How I use AI in my work

Tomorrow we will have a discussion in our lab about how to use AI as a scientist. I have increasingly started to use AI, having moved from Copilot Chat that our university provides last year, to some free credit on GitHub Copilot under their educational program, to Claude Pro integrated in Visual Studio Code, to a Claude 20x max subscription (in part to also make these AI tools available to my lab in case they want to use them).

I work with collaborators whose opinions and values are important to me, and several of them are more skeptical about the use of AI. I see their concerns, both in how my students use AI, and how some peers use AI. I am as annoyed by people use write texts where AI has made up references as anyone. I also see how tempting it is to use AI mindlessly. It does so much, so quickly, and validating that it has done things well is so effortful, that you get sucked into overtrusting AI. Some people don’t want to use AI at all, but for me, the question is not whether to use AI, but how to use it well. Of course, I don’t see any problems with how I use AI, and like in the rest of my work, I need others to criticize me to point out my own biases. So I am writing down my current thoughts, to make them as explicit as possible.

Using AI as a scientist matches my personality and work

I use smart home tools, and have been using If This Than That (IFTTT) for years. I like to use technology to make my life more efficient. In the last year, I have been using AI to create tools that improve my life. For example, I have asked an AI to create a pipeline where I can transform text (books and papers) to audio on my laptop, sync it to my phone, where I have a custom made Android app that I can use to listen to these audio files. When I swipe back, I can mark the section I listen to, and the text (with the use of meta-data files that link the time stamps in the audio to the sentences in the text) of this section gets automatically synced to my Obsidian note-taking tool. If that feels like overdoing it to you, and that you might go as far as buying a tablet to read, but that building custom software is a bridge too far, I understand. My personality is such that I like to be efficient. I would predict that the more efficiency matters to you in your job, the more you use technology, including AI.

AI use also aligns with work I do. I spend a lot of time behind my computer, interacting with software, working on websites, and I program R packages and tools such as Metacheck. Of course a computer already automates a ton of our activities, so AI use is an extension of the tasks we automate. But if you spend less time behind a computer, and especially if you do not code, there will be less use-cases for AI.

When and how to use automation?

I taught a Human Factors course, and the book we used has a chapter on human-automation interaction. It starts with the question “Why automate” and offers 4 reasons: 1) it is impossible or hazardous for people to perform these tasks, 2) tasks are difficult or unpleasant, 3) automation can extend human capability, or 4) we do it just because it is possible, even where it has no benefits. To the extent that we see AI as automation, I have never used it to do impossible or hazardous tasks, but I think I have experienced the other 3 categories. Sometimes the use of tools has been useful, sometimes less so (but learning when AI use is useful is for me just part of learning to use new technology).

Doing tasks that are difficult or unpleasant

Let’s start with unpleasant tasks. I often perform tasks that take a long time, and/or need to be monitored, but that require very little cognitive resources, and that an AI can do.

Grading

I teach Introduction to Psychology and Technology in the bachelor this quarter, and Metascience in the master. Both courses have weekly assignments that should be graded. The colleague who was supposed to assist with grading fell ill. We use the Canvas learning management system. Many of the assignments are mainly intended to activate students – for example, they have to walk around campus and take pictures of things in the built environment to assist blind and/or deaf people to find their way, or they perform an online experiment and have to copy-paste their results. All I do for these assignments is check if they have completed them, and then I assign them the points. There are also open questions that need actual grading by comparing answers to a rubric.

Canvas has an API, but figuring out how it works is too difficult and unpleasant so I never used it in the past. But AI can figure out how the API works. I use AI to write R scripts that download all assignments, and store the questions and answers in a spreadsheet. I use that spreadsheet to grade, which prevents me from having to click 20 times for all 160 students in the slow online Canvas system. When I am done with grading, I use an R script to upload the results to Canvas.

I also use Claude to check if assignments are completed. In the past, I would give all students a point for some questions without checking if they actually uploaded real pictures, as the assignment instructed. I knew some students uploaded images unrelated to the assignment in an attempt to make it look like they did the assignment, and I disliked that I did not have the time to grade these assignments, but I found it an acceptable trade-off for more engaging class assignments. Now, I can use AI to check the assignments I would otherwise just assign points for, thereby keeping the more engaging assignments, while also checking that students who try to get a point without doing the assignment receive no points.

