Michael Schulte-Mecklenbeck

Political polarization and increasing exposure to extreme weather can shape public engagement with climate change, yet the underlying psychological mechanisms remain unclear. In this Registered Report, we examine how political affiliation, extreme weather exposure and subjective attribution influence climate-relevant information search and decision-making in a US sample ( N = 1,451). We combined a process-tracing paradigm within an incentivized decision task involving real carbon emissions with objective data on extreme weather exposure. Democrats attended more to carbon emission information and made more pro-climate choices than Republicans, with no evidence of moderation by objective extreme weather exposure. The subjective attribution of extreme weather to climate change was associated with greater attention to carbon information, more pro-climate choices and moderated the effects of exposure on both outcomes. These findings highlight the importance of political affiliation in climate-relevant decision-making and suggest that extreme weather effects depend less on exposure itself than on how these experiences are interpreted.

Generation Alpha, a cohort growing up with smartphones and generative AI as ordinary features of everyday life, will soon enter a higher education system that is itself undergoing profound digital transformation. We argue that the central challenge of this transformation is not technological alone but human and that it is shaped by three fundamental tensions: upskilling vs. deskilling, autonomy vs. dependence, and individualization vs. collaboration. Developed by an interdisciplinary Community of Practice at the University of Bern (UniBE), we introduce the BRIDGE model, which conceptualizes the university as an ecosystem of three interdependent groups: students, academic (researchers and lecturers) and organizational support staff whose psycho-social, health, well-being, and performance competencies must be understood across the trajectory from school to working life. Building on this model, we propose a two-phase research agenda: systematic identification of competencies, expectations, and institutional processes through a multi-year, longitudinal research program spanning all three groups, followed by collaborative transformation of higher education through empirically evaluated interventions. This agenda provides what digital transformation in higher education currently lacks: an empirical basis for deciding where digital tools strengthen learning, work, and well-being--and where they undermine them--and tested interventions to shift that balance. It thereby operationalizes UniBE's Digitalization Strategy 2030 and responds to national as well as international calls for evidence-based, human-centered digital transformation.

Human-machine interaction is becoming increasingly relevant in traffic, where autonomous vehicles (AVs) and manual vehicles (MVs) share the same roads. When encountering AVs, drivers must interpret a machine agent’s behavior rather than a human’s. Does this impact human drivers’ behavior? Prior research suggests that people engage in mentalizing processes toward robots and autonomous agents, resulting in adjusted social behavior. Whether such processes similarly influence how human drivers respond to AVs remains unclear. We tested whether the perceived autonomy of vehicles impacts their perceived predictability and how this influences driving behavior of other drivers in a within-subjects virtual reality (VR) lab study (N = 690,094 observations from 61 participants) and in a field experiment (N = 27,198 observations from 1,414 vehicles). Across both studies, perceived autonomy of the vehicle was associated with greater following distance, though the evidence varied in strength. In the VR study, drivers maintained a significantly larger following distance (9%) to AVs relative to MVs. In the field experiment, manipulating perceived autonomy produced no overall difference in following distance to the same AV presented under different autonomy framings; however, an exploratory growth curve model revealed that following distance developed differently over time depending on perceived autonomy. Overall, the data offers indications that perceived autonomy might impact driving behavior, as the mental model could adjust differently for AVs during the first encounters. We quantify following distances in mixed-traffic, with implications for traffic planning regarding lane capacity and traffic flow at merges and bottlenecks when adopting AVs.

We attempted replications of 274 claims of positive results from 164 quantitative papers published from 2009 to 2018 in 54 journals in the social and behavioral sciences. Replications were high-powered on average to detect the original effect size (Median = 99.6%), used original materials when relevant and available, and were peer-reviewed in advance through a standardized internal protocol. Replications showed statistically significant results in the original pattern for 151 of 274 claims (55.1% [95% CI 49.2 - 60.9%]) and for 80.8 of 164 papers (49.3% [95% CI 43.8 - 54.7%]) weighed for replicating multiple claims per paper. We observed modest variation in replication rates across disciplines (42.5% to 63.1%) though some estimates had high uncertainty. For claims with Pearson’s r effect sizes, the median was 0.25 [95% CI 0.21 - 0.27] for original studies and 0.10 [95% CI 0.09 - 0.13] for replication studies, a 82.4% [95% CI 67.8 - 88.2%] reduction in shared variance. Thirteen methods for evaluating replication success provided estimates ranging from 28.6% to 74.8% (median = 49.3%). Some decline in effect size and significance is expected based on power to detect original effects and regression to the mean due to replicating only positive results. The conditions that promote or inhibit replicability are worthy of additional investigation.

