đŸ€– Must-Read Articles đŸ€–

Experimental Feature

Even just looking backwards a week, there are a lot more articles published than most of us could hope to read. We can always skim the titles and abstracts ourselves, but I wanted to test out some automation. The articles below, all of which can be found elsewhere on this site among other new publications, were chosen by Google Gemini Flash Thinking as "must-read" articles. The proper criteria are in the eyes of the beholder and Gemini doesn't apply my criteria without error. I may continue to refine the prompting based on experience and feedback. Like the rest of the site, this will update daily!

Social Media Addiction: The Mediating Role Between Teacher Relationships and Work Performance in Primary and Secondary Schools with the Moderating Role of School Climate
Cyberpsychology, Behavior, and Social Networking
ManLiu Wang, Yinghua Ye
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Good teacher–student and parent–teacher relationships enhance teachers’ sense of accomplishment and efficacy, thereby contributing to their professional development. Given the increasing role of social media as a primary source of relaxation and entertainment, understanding its impact on teachers is crucial. This study, involving 1,840 primary and secondary school teachers ( M age = 40.44, SD = 9.68, 38.8 percent male), employed a questionnaire to explore how teacher–student relationships, parent–teacher relationships, and social media addiction (SMA) influence teachers’ work performance. The results revealed that (1) nearly half of the teachers were addicted to social media; (2) both parent–teacher and teacher–student relationships positively affected work performance, while SMA negatively impacted it; (3) when both relationships were present, parent–teacher relationships had a more significant effect, with SMA mediating the link between teacher–parent relationships and work performance; and (4) school climate moderated the impact of SMA on work performance. These findings clarify the interactions between interpersonal relationships, social media behavior, and work performance, suggesting strategies to improve performance through optimized relationships, guided social media use, and a positive school environment.
The Relationship Between Adolescent Experiential Avoidance and Smartphone Addiction: A Study Based on Latent Transition Analysis and Cross-Lagged Panel Network Analysis
Cyberpsychology, Behavior, and Social Networking
Xiaoxiao Song, Xiaoyan Wang, Xindi Wang, Xu Zhang, Quanwei Zheng, Xiujun Zhang, Shaobo Lyu
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With the widespread use of smartphones among adolescents, smartphone addiction has become a growing mental health concern. Adolescents’ limited self-regulation makes them particularly vulnerable to using smartphones to escape real-life stress, heightening addiction risk. However, the heterogeneity of addictive behaviors and the dynamic role of experiential avoidance have been underexplored. This 6-month longitudinal study surveyed 547 Chinese primary and secondary students using the Smartphone Addiction Scale (SAS) and the Acceptance and Action Questionnaire-II (AAQ-II). Latent profile analysis (LPA) and latent transition analysis (LTA) were applied to identify subgroups and examine transitions between these subgroups. Cross-lagged panel network analysis (CLPN) revealed key symptom interactions between experiential avoidance and addiction. The study identified two addiction subgroups: a stable “low-risk group” (84.9 percent) and a “high-risk group,” 51.4 percent of whom transitioned to low risk over time. Logistic regression showed that experiential avoidance significantly predicted high-risk membership (odds ratios [OR] = 1.083–1.102) and deterioration within the low-risk group (OR = 1.036). The CLPN identified “online intimacy” (SPA-3) and “hesitation and overcautious” (EA-7) as driver nodes, with “withdrawal symptoms” (SPA-2) serving as a central node. These findings emphasize the crucial role of experiential avoidance in adolescent smartphone addiction and suggest symptom-level targets for early intervention. The results support acceptance and commitment therapy (ACT) as a promising approach for reducing smartphone addiction among youth.
All Roads Lead to Hate? Examining Five Prediction Paths for Online Incivility and Intolerance Perpetration
Journalism & Mass Communication Quarterly
Kevin Koban, Stephanie BĂŒhrer, Jörg Matthes
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This two-wave panel survey puts five pathways (i.e., antisocial and prosocial personality, intergroup identity conflict, recent victimization experiences, social media habits) against each other to test their unique over-time relationship with digital hate perpetration. Using autoregressive structural equation modeling, psychopathy, need for social approval, and recent victimization emerged as universally relevant for both incivility and intolerance. Notably, recent victimization had the strongest link, highlighting that breaking victimization-perpetration cycles may be key in combating digital hate. Furthermore, Machiavellianism and social media habits selectively predicted online intolerance. All these generally and selectively relevant paths point toward distinct opportunities to reduce aggressive communication.
