Relational processing demands and the role of spatial context in the construction of episodic simulations

Reports on differences between remembering the past and imagining the future have led to the hypothesis that constructing future events is a more cognitively demanding process. However, factors that influence these increased demands, such as whether the event has been previously constructed and the types of details comprising the event, have remained relatively unexplored. Across two experiments, we examined how these factors influence the process of constructing event representations by having participants repeatedly construct events and measuring how construction times and a range of phenomenological ratings changed across time points. In Experiment 1, we contrasted the construction of past and future events and found that, relative to past events, the constructive demands associated with future events are particularly heightened when these events are imagined for the first time. Across repeated simulations, future events became increasingly similar to past events in terms of construction times and incorporated detail. In Experiment 2, participants imagined future events involving two memory details (person, location) and then reimagined the event either (a) exactly the same, (b) with a different person, or (c) in a different location. We predicted that if generating spatial information is particularly important for event construction, a change in location will have the greatest impact on constructive demands. Results showed that spatial context contributed to these heightened constructive demands more so than person details, consistent with theories highlighting the central role of spatial processing in episodic simulation. We discuss the findings from both studies in the light of relational processing demands and consider implications for current theoretical frameworks

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

A Many-analysts Approach to the Relation Between Religiosity and Well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β = 0.120). For the second research question, this was the case for 65% of the teams (median reported β = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

Dynamic Data Visualizations to Enhance Insight and Communication Across the Lifecycle of a Scientific Project

In scientific communication, figures are typically rendered as static displays. This often prevents active exploration of the underlying data, for example to gauge the influence of particular data points or of particular analytic choices. Yet modern data visualization tools, from animated plots to interactive notebooks and reactive web applications, allow psychologists to share and present their findings in dynamic and transparent ways. In this tutorial, we present a number of recent developments to build interactivity and animations into scientific communication and publications, using examples and illustrations in the R language (basic knowledge of R is assumed). In particular, we discuss when and how to build dynamic figures, with step-by-step reproducible code that can easily be extended to the reader’s own projects. We illustrate how interactivity and animations can facilitate insight and communication across a project lifecycle—from initial exchanges and discussions within a team to peer-review and final publication—and provide a number of recommendations to use dynamic visualizations effectively. We close with a reflection on how the scientific publishing model is currently evolving, and consider the challenges and opportunities this shift might bring along for data visualization

Psychological Constructs as Local Optima

Psychological constructs are necessary abstractions to operationalize otherwise intractable entities. However, the way constructs are defined and refined over time introduces notable bias into models of behavior, which prevents effective knowledge building within and across subfields

Assessing Change in Intervention Research: The Benefits of Composite Outcomes

Intervention research is often time- and resource-intensive, with numerous participants involved over extended periods of time. In order to maximize the value of intervention studies, multiple outcome measures are often included, either to ensure a diverse set of outcomes is being assessed or to refine assessments of specific outcomes. Here, we advocate for combining assessments, rather than relying on individual measures assessed separately, to better evaluate the effectiveness of interventions. Specifically, we argue that by pooling information from individual measures into a single outcome, composite scores can provide finer estimates of the underlying theoretical construct of interest, while retaining important properties more sophisticated methods often forego, such as transparency and interpretability. We describe different methods to compute, evaluate, and use composites, depending on the goals, design, and data. To promote usability, we also provide a preregistration template that includes examples in the context of psychological interventions, with supporting R code. Finally, we make a number of recommendations to help ensure that intervention studies are designed in a way that maximizes discoveries. A Shiny app and detailed R code accompany this paper, and are available at: https://osf.io/u96em/

Open Meta-Analysis Checklist

Cite as: Moreau, D. & Wiebels, K. (under review). Nine Quick Tips for Open Meta-Analyses. PLoS Computational Biology Open science principles are revolutionizing the transparency, reproducibility, and accessibility of research. Meta-analyses have become key for synthesizing data across studies in a principled way, however their impact is contingent on adherence to open science practices. Here, we outline nine quick tips for open meta-analyses, aimed at guiding researchers to maximize the reach and utility of their findings. We advocate for outlining preregistering clear protocols, opting for open tools and software, and the use of version control systems to ensure transparency and facilitate collaboration. We further emphasize the importance of reproducibility, for example by sharing search syntax and analysis scripts, and discuss the benefits of planning for dynamic updating to enable living meta-analyses. We also recommend publication in open-access formats, as well as open data, open code, and open access publication. We close by encouraging active promotion of research findings to bridge the gap between complex syntheses and public discourse, and provide a detailed submission checklist to equip researchers, reviewers and journal editors with a structured approach to conducting and reporting open meta-analyses

Nine quick tips for open meta-analyses.

Open science principles are revolutionizing the transparency, reproducibility, and accessibility of research. Meta-analysis has become a key technique for synthesizing data across studies in a principled way; however, its impact is contingent on adherence to open science practices. Here, we outline 9 quick tips for open meta-analyses, aimed at guiding researchers to maximize the reach and utility of their findings. We advocate for outlining preregistering clear protocols, opting for open tools and software, and the use of version control systems to ensure transparency and facilitate collaboration. We further emphasize the importance of reproducibility, for example, by sharing search syntax and analysis scripts, and discuss the benefits of planning for dynamic updating to enable living meta-analyses. We also recommend publication in open-access formats, as well as open data, open code, and open access publication. We close by encouraging active promotion of research findings to bridge the gap between complex syntheses and public discourse, and provide a detailed submission checklist to equip researchers, reviewers and journal editors with a structured approach to conducting and reporting open meta-analyses

Assessing Change in Intervention Research: The Benefits of Composite Outcomes

Intervention research is often time- and resource-intensive, with numerous participants involved over extended periods of time. In order to maximize the value of intervention studies, multiple outcome measures are often included, either to ensure a diverse set of outcomes is being assessed or to refine assessments of specific outcomes. In the paper, we advocate for combining assessments, rather than relying on individual measures assessed separately, to better evaluate the effectiveness of interventions. Specifically, we argue that by pooling information from individual measures into a single outcome, composite scores can provide finer estimates of the underlying theoretical construct of interest, while retaining important properties more sophisticated methods often forego, such as transparency and interpretability. Shiny app: https://kwiebels.shinyapps.io/Multiple_outcomes_in_interventions