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Abstract Misinformation can profoundly impact an individual’s attitudes—sometimes even after the misinformation has been corrected. In two preregistered experiments (N 1 = 355, N 2 = 725), we investigated whether individual differences in the ability and motivation to process information thoroughly influence the impact of misinformation in a news media context. More specifically, we tested whether fluid intelligence and need for cognition predicted the degree to which individuals who were exposed to misinformation changed their attitudes after receiving a correction message. We found consistent evidence that higher fluid intelligence is associated with a more pronounced correction effect, while need for cognition did not have a significant effect. This suggests that integrating a correction message with a previously encountered piece of misinformation can be challenging and that correction messages consequently need to be communicated in a way that is accessible to a broad audience.

Communication research has long recognized the dynamic nature of most media stimuli and the corresponding dynamic emotional processing implied on the side of the audience. Capturing and analyzing these emotional dynamics during media reception is imperative for advancing our understanding of media processing and effects, but is not common practice in most communication research. This article highlights several methodological approaches to measuring the physiological, behavioral, and experiential components of emotions during media exposure: Electrodermal activity, automated facial expression analysis, continuous response measurement, and self-probed emotional retrospections. We discuss these methods in terms of what they measure, their practical application to different research contexts, and aspects of data-analysis. We further highlight ways to adapt and apply these methods to advance research on hot topics in communication science, psychology, and related fields and provide recommendations for scholars who wish to integrate continuous measures of emotional responses into their research.

Artificial intelligence (AI) has profoundly transformed numerous facets of both private and professional life. Understanding how people evaluate AI is crucial for predicting its future adoption and addressing potential barriers. However, existing instruments measuring attitudes towards AI often focus on specific technologies or cross-domain evaluations, while domain-specific measurement instruments are scarce. Therefore, this study introduces the nine-item Attitudes towards Artificial Intelligence in Work, Healthcare, and Education (ATTARI-WHE) scale. Using a diverse sample of N = 1083 respondents from Germany, the psychometric properties of the instrument were evaluated. The results demonstrated low rates of missing responses, minimal response biases, and a robust measurement model that was invariant across sex, age, education, and employment status. These findings support the use of the ATTARI-WHE to assess AI attitudes in the work, healthcare, and education domains, with three items each. Its brevity makes it particularly well-suited for use in social surveys, web-based studies, or longitudinal research where assessment time is limited.

Theory suggests that robots with human-like mental capabilities (i.e., high agency and experience) evoke stronger aversion than robots without these capabilities. Yet, while several studies support this prediction, there is also evidence that the mental prowess of robots could be evaluated positively, at least by some individuals. To help resolving this ambivalence, we focused on rather stable individual differences that may shape users’ responses to machines with different levels of (perceived) mental ability. Specifically, we explored four key variables as potential moderators: monotheistic religiosity, the tendency to anthropomorphize, prior attitudes towards robots, and the general affinity for complex technology. Two pre-registered online experiments (N1 = 391, N2 = 617) were conducted, using text vignettes to introduce participants to a robot with or without complex, human-like capabilities. Results showed that negative attitudes towards robots increased the relative aversion against machines with (vs. without) complex minds, whereas technology affinity weakened the difference between conditions. Results for monotheistic religiosity turned out mixed, while the tendency to anthropomorphize had no significant impact on the evoked aversion. Overall, we conclude that certain individual differences play an important role in perceptions of machines with complex minds and should be considered in future research.

Abstract Human information processing is not always rational but influenced by prior attitudes, a phenomenon commonly known as motivated reasoning. We conducted two studies (N 1 = 556, N 2 = 1198; UK samples) investigating motivated reasoning in the context of climate change with a focus on individual differences as potential moderating factors. While previous research investigated motivated reasoning regarding the debate whether climate change is anthropogenic, we focused on current discourses about the effectiveness of different countermeasures. To this end, participants evaluated fictitious scientific data on the effectiveness of regulations to reduce CO2 emissions. In both studies, participants exhibited motivated reasoning as indicated by the observation that prior attitudes about CO2 reduction policies predicted evaluation of the scientific data. The degree of motivated reasoning was not related to individual difference variables, namely the ability to understand and reason with numbers (Numeracy), the willingness to show this ability (Need for Cognition), and the tendency to maximize one’s individual utility (Dark Factor of Personality). However, numeracy was associated with a less biased interpretation of the presented information. Our research demonstrates that motivated reasoning is a general phenomenon, and points to numerical training as one way to improve reasoning.

