Exploring physiologic reactions to persuasive information.

Persuasion aims at changing peoples' motivations and/or behaviors. This study explores how and when physiology reflects persuasion processes and specifically whether individual differences in motivations and behaviors affect psychophysiologic reactions to persuasive information. Participants (N = 70) with medium or high meat consumption patterns watched a persuasive video advocating limited meat consumption, while their electrodermal and cardiovascular physiology was measured. Results indicated that the video increased participants' moral beliefs, perceived behavioral control, and reduction intentions. This study also found an increase in physiologic arousal during the persuasive video and that people with motivations less aligned to the persuasion objective had more physiologic arousal. The findings encourage further psychophysiologic persuasion research, especially as these insights can potentially be used to personalize persuasive messages of behavior change applications.

Personalizing HRI in Musical Instrument Practicing: The Influence of Robot Roles (Evaluative Versus Nonevaluative) on the Child's Motivation for Children in Different Learning Stages.

Learning to play a musical instrument involves skill learning and requires long-term practicing to reach expert levels. Research has already proven that the assistance of a robot can improve children's motivation and performance during practice. In an earlier study, we showed that the specific role (evaluative role versus nonevaluative role) the robot plays can determine children's motivation and performance. In the current study, we argue that the role of the robot has to be different for children in different learning stages (musical instrument expertise levels). Therefore, this study investigated whether children in different learning stages would have higher motivation when assisted by a robot in different supporting roles (i.e., evaluative role versus nonevaluative role). We conducted an empirical study in a real practice room of a music school with 31 children who were at different learning stages (i.e., beginners, developing players, and advanced players). In this study, every child practiced for three sessions: practicing alone, assisted by the evaluative robot, or assisted by the nonevaluative robot (in a random order). We measured motivation by using a questionnaire and analyzing video data. Results showed a significant interaction between condition (i.e., alone, evaluative robot, and nonevaluative robot) and learning stage groups indicating that children in different learning stage groups had different levels of motivation when practicing alone or with an evaluative or nonevaluative robot. More specifically, beginners had higher persistence when practicing with the nonevaluative robot, while advanced players expressed higher motivation after practicing with a robot than alone, but no difference was found between the two robot roles. Exploratory results also indicated that gender might have an interaction effect with the robot roles on child's motivation in music practice with social robots. This study offers more insight into the child-robot interaction and robot role design in musical instrument learning. Specifically, our findings shed light on personalization in HRI, that is, from adapting the role of the robot to the characteristics and the development level of the user.

Why Exercise at Work: Development of the Office Exercise Behavior Determinants Scale.

The constant increase in work pressure and the penetration of labor-saving technologies have significantly reduced physical activity in office-based work routines, threatening employees' physical and mental well-being. Encouraging physical exercises at the office seems a potential solution. However, while there is a wealth of research into occupational health and workplace exercise promotion, little is known about which factors can influence the engagement of physical exercises in the office context. It is crucial to understand these determinants, in order to support the design of office exercise promoting intervention. This study explored the determinants of office workers' exercise behavior by proposing and developing the Office Exercise Behavior Determinants (OEBD) scale based on existing behavioral and environmental research. The OEBD scale was assessed through an online questionnaire study involving 479 office workers. The results indicated that four factors (Intrinsic Motivation, Extrinsic Motivation, Social Environment, and Work Environment) contribute to office workers' exercise behavior. Furthermore, confirmatory factor analysis of our obtained data provided evidence for the internal validity of the OEBD scale. Thereby, this research can support increased office exercise with valid measurements for behavioral determinants.

A dual-process view on medication adherence: The role of affect.

Medication non-adherence has been recognised as one of the major problems in health care that leads to preventable costs and hospitalisations. This study aimed to assess the role of affect in medication adherence. We propose a dual-process framework of medication adherence based on the reflective-impulsive model, which includes both cognitive and affective processes. We tested this framework in a cross-sectional study (N = 525). The results supported this framework by illustrating the clear role of both cognitive and affective processes.

Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction.

The growing interest in social robotics makes it relevant to examine the potential of robots as persuasive agents and, more specifically, to examine how robot characteristics influence the way people experience such interactions and comply with the persuasive attempts by robots. The purpose of this research is to identify how the (ostensible) gender and the facial characteristics of a robot influence the extent to which people trust it and the psychological reactance they experience from its persuasive attempts. This paper reports a laboratory study where SociBot™, a robot capable of displaying different faces and dynamic social cues, delivered persuasive messages to participants while playing a game. In-game choice behavior was logged, and trust and reactance toward the advisor were measured using questionnaires. Results show that a robotic advisor with upturned eyebrows and lips (features that people tend to trust more in humans) is more persuasive, evokes more trust, and less psychological reactance compared to one displaying eyebrows pointing down and lips curled downwards at the edges (facial characteristics typically not trusted in humans). Gender of the robot did not affect trust, but participants experienced higher psychological reactance when interacting with a robot of the opposite gender. Remarkably, mediation analysis showed that liking of the robot fully mediates the influence of facial characteristics on trusting beliefs and psychological reactance. Also, psychological reactance was a strong and reliable predictor of trusting beliefs but not of trusting behavior. These results suggest robots that are intended to influence human behavior should be designed to have facial characteristics we trust in humans and could be personalized to have the same gender as the user. Furthermore, personalization and adaptation techniques designed to make people like the robot more may help ensure they will also trust the robot.

A multimodal dataset for authoring and editing multimedia content: The MAMEM project.

We present a dataset that combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals collected from 34 individuals (18 able-bodied and 16 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. The presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.

