Search for: All records

In affect-aware task adaptation, users' psychological states are recognized with diverse measurements and used to adapt computer-based tasks. User experience with such adaptation improves as the accuracy of psychological state recognition and task adaptation increases. However, it is unclear how user experience is influenced by algorithmic transparency: the degree to which users understand the computer's decision-making process. We thus created an affect-aware task adaptation system with 4 algorithmic transparency levels (none/low/medium/high) and conducted a study where 93 participants first experienced adaptation with no transparency for 16 minutes, then with one of the other 3 levels for 16 minutes. User experience questionnaires and physiological measurements (respiration, skin conductance, heart rate) were analyzed with mixed 2×3 analyses of variance (time × transparency group). Self-reported interest/enjoyment and competence were lower with low transparency than with medium/high transparency, but did not differ between medium and high transparency. The transparency level may also influence participants' respiratory responses to adaptation errors, but this finding is based on ad-hoc t-tests and should be considered preliminary. Overall, results show that the degree of algorithmic transparency does influence self-reported user experience. Since transparency information is relatively easy to provide, it may represent a worthwhile design element in affective computing. 

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.