Robots are a source of evaluative conflict and thus elicit ambivalence. In fact, psychological research has shown across domains that people simultaneously report strong positive and strong negative evaluations about one and the same attitude object. This is defined as ambivalence. In the current research, we extended existing ambivalence research by measuring ambivalence towards various robot-related stimuli using explicit (i.e., self-report) and implicit measures. Concretely, we used a mouse tracking approach to gain insights into the experience and resolution of evaluative conflict elicited by robots. We conducted an extended replication across four experiments with N = 411 overall. This featured a mixed-methods approach and included a single paper meta-analysis. Thereby, we showed that the amount of reported conflicting thoughts and feelings (i.e., objective ambivalence) and self-reported experienced conflict (i.e., subjective ambivalence) were consistently higher towards robot-related stimuli compared to stimuli evoking univalent responses. Further, implicit measures of ambivalence revealed that response times were higher when evaluating robot-related stimuli compared to univalent stimuli, however results concerning behavioral indicators of ambivalence in mouse trajectories were inconsistent. This might indicate that behavioral indicators of ambivalence apparently depend on the respective robot-related stimulus. We could not obtain evidence of systematic information processing as a cognitive indicator of ambivalence, however, qualitative data suggested that participants might focus on especially strong arguments to compensate their experienced conflict. Furthermore, interindividual differences did not seem to substantially influence ambivalence towards robots. Taken together, the current work successfully applied the implicit and explicit measurement of ambivalent attitudes to the domain of social robotics, while at the same time identifying potential boundaries for its application.

When encountering social robots, potential users are often facing a dilemma between privacy and utility. That is, high utility often comes at the cost of lenient privacy settings, allowing the robot to store personal data and to connect to the internet permanently, which brings in associated data security risks. However, to date, it still remains unclear how this dilemma affects attitudes and behavioral intentions towards the respective robot. To shed light on the influence of a social robot’s privacy settings on robot-related attitudes and behavioral intentions, we conducted two online experiments with a total sample of N = 320 German university students. We hypothesized that strict privacy settings compared to lenient privacy settings of a social robot would result in more favorable attitudes and behavioral intentions towards the robot in Experiment 1. For Experiment 2, we expected more favorable attitudes and behavioral intentions for choosing independently the robot’s privacy settings in comparison to evaluating preset privacy settings. However, those two manipulations seemed to influence attitudes towards the robot in diverging domains: While strict privacy settings increased trust, decreased subjective ambivalence and increased the willingness to self-disclose compared to lenient privacy settings, the choice of privacy settings seemed to primarily impact robot likeability, contact intentions and the depth of potential self-disclosure. Strict compared to lenient privacy settings might reduce the risk associated with robot contact and thereby also reduce risk-related attitudes and increase trust-dependent behavioral intentions. However, if allowed to choose, people make the robot ‘their own’, through making a privacy-utility tradeoff. This tradeoff is likely a compromise between full privacy and full utility and thus does not reduce risks of robot-contact as much as strict privacy settings do. Future experiments should replicate these results using real-life human robot interaction and different scenarios to further investigate the psychological mechanisms causing such divergences.

Various works show that proxemics occupies an important role in human-robot interaction and that appropriate proxemic interaction depends on many characteristics of humans and robots. However, there is none that shows the relationship between an emotional state expressed by a user and a proxemic reaction of the robot to it, in a social interaction between these interactants. In the current experiment (N = 82), we investigate this using an online study in which we examine which proxemic response (i.e., approaching, not moving, moving away) to a person’s expressed emotional state (i.e., anger, fear, disgust, surprise, sadness, joy) is perceived as appropriate. The quantitative and qualitative data collected suggests that the robot’s approach was considered appropriate for the expressed fear, sadness, and joy, whereas moving away was perceived as inappropriate in most scenarios. Further exploratory findings underline the importance of appropriate nonverbal behavior on the perception of the robot.

Ambivalence, the simultaneous experience of both positive and negative feelings about one and the same attitude object, has been investigated within psychological attitude research for decades. Ambivalence is interpreted as an attitudinal conflict with distinct affective, behavioral, and cognitive consequences. In social psychological research, it has been shown that ambivalence is sometimes confused with neutrality due to the use of measures that cannot distinguish between neutrality and ambivalence. Likewise, in social robotics research the attitudes of users are often characterized as neutral. We assume that this is due to the fact that existing research regarding attitudes towards robots lacks the opportunity to measure ambivalence. In the current experiment (N = 45), we show that a neutral and a robot stimulus were evaluated equivalently when using a bipolar item, but evaluations differed greatly regarding self-reported ambivalence and arousal. This points to attitudes towards robots being in fact highly ambivalent, although they might appear neutral depending on the measurement method. To gain valid insights into people’s attitudes towards robots, positive and negative evaluations of robots should be measured separately, providing participants with measures to express evaluative conflict instead of administering bipolar items. Acknowledging the role of ambivalence in attitude research focusing on robots has the potential to deepen our understanding of users’ attitudes and their potential evaluative conflicts, and thus improve predictions of behavior from attitudes towards robots.