Experimentation on TikTok, Standardisation on Reels? Party Short-Form Video Use in the 2024 UK General Election
Media and Communication
Rosalynd Southern, Niamh Cashell, Liam McLoughlin, Ploykamol Suwantawit
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Campaign practices evolve alongside technological change. We examine one of the most salient current developments: the rise of short-form video on platforms such as TikTok and Instagram Reels—often termed the “TikTokification” of election campaigns (Gerbaudo, 2024). The adoption of short-form video may signal the arrival of Römmele and Gibson’s (2020) “subversive” fourth era of campaigning, characterised by emotion, disruption, spontaneity, and the mimicry of authenticity. Here, we examine how the five main UK parties used short-form content during the 2024 UK General Election through a manual content analysis of all TikToks and Instagram Reels posted during the campaign period (<em>N</em> = 887). We find evidence of extensive but uneven adoption of short-form video across parties, with TikTok generating substantially higher reach and engagement than Instagram Reels. Whereas Reels were largely used to repurpose traditional campaign material, TikTok served as a site of experimentation, with parties more frequently deploying humour, memes, and in-app music. Leader-centred communication remained dominant overall, but traditional campaign functions were more pronounced on Reels than on TikTok. Thus, results suggest a compressed cycle of experimentation and standardisation. Furthermore, TikTokification occurred mainly on TikTok itself rather than diffusing across short-form platforms.
Effects of Framing and Identity Cues in Science Communication With and About AI
Media and Communication
Daniel Silva Luna, Helena Bilandzic, Martin BĂŒrger
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As AI increasingly participates in science communication, it is unclear how people evaluate AI as a source of scientific information. This study examines how message framing and identity cues shape public evaluations of communicative AI and whether these effects differ when AI is encountered through reading or direct interaction. Two preregistered online experiments in Germany contrasted science communication <em>about</em> AI (reading a news-style article) with science communication <em>with</em> AI (interacting with a chatbot), manipulating risk versus progress framing and human-like versus machine-like cues. In an article-based context (Experiment 1, <em>N</em> = 862), progress framing increased trust in AI, while machine-like wording further improved trust. In an interactive context (Experiment 2, <em>N</em> = 868), framing shaped evaluations indirectly by reducing fear, while human-like cues increased social presence and parasocial connection, producing indirect gains across key outcomes. Across both experiments, higher AI competence was associated with more positive evaluations. Overall, the findings show that framing and design cues exert modest but systematic effects that depend on the communicative format.
Are smartphones stress-inducing or stress-buffering for adolescents? An experience-sampling study
New Media & Society
Michaela Ć aradĂ­n LebedĂ­kovĂĄ, David Lacko, Ine Beyens, David Smahel
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Despite the far-reaching impact of stress on overall well-being, current research offers little information on whether smartphone use (SU) is stress-inducing or stress-buffering for adolescents. Building on existing media effects theories and the transactional theory of stress, this study is the first to address the effect of SU on perceived stress in adolescents (17,152 observations, N = 184, 13–17 years old) with an experience-sampling design combined with trace data. We found no effects of time spent using smartphones on stress for approximately 80% of our sample. For 20%, SU was stress-inducing, albeit with a small effect size. The results point to the importance of smartphone usage patterns besides the time spent using smartphones. Furthermore, the results provide evidence that media effects are not universal and that adolescents cannot be regarded as a homogeneous group. Our work has important implications for future research, as well as for parents and educators.
Growing up with misinformation: A mixed-methods study of the perceptions, experiences, and responses among the 5- to 12-year-olds
New Media & Society
Freja SĂžrine Adler Berg, Lene Heiselberg, Thomas Enemark Lundtofte, Lena Frischlich
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This mixed-methods study explores how children aged 5–12 years experience and respond to perceived misinformation on social media. Drawing on mobile ethnographic data and a survey of 401 Danish children, the research shows that children perceive misinformation as a frequent part of their digital lives, especially on platforms such as YouTube. Children encounter diverse forms of misleading content—from playful videos to harmful challenges and fake science—and react with emotions ranging from amusement and curiosity to confusion, fear, and anger. Older children display greater awareness and vocabulary related to misinformation, while younger ones engage with it more imaginatively. The study also highlights the role of parents in shaping how children interpret and react to misleading content and underscores the need for age-sensitive approaches to media literacy. By centering children’s perspectives, the study offers new insights into how misinformation affects the youngest users.
