Open science for psychology and cognitive neuroscience

A short summary of our lab’s efforts and ambitions to embrace recent deveopments in methods and meta-science


May 16, 2023

A bit of historical context

A cornerstone of scientific study is the ability to accumulate knowledge over time in a progressive manner. However, threats exist that limit the steady accumulation of knowledge.

As poignantly remarked in 1974, Richard Feynman warned against the perils of pseudoscience and the relative ease with which scientists can be fooled into believing things that do not stand up to scientific study or reflect reality (.pdf). His now famous quote is as relevant as ever:

The first principle is that you must not fool yourself - and you are the easiest person to fool. So you have to be very careful about that. After you’ve not fooled yourself, it’s easy not to fool other scientists. You just have to be honest in a conventional way after that.

The open science movement is concerned with providing a scientific platform to prevent scientists from fooling themselves, as well as each other.​

Recent developments in open science and meta-science

Turning to recent developments in psychology and neuroscience, threats that limit the ability to build cumulative knowledge have been brought into sharp focus, which ignited the open science movement and led to suggestions for reform (Simmons et al., 2011; Munafo et al., 2017). Indeed, a cycle of studies with low power and a publication bias skewed towards positive results has produced a poor level of evidence for many claims made (Button et al., 2013; Open Science Collaboration, 2015).

Low reproducibility is a problem for the accumulation of knowledge, as well as an inefficient use of public funds. As such, improving the efficiency and robustness of science has important societal impact. Unfortunately, however, there is no easy fix for low reproducibility in science because it is the result of a complex and system-wide problem, which requires a diverse set of solutions (Nelson et al., 2018; Munafo et al., 2017; Nosek et al., 2022).

Our focus in this article

We focus here on the level of the research group, rather than the role of the wider scientific community (e.g., editors, journal policy, peer review, incentives and hiring committees). Below are seven methodological approaches that the SoBA Lab has embraced in recent years to improve research efficiency and the robustness of our findings:

1. Pre-registration

All confirmatory studies in our lab are pre-registered to reduce the opportunity for p-hacking1 and to provide pre-specified limits on researcher degrees of freedom (Simmons et al., 2011; 2018). Free, online resources are available to enable pre-registration (Open Science Framework; and a growing number of journals offer a registered report format.

2. Statistical power and sample size

Power analyses and other ways to determine sample sizes are becoming routine for all experiments. The consequence for research in psychology, whereby small and medium effects are common, will be that much larger sample sizes and/or much more data per particiapnt will be required. Of course, larger sample sizes are not always practical for some types of research, such as training studies, multi-session fMRI studies and when studying atypical populations. As such, we try to clearly justify statistical choices (Lakens et al., 2018), as well as consider alternative ways to increase power and determine target effect sizes (Albers & Lakens, 2018; Open Science Collaboration, 2017; Lakens, Scheel, Isager, 2018).

3. Replication

Multi-experiment replications are used to minimise the likelihood that we publish and pursue false positives (Zwaan et al., 2018). 2

4. Meta-analysis.

Meta-analyses and meta-analytical thinking are being incorporated wherever possible Cumming, 2014.

5. Open data, materials and code.

Whenever possible, data should be made freely available to facilitate meta-analysis, synthesis, and further analyses. And if it is not possible, there should be an explicit justification provided in the publication3. User-friendly data repositories are available for data in general (e.g., Open Science Framework), as well as for specialised data formats such as neuroimaging data (e.g.,, We are also trying to make the analysis pipeline open and available to others via the use of R and R Markdown and GitHub. As reviewers, you can also incentivise the sharing of data by signing and following the Peer Reviewers’ Openness Initiative.

6. Pre-prints.

We try to post pre-print versions of articles online to widen the “pre-review” process and to speed up the communication of research findings. Preprint servers are readily available and easy to use (e.g., bioRxiv, PsyArXiv).

7. More modest claims

The replication crisis has taught us that we need to become more modest in our assertions and to steer clear of confident proclamations based on isolated positive results. Lilienfeld, 2017

Exaggerated claims should be dialed down and the limits of work highlighted more (Hoekstra & Vazire, 2021; Ramsey, 2021). In other words, more modest claims should be made as a general rule. This would provide a stronger foundation for replication and the building of a more trustworthy and cumulative science.

Finally, we welcome feedback

We welcome feedback on our attempts to embrace open science initiatives. Please get in touch if you have any relevant suggestions.


  1. p-hacking or data dredging refers to the process of analysing data until a statistically significant pattern emerges, without first devising a specific hypothesis or a pre-determined analysis pipeline.↩︎

  2. A complementary approach would be to employ approaches developed from machine learning that aim to predict out-of-sample effects using cross-validation methods (Yarkoni & Westfall, 2017). Such machine learning approaches can be data-efficient by avoiding the requirement to collect an additional dataset and permit left-out runs to be used as independent data.↩︎

  3. There are several reasons why data cannot be shared publicly, such as if individuals could be identified or if another organisation controls access to the data. But in our experience, the vast majority of data produced in psychology and human neuroscience research can be shared publicly.↩︎


BibTeX citation:
  author = {Ramsey, Richard and S. Cross, Emily},
  title = {Open Science for Psychology and Cognitive Neuroscience},
  date = {2023-05-16},
  url = {},
  langid = {en}
For attribution, please cite this work as:
Ramsey, Richard, and Emily S. Cross. 2023. “Open Science for Psychology and Cognitive Neuroscience.” May 16, 2023.