RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality

Dominic Potts; Miloni Gada; Aastha Gupta; Kavya Goel; Klaus Philipp Krzok; Genevieve Pate; Joe Hartley; Mark Weston-Arnold; Jakob  Aylott; Christopher Clarke; Crescent Jicol; Christof Lutteroth

doi:10.1145/3706598.3713957

RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality

Dominic Potts, Miloni Gada, Aastha Gupta, Kavya Goel, Klaus Philipp Krzok, Genevieve Pate, Joe Hartley, Mark Weston-Arnold, Jakob Aylott, Christopher Clarke, Crescent Jicol, Christof Lutteroth

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Abstract

Virtual Reality (VR) designers and researchers often need to measure emotions and presence as they evolve over time. The experience sampling method (ESM) is a common way to achieve this, however, ESM disrupts the experience and lacks granularity. We propose RetroSketch, a new method for measuring subjective emotions and presence in VR, where users watch back their VR experience and retrospectively sketch a plot of their feelings. RetroSketch leaves the VR experience undisturbed and yields highly granular data, including information about salient events and qualitative descriptions of their feelings. We compared RetroSketch and ESM in a large study (n=140) using five different VR experiences over one-hour sessions. Our results show that RetroSketch and ESM measures are highly correlated with each other, as well as physiological measures indicative of emotion. The correlations are robust across different VR experiences and user demographics. They also highlight the impact of ESM on users’ experience.

Original language	English
Title of host publication	CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery
ISBN (Print)	9798400713941
DOIs	https://doi.org/10.1145/3706598.3713957
Publication status	Published - 25 Apr 2025

Data Availability Statement

[...] we make the following contributions: [...] A large open dataset of emotions, presence, and ten physiological measures across five VR experiences (n=140), see [172].

[172] Dominic Potts, Miloni Gada, Aastha Gupta, Kavya Goel, Klaus Phillip Krzok, Genieve Pate, Joseph Hartley, Mark Weston-Arnold, Jakob Aylott, Christopher Clarke, Crescent Jicol, and Christof Lutteroth. 2025. Dataset for "RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality". Bath: University of Bath Research Data Archive. https://doi.org/10.15125/BATH-01489

Acknowledgements

We would like to acknowledge Jovana Cuxac, Jasmine Chung, Katherine Baldrian, and Tarini Sehgal for their support and assistance in conducting the study.

Funding

This work is supported by the European Union’s Horizon Europe research and innovation program and Innovate UK under grant agreement No 101070533, project EMIL (The European Media and Immersion Lab: https://emil-xr.eu/), as well as the Royal Society Industry Fellowship under grant agreement No INF\R1\221058.

Access to Document

10.1145/3706598.3713957Licence: CC BY

Cite this

Potts, D., Gada, M., Gupta, A., Goel, K., Krzok, K. P., Pate, G., Hartley, J., Weston-Arnold, M., Aylott, J., Clarke, C., Jicol, C., & Lutteroth, C. (2025). RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality. In CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems Association for Computing Machinery. https://doi.org/10.1145/3706598.3713957

RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality. / Potts, Dominic; Gada, Miloni; Gupta, Aastha et al.
CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2025.

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Potts, D, Gada, M, Gupta, A, Goel, K, Krzok, KP, Pate, G, Hartley, J, Weston-Arnold, M, Aylott, J, Clarke, C , Jicol, C & Lutteroth, C 2025, RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality. in CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. https://doi.org/10.1145/3706598.3713957

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AU - Krzok, Klaus Philipp

AU - Pate, Genevieve

AU - Hartley, Joe

AU - Weston-Arnold, Mark

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AB - Virtual Reality (VR) designers and researchers often need to measure emotions and presence as they evolve over time. The experience sampling method (ESM) is a common way to achieve this, however, ESM disrupts the experience and lacks granularity. We propose RetroSketch, a new method for measuring subjective emotions and presence in VR, where users watch back their VR experience and retrospectively sketch a plot of their feelings. RetroSketch leaves the VR experience undisturbed and yields highly granular data, including information about salient events and qualitative descriptions of their feelings. We compared RetroSketch and ESM in a large study (n=140) using five different VR experiences over one-hour sessions. Our results show that RetroSketch and ESM measures are highly correlated with each other, as well as physiological measures indicative of emotion. The correlations are robust across different VR experiences and user demographics. They also highlight the impact of ESM on users’ experience.

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RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality

Abstract

Data Availability Statement

Acknowledgements

Funding

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Dataset for "RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality"

Cite this

RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality

Abstract

Data Availability Statement

Acknowledgements

Funding

Access to Document

Other files and links

Fingerprint

Datasets

Dataset for "RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality"

Cite this