There are a number of similar use-cases for grading. I have compared the quality of grading of low-stakes assignments last year, compared to this year, and the use of AI has improved the accuracy of the grades I assign. To be clear, I was aware that my grading was too lenient, but I think weekly graded assignments are more educational than removing them due to a lack of time to grade them carefully. For both courses, the assignments that are graded with AI are a small percentage of the final grade, unlikely to determine who passes a course, and who not. The psychology course has an in person final exam and for the Metascience course I have switched to (rather labor intensive) oral exams to make that master course AI proof.

The time saved on grading with the use of AI is substantial (approximately 2 hours per week, for 8 weeks), and the quality has increased. At the same time, I invest some of this time back into education, making new and more engaging assignments, and more personal assignments and tests. I think it has made my education better.

Monitoring

I perform many tasks on a computer that take a long time. In the past, I ran simulations that would last days, and currently, I run automated checks that Metacheck performs on thousands of scientific articles. When a computer runs for days, there are many things that can go wrong. AI helps me to create scripts that are more reliable, that store intermediate results and can restart easily, informs me when there are unforeseen problems, and allows me to quickly resolve them. Claude allows you to chat with another computer through the browser, which makes it very easy to leave a computer at work on over the weekend, while monitoring its progress, and asking Claude to resolve problems that emerge. For example, if during a run it becomes clear that there is a bug in a script, Claude can notice this before the run has continued for days, fix the code, and restart. I remember how in the past I would often rerun the same thing for multiple times across multiple days (often a real source of frustration in my work).

Adopting new Best Practices

I try to improve my research practices, but there is only so much I am willing to learn. I have been using GitHub, as an amateur, but at least I managed to made code open and have version control. But I never received training in Git, and I have used it suboptimally. Claude makes it much easier to adopt good coding practices, as well as cleaning up a repository after I made a mess of things. Creating new branches, using Large File Storage, creating public releases, making code reproducible, and documentation have improved a lot.

I am also increasingly using Docker, which was another tool I knew I had to learn to use, but found difficult. I was able to run containers in the past if there was a clear tutorial, but now I can also make docker containers and share them (for example as part of Metacheck’s reproducibility_check where we automatically check if code in repositories is computationally reproducible in a docker environment where most R or Python packages are pre-installed to speed up the checks).

In an ongoing project, where I am iteratively improving the code I am using, and therefore the data I am generating is updated, I was losing track of which files where updated, and which not, with the risk that the manuscript described stale results. I knew targets was the tool I should use to prevent this confusion, but I really did not have the time to learn it. With the help of Claude, I reorganized my repo and implemented it anyway.

Extending Human Capabilities

There are only so many things I can learn, and my lack of knowledge limits the things I can do. This is especially true when it comes to writing software and code.

AI helps me to complete ideas I have that I would otherwise never get around to complete. For example, I turned static pictures (generated by code I had written and validated that performs the correct calculations underlying these code-generated pictures in earlier versions of the textbook) into interactive visualizations. I want free open textbooks to be better than paid textbooks by commercial publishers, and interactive apps are one way to achieve this. But I don’t have the time to learn how to turn my code into an interactive app. AI is very good at this. This is also true for the creation of Shiny apps. It took me a long time to learn Shiny at a mediocre level, but with AI, turning code into an R Shiny app is trivially easy. I have to load Shiny apps on my server, which runs on Linux, but I don’t know Linux well, and AI is also of great help here. Shiny apps are now more up to date, because maintaining them and updating them takes less time.

One area where the use of AI becomes more problematic is when ‘extending human capabilities’ means ‘doing more in less time’. Where scientists used to have to bike to the university computer, hand in their programming cards, and return a day later to get the results, we can now perform statistics on our laptop in seconds. Where we used to browse through paper journals, we can now download any pdf version of an article we want. These innovations mean we can do things more quickly. Where should we use AI to speed up our work, even if we can also do this work manually? And, how do we know we are extending our capabilities, instead of using AI to create flawed work?

Validating code written with AI

When I let AI write code, I extensively validate the code against ground truth. This is easy when I know what results code should return, and not possible when I do not know what code should return. When I program on Metacheck and I develop a new module, for example one that automatically retrieves datafiles from repositories, I use manually coded ground truth (so, a datafile where people coded the correct information as accurately as possible) to validate the code I create with AI. I know that my code should find repositories, and I can check if it actually finds them. Even in this validating step, AI can be very helpful. Where I might manually check 20 repositories, I can use AI to check all repositories.

Here, we get to some challenges I experience when using AI. Manual validation against ground truth sounds like a good golden standard. But it is effortful, so using AI to validate AI is tempting. It is less work, and in all fairness, we are also fallible human beings, so sometimes our manually coded ground truth is wrong, and AI points this out. I think I spent more time validating code as I did before I used AI, but validation efforts are more extensive and more systematic, and they often occur at a higher level. I can feel a greater distance between the code and data and my validation work, towards an evaluation of the output of an entire piece of code.

