The Curiosity Engine: A Reflexive Framework for Accelerating Experimental Research

Abstract

VR research rarely fails in dramatic ways. It frays in the margins. It slips. It drifts. A timestamp is off. A controller desyncs for a moment. A study crashes right as a participant reaches for the final task. These moments feel small and forgettable, yet they accumulate. Bit by bit, they turn smooth inquiry into slow erosion. What researchers call friction is this steady drag on momentum, the quiet resistance that grows from mismatched tools, scattered workflows, and the constant need to hold a study together with focus alone.

This thesis begins from that everyday reality. It argues that friction is not accidental. It shows how the systems surrounding us do more than record data. They shape how we think, how we adapt, and how we make decisions in the moment of research itself.

To address this, the thesis introduces a meta-cognitive framework for experimental research. The framework treats friction as a meaningful signal. When things slow down or fall out of sync, it points to gaps in structure, preparation, or workflow clarity. By paying attention to these signals, researchers can design studies and tools that protect momentum, make intentions visible, and support reflective, adaptive work.

ScryVR serves as practical demonstration of this idea. The system offers clear templates, organized study structures, and simple building blocks that help researchers understand and adjust their experiments without getting lost in technical details.

This thesis shows that the meta-cognitive approach improves clarity, supports exploration, and reduces the fragility that often disrupts VR studies. The work concludes by arguing for research tools that act like partners. When systems support reflection and reveal structure, researchers gain a clearer sense of their own process and can build studies that are more sustainable, more intentional, and more creative.