Americans harbor much less favorable explicit sentiments toward young adults than toward older adults.

Public and academic discourse on ageism focuses primarily on prejudices targeting older adults, implicitly assuming that this age group experiences the most age bias. We test this assumption in a large, preregistered study surveying Americans' explicit sentiments toward young, middle-aged, and older adults. Contrary to certain expectations about the scope and nature of ageism, responses from two crowdsourced online samples matched to the US adult population (N = 1,820) revealed that older adults garner the most favorable sentiments and young adults, the least favorable ones. This pattern held across a wide range of participant demographics and outcome variables, in both samples. Signaling derogation of young adults more than benign liking of older adults, participants high on SDO (i.e., a key antecedent of group prejudice) expressed even less favorable sentiments toward young adults-and more favorable ones toward older adults. In two follow-up, preregistered, forecasting surveys, lay participants (N = 500) were generally quite accurate at predicting these results; in contrast, social scientists (N = 241) underestimated how unfavorably respondents viewed young adults and how favorably they viewed older adults. In fact, the more expertise in ageism scientists had, the more biased their forecasts. In a rapidly aging world with exacerbated concerns over older adults' welfare, young adults also face increasing economic, social, political, and ecological hardship. Our findings highlight the need for policymakers and social scientists to broaden their understanding of age biases and develop theory and policies that ponder discriminations targeting all age groups.

Failing to replicate predicts citation declines in psychology.

With a sample of 228 psychology papers that failed to replicate, we tested whether the trajectory of citation patterns changes following the publication of a failure to replicate. Across models, we found consistent evidence that failing to replicate predicted lower future citations and that the size of this reduction increased over time. In a 14-y postpublication period, we estimated that the publication of a failed replication was associated with an average citation decline of 14% for original papers. These findings suggest that the publication of failed replications may contribute to a self-correcting science by decreasing scholars' reliance on unreplicable original findings.

Competition and moral behavior: A meta-analysis of forty-five crowd-sourced experimental designs.

Does competition affect moral behavior? This fundamental question has been debated among leading scholars for centuries, and more recently, it has been tested in experimental studies yielding a body of rather inconclusive empirical evidence. A potential source of ambivalent empirical results on the same hypothesis is design heterogeneity-variation in true effect sizes across various reasonable experimental research protocols. To provide further evidence on whether competition affects moral behavior and to examine whether the generalizability of a single experimental study is jeopardized by design heterogeneity, we invited independent research teams to contribute experimental designs to a crowd-sourced project. In a large-scale online data collection, 18,123 experimental participants were randomly allocated to 45 randomly selected experimental designs out of 95 submitted designs. We find a small adverse effect of competition on moral behavior in a meta-analysis of the pooled data. The crowd-sourced design of our study allows for a clean identification and estimation of the variation in effect sizes above and beyond what could be expected due to sampling variance. We find substantial design heterogeneity-estimated to be about 1.6 times as large as the average standard error of effect size estimates of the 45 research designs-indicating that the informativeness and generalizability of results based on a single experimental design are limited. Drawing strong conclusions about the underlying hypotheses in the presence of substantive design heterogeneity requires moving toward much larger data collections on various experimental designs testing the same hypothesis.

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty.

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.

Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference.

The cost of drug development continues to rise and may be prohibitive in cases of unmet clinical need, particularly for rare diseases. Artificial intelligence-based methods are promising in their potential to discover new treatment options. The task of drug repurposing hypothesis generation is well-posed as a link prediction problem in a knowledge graph (KG) of interacting of drugs, proteins, genes and disease phenotypes. KGs derived from biomedical literature are semantically rich and up-to-date representations of scientific knowledge. Inference methods on scientific KGs can be confounded by unspecified contexts and contradictions. Extracting context enables incorporation of relevant pharmacokinetic and pharmacodynamic detail, such as tissue specificity of interactions. Contradictions in biomedical KGs may arise when contexts are omitted or due to contradicting research claims. In this review, we describe challenges to creating literature-scale representations of pharmacological knowledge and survey current approaches toward incorporating context and resolving contradictions.

Cognition of Time and Thinking Beyond.

A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.

Where do we intervene to optimize sports systems? Leverage Points the way.

