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Fostering Flow Experience in HCI to Enhance and Allocate Human Energy

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Engineering Psychology and Cognitive Ergonomics. Mental Workload, Human Physiology, and Human Energy (HCII 2020)

Abstract

Motivation explains the direction, intensity and persistence of human behavior and thus plays a crucial role in the mobilization and allocation of available energy. An experience that occurs during motivated action is flow. Flow is perceived as highly rewarding for its own sake and, thus, in flow all attention is directed towards the task at hand, leading to an experience of absorption. At the same time, attention is shielded from irrelevant stimuli and the activity feels easy and effortless. This suggests that flow is a highly efficient state in terms of energy expenditure. Studies addressing the physiology of flow support this assumption. Accordingly, for an optimal use of energy, it is of interest to promote flow in relevant work processes. In HCI, for example, in production work, flow promotion could be enabled by a real-time measure of the operator’s flow state in combination with automated adjustments in the work system to achieve, sustain, or extend flow. Such a real-time measure should not interrupt a person, as traditional self-report measures do. A combination of physiological measures (e.g., heart rate variability, skin conductance, and blink rate) provides a promising starting point to find such a real-time measure. Automated adjustments first require the identification of design approaches that affect flow within the work system. Using the example of work in manufacturing, the concept of flow, its measurement, and potential design approaches for automated adaptation are presented, and their application in HCI processes is discussed.

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Peifer, C., Kluge, A., Rummel, N., Kolossa, D. (2020). Fostering Flow Experience in HCI to Enhance and Allocate Human Energy. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. Mental Workload, Human Physiology, and Human Energy. HCII 2020. Lecture Notes in Computer Science(), vol 12186. Springer, Cham. https://doi.org/10.1007/978-3-030-49044-7_18

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