Gender bias in text-to-image generative artificial intelligence: Neglect and stereotypical presentations across three popular platforms
New Media & Society
Hannah Weinmann, Tanja V Messingschlager, Markus Appel
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Images generated by artificial intelligence (AI) were assumed to underrepresent women and to contain stereotypical portrayals. A total of 1344 images were generated at two times of measurement by DALL·E, Midjourney, and Stable Diffusion and were analyzed via a preregistered content analysis. Results revealed representational and presentational bias, varying between prompts and between AI platforms. Women were depicted less frequently than men in images generated with the prompt “competent person,” whereas women were generated more often with a neutral prompt (i.e. “person”) or with the prompt “warm person.” Regarding stereotypical presentations, images representing women (vs men) showed lower facial prominence (face-ism), higher intensity smiles, and more pronounced lateral head tilts (head canting). These biases varied significantly between AI platforms. The findings suggest that gender stereotypes are spread by generative AI systems and highlight the need for interventions in the development and deployment of image-generating AI.
Generics in science communication: Misaligned interpretations across laypeople, scientists, and large language models
Public Understanding of Science
Uwe Peters, Andrea Bertazzoli, Jasmine M. DeJesus, Gisela J. van der Velden, Benjamin Chin-Yee
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Scientists often use generics , that is, unquantified statements about whole categories of people or phenomena, when communicating research findings (e.g. “statins reduce cardiovascular events”). Large language models, such as ChatGPT, frequently adopt the same style when summarizing scientific texts. However, generics can prompt overgeneralizations, especially when they are interpreted differently across audiences. In a study comparing laypeople, scientists, and two leading large language models (ChatGPT-5 and DeepSeek), we found systematic differences in interpretation of generics. Compared with most scientists, laypeople judged scientific generics as more generalizable and credible, while large language models rated them even higher. These mismatches highlight significant risks for science communication. Scientists may use generics and incorrectly assume laypeople share their interpretation, while large language models may systematically overgeneralize scientific findings when summarizing research. Our findings underscore the need for greater attention to language choices in both human and large language model-mediated science communication.
Pausing for Reflection: How Design Friction Shapes Environmentally Responsible Artificial Intelligence Use and Trust
Science Communication
Cheng Chen, Cassandra Troy, Maggie Mengqing Zhang
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The rapid growth of artificial intelligence (AI) has significant environmental impacts that are rarely communicated. This study leverages the concept of design friction to communicate AI’s environmental costs during user interaction with a system, and examines its effects on trust, perceived trust calibration, and responsible AI use. In an experiment ( N = 171), cue-based friction indirectly enhanced trust-related outcomes through the transparency heuristic and perceived social responsibility, whereas action-based friction influenced both trust-related outcomes and responsible AI use through heightened cognitive elaboration but reduced perceptions of user agency. Implications for conceptualizing different forms of design friction and promoting responsible AI design are discussed.
Bystanders and Reporters: Who Acts Against Illegal Online Content?
Social Media + Society
Friederike Quint, Yannis Theocharis, Spyros Kosmidis, Margaret E. Roberts
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Harmful and illegal content on social media is widespread, but what should be taken down is widely disputed, creating ongoing challenges for resolving the tension between free speech and user safety. User reporting is a key mechanism for addressing such content, yet little is known about who reports, what motivates them, and how they compare to the general population. We study these questions using two datasets: (1) a unique survey with individuals verified to have previously reported potentially illegal content to a third-party organization in Germany and (2) a quota-based sample approximating the German population. We show that individuals who have previously reported potentially illegal content via a third-party reporting service represent a distinct, civically engaged subset of users. They tend to be older, more often men than women, highly educated, highly politically active, and markedly left-leaning. They are not politically representative of the German population and take a distinctly different position when balancing free speech and protection from harm, putting more emphasis on protecting from harm. Reporting users’ motivations appear primarily civic-minded rather than reactive, especially among those who do it frequently and those intervening on behalf of others. These insights highlight reporting as a form of digital civic participation and offer perspectives relevant for understanding political engagement online, platform governance, user agency, and trust and safety regulation.
