Designing for Deep Engagement

Abstract

Flow state represents the quality of meaningful experience-- an effortless, depth of attention that is often undermined in our interrupt-driven, modern society. In this thesis, I present four novel interventions to promote states of deep engagement.

Evaluating whether one of these interventions has a meaningful impact on flow state is difficult to do. The bulk of my work, then, focuses on the methodological challenges of flow state research. Herein I tackle three weaknesses in our ability to make strong, generalizable predictions about the causal link between environmental stimuli and flow states: (1) I discuss advancing how we represent the environment (specifically for aural stimuli) using phenomenological principles; (2) I advance the state-of-the-art in how we represent and measure flow bio-behaviorally (with the goal of integrating physiology into our judgements); and (3) I evaluate methodological weaknesses in current experimental flow work. To do this, I present experimental work on models of auditory attention, new wearables and survey instruments for flow estimation, and an experiment that compares flow as measured in lab and at home across varying task structures.

This thesis contributes a suite of state-of-the-art psychophysiological and behavioral hardware tools designed to inform inference about flow in-the-wild; it also contributes two unique, open-source, naturalistic datasets collected with them. Combined with time-aware, probabilistic representations of cognition, this work sets the stage for a precise and explicit bio-behavioral definition of flow states that will improve our ability to understand its relationship to our environment. In so doing, it points to an improved approach for social psychology more generally.