Sport and sports research are inherently complex systems. This appears to be somewhat at odds with the current research paradigm in sport in which interventions are aimed are fixing or solving singular broken components within the system. In any complex system, such as sport, there are places where we can intervene to change behaviour and, ideally, system outcomes. Meadows influential work describes 12 different points with which to intervene in complex systems (termed "Leverage Points"), which are ordered from shallow to deeper based on their potential effectiveness to influence transformational change. Whether research in sport is aimed at shallow or deeper Leverage Points is unknown. This study aimed to assess highly impactful research in sports science, sports nutrition/metabolism, sports medicine, sport and exercise psychology, sports management, motor control, sports biomechanics and sports policy/law through a Leverage Points lens. The 10 most highly cited original-research manuscripts from each journal representing these fields were analysed for the Leverage Point with which the intervention described in the manuscript was focused. The results indicate that highly impactful research in sports science, sports nutrition/metabolism, sports biomechanics and sports medicine is predominantly focused at the shallow end of the Leverage Points hierarchy. Conversely, the interventions drawn from journals representing sports management and sports policy/law were focused on the deeper end. Other journals analysed had a mixed profile. Explanations for these findings include the dual practitioner/academic needing to "think fast" to solve immediate questions in sports science/medicine/nutrition, limited engagement with "working slow" systems and method experts and differences in incremental vs. non-incremental research strategies.

Increasing Value and Reducing Waste of Research on Neurofeedback Effects in Post-traumatic Stress Disorder: A State-of-the-Art-Review.

Post-Traumatic Stress Disorder (PTSD) is often considered challenging to treat due to factors that contribute to its complexity. In the last decade, more attention has been paid to non-pharmacological or non-psychological therapies for PTSD, including neurofeedback (NFB). NFB is a promising non-invasive technique targeting specific brainwave patterns associated with psychiatric symptomatology. By learning to regulate brain activity in a closed-loop paradigm, individuals can improve their functionality while reducing symptom severity. However, owing to its lax regulation and heterogeneous legal status across different countries, the degree to which it has scientific support as a psychiatric treatment remains controversial. In this state-of-the-art review, we searched PubMed, Cochrane Central, Web of Science, Scopus, and MEDLINE and identified meta-analyses and systematic reviews exploring the efficacy of NFB for PTSD. We included seven systematic reviews, out of which three included meta-analyses (32 studies and 669 participants) that targeted NFB as an intervention while addressing a single condition-PTSD. We used the MeaSurement Tool to Assess systematic Reviews (AMSTAR) 2 and the criteria described by Cristea and Naudet (Behav Res Therapy 123:103479, 2019, https://doi.org/10.1016/j.brat.2019.103479 ) to identify sources of research waste and increasing value in biomedical research. The seven assessed reviews had an overall extremely poor quality score (5 critically low, one low, one moderate, and none high) and multiple sources of waste while opening opportunities for increasing value in the NFB literature. Our research shows that it remains unclear whether NFB training is significantly beneficial in treating PTSD. The quality of the investigated literature is low and maintains a persistent uncertainty over numerous points, which are highly important for deciding whether an intervention has clinical efficacy. Just as importantly, none of the reviews we appraised explored the statistical power, referred to open data of the included studies, or adjusted their pooled effect sizes for publication bias and risk of bias. Based on the obtained results, we identified some recurrent sources of waste (such as a lack of research decisions based on sound questions or using an appropriate methodology in a fully transparent, unbiased, and useable manner) and proposed some directions for increasing value (homogeneity and consensus) in designing and reporting research on NFB interventions in PTSD.

Many nonnormalities, one simulation: Do different data generation algorithms affect study results?