Abstract Artificial intelligence (AI) has become an integral part of many contemporary technologies, such as social media platforms, smart devices, and global logistics systems. At the same time, research on the public acceptance of AI shows that many people feel quite apprehensive about the potential of such technologies—an observation that has been connected to both demographic and sociocultural user variables (e.g., age, previous media exposure). Yet, due to divergent and often ad-hoc measurements of AI-related attitudes, the current body of evidence remains inconclusive. Likewise, it is still unclear if attitudes towards AI are also affected by users’ personality traits. In response to these research gaps, we offer a two-fold contribution. First, we present a novel, psychologically informed questionnaire (ATTARI-12) that captures attitudes towards AI as a single construct, independent of specific contexts or applications. Having observed good reliability and validity for our new measure across two studies (N 1 = 490; N 2 = 150), we examine several personality traits—the Big Five, the Dark Triad, and conspiracy mentality—as potential predictors of AI-related attitudes in a third study (N 3 = 298). We find that agreeableness and younger age predict a more positive view towards artificially intelligent technology, whereas the susceptibility to conspiracy beliefs connects to a more negative attitude. Our findings are discussed considering potential limitations and future directions for research and practice.

Abstract Reading is a popular leisure activity for children, teenagers, and adults. Several theories agree that reading might improve social cognition, but the empirical evidence remains tentative, with research on adolescents especially lacking. We employed a very large, and nationally representative, longitudinal dataset from the National Educational Panel Study (NEPS) in Germany to examine this hypothesis. Specifically, we tested whether reading prospectively predicted future self-reported prosocial behavior and social adjustment in adolescents, controlling for a number of covariates. Two-way cross-lagged panel analyses probed the longitudinal relationship between leisure reading and these social outcomes from Grade 6 to Grade 9. In addition, we examined the effect of cumulative reading experience across Grades 5–8 on future social outcomes, using structural equation modeling. We also explored the unique contributions of cumulative reading experience in different literary genres (classic literature, popular literature, nonfiction, comic books). Cumulative reading in general did not predict future prosocial behavior and social adjustment. However, cumulative reading of modern classic literature was positively associated with later prosocial behavior and social adjustment. Protocol registration The stage 1 protocol for this Registered Report was accepted in principle on 08 November 2021. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/KSWY7 .

Abstract A substantial number of people refused to get vaccinated against COVID-19, which prompts the question as to why. We focus on the role of individual worldviews about the nature and generation of knowledge (epistemic beliefs). We propose a model that includes epistemic beliefs, their relationship to the Dark Factor of Personality (D), and their mutual effect on the probability of having been vaccinated against COVID-19. Based on a US nationally representative sample (N = 1268), we show that stronger endorsement of post-truth epistemic beliefs was associated with a lower probability of having been vaccinated against COVID-19. D was also linked to a lower probability of having been vaccinated against COVID-19, which can be explained by post-truth epistemic beliefs. Our results indicate that the more individuals deliberately refrain from adhering to the better argument, the less likely they are vaccinated. More generally, post-truth epistemic beliefs pose a challenge for rational communication.

The philosophical concept of informal fallacies-arguments that fail to provide sufficient support for a claim-is introduced and connected to the topic of fake news detection. We assumed that the ability to identify informal fallacies can be trained and that this ability enables individuals to better distinguish between fake news and real news. We tested these assumptions in a two-group between-participants experiment (N = 116). The two groups participated in a 30-minute-long text-based learning intervention: either about informal fallacies or about fake news. Learning about informal fallacies enhanced participants' ability to identify fallacious arguments one week later. Furthermore, the ability to identify fallacious arguments was associated with a better discernment between real news and fake news. Participants in the informal fallacy intervention group and the fake news intervention group performed equally well on the news discernment task. The contribution of (identifying) informal fallacies for research and practice is discussed.