Pursuing replicability — independent evidence for previous claims — is important for creating generalizable knowledge^1,2. Here we attempted replications of 274 claims of positive results from 164 quantitative papers published from 2009 to 2018 in 54 journals in the social and behavioural sciences. Replications were high powered on average to detect the original effect size (median of 99.6%), used original materials when relevant and available, and were peer reviewed in advance through a standardized internal protocol. Replications showed statistically significant results in the original pattern for 151 of 274 claims (55.1% (95% confidence interval (CI) 49.2–60.9%)) and for 80.8 of 164 papers (49.3% (95% CI 43.8–54.7%)), weighed for replicating multiple claims per paper. We observed modest variation in replication rates across disciplines (42.5–63.1%), although some estimates had high uncertainty. The median Pearson’s r effect size was 0.25 (95% CI 0.21–0.27) for original studies and 0.10 (95% CI 0.09–0.13) for replication studies, an 82.4% (95% CI 67.8–88.2%) reduction in shared variance. Thirteen methods for evaluating replication success provided estimates ranging from 28.6% to 74.8% (median of 49.3%). Some decline in effect size and significance is expected based on power to detect original effects and regression to the mean because we replicated only positive results. We observe that challenges for replicability extend across social–behavioural sciences, illustrating the importance of identifying conditions that promote or inhibit replicability^3,4.

Practice exams support learning but are labor-intensive to create, prepare, and implement. Large language models (LLMs) may help to reduce this burden, but the quality of LLM-generated assessment items has varied in research to date. Across two studies, we evaluate PREPARE, a comprehensive LLM-based workflow aimed at high-quality multiple-choice question generation. In Study 1, we examined the perceived relative quality of LLM-generated items, and found that university lecturers’ (N = 109) perceptions of LLM-generated item quality were broadly similar to their perceptions of human-generated items. Specifically, when required to select questions for a mock exam, lecturers’ chosen exams consisted of 45% LLM-generated questions. In Study 2, we then examined the relative psychometric integrity of LLM-generated items, with first-year undergraduate students (N = 336) completing a series of assessments containing both LLM-generated and instructor-generated items. In contrast to Study 1, we found that LLM-generated items appeared overall psychometrically weaker than their human-generated counterparts. Together, our results suggest a disconnect between perceived quality and psychometric performance: LLM-generated questions may look the part, but this obfuscates psychometric deficits. LLM-based item generation may be useful for low-stakes practice, but further refinement is needed to achieve item quality equivalent to human instructors.

Movement tracking is a novel process-tracing method that promises unique access to the temporal dynamics of psychological processes. The method involves high-resolution tracking of a hand or handheld device (e.g., a computer mouse) while it is used to make a choice. In contrast to other process-tracing methods, which mostly focus on information acquisition, movement tracking focuses on the processes of information integration and preference formation. In this article, we present a tutorial on movement tracking of psychological processes with the mousetrap R package. We address all steps of the research process, from design to interpretation, with a particular focus on data processing and analysis and featuring both established and novel approaches. Using a representative working example, we demonstrate how the various steps of movement-tracking analysis can be implemented with mousetrap and provide thorough explanations of their theoretical background and interpretation. Finally, we present a list of recommendations to assist researchers in addressing their own research questions using movement tracking of psychological processes.

According to the justified true belief (JTB) account of knowledge, people can truly know something only if they have a belief that is both justified and true (i.e., knowledge is JTB). This account was challenged by Gettier, who argued that JTB does not explain knowledge attributions in certain situations, later called “Gettier-type cases,” wherein protagonists are justified in believing something to be true, but their belief was correct only because of luck. Laypeople may not attribute knowledge to protagonists with justified but only luckily true beliefs. Although some research has found evidence for these so-called Gettier intuitions, Turri et al. found no evidence that participants attributed knowledge in a counterfeit-object Gettier-type case differently than in a matched case of JTB. In a large-scale, cross-cultural conceptual replication of Turri and colleagues’ Experiment 1 ( N = 4,724) using a within-participants design and three vignettes across 19 geopolitical regions, we did find evidence for Gettier intuitions; participants were 1.86 times more likely to attribute knowledge to protagonists in standard cases of JTB than to protagonists in Gettier-type cases. These results suggest that Gettier intuitions may be detectable across different scenarios and cultural contexts. However, the size of the Gettier intuition effect did vary by vignette, and the Turri et al. vignette produced the smallest effect, which was similar in size to that observed in the original study. Differences across vignettes suggest that epistemic intuitions may also depend on contextual factors unrelated to the criteria of knowledge, such as the characteristics of the protagonist being evaluated.