Attitudes towards robots are not always unequivocally positive or negative: when attitudes encompass both strong positive and strong negative evaluations about an attitude object, people experience an unpleasant state of evaluative conflict, called ambivalence. To shed light on ambivalence towards robots, we conducted a mixed-methods experiment with N = 163 German university students that investigated the influence of robot autonomy on robot-related attitudes. With technological progress, robots become increasingly autonomous. We hypothesized that high levels of robot autonomy would increase both positive and negative robot-related evaluations, resulting in more attitudinal ambivalence. We experimentally manipulated robot autonomy through text vignettes and assessed objective ambivalence (i.e., the amount of reported conflicting thoughts and feelings) and subjective ambivalence (i.e., self-reported experienced conflict) towards the robot 'VIVA' using qualitative and quantitative measures. Autonomy did not impact objective ambivalence. However, subjective ambivalence was higher towards the robot high versus low in autonomy. Interestingly, this effect turned non-significant when controlling for individual differences in technology commitment. Qualitative results were categorized by two independent raters into assets (e.g., assistance, companionship) and risks (e.g., privacy/data security, social isolation). Taken together, the present research demonstrated that attitudes towards robots are indeed ambivalent and that this ambivalence might influence behavioral intentions towards robots. Moreover, the findings highlight the important role of technology commitment. Finally, qualitative results shed light on potential users' concerns and aspirations. This way, these data provide useful insights into factors that facilitate human-robot research.

We conducted a preregistered multilaboratory project (k = 36; N = 3,531) to assess the size and robustness of ego-depletion effects using a novel replication method, termed the paradigmatic replication approach. Each laboratory implemented one of two procedures that was intended to manipulate self-control and tested performance on a subsequent measure of self-control. Confirmatory tests found a nonsignificant result (d = 0.06). Confirmatory Bayesian meta-analyses using an informed-prior hypothesis (δ = 0.30, SD = 0.15) found that the data were 4 times more likely under the null than the alternative hypothesis. Hence, preregistered analyses did not find evidence for a depletion effect. Exploratory analyses on the full sample (i.e., ignoring exclusion criteria) found a statistically significant effect (d = 0.08); Bayesian analyses showed that the data were about equally likely under the null and informed-prior hypotheses. Exploratory moderator tests suggested that the depletion effect was larger for participants who reported more fatigue but was not moderated by trait self-control, willpower beliefs, or action orientation.

Background: Health literacy (HL) refers to the capacity to access, understand, appraise and apply information for decision-making and acting in health-related matters. In the field of Alzheimer’s disease (AD), expanding technologies of early disease detection, disease course prediction and eventually personalized prevention confront individuals at-risk with increasingly complex information, which demand substantial HL skills. Here we report current findings of HL research in at-risk groups. Methods: Search strings, referring to HL, AD, amyloid and risk, were developed. A systematic review was conducted in PUBMED, Cochrane Library, PsycINFO, and Web of Science to summarize the state of evidence on HL in at-risk individuals for Alzheimer’s dementia. Eligible articles needed to employ a validated tool for HL, mention the concept or one dimension (access, understand, appraise and apply information for decision-making and acting). Results: 26 quantitative and 9 qualitative studies addressing at least one dimension of HL were included. Overall, there is evidence for a wish to gain knowledge about the own brain status and risk of dementia. Psychological distress may occur and the subjective benefit-risk estimation may be modified after risk disclosure. Effects on lifestyle and planning may occur. Overall understanding and appraisal of information related to AD risk seem variable with several impacting factors. In mild cognitive impairment (MCI) basic HL skill seem to be affected by cognitive dysfunction. Conclusions: Systematic assessment of HL in at-risk population for AD is sparse. Findings indicate the paramount importance of adequate communication with persons at risk, being sensitive to individual needs and preferences. Substantial research needs were identified.

In language, people often refer to decision difficulty in terms of spatial distance. Specifically, decision-difficulty is expressed as proximity, for instance when people say that a decision was "too close to call". Although these expressions are metaphorical, we argue, in line with research on conceptual metaphor theory, that they reflect how people think about difficult decisions. Thus, here we examine whether close spatial distance can actually make decision-making harder. In six experiments (total N = 672), participants chose between two choice options presented either close together or far apart. As predicted, close (rather than far) choice options led to more difficulty, both in self-report (Experiment 1A-1C) and behavioral measures (decision-time, Experiment 2 and 3). Identifying a boundary condition, we show that close choice options lead to more difficulty only for within-category choices (Experiment 3). The too-close-to-call effect is theoretically and methodologically relevant for a broad array of research where choice options are visually presented, ranging from social cognition, judgment and decision-making to more applied settings in consumer psychology and marketing.

Health literacy (HL) can be described by specific skills that allow individuals to access, understand, appraise and apply information for decision-making and acting in health-related matters. In the field of early detection of Alzheimer's disease (AD), knowledge about HL in at risk individuals is limited. In the light of increasing technologies of early disease detection, risk assessment and prevention, individuals at risk are faced with complex information. This systematic review aims at analysing the status quo of empirical evidence on the role of health literacy for individuals at risk for developing AD. In the multiple-step search strategy, search terms and search strings were developed and pretested in PubMed. Search strings consisted of three sections, referring to HL, AD and risk factors for developing AD. The search was carried out in PUBMED, Cochrane Library, PsycINFO and Web of Science. The complexity of the research question made it necessary to conduct a mixed-methods review, including both quantitative and qualitative study types. To be eligible, articles needed to report on empirical studies focusing on individuals at risk for developing AD, and using either (a) a validated tool for assessing HL, or (b) mention the concept of HL as well as one of its four dimensions (access, understand, appraise, and apply). A total of 3672 articles were identified and screened for eligibility by two independent reviewers. 211 articles were selected for full text review. None of the studies considered HL as a basic concept or used established HL assessment tools. However, 26 quantitative and 5 qualitative studies addressed at least one aspect of HL and were included for data extraction and analysis. This review reveals that systematic assessment of HL in an at-risk population for AD is sparse. Based on the outcomes of this review, qualitative interviews will investigate in more depth the meaning of HL for people at risk of developing AD. Eventually, the findings of this project will lay the foundation for the development of HL tools within the field of early AD diagnosis, and for interventional approaches to a competent handling of health-related risk information.