Monte Carlo simulation studies are among the primary scientific outputs contributed by methodologists, guiding application of various statistical tools in practice. Although methodological researchers routinely extend simulation study findings through follow-up work, few studies are ever replicated. Simulation studies are susceptible to factors that can contribute to replicability failures, however. This paper sought to conduct a meta-scientific study by replicating one highly cited simulation study (Curran et al., Psychological Methods, 1, 16-29, 1996) that investigated the robustness of normal theory maximum likelihood (ML)-based chi-square fit statistics under multivariate nonnormality. We further examined the generalizability of the original study findings across different nonnormal data generation algorithms. Our replication results were generally consistent with original findings, but we discerned several differences. Our generalizability results were more mixed. Only two results observed under the original data generation algorithm held completely across other algorithms examined. One of the most striking findings we observed was that results associated with the independent generator (IG) data generation algorithm vastly differed from other procedures examined and suggested that ML was robust to nonnormality for the particular factor model used in the simulation. Findings point to the reality that extant methodological recommendations may not be universally valid in contexts where multiple data generation algorithms exist for a given data characteristic. We recommend that researchers consider multiple approaches to generating a specific data or model characteristic (when more than one is available) to optimize the generalizability of simulation results.

Biomarker adoption in developmental science: A data-driven modelling of trends from 90 biomarkers across 20 years.

Developmental scientists have adopted numerous biomarkers in their research to better understand the biological underpinnings of development, environmental exposures, and variation in long-term health. Yet, adoption patterns merit investigation given the substantial resources used to collect, analyse, and train to use biomarkers in research with infants and children. We document trends in use of 90 biomarkers between 2000 and 2020 from approximately 430,000 publications indexed by the Web of Science. We provide a tool for researchers to examine each of these biomarkers individually using a data-driven approach to estimate the biomarker growth trajectory based on yearly publication number, publication growth rate, number of author affiliations, National Institutes of Health dedicated funding resources, journal impact factor, and years since the first publication. Results indicate that most biomarkers fit a "learning curve" trajectory (i.e., experience rapid growth followed by a plateau), though a small subset decline in use over time.

Toward Open and Reproducible Epidemiology.

Starting in the 2010s, researchers in the experimental social sciences rapidly began to adopt increasingly open and reproducible scientific practices. These practices include publicly sharing deidentified data when possible, sharing analytical code, and preregistering study protocols. Empirical evidence from the social sciences suggests such practices are feasible, can improve analytical reproducibility, and can reduce selective reporting. In academic epidemiology, adoption of open-science practices has been slower than in the social sciences (with some notable exceptions, such as registering clinical trials). Epidemiologic studies are often large, complex, conceived after data have already been collected, and difficult to replicate directly by collecting new data. These characteristics make it especially important to ensure their integrity and analytical reproducibility. Open-science practices can also pay immediate dividends to researchers' own work by clarifying scientific reasoning and encouraging well-documented, organized workflows. We consider how established epidemiologists and early-career researchers alike can help midwife a culture of open science in epidemiology through their research practices, mentorship, and editorial activities.

Effective or predatory funding? Evaluating the hidden costs of grant applications.

Researchers are spending an increasing fraction of their time on applying for funding; however, the current funding system has considerable deficiencies in reliably evaluating the merit of research proposals, despite extensive efforts on the sides of applicants, grant reviewers and decision committees. For some funding schemes, the systemic costs of the application process as a whole can even outweigh the granted resources-a phenomenon that could be considered as predatory funding. We present five recommendations to remedy this unsatisfactory situation.

Harm Hypervigilance in Public Reactions to Scientific Evidence.

Two preregistered studies from two different platforms with representative U.S. adult samples (N = 1,865) tested the harm-hypervigilance hypothesis in risk assessments of controversial behavioral science. As expected, across six sets of scientific findings, people consistently overestimated others' harmful reactions (medium to large average effect sizes) and underestimated helpful ones, even when incentivized for accuracy. Additional analyses found that (a) harm overestimations were associated with support for censoring science, (b) people who were more offended by scientific findings reported greater difficulty understanding them, and (c) evidence was moderately consistent for an association between more conservative ideology and harm overestimations. These findings are particularly relevant because journals have begun evaluating potential downstream harms of scientific findings. We discuss implications of our work and invite scholars to develop rigorous tests of (a) the social pressures that lead science astray and (b) the actual costs and benefits of publishing or not publishing potentially controversial conclusions.

Replicability, Robustness, and Reproducibility in Psychological Science.

