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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. 

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. 

Dyadic physiological responses are correlated with the quality of interpersonal processes – for example, the degree of “connectedness” in education and mental health counseling. Pattern recognition algorithms could be applied to such dyadic responses to identify the states of specific dyads, but such pattern recognition has primarily focused on classification. This paper instead uses regression algorithms to estimate three conversation aspects (valence, arousal, balance) from heart rate, skin conductance, respiration, and skin temperature. Data were collected from 35 dyads who engaged in 20 minutes of conversation, divided into 10 two-minute intervals. Each interval was rated with regard to conversation valence, arousal, and balance by an observer. When regression algorithms (support vector machines and Gaussian process regression) were trained on other data from the same dyad, they were able to estimate valence, arousal and balance with lower errors than a simple baseline estimator. However, when algorithms were trained on data from other dyads, errors were not lower than those of the baseline estimator. Overall, results indicate that, as long as training data from the same dyad are available, autonomic nervous system responses can be combined with regression algorithms to estimate multiple dyadic conversation aspects with some accuracy. This has applications in education and mental health counseling, though fundamental issues remain to be addressed before the technology is used in practice. 

Public stereotypes about trauma exposure and its likely consequences have the potential to influence levels of support extended to survivors in the larger community. The current project sought to examine unique profiles of stereotype endorsement both within and across participants sampled from distinct populations. Trauma-related stereotypes involving symptom course, dangerousness, employability, social functioning, predictability, character, and treatment need were examined in undergraduate (N1 = 404; N2 = 502) and MTurk (N3 = 364) samples. Sympathizing [low overall endorsement], Fearful [high overall endorsement], Pejorative [high endorsement + moralizing beliefs], Safety-Focused [intermediate endorsement + dangerousness], and Performance-Focused [intermediate endorsement + employability] groups were replicated in latent profile models across all samples. Stereotype profiles demonstrated hypothesized associations with general perspectives of mental illness although support for consistent relations with respondent characteristics (e.g., sex; personal exposure to trauma; reported exposure in friends/family) was limited. Data suggest that trauma stereotypes are endorsed at high frequencies in the general community and conform to systematic patterns of prejudice that may be overlooked in more global assessments of stigma.