Combining Personality and Physiology to Investigate the Flow Experience in Virtual Reality Games

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Authors: Michailidis, L., Lucas Barcias, J., Charles, F., He, X. and Balaguer-Ballester, E.

Start date: 26 July 2019

ISBN: 9783030235277

ISSN: 1865-0929

DOI: 10.1007/978-3-030-23528-4_7

© Springer Nature Switzerland AG 2019. Immersive experiences are typically considered an indicator of successful game design. The ability to maintain the player’s focus and enjoyment in the game lies at the core of game mechanics. In this work, we used a custom virtual reality game aiming to induce flow, boredom and anxiety throughout specific instances in the game. We used self-reports of personality and flow in addition to physiological measures (heart rate variability) as a means of evaluating the game design. Results yielded a consistently high accuracy in the classification of low flow versus high flow conditions across multiple classifiers. Moreover, they suggested that the anticipated model-by-design was not necessarily consistent with the player’s subjective and objective data. Our approach lays promising groundwork for the automatic assessment of game design strategies and may help explain experiential variability across video game players.

This data was imported from Scopus:

Authors: Michailidis, L., Lucas Barcias, J., Charles, F., He, X. and Balaguer-Ballester, E.

Journal: Communications in Computer and Information Science

Volume: 1033

Pages: 45-52

eISSN: 1865-0937

ISBN: 9783030235277

ISSN: 1865-0929

DOI: 10.1007/978-3-030-23528-4_7

© Springer Nature Switzerland AG 2019. Immersive experiences are typically considered an indicator of successful game design. The ability to maintain the player’s focus and enjoyment in the game lies at the core of game mechanics. In this work, we used a custom virtual reality game aiming to induce flow, boredom and anxiety throughout specific instances in the game. We used self-reports of personality and flow in addition to physiological measures (heart rate variability) as a means of evaluating the game design. Results yielded a consistently high accuracy in the classification of low flow versus high flow conditions across multiple classifiers. Moreover, they suggested that the anticipated model-by-design was not necessarily consistent with the player’s subjective and objective data. Our approach lays promising groundwork for the automatic assessment of game design strategies and may help explain experiential variability across video game players.

The data on this page was last updated at 05:10 on February 17, 2020.