Clicks and Stones: Women Politicians and Gendered Hostility Online
British Journal of Political Science
Annie Jarman
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Women politicians report that social media abuse harms their personal and professional lives. However, prior text-based research finds that men receive more general online hostility than women – except among the most visible politicians. I hypothesize that backlash to perceived gender-role violations – such as public visibility – will include distinctly gendered content, such as slurs and references to appearance. Using a novel and replicable method, I analyze hostile and gendered language in three million social media mentions of US state representatives. I find that hostility towards visible women differs from men in content, not volume. Visible women face similar volumes of generic hostility but twice as much gender-specific abuse as men. This pattern holds across two alternate measures of perceived conformity to traditional gender roles: legislator tone and the presence of women in the chamber. Incorporating gendered content into text-based analyses reconciles discrepancies between observational and self-reported data and validates women politicians’ reports.
Elite Misperceptions in Foreign Policy
British Journal of Political Science
Joshua D. Kertzer, Joshua Busby, Jonathan Monten, Jordan Tama, Craig Kafura
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Many models of domestic politics in international relations presume that political elites correctly perceive public preferences, even as a growing body of research in political behavior calls this assumption into question. Leveraging seven paired surveys of 4,852 foreign policy elites and 13,687 members of the American public from 2004–24 on twenty-four different questions, we show elites systematically misperceive public opinion in foreign policy, misperceiving the public as more isolationist and inward-looking than it actually is. We replicate this finding with a paired experiment showing that elites effectively underestimate the public’s responsiveness to cues from international organizations, and that elites with isolationist stereotypes underestimate public approval the most. These dynamics – which operate predominantly through stereotyping, rather than projection – have important implications for the study of political elites, public opinion about foreign policy, and efforts to test theoretical models of domestic politics in international relations using public opinion data alone.
“Good job reporting this!”: Examining psychological needs and community building in YouTube conspiracy narratives
Political Psychology
Darja Wischerath, Lukasz Piwek, Jonathan F. Roscoe, Brittany I. Davidson
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The proliferation of conspiracy theories online has tangible offline consequences, both on an individual and collective level. Conspiracy narratives have been associated with reduced belief in democracy, the rise of populist parties, and can act as a radicalization multiplier in such contexts. These narratives capitalize on pre‐existing beliefs and grievances and add urgency to act through a narrative of imminent danger. Previous research has proposed that belief in conspiracy narratives is driven by unfulfilled psychological needs such as existential threat, epistemic motives, and social motives and calls have been made to examine conspiracy belief as a form of affective community investment. In the present research, we explored how conspiracy narratives address grievances and psychological needs through a 1‐month digital observation of conspiracy‐related YouTube videos. We performed an LDA topic model analysis of 102 videos and 455,738 comments and qualitatively examined 24 videos and 1200 comments using an abductive approach. This study validated and extended existing models of conspiracy beliefs, highlighting how conspiracy narratives address and amplify grievances and psychological needs in both official content and community‐generated discourse. This research contributes to a deeper understanding of the mechanisms underlying the spread and impact of conspiracy theories in online environments.
Toward a Margin of Total Error
Journal of Survey Statistics and Methodology
Sharon L Lohr, Andrew Mercer, Courtney Kennedy, J Michael Brick
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The margin of error that is typically reported with a poll is based on sampling variability. This measure of uncertainty excludes bias from nonresponse and measurement error, which can in practice be much larger than the sampling error. We examine empirical models for predicting a “margin of total error” that includes both sampling and nonsampling error. Models considered include a multiplicative adjustment to the standard error, a linear regression model predicting squared bias from covariates, and linear and logistic mixed models that predict systematic bias components through fixed effects and incorporate extra non-sampling variability through a vector of random effects. These models are fit to two sets of polls that have benchmark estimates available from election results and high-quality probability surveys. The linear regression model and linear mixed model had the best performance for the datasets considered. These models are easy to interpret, offer flexibility for modeling the relationship between bias and the benchmark proportion, and can include covariates about sampling protocols and question topics. The oft-cited recommendation to double the margin of sampling error achieved approximately correct coverage for confidence intervals in the election dataset but achieved poor coverage for other polls. Recommendations for inflating standard errors of election polls do not necessarily carry over to polls on other subjects. The models in this article could be used to derive standard error adjustments for a database of polls having benchmark estimates. Poll producers could then draw on predictions of excess variability that have been calculated for similar poll designs and topics to provide measures of uncertainty for future polling estimates.
