Temporal Taylor's scaling of facial electromyography and electrodermal activity in the course of emotional stimulation

High frequency psychophysiological data create a challenge for quantitative modeling based on Big Data tools since they reflect the complexity of processes taking place in human body and its responses to external events. Here we present studies of fluctuations in facial electromyography (fEMG) and electrodermal activity (EDA) massive time series and changes of such signals in the course of emotional stimulation. Zygomaticus major (ZYG, "smiling" muscle) activity, corrugator supercilii (COR, "frowning"bmuscle) activity, and phasic skin conductance (PHSC, sweating) levels of 65 participants were recorded during experiments that involved exposure to emotional stimuli (i.e., IAPS images, reading and writing messages on an artificial online discussion board). Temporal Taylor's fluctuations scaling were found when signals for various participants and during various types of emotional events were compared. Values of scaling exponents were close to 1, suggesting an external origin of system dynamics and/or strong interactions between system's basic elements (e.g., muscle fibres). Our statistical analysis shows that the scaling exponents enable identification of high valence and arousal levels in ZYG and COR signals

How to reap the benefits of flexible work time

The golden strategy may be to use flextime only occasionally when really needed, write Ines Spieler, Susanne Scheibe, Christian Stamov Roßnagel and Arvid Kappa

Building Long-Term Human–Robot Relationships: Examining Disclosure, Perception and Well-Being Across Time

While interactions with social robots are novel and exciting for many people, one concern is the extent to which people’s behavioural and emotional engagement might be sustained across time, since during initial interactions with a robot, its novelty is especially salient. This challenge is particularly noteworthy when considering interactions designed to support people’s well-being, with limited evidence (or empirical exploration) of social robots’ capacity to support people’s emotional health over time. Accordingly, our aim here was to examine how long-term repeated interactions with a social robot affect people’s self-disclosure behaviour toward the robot, their perceptions of the robot, and how such sustained interactions influence factors related to well-being. We conducted a mediated long-term online experiment with participants conversing with thesocial robot Pepper 10 times over 5 weeks. We found that people self-disclose increasingly more to a social robot over time, and report the robot to be more social and competent over time. Participants’ moods also improved after talking to the robot, and across sessions, they found the robot’s responses increasingly comforting as well as reported feeling less lonely. Finally, our results emphasize that when the discussion frame was supposedly more emotional (in this case, framing questions in the context of the COVID-19 pandemic), participants reported feeling lonelier and more stressed. These results set the stage forsituating social robots as conversational partners and provide crucial evidence for their potential inclusion in interventions supporting people’s emotional health through encouraging self-disclosure

Opening Up to Social Robots: How Emotions Drive Self-Disclosure Behavior

Self-disclosing to others can benefit emotional well-being, but socio-emotional barriers can limit people’s ability to do so. Self-disclosing towards social robots can help overcome these obstacles as robots lack judgment and can establish rapport. To further understand the influence of affective factors on people’s self-disclosure to social robots, this study examined the relationship between self-disclosure behaviour towards a social robot and people’s emotional states and their perception of the robot’s responses as comforting (i.e., being emphatic). The study included 1160 units of observation collected from 39 participants who conversed with the social robot Pepper (SoftBank Robotics) twice a week for 5 weeks (10 sessions in total), answering three personal questions in each session. Results show that perceiving the robot’s responses as more comforting was positively related to self-disclosure behaviour (in terms of disclosure duration in seconds, and disclosure length in number of words), and negative emotional states, such as lower mood, and higher feelings of loneliness and stress, were associated with higher rates of self-disclosure towards the robot. Additionally, higher rates of introversion significantly predicted higher rates of self-disclosure towards the robot. The study reveals the meaningful influence of affective states on how people behave when talking to social robots, especially when experiencing negative emotions. These findings may have implications for designing and developing social robots in therapeutic contexts

Informal Caregivers Disclose Increasingly More to a Social Robot Over Time

Informal caregivers often struggle in managing to cope with both the stress and the practical demands of the caregiving situation. It has been suggested that digital solutions might be useful to monitor caregivers’ health and well-being, by providing early intervention and support. Given the importance of self-disclosure for psychological health, here we aimed to investigate the potential of employing a social robot for eliciting self-disclosure among informal caregivers over time. We conducted a longitudinal experiment across a five-week period, measuring participants’ disclosure duration (in seconds) and length (in number of words). Our preliminary results show a positive trend where informal caregivers speak for a longer time and share more information in their disclosures to a social robot across the five-week period. These results provide useful evidence supporting the potential deployment of social robots as intervention tools to help provide support for individuals suffering from stress and experiencing challenging life situations

User Experience of Human-Robot Long-Term Interactions

Since interactions with social robots are novel and exciting for many people, one particular concern in this specific area of human-robot interaction (HRI) is the extent to which human users will experience the interactions positively over time, when the robot’s novelty is particularly salient. In the current paper, we investigated users’ experience in long-term HRIs; how users perceive the ongoing interactions and the robot’s ability to sustain it over time. Therefore, here we examine the effect of the repeated measures (10 testing sessions) and the discussion theme (Covid-19 related vs general) on the way participants experienced the interaction quality with a social robot and perceived the robot’s communication competency over time. We found that despite individual differences between the participants, over time participants found the interactions with Pepper to be of higher quality and that Pepper’s communication skills got better. Nevertheless, our results also stressed that the discussion theme has no meaningful nor significant effect on the way people perceive Pepper and the interaction