FPV Drone Flight as a Flow Induction Platform: Structural Properties and Theoretical Mechanisms

First-Person View (FPV) drone flight is a form of remote piloting in which the operator's visual field is entirely replaced by a live camera feed from a small aircraft received through head-mounted display goggles, with control inputs transmitted via low-latency proportional links to the aircraft's flight controller. Despite growing adoption as a recreational and competitive practice, the psychological properties of FPV flight have received limited formal scientific attention. This paper proposes and develops a theoretical account of FPV drone flight as an unusually reliable flow induction environment. Drawing on Csikszentmihalyi's (1990) structural analysis of flow conditions, Kotler and Wheal's (2014) neurochemical account of flow states, embodied cognition theory (Wilson, 2002; Varela, Thompson, Sheridan, 1992), the paper identifies four structural properties of FPV that together constitute a near-optimal configuration for flow induction: real-consequence feedback, challenge-skill calibration, distributed embodiment, and high-frequency deliberate practice cycles. A novel construct — distributed embodiment — is introduced to describe the condition in which the pilot's perceptual and motor processes become continuously coupled with a remote physical system, with the drone serving as an extension of the operator's sensorimotor apparatus. Observable developmental and state-dependent behavioral markers of embodiment depth are identified. The paper further argues that FPV occupies a distinct technological position from simulation-based and virtual reality systems, extending the operator into a genuinely physical environment rather than approximating one, a distinction with direct implications for the depth and reliability of flow induction. FPV environments are proposed as high-value platforms for studying flow induction and the psychological mechanisms of human-machine integration at scale.