Variations in Self-Reported Happiness Across Different Response Scales: Evidence From 34 Chinese Surveys From 2002 to 2021
Journal of Survey Statistics and Methodology
Shuaiying Cao, Minglei Wang, Ting Yan, Lirui He, Chan Zhang
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Happiness is a widely used measure of subjective well-being, often assessed through a single survey item (e.g., “How happy are you these days?”) in global social surveys. However, variations in response scale design across surveys complicate comparisons of happiness both within and across countries. To examine the impact of response scale design on happiness ratings, we analyzed data from 34 interviewer-administered (mostly face-to-face) national probability-based surveys conducted in China, spanning six survey programs and comprising approximately half a million responses. We focused on three key scale design features that varied across surveys: scale direction, presence or absence of a midpoint, and scale length. To enable meaningful comparisons across surveys with different scale lengths, we standardized self-reported happiness scores using z-score transformations within each survey. This approach allowed us to evaluate how scale features are associated with respondents’ relative positions within each survey’s distribution, rather than comparing raw mean happiness scores across studies. Our analyses revealed no differences in standardized happiness ratings across scale lengths, but scales without a midpoint resulted in higher ratings. The relationship between scale direction and standardized happiness ratings differed markedly across education subgroups. Specifically, among respondents with lower levels of education, descending scales (e.g., from “a lot of happiness” to “a lot of unhappiness”) were associated with higher standardized happiness ratings compared to ascending scales. This pattern aligns with findings from Western research on scale direction effects. In contrast, among respondents with higher education—particularly those in the highest education category—descending scales were associated with lower standardized happiness ratings. These findings suggest that culturally specific mechanisms may interact with scale direction to influence how individuals report their happiness.
Leader-Motivated Behavior: Voting by Mail in the 2020 General Election
Public Opinion Quarterly
Seth C McKee, Enrijeta Shino, Daniel A Smith
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There is a large body of scholarship that probes the association of elite messaging and motivated reasoning with political behavior. Few studies, though, consider how these factors relate to validated mass behavior. Leveraging two national surveys, we assess voter alignment with President Trump and who voted by mail (VBM). Drawing on 2016 and 2020 Cooperative Election Study (CES) data, we analyze the likelihood that Trump supporters: (1) voted by mail, (2) self-reported voting by mail, and (3) self-reported not voting by mail when they did (misreporting VBM). In 2016, candidate Trump spoke of a “rigged” election, but it was in the 2020 contest that President Trump indicted VBM as the principal reason he might lose reelection. Analyzing VBM before (2016) and during (2020) Trump’s attack on this vote method suggests that leader opinion activation shaped the views of Trump voters and their actual voting behavior. In 2020, Trump supporters were markedly less likely to cast a VBM ballot and were also significantly more likely to disclaim voting by mail when they actually did. These findings have important implications regarding the extent to which purported and verified mass political behavior comports with leader-directed messaging.
Gender-of-Interviewer Effects in the Measurement of Gender Attitudes: Lessons from 18 Countries of the European Social Survey
Public Opinion Quarterly
Ádåm Stefkovics
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Measuring gender-related questions in face-to-face surveys can be challenging due to the potential influence of the interviewer’s gender. First, people may feel more comfortable disclosing sensitive information (e.g., gender-based discrimination) to a female interviewer or an interviewer of the same gender. Second, respondents may alter their responses in light of what they perceive as desirable, for instance, by reporting more feminist views when interviewed by a female interviewer. To test these assumptions, we use the gender module of the eleventh round of the European Social Survey. We analyze patterns from 28,567 respondent-interviewer dyads from 18 countries. We find no evidence of the interviewer’s gender influencing disclosure and limited evidence of gender-of-interviewer (GOI) effects on distributions. These findings are consistent across countries with varying levels of gender equality. Our results indicate that in European societies, GOI effects may not cause significant harm in social surveys.