Still, where needed, I can easily jump back in, and look at the dataframes my code generates, to see if the contents match my expectations. This requires coding skills, so AI can’t replace learning to code (even if it might change how we learn to code). For most of what I am building, AI seems to be doing very well, so this is increasingly less necessary – or maybe I am overtrusting my AI, and I think it is not necessary.

I spent a huge amount of time validating code (as I spend less time coding, I spend more time validating, and for many of the modules in Metacheck that I have created I spend weeks (often hundreds of hours) validating that they work, and iteratively improving them. For this reason, I don’t like the term ‘vibe-coding’. I feel this is a pejorative term that is used when people throw together code in a short amount of time without knowing what their code does, and whether it works. This is not what I do when I code with AI.

When I started coding, I did everything manually. Then I moved to copying code from stack overflow, then to copy-pasting code from ChatGPT, and now I ask Claude to create the code. This is an improvement over asking ChatGPT (and if you have not used AI inside your programming software, you don’t really known how coding with AI works and can’t judge it). Because Claude has access to the code, and the results, it can check the input, code, and output. If you have a good default prompt (e.g., you tell it to always check everything, never make assumptions, etc.) the quality of the code is hundreds of times better (and really not comparable) to asking ChatGPT ‘how do I create a dataframe with data organized like I want’.

I also think there is a lot to learn about how to set up a workflow where I am even more likely to notice when AI created code that is not in line with what I intend to create. I am just learning how to use these tools – of course in a decade I will be much better at it. When I learned to share my data openly, I linked to a dropbox folder in my scientific articles. Of course these links are dead now, and this was really bad practice. I am sure I am making similar mistakes when I use AI. But the mistakes might already be smaller than when I figured out new technology (like data sharing) myself.

When there is no ground truth, I would be much more careful in using AI. I also use AI to write reproducible manuscripts where I do not know what the results should be. In these cases, I don’t know if a number should be high, or low. I might still ask AI to create a first version of the calculation of the descriptive report. But then I have to double check every number, and here, I often intended to compute other numbers than AI thinks I wanted to generate. It is still helpful, and the corrections are less work than writing all reproducible analysis code from scratch, but the required oversight is much larger, and I really need to look at the raw data and code to make sure everything is accurate. Here, I will still use AI to ask how to compute certain output, then I check the code, I correct it where needed, but then I would also ask AI to check my corrections. After all, I also make mistakes, and AI can help to catch these more quickly.

So, there is a lot of contextual variation in how much I use AI, and what role it plays in the work I am doing. I don’t spend less time checking my work – if anything, I spend more time on it – but the nature of the checks have moved to a slightly higher level, often more away from the data and individual code lines, to the results that the code should produce. This is actually not that different from when I used to code simulations manually. I would also evaluate the results on a higher level (the results of the simulation) and only jump into the dataframes and individual lines if I expected that there was something wrong. At the same time, those simulations were built in smaller steps, and the individual sections of code would receive at least a basic check. Now, these lower level checks are still present, but instead of manual checks, I often ask AI to write explicit tests (e.g., in this dataframe, column 1 should be a number between 0 and 1). This is on the one hand a better practice, but sometimes also comes closer to letting AI check AI. It’s a space where I still need to explore how to create a reliable and efficient workflow.

Using AI for Writing

As enthusiastic I am about using AI for code, as disappointing I am in using AI to write. Maybe it’s because I have a lot of experience in writing, but I find AI written text mostly mediocre. This also holds for Peer Review. The only use I find in AI for peer review is (beyond maybe using Claude to automatically reproduce your results) is to remove the Dutch Directness that can creep in some reviews.

There are some exceptions. Sometimes AI can improve parts of text I write. I write too long sentences, and sometimes as a non-native English speaker I create weirdly formatted sentence constructions, I don’t know the right words, or the thoughts just appear on paper somewhat unclear. Asking AI to suggest an alternative sometimes (but not always) provides inspiration for how to improve the sentence.

Another exception is low-stakes writing. Sometimes as academics we need to create text that will not be read (or not be read by many people), where we would otherwise invest so little effort that our writing would not be better than what AI produces. Of course, you could also say ‘why not reduce bureaucracy in institutions’? I agree. With many things, AI use amplifies underlying problems. If people use AI to write, maybe what they are writing does not need to exist. If AI increases our reflection on this topic, that is a good thing.

