From Virtual Reality to the Emerging Discipline of Perception Engineering
Steven M. LaValle, Evan G. Center, Timo Ojala, Matti Pouke, Nicoletta Prencipe, Basak Sakcak, Markku Suomalainen, Kalle G. Timperi, Vadim K. Weinstein
TL;DR
Perception engineering formalizes how a producer can deliberately alter an environment to elicit a targeted, plausible perceptual experience in a receiver, extending VR beyond devices to a unified mathematical framework spanning humans, animals, and robots. The authors introduce a coupled $\mathcal I$-space/$\mathcal X$-space dynamical model with $f$, $h$, $\phi$, and a reality relation $R$, and they develop stationary and dynamic producer–receiver scenarios, including multiagent extensions and sensor-spoofing implications. They apply the framework to robots (white-box modeling and spoofing), humans (VR and psychophysics), and other biological agents (animals), and introduce metrics such as plausibility robustness $PR$ and forced-fusion magnitude $FFM$ to quantify perceptual integrity. The work highlights significant practical impact in VR design, robotics testing, social information dynamics, and cross-disciplinary insights, while outlining open computational, methodological, and ethical challenges for maturing perception engineering as a discipline.
Abstract
This paper makes the case that a powerful new discipline, which we term perception engineering, is steadily emerging. It follows from a progression of ideas that involve creating illusions, from historical paintings and film, to video games and virtual reality in modern times. Rather than creating physical artifacts such as bridges, airplanes, or computers, perception engineers create illusory perceptual experiences. The scope is defined over any agent that interacts with the physical world, including both biological organisms (humans, animals) and engineered systems (robots, autonomous systems). The key idea is that an agent, called a producer, alters the environment with the intent to alter the perceptual experience of another agent, called a receiver. Most importantly, the paper introduces a precise mathematical formulation of this process, based on the von Neumann-Morgenstern notion of information, to help scope and define the discipline. It is then applied to the cases of engineered and biological agents with discussion of its implications on existing fields such as virtual reality, robotics, and even social media. Finally, open challenges and opportunities for involvement are identified.