Humanoid robots (i.e., robots with a human-like body) are projected to be mass marketed in the future in several fields of application. Today, however, user evaluations of humanoid robots are often based on mediated depictions rather than actual observations or interactions with a robot, which holds true not least for scientific user studies. People can be confronted with robots in various modes of presentation, among them (1) 2D videos, (2) 3D, i.e., stereoscopic videos, (3) immersive Virtual Reality (VR), or (4) live on site. A systematic investigation into how such differential modes of presentation influence user perceptions of a robot is still lacking. Thus, the current study systematically compares the effects of different presentation modes with varying immersive potential on user evaluations of a humanoid service robot. Participants (N = 120) observed an interaction between a humanoid service robot and an actor either on 2D or 3D video, via a virtual reality headset (VR) or live. We found support for the expected effect of the presentation mode on perceived immediacy. Effects regarding the degree of human likeness that was attributed to the robot were mixed. The presentation mode had no influence on evaluations in terms of eeriness, likability, and purchase intentions. Implications for empirical research on humanoid robots and practice are discussed.

e-Learning systems increasingly support learning management and self-organized learning processes. Since the latter have been studied in the field of progressive education extensively, it is worthwhile to consider them for developing digital learning environments to support self-regulated learning processes. In this paper we aim at transforming one of the most prominent and sustainable approaches to self-organized learning, the “Dalton Plan” as proposed by Helen Parkhurst. Its assignment structure supports learners when managing their learning tasks, thus triggering self-organized acquisition of knowledge, and its feedback graphs enable transparent learning processes. Since e-learning environments have become common use, rather than creating another system, we propose a modular approach that can be used for extending existing e-learning environments. In order to design a respective component, we interviewed experts in self-organized e-learning. Their input facilitated integrating the Dalton Plan with existing features of e-learning environments. After representing each interview in concept maps, we were able to aggregate them for deriving e-learning requirements conform to the Dalton Plan instruments. In the course of implementing them, particular attention had to be paid to the asynchrony of interaction during runtime. Java Server Faces technology enable the Dalton Plan component to be migrated into existing web 2.0 e-learning platforms. The result was evaluated based on the acquired concept maps, as they also captured the transformation process of the Dalton Plan to e-learning features. The findings encourage embodying further progressive education approaches in this way, since the structured (concept) mapping of the Dalton Plan to e-learning features turned out to be accurate. The experts were able to recognize the potential of the approach both in terms of structuring the knowledge acquisition process, and in terms of developing progressive learning support features.

Social robots are becoming increasingly prevalent in everyday life and sex robots are a sub-category of especially high public interest and controversy. Starting from the concept of the otaku, a term from Japanese youth culture that describes secluded persons with a high affinity for fictional manga characters, we examine individual differences behind sex robot appeal (anime and manga fandom, interest in Japanese culture, preference for indoor activities, shyness). In an online-experiment, 261 participants read one out of three randomly assigned descriptions of future technologies (sex robot, nursing robot, genetically modified organism) and reported on their overall evaluation, eeriness, and contact/purchase intentions. Higher anime and manga fandom was associated with higher appeal for all three future technologies. For our male subsample, sex robots and GMOs stood out as shyness yielded a particularly strong relationship to contact/purchase intentions for these new technologies.

In many situations red is associated with hazard and danger. As a consequence, it was expected that task-irrelevant color cues in online environments would affect risk-taking behaviors. This assumption was tested in two web-based experiments. The first study (N = 383) demonstrated that in risky choice dilemmas respondents preferred the less risky option when the displayed university logo was in red (versus gray); but only when both choice alternatives were at least moderately risky. The second study (N = 144) replicated these results with a behavioral outcome: Respondents showed more cautious behavior in a web-based game when the focal stimuli were colored red (versus blue). Together, these findings demonstrate that variations in the color design of a computerized environment affect risk taking: Red color leads to more conservative choices and behaviors.