approach contradicts the theories of optimal decision making and Bayesian brain [117; 118]. These theories suggest that an optimal decision can be made only integrating the utility of all actions while considering all possible interpretations of sensory data.