'A necessary evil': associations with taking medication and their relationship with medication adherence.

Cognitive factors, like beliefs, have been studied extensively as determinants of medication adherence, while affect associated with taking medicines has been studied much less. In the present study (N = 525), we investigated affect by assessing patients' first associations with taking their medicines. Results showed that these associations were related to self-reported medication adherence: Patients who associated taking medicines with negative affect were the least adherent, while those associating taking medicines with the need to take medicines were the most adherent. Our results support the idea that affect should be considered an important determinant of adherence.

Trusting a Virtual Driver That Looks, Acts, and Thinks Like You.

OBJECTIVE: We examined whether participants would trust an agent that was similar to them more than an agent that was dissimilar to them. BACKGROUND: Trust is an important psychological factor determining the acceptance of smart systems. Because smart systems tend to be treated like humans, and similarity has been shown to increase trust in humans, we expected that similarity would increase trust in a virtual agent. METHODS: In a driving simulator experiment, participants (N = 111) were presented with a virtual agent that was either similar to them or not. This agent functioned as their virtual driver in a driving simulator, and trust in this agent was measured. Furthermore, we measured how trust changed with experience. RESULTS: Prior to experiencing the agent, the similar agent was trusted more than the dissimilar agent. This effect was mediated by perceived similarity. After experiencing the agent, the similar agent was still trusted more than the dissimilar agent. CONCLUSION: Just as similarity between humans increases trust in another human, similarity also increases trust in a virtual agent. When such an agent is presented as a virtual driver in a self-driving car, it could possibly enhance the trust people have in such a car. APPLICATION: Displaying a virtual driver that is similar to the human driver might increase trust in a self-driving car.

The development of the ProMAS: a Probabilistic Medication Adherence Scale.

Current self-report medication adherence measures often provide heavily skewed results with limited variance, suggesting that most participants are highly adherent. This contrasts with findings from objective adherence measures. We argue that one of the main limitations of these self-report measures is the limited range covered by the behaviors assessed. That is, the items do not match the adherence behaviors that people perform, resulting in a ceiling effect. In this paper, we present a new self-reported medication adherence scale based on the Rasch model approach (the ProMAS), which covers a wide range of adherence behaviors. The ProMAS was tested with 370 elderly receiving medication for chronic conditions. The results indicated that the ProMAS provided adherence scores with sufficient fit to the Rasch model. Furthermore, the ProMAS covered a wider range of adherence behaviors compared to the widely used Medication Adherence Report Scale (MARS) instrument, resulting in more variance and less skewness in adherence scores. We conclude that the ProMAS is more capable of discriminating between people with different adherence rates than the MARS.

Brightness differences influence the evaluation of affective pictures.

We explored the possibility of a general brightness bias: brighter pictures are evaluated more positively, while darker pictures are evaluated more negatively. In Study 1 we found that positive pictures are brighter than negative pictures in two affective picture databases (the IAPS and the GAPED). Study 2 revealed that because researchers select affective pictures on the extremity of their affective rating without controlling for brightness differences, pictures used in positive conditions of experiments were on average brighter than those used in negative conditions. Going beyond correlational support for our hypothesis, Studies 3 and 4 showed that brighter versions of neutral pictures were evaluated more positively than darker versions of the same picture. Study 5 revealed that people categorised positive words more quickly than negative words after a bright picture prime, and vice versa for negative pictures. Together, these studies provide strong support for the hypotheses that picture brightness influences evaluations.

Trust in smart systems: sharing driving goals and giving information to increase trustworthiness and acceptability of smart systems in cars.

OBJECTIVE: We examine whether trust in smart systems is generated analogously to trust in humans and whether the automation level of smart systems affects trustworthiness and acceptability of those systems. BACKGROUND: Trust is an important factor when considering acceptability of automation technology. As shared goals lead to social trust, and intelligent machines tend to be treated like humans, the authors expected that shared driving goals would also lead to increased trustworthiness and acceptability of adaptive cruise control (ACC) systems. METHOD: In an experiment, participants (N = 57) were presented with descriptions of three ACCs with different automation levels that were described as systems that either shared their driving goals or did not. Trustworthiness and acceptability of all the ACCs were measured. RESULTS: ACCs sharing the driving goals of the user were more trustworthy and acceptable than were ACCs not sharing the driving goals of the user. Furthermore, ACCs that took over driving tasks while providing information were more trustworthy and acceptable than were ACCs that took over driving tasks without providing information. Trustworthiness mediated the effects of both driving goals and automation level on acceptability of ACCs. CONCLUSION: As when trusting other humans, trusting smart systems depends on those systems sharing the user's goals. Furthermore, based on their description, smart systems that take over tasks are judged more trustworthy and acceptable when they also provide information. APPLICATION: For optimal acceptability of smart systems, goals of the user should be shared by the smart systems, and smart systems should provide information to their user.

Dispositional attribution: multiple inferences about motive-related traits.

This research views dispositional inference as a process whereby perceivers integrate multiple inferences about a target person's motives and traits. The findings suggest that although perceived motives may stimulate extra attributional processing (S. Fein, 1996), the content of the inferred motive is important as well. Perceivers learned about situational forces implying that a target person had free choice, no choice, or an ulterior motive for helpful behavior. Inferences about the target's helpfulness differed depending on whether the target's behavior was attributed to an obedience motive (no-choice condition) or to a selfish motive (ulterior-motive condition). In general, inferences about motives were more predictive of dispositional inferences than were global causal attributions (to situational vs. dispositional forces) or base rate assumptions.