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In affective computing, classification algorithms are used to recognize users’ psychological states and adapt tasks to optimize user experience. However, classification is never perfect, and the relationship between adaptation accuracy and user experience remains understudied. It is also unclear whether the adaptation magnitude (‘size’ of action taken to influence user states) influences effects of adaptation accuracy. To evaluate impacts of adaptation accuracy (appropriate vs. inappropriate actions) and magnitude on user experience, we conducted a ‘Wizard of Oz’ study where 112 participants interacted with the Multi-Attribute Task Battery over three 11-minute intervals. An adaptation accuracy (50 % to 80 %) was preassigned for the first 11-minute interval, and accuracy increased by 10 % in each subsequent interval. Task difficulty changed every minute, and participant preferences for difficulty changes were assessed at the same time. Adaptation accuracy was artificially induced by fixing the percentage of times the difficulty changes matched participant preferences. Participants were also randomized to two magnitude conditions, with difficulty modified by 1 (low) or 3 (high) levels each minute. User experience metrics were assessed after each interval. Analysis with latent growth models offered support for linear increases in user experience across increasing levels of adaptation accuracy. For each 10 % gain in accuracy, results indicate a 1.3 (95 % CI [.35, 2.20]) point increase in NASA Task Load Index scores (range 6–60), a 0.40 (95 % CI [.18, 0.57]) increase in effort/importance (range 2–14), and 0.48 (95 % CI [.24, 0.72]) increase in perceived competence (range 2–14). Furthermore, the effect of accuracy on Task Load Index scores was modulated by adaptation magnitude. No effects were observed for interest/enjoyment or pressure/tension. By providing quantitative estimates of effects of adaptation accuracy on user experience, the study provides guidelines for researchers and developers of affect-aware technologies. Furthermore, our methods could be adapted for use in other affective computing scenarios.
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Effects of Sensor Setup Time and Comfort on User Experience in Physiological Computing *
Physiological sensors are commonly applied for user state monitoring and consequent machine behavior adaptation in applications such as rehabilitation and intelligent cars. While more accurate user state monitoring is known to lead to better user experience, increased accuracy often requires more sensors or more complex sensors. The increased setup time and discomfort involved in the use of such sensors may itself worsen user experience. To examine this effect, we conducted a study where 72 participants interacted with a computer-based multitasking scenario whose difficulty was periodically adapted - ostensibly based on data from either a remote eye tracker or a lab-grade “wet” electroencephalography sensor. Deception was used to ensure consistent difficulty adaptation accuracies, and user experience was measured with the Intrinsic Motivation Inventory, NASA Task Load Index, and an ad-hoc scale. We found few user experience differences between the eye tracker and electroencephalography sensor - while one interaction effect was noted, it was small, and there were no other differences. This result is at first surprising and seems to indicate that comfort and setup time are not major factors for laboratory-based user experience evaluations of such technologies. However, the result is likely due to a suboptimal study protocol where each participant interacted with only one sensor. In future work, we will use an alternate protocol to further explore the effects of user comfort and setup time on user experience.
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- Award ID(s):
- 2151464
- PAR ID:
- 10579588
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 978-1-6654-1020-5
- Page Range / eLocation ID:
- 3776 to 3780
- Format(s):
- Medium: X
- Location:
- Kuching, Malaysia
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SMC54092.2024.10831219