Replication-an important, uncommon, and misunderstood practice-is gaining appreciation in psychology. Achieving replicability is important for making research progress. If findings are not replicable, then prediction and theory development are stifled. If findings are replicable, then interrogation of their meaning and validity can advance knowledge. Assessing replicability can be productive for generating and testing hypotheses by actively confronting current understandings to identify weaknesses and spur innovation. For psychology, the 2010s might be characterized as a decade of active confrontation. Systematic and multi-site replication projects assessed current understandings and observed surprising failures to replicate many published findings. Replication efforts highlighted sociocultural challenges such as disincentives to conduct replications and a tendency to frame replication as a personal attack rather than a healthy scientific practice, and they raised awareness that replication contributes to self-correction. Nevertheless, innovation in doing and understanding replication and its cousins, reproducibility and robustness, has positioned psychology to improve research practices and accelerate progress.

How do participants feel about the ethics of rich false memory studies?

ABSTRACTDeception is often a necessity in rich false memory studies, but is this deception acceptable to participants? In the current study, we followed up with 175 participants who had taken part in a replication of the Lost in the Mall childhood false memory study (Loftus & Pickrell, 1995), as either a research subject or a familial informant. We found that both participants and informants were generally very positive about their experience, did not regret taking part and found the deceptive methods acceptable. Importantly, the vast majority reported that they would still have taken part had they known the true objectives from the beginning. Participants also reported learning something interesting about memory and enjoying the nostalgia and family discussions that were prompted by the study. We would encourage other researchers to assess the ethical implications of false memory research paradigms and to incorporate the valuable feedback from participants and informants.

Predicting research trends with semantic and neural networks with an application in quantum physics.

The vast and growing number of publications in all disciplines of science cannot be comprehended by a single human researcher. As a consequence, researchers have to specialize in narrow subdisciplines, which makes it challenging to uncover scientific connections beyond the own field of research. Thus, access to structured knowledge from a large corpus of publications could help push the frontiers of science. Here, we demonstrate a method to build a semantic network from published scientific literature, which we call SemNet We use SemNet to predict future trends in research and to inspire personalized and surprising seeds of ideas in science. We apply it in the discipline of quantum physics, which has seen an unprecedented growth of activity in recent years. In SemNet, scientific knowledge is represented as an evolving network using the content of 750,000 scientific papers published since 1919. The nodes of the network correspond to physical concepts, and links between two nodes are drawn when two concepts are concurrently studied in research articles. We identify influential and prize-winning research topics from the past inside SemNet, thus confirming that it stores useful semantic knowledge. We train a neural network using states of SemNet of the past to predict future developments in quantum physics and confirm high-quality predictions using historic data. Using network theoretical tools, we can suggest personalized, out-of-the-box ideas by identifying pairs of concepts, which have unique and extremal semantic network properties. Finally, we consider possible future developments and implications of our findings.

Using Normative Language When Describing Scientific Findings: Randomized Controlled Trial of Effects on Trust and Credibility.

BACKGROUND: Scientists often make cognitive claims (eg, the results of their work) and normative claims (eg, what should be done based on those results). Yet, these types of statements contain very different information and implications. This randomized controlled trial sought to characterize the granular effects of using normative language in science communication. OBJECTIVE: Our study examined whether viewing a social media post containing scientific claims about face masks for COVID-19 using both normative and cognitive language (intervention arm) would reduce perceptions of trust and credibility in science and scientists compared with an identical post using only cognitive language (control arm). We also examined whether effects were mediated by political orientation. METHODS: This was a 2-arm, parallel group, randomized controlled trial. We aimed to recruit 1500 US adults (age 18+) from the Prolific platform who were representative of the US population census by cross sections of age, race/ethnicity, and gender. Participants were randomly assigned to view 1 of 2 images of a social media post about face masks to prevent COVID-19. The control image described the results of a real study (cognitive language), and the intervention image was identical, but also included recommendations from the same study about what people should do based on the results (normative language). Primary outcomes were trust in science and scientists (21-item scale) and 4 individual items related to trust and credibility; 9 additional covariates (eg, sociodemographics, political orientation) were measured and included in analyses. RESULTS: From September 4, 2022, to September 6, 2022, 1526 individuals completed the study. For the sample as a whole (eg, without interaction terms), there was no evidence that a single exposure to normative language affected perceptions of trust or credibility in science or scientists. When including the interaction term (study arm × political orientation), there was some evidence of differential effects, such that individuals with liberal political orientation were more likely to trust scientific information from the social media post's author if the post included normative language, and political conservatives were more likely to trust scientific information from the post's author if the post included only cognitive language (ß=0.05, 95% CI 0.00 to 0.10; P=.04). CONCLUSIONS: This study does not support the authors' original hypotheses that single exposures to normative language can reduce perceptions of trust or credibility in science or scientists for all people. However, the secondary preregistered analyses indicate the possibility that political orientation may differentially mediate the effect of normative and cognitive language from scientists on people's perceptions. We do not submit this paper as definitive evidence thereof but do believe that there is sufficient evidence to support additional research into this topic, which may have implications for effective scientific communication. TRIAL REGISTRATION: OSF Registries osf.io/kb3yh; https://osf.io/kb3yh. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/41747.

