Erin Michelle Buchanan’s research while affiliated with Harrisburg University of Science and Technology and other places

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. Although previous studies provide insight into the cognitive underpinnings of semantic representations, they have suffered from small sample sizes and a lack of linguistic and cultural diversity. In this Registered Report, we measured the size and the variability of the semantic priming effect across 19 languages (N = 25,163 participants analyzed) by creating the largest available database of semantic priming values based on an adaptive sampling procedure. We found evidence for semantic priming in terms of differences in response latencies between related word-pair conditions and unrelated word-pair conditions. Model comparisons showed that inclusion of a random intercept for language improved model fit, providing support for variability in semantic priming across languages. This study highlights the robustness and variability of semantic priming across languages and provides a rich, linguistically diverse dataset for further analysis.

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. Although previous studies provide insight into the cognitive underpinnings of semantic representations, they have suffered from small sample sizes and a lack of linguistic and cultural diversity. In this Registered Report, we measured the size and the variability of the semantic priming effect across 19 languages (N = 25,163 participants analyzed) by creating the largest available database of semantic priming values based on an adaptive sampling procedure. We found evidence for semantic priming in terms of differences in response latencies between related word-pair conditions and unrelated word-pair conditions. Model comparisons showed that inclusion of a random intercept for language improved model fit, providing support for variability in semantic priming across languages. This study highlights the robustness and variability of semantic priming across languages and provides a rich, linguistically diverse dataset for further analysis.

In response to the replication and confidence crisis across various empirical disciplines, ensuring the validity of research has gained attention. High validity is crucial for obtaining replicable and robust study outcomes when both exploring new questions and replicating previous findings. In this study, we aimed to address this issue by developing a comprehensive checklist to assist researchers in enhancing and monitoring the validity of their research. After systematically analyzing previous findings on validity, a comprehensive list of potential checklist items was compiled. Over the course of three rounds, more than 30 interdisciplinary and psychological-science experts participated in a Delphi study. Experts rated items on their importance and were given the opportunity to propose novel items as well as improve existing ones. This process resulted in a final set of 91 items, organized according to common stages of a research project. The VALID checklist is accessible online ( https://www.validchecklist.com/ ) and provides researchers with an adaptable, versatile tool to monitor and improve the validity of their research and to suit their specific needs. By focusing on adaptiveness during its development, VALID encompasses 331 unique checklist versions, making it a one-stop solution suitable for a wide range of projects, designs, and requirements.

Intuition often guides our thinking effectively, but it can also lead to consequential reasoning errors, underpinning poor decisions and biased judgments. Little is known about how people globally self-correct such intuitive reasoning errors and what enhances their correction. Defying prevailing models of reasoning, recent research suggests that people spontaneously correct only a few errors during deliberation; however, enhancing error monitoring and motivating further effort should increase error correction. Here, we study whether these mechanisms apply to reasoning across individualistic and collectivistic cultures (expected N = 33,000 participants from 67 regions). Participants will solve problems that elicit incorrect intuitions twice: first intuitively and then reflectively, allowing them to correct initial errors, in a 2 (feedback: absent vs present) × 2 (answer justification: absent vs present) between-participants design. The study will shed more light on the nature, generalisability, and promotion of corrective behaviour, crucial for understanding and improving reasoning worldwide.

Intuition often guides our thinking effectively, but it can also lead to consequential reasoning errors, underpinning poor decisions and biased judgments. Little is known about how people globally self-correct such intuitive reasoning errors and what enhances their correction. Defying prevailing models of reasoning, recent research suggests that people spontaneously correct only a few errors during deliberation; however, enhancing error monitoring and motivating further effort should increase error correction. Here, we study whether these mechanisms apply to reasoning across individualistic and collectivistic cultures (expected N = 33,000 participants from 67 regions). Participants will solve problems that elicit incorrect intuitions twice: first intuitively and then reflectively, allowing them to correct initial errors, in a 2 (feedback: absent vs present) × 2 (answer justification: absent vs present) between-participants design. The study will shed more light on the nature, generalisability, and promotion of corrective behaviour, crucial for understanding and improving reasoning worldwide.

An observational study might support a causal claim if the association found cannot be explained by bias due to unconsidered confounders. This bias depends on how strongly the common predisposition, a summary of unconsidered confounders, is related to the factor and the outcome. For a positive effect to be supported, the product of these two relations must be smaller than the left boundary of the confidence interval for, e.g., a standardised mean difference (d). We suggest means to derive heuristics for how large this product must be to serve as a confirmatory threshold. We also provide non-technical, visual means to express researchers’ assumptions on the two relations to assess whether a finding on d is explainable by omitted confounders. The ViSe tool, available as an R package and Shiny application, allows users to choose between various effect sizes and apply it to their own data or published summary results.

Replications are important for assessing the reliability of published findings. However, they are costly, and it is infeasible to replicate everything. Accurate, fast, lower-cost alternatives such as eliciting predictions could accelerate assessment for rapid policy implementation in a crisis and help guide a more efficient allocation of scarce replication resources. We elicited judgements from participants on 100 claims from preprints about an emerging area of research (COVID-19 pandemic) using an interactive structured elicitation protocol, and we conducted 29 new high-powered replications. After interacting with their peers, participant groups with lower task expertise (‘beginners’) updated their estimates and confidence in their judgements significantly more than groups with greater task expertise (‘experienced’). For experienced individuals, the average accuracy was 0.57 (95% CI: [0.53, 0.61]) after interaction, and they correctly classified 61% of claims; beginners’ average accuracy was 0.58 (95% CI: [0.54, 0.62]), correctly classifying 69% of claims. The difference in accuracy between groups was not statistically significant and their judgements on the full set of claims were correlated (r(98) = 0.48, P < 0.001). These results suggest that both beginners and more-experienced participants using a structured process have some ability to make better-than-chance predictions about the reliability of ‘fast science’ under conditions of high uncertainty. However, given the importance of such assessments for making evidence-based critical decisions in a crisis, more research is required to understand who the right experts in forecasting replicability are and how their judgements ought to be elicited.

Latent variable modeling as a lens for psychometric theory is a popular tool for social scientists to examine measurement of constructs. Journals, such as Assessment regularly publish articles supporting measures of latent constructs wherein a measurement model is established. Confirmatory factor analysis can be used to investigate the replicability and generalizability of the measurement model in new samples, while multigroup confirmatory factor analysis is used to examine the measurement model across groups within samples. With the rise of the replication crisis and “psychology’s renaissance,” interest in divergence in measurement has increased, often focused on small parameter differences within the latent model. This article presents visualizemi, an R package that provides functionality to calculate multigroup models, partial invariance, visualizations for (non)-invariance, effect sizes for models and parameters, and potential replication rates compared with random models. Readers will learn how to interpret the impact and size of the proposed non-invariance in models with a focus on potential replication and how to plan for registered reports.

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.

The replication crisis in psychology and related sciences contributed to the adoption of large-scale research initiatives known as Big Team Science (BTS). BTS has made significant advances in addressing issues of replication, statistical power, and diversity through the use of larger samples and more representative cross-cultural data. However, while these collaborations hold great potential, they also introduce unique challenges related to their scale. Drawing on experiences from successful BTS projects, we identified and outlined key strategies for overcoming diversity, volunteering, and capacity challenges. We emphasize the need for the implementation of strong organizational practices and the distribution of responsibility to prevent common pitfalls. More fundamentally, BTS requires a shift in mindset toward prioritizing collaborative effort, diversity, transparency, and inclusivity. Ultimately, we call for reflection on the strengths and limitations of BTS to enhance the quality, generalizability, and impact of research across disciplines.