Is There a Partisan Divide in Citizens’ Preferences on Ukraine Support? Survey-Experimental Evidence from the US
Public Opinion Quarterly
Lukas Rudolph
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Models of democratic responsiveness imply that consistent US foreign policy is furthered by bipartisan agreement on core US strategy. A broad literature indicates, however, that politics does not stop “at water’s edge,” suggesting relevant differences between more “dovish” Democratic-leaning and more “hawkish” Republican-leaning voters. I revisit this question for the case of US support strategies aiding Ukraine against the Russian invasion in 2022. In particular, I investigate the extent of partisan differences in both overall agreement and the hard trade-offs between competing normative, strategic, and economic considerations connected to supporting Ukraine. Do Democratic- and Republican-leaning voters deal with these trade-offs differently? I bring conjoint and vignette survey experimental evidence from 2,334 US citizens to this question. I show that absolute levels of support for Ukraine are lower among Republicans. However, irrespective of partisanship, citizens resolve the fundamental trade-offs involved in Ukraine policy similarly. Strategies that sustain Ukraine’s territorial integrity and political sovereignty, rather than concessions to Russia, are substantively more likely to be chosen, irrespective of partisanship, even if less strongly so among (moderate) US Republicans. Both Democratic and Republican voters show similarly high concern for the human costs of war, domestic economic costs, and escalation risk—with economic costs resonating more among Republicans. These results imply that for a case where both normative and strategic motivations for intervention loom large, public support is not structured as expected from “partisan types.” Tentatively, this indicates that differences in partisan preferences are no constraint for persisting US support to Ukraine.
Models as Prediction Machines: How to Convert Confusing Coefficients Into Clear Quantities
Advances in Methods and Practices in Psychological Science
Julia M. Rohrer, Vincent Arel-Bundock
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Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models but is challenging for more complex models with, for example, categorical variables, interactions, nonlinearities, or hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in consistent fashion to draw inferences from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports more than 100 different classes of models in R and Python , and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational studies, randomized experiments) to answer different research questions (e.g., about associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modeling ordinal outcomes, and interpreting nonlinear models).
Stage 2 Registered Report: Restriction of Researcher Degrees of Freedom Through the Psychological Research Preregistration-Quantitative Template
Advances in Methods and Practices in Psychological Science
Lisa Spitzer, Amelie Kroeger, Stefanie Mueller
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Preregistration can help to restrict researcher degrees of freedom and thereby ensure the integrity of research findings. However, its ability to restrict such flexibility depends on whether researchers specify their study plan in sufficient detail and adhere to this plan. Previous research indicates higher restrictiveness when preregistrations are based on structured versus unstructured template formats, although there is room for further improvement. In this study, we built on these findings and investigated the restrictiveness of preregistrations based on the Psychological Research Preregistration-Quantitative (PRP-QUANT) Template, an extensive template that aids the preregistration of quantitative studies in psychology. Preregistrations were sampled from PsychArchives and coded for their level of restrictiveness using the coding schemes of Bakker et al. and Heirene et al. We predicted that preregistrations based on the PRP-QUANT Template ( N = 103) are more restrictive than preregistrations based on the OSF Preregistration Template ( N = 52; Hypothesis 1). We also inspected whether peer review can contribute further to restricting flexibility using nested Wilcoxon-Mann-Whitney tests and predicted higher restrictiveness for peer-reviewed ( n = 29) than non-peer-reviewed preregistrations ( n = 74; Hypothesis 2). In addition, we examined adherence to the preregistered plans in the associated publications ( N = 19). In line with Hypothesis 1, PRP-QUANT preregistrations had significantly higher restrictiveness scores than OSF preregistrations. Moreover, consistent with Hypothesis 2, peer-reviewed preregistrations had significantly higher restrictiveness than non-peer-reviewed ones. Of the associated articles, 73.68% included undeclared deviations. We discuss the implications of our findings for the PRP-QUANT Template and structured templates in general.
Introducing the Truth Effect Database (TED): An open trial-level resource promoting FAIR data in truth effect research
Behavior Research Methods
Sven Lesche, Annika Stump
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The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles form the foundation of the open data movement. However, while many current practices ensure data are findable and accessible, true interoperability and reusability remain limited. This paper introduces the Truth Effect Database (TED), a large-scale, trial-level, open database harmonizing data from illusory truth effect studies designed to enhance interoperability and reusability. TED currently integrates data from 59 studies in 29 publications, spanning 12,249 participants and 808,231 trials, accounting for a wide range of dispositional and contextual variables. To promote usability, TED focuses on user-friendly data submission using a custom entry website and data extraction using the R package acdcquery. These tools guide researchers through both data entry and retrieval, eliminating the need for direct interaction with the database’s internal structure. We illustrated the utility of TED through Bayesian multilevel analyses, highlighting substantial variance in the illusory truth effect at the subject level, moderated by the delay between exposure and judgment phases in truth effect paradigms. Beyond this first demonstration, TED provides the foundation for a wide range of future research. These include (living) meta-analyses, simulation-based power analyses, rigorous replication and reanalysis of existing studies, and the validation and development of formal cognitive models. As an open and extensible infrastructure, TED serves as a blueprint for sustainable, community-driven database development in psychological science.