Survey on Open Science Practices in Functional Neuroimaging.

Replicability and reproducibility of scientific findings is paramount for sustainable progress in neuroscience. Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. Email invitations to participate in the survey were sent to addresses received through a PubMed search of human functional magnetic resonance imaging studies that were published between 2010 and 2020. 283 persons completed the questionnaire. Although half of the participants were experienced with preregistration, the willingness to preregister studies in the future was modest. The majority of participants had experience with the sharing of primary neuroimaging data. Most of the participants were interested in implementing a standardized data structure such as BIDS in their labs. Based on demographic variables, we compared participants on seven subscales, which had been generated through factor analysis. Exploratory analyses found that experienced researchers at lower career level had higher fear of being transparent and researchers with residence in the EU had a higher need for data governance. Additionally, researchers at medical faculties as compared to other university faculties reported a more unsupportive supervisor with regards to open science practices and a higher need for data governance. The results suggest growing adoption of open science practices but also highlight a number of important impediments.

FENS-Kavli Network of Excellence: Postponed, non-competitive peer review for research funding.

Receiving research grants is among the highlights of an academic career, affirming previous accomplishments and enabling new research endeavours. Much of the process of acquiring research funding, however, belongs to the less favourite duties of many researchers: It is time consuming, often stressful and, in the majority of cases, unsuccessful. This resentment towards funding acquisition is backed up by empirical research: The current system to distribute research funding, via competitive calls for extensive research applications that undergo peer review, has repeatedly been shown to fail in its task to reliably rank proposals according to their merit, while at the same time being highly inefficient. The simplest, fairest and broadly supported alternative would be to distribute funding more equally across researchers, for example, by an increase of universities' base funding, thereby saving considerable time that can be spent on research instead. Here, I propose how to combine such a 'funding flat rate' model-or other efficient distribution strategies-with quality control through postponed, non-competitive peer review using open science practices.

Slow improvement to the archiving quality of open datasets shared by researchers in ecology and evolution.

Many leading journals in ecology and evolution now mandate open data upon publication. Yet, there is very little oversight to ensure the completeness and reusability of archived datasets, and we currently have a poor understanding of the factors associated with high-quality data sharing. We assessed 362 open datasets linked to first- or senior-authored papers published by 100 principal investigators (PIs) in the fields of ecology and evolution over a period of 7 years to identify predictors of data completeness and reusability (data archiving quality). Datasets scored low on these metrics: 56.4% were complete and 45.9% were reusable. Data reusability, but not completeness, was slightly higher for more recently archived datasets and PIs with less seniority. Journal open data policy, PI gender and PI corresponding author status were unrelated to data archiving quality. However, PI identity explained a large proportion of the variance in data completeness (27.8%) and reusability (22.0%), indicating consistent inter-individual differences in data sharing practices by PIs across time and contexts. Several PIs consistently shared data of either high or low archiving quality, but most PIs were inconsistent in how well they shared. One explanation for the high intra-individual variation we observed is that PIs often conduct research through students and postdoctoral researchers, who may be responsible for the data collection, curation and archiving. Levels of data literacy vary among trainees and PIs may not regularly perform quality control over archived files. Our findings suggest that research data management training and culture within a PI's group are likely to be more important determinants of data archiving quality than other factors such as a journal's open data policy. Greater incentives and training for individual researchers at all career stages could improve data sharing practices and enhance data transparency and reusability.