Another exception is documentation. If I want to summarize the code in a repository, AI will do so more diligently that I can. Similarly, the Metacheck Manual has large sections that are written by AI (although I also rewrote a lot of sections) because it summarizes what code does, and AI can do this relatively well. Ideally I would write every line myself, but I don’t have the time, and the entire team is busy making software. So, AI makes documenting things less work, and although it is certainly not as good as what a human would produce, given our priorities and limited resources, it suffices. If you prefer manually written documentation, as I do, and you have time, it would be a great way to help with Metacheck.

Other uses of AI

I personally like the ability to automate boring tasks (e.g., asking AI to prepare a meeting, copy files from my computer to a harddrive, cleaning up messy folders, look at which of 10 meetings dates is free in my calendar, etc.). I also like the voice interface of Claude. Instead of reading Wikipedia (which I often do), I can ask Claude questions while am walking around. The answers largely come from public sources like Wikipedia, but I can more quickly switch and develop my thoughts about a topic I don’t know a lot about. I don’t like the use of AI to generate pictures, videos, or presentations. I find them bland and uninteresting. But, this drifts away from the specific use of AI as a scientist, and this blog is long enough as it is, so I will not go into details about other uses of AI too much.

Ethical aspects of AI

I am regularly reflecting on the ethical aspects of using AI. I am not an expert on this, so I can’t say sensible things. I hope there will soon be a European Union version of Claude for scientists, and I will switch over to one if it exists. I prefer to spend my money on non-commercial and open access tools. I am hopeful this will soon become possible. But in the meantime, I think Anthropic is not that different from my bank. There are a lot of values I disagree with, but the benefits of having a bank outweigh the problems I have with banks in general. I am supportive of the general policy in the EU around the use and development of AI, so I hope that in the future I will be happier with the organizations that provide me access to AI than I am with banks. I do not have a drivers license, but if you do, you should probably be more upset about the companies behind the oil you buy than about the company that provides your AI inference, in my humble opinion.

Then there is the climate. Data servers use a lot of energy. Here, the question is how much energy we want to use, and what we want to use it on. I think people using AI to generate or adjust pictures and videos should stop using AI. I don’t see any societal benefit, and it uses quite some energy. I also think we should reduce our reliance on data servers where possible. I personally have stopped automatically syncing everything to the cloud. For example, I maintain a local back-up of my pictures, but I no longer store them in a datacenter. If we want to reduce the use of datacenters, I would personally prioritize a reduced use of video streaming. I feel services like Instagram reels waste everyone's time and are overly addictive, and I would not mind it if people who want to use them need to pay the environmental cost (compared to the current free use) of streaming, and I think the world is better off if these services do not exist. I also think we should stop streaming video with services such as Netflix. As someone who likes vinyl, I think we should own the media we consume. If we want to rely less on datacenters, I would prefer a societal movement away from all streaming services such as Netflix. As video streaming takes up a much larger percentage of the use of datacenters, I think for my own personal life, I prefer to use energy more on AI, and less on things like video streaming.

This blog is mainly an attempt to make my current behavior and beliefs more explicit, so that they become easier to criticize. I am sure I am getting things wrong, I am missing things I should weigh more, I have not discussed all important aspects, and in 10 years I will read this and know better. But I at least feel prepared to go into our lab meeting next Monday, and spend some time with the old-fashioned use of a keyboard to organize my thoughts. (Yes, this 100% human writing but it would be funny if you thought otherwise).

Which Data Repository Should You Use?

The Center for Open Science has announced that from November 16, 2026, no new projects can be created on the Open Science Framework. After February 19 all projects will become read-only, and eventually, they anticipate that all private projects will be deleted. The OSF has raised a generation of scientists that is able to practice open science. These scientists will need to take these skills to a new data repository in the future.

 

Choosing a New Data Repository

 

There are many data repositories, each with their own strengths and weaknesses. Before I compare them, I want to make it clear that the fact that organizations are willing to store your data for free for a very long time is an amazing service. You should be incredibly grateful that any and all of these services exist. As someone who had a free Hotmail email account in 1997 with 2 mb of free storage, the fact that we can now store up to 50GB for every project we create in a data repository blows my mind.

Some common data repositories used in psychology are Zenodo, GitHub, ResearchBox, Dataverse, and PsyArchives. I will compare these services on properties that I think are important.