Bipartisan-cited science is rare, unevenly distributed, and disproportionately influential
Proceedings of the National Academy of Sciences
Alexander C. Furnas, Dashun Wang
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This study offers a systematic analysis of scientific papers cited in both Republican and Democratic policy documents. Using data from Overton and Dimensions, we examine congressional reports, hearings, and think tank publications. We find that bipartisan citations, while rare, highlight papers of exceptional scientific influence. Policy documents citing these papers also receive more citations, amplifying their policy impact. Yet, bipartisan-cited science is unevenly distributed—concentrated in monetary policy and healthcare, but notably absent in climate, inequality, and race and gender. These results show that bipartisan engagement, though limited, marks a uniquely influential core of science in both research and policy.
Eye of the beholder: Pupillary response reflects how subjective prior beliefs shape reinforcement learning with fake news
Proceedings of the National Academy of Sciences
Silvana Lozito, Valentina Piga, Sara Lo Presti, Angelica Scuderi, Fabrizio Doricchi, Massimo Silvetti, Stefano Lasaponara
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Information selection plays a crucial role in how individuals navigate online content. While confirmation bias has been implicated in this phenomenon, its interaction with reinforcement learning dynamics and internal confidence signals remains poorly understood. Here, we examined how veracity judgments and confidence shape choices when probabilistic rewards are tied to different epistemic attributes of news headlines. Participants completed a three-phase paradigm that combined news classification, a probabilistic learning task with varying reward contingencies, and a final reevaluation phase. Using real and false headlines judged for veracity and confidence, we created personalized sets of stimulus categories that were later used in a two-armed bandit task. In different blocks of trials, reinforcement was probabilistically associated with either the perceived truthfulness or confidence of each item. Across all experimental phases, pupil dilation provided neurophysiological signatures of belief-related processing. At a behavioral level, participants showed higher accuracies and learning rates when rewards were contingent on their previous judgments of veracity, whereas performance was markedly reduced when reinforcement favored confidence, especially low-confidence options. Pupillometric data revealed predecisional modulations tied to subjective confidence, while computational modeling showed that participants relied on feature-based generalization when veracity predicted reward and shifted toward valence-sensitive updating when contingencies no longer matched their prior epistemic structure. Together, these results reveal how veracity and confidence jointly guide reinforcement-driven choices and modulate the flexibility of belief-related decisions. By integrating cognitive, computational, and physiological data, our study provides a mechanistic understanding of how prior beliefs shape learning in complex and misinformation-rich contexts.
Expression at the edge: Free speech boundaries amidst the Gaza crisis
Science Advances
Ran Abramitzky, Guy Grossman, Yphtach Lelkes, Hani Mansour, Tamar Mitts
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Universities have become key arenas in national debates over the boundaries of free expression. Using preregistered online survey experiments with a nationally representative sample of 3065 US college students, this study examines how individuals navigate the tension between free speech and harm prevention, an issue sharpened by recent campus protests over Gaza. We test how variation in the severity of speech and the identity of its target (white, Black, Jewish, Muslim, or transgender individuals) shapes judgments about appropriate institutional responses. Our preregistered analyses show that students generally oppose punishing objectionable speech unless it is perceived as highly harmful and that identical statements directed at minority groups elicit stronger punitive responses than those targeting white individuals. Exploratory analyses reveal that these patterns reflect distinct normative principles: Most students adopt a particularist stance, favoring greater protection for marginalized groups, while a sizable minority adhere to a universalist view emphasizing equal treatment regardless of identity. These principles predict attitudes across contexts, but adherence weakens when individuals hold strong views on the issue at hand. Our findings show that campus conflicts over speech boundaries reflect not only disagreement about norms but also unequal application of these norms across groups and issues.