1. Longevity

We are all grains of sand in the desert of eternity. Nothing lasts forever. But it would be nice if our data repositories will keep going for a while. Zenodo is hosted by CERN and commissioned by the European Commission through the OpenAIRE project. CERN has been around since 1954, and it will be around well until anyone who will ever read this is dead. GitHub is too big to fail, and was bought by Microsoft in 2018 for 7.5 US billion. It will be around for a while. PsychArchives is hosted by ZPID, which started in 1971, and which I expect to exist many years from now. The first Dataverse repository was hosted by Harvard in 2006. Dataverse is not a single repository. For example, Harvard Dataverse and DataverseNL are different instantiations of the service. The Open Science Framework started in 2013 and is hosted by the Center for Open Science. ResearchBox started in 2020, and is hosted by the Wharton Credibility Lab, and funded by the Wharton School of the University of Pennsylvania.

Predicting the future is notoriously difficult. I think the order in which I discussed the platforms is also the order in which I would rank their longevity, but I would not dare to give a number on how long each service will last. As the team behind ResearchBox explains, they set up the system to minimize misuse, but their costs will continue to grow, especially if more researchers start to use the platform. I would guess that for the next decade, all these services are likely to continue to exist, and the ones earlier in the list will continue to exist for a long time. Note that data hosted on these platforms will continue to exist in any case. ResearchBox uses Zenodo as one of its long term back-up solutions.

2. API

Although you might think that data re-use will mean that a person will browse through your data, data and code sharing is done as much for re-use by people, as re-use by machines. To enable re-use by machines, platforms need a good API (Application Programming Interface). GitHub, Zenodo, and Dataverse have good APIs. The OSF has a usable API, but it is not well-designed, and requires some effort to use. PsychArchives has an API, but it is hidden – it is not documented, so if you want to use it you have to discover it yourself. This is suboptimal. There is an additional challenge for PsychArchives: While public files on PsychArchives can be downloaded automatically, files shared under more restrictive licenses can be downloaded manually, but not by software.

ResearchBox has no API. This is the biggest weakness of ResearchBox, and it is serious enough such that it prevents me from recommending ResearchBox as the main alternative for people looking for a new data repository. ResearchBox can make an API, and if it does (and it does it well) the platform is an excellent alternative to the OSF. But currently, software can’t automatically interact with (e.g., download from or upload to) ResearchBox, and this is a problem for re-use. It is possible to scrape the ResearchBox website, but this breaks too easily and is not a long-term solution.

Dataverse is open-source repository software rather than a single service: it is run independently by many institutions (e.g., Harvard Dataverse, DataverseNL, and various university-hosted installations), each on its own domain. They all have the same API, but a tool first needs to recognize which of the many independent Dataverse installations a given link belongs to. It is possible to work around this if one has a list of all Dataverse repositories, but it adds complexity that is not present for all other services.

3. Metadata

Metadata might be a bit boring, but it is how re-users will find your data and code when they are not approaching it through a link in a paper. That makes it very important (but boring).

On the OSF, a project (or component) carries a title, a description, a controlled `category` (e.g., project, data, hypothesis, methods and measures, software), an optional custom citation, free-form tags, a license, a discipline classification from OSF's own subject taxonomy, and contributors. This is rather limited as we will see compared to other repositories. When it comes to metadata, there is a lot to gain by using other repositories.

Zenodo's metadata model is richer and more structured for citation purposes: alongside title, description, and creators (name, affiliation, ORCID, GND), a deposit requires an `upload_type` (e.g., publication, dataset, software, poster) and, for publications, a `publication_type` (e.g., journal article, preprint, report, thesis). It also supports free-form keywords, and a controlled `access_right` (open, embargoed, restricted, closed) with an associated license or embargo date. It also has `related_identifiers` linking the deposit to other DOIs with a typed relationship (e.g., isSupplementTo, cites, isNewVersionOf). You are not as excited about this as you should be. Links between files matter a lot in understanding what files are, and how they can be re-used. There are also `contributors` (e.g., DataCurator, Supervisor, RightsHolder) distinct from creators, and `grants`, which link the deposit to a funder-registered grant ID so it appears in that funder's own reporting.

Dataverse is even better when it comes to metadata, and it places strong emphasis on structured, repository-level metadata that supports discovery, citation, and interoperability. Each dataset is described through a citation metadata block containing fields such as title, author, contact, description, subject classification, keywords, producer, distributor, publication date, and related publications. Dataverse repositories can also enable domain-specific metadata schemas, including social science, geospatial, life science, and astronomy extensions, allowing much richer description than a generic file upload. In addition, datasets receive persistent identifiers (typically DOIs), versioning metadata, and machine-readable metadata exports in standards such as Dublin Core and DataCite, facilitating integration with external discovery services and repository networks.

PsychArchives follows a metadata model designed around the needs of psychological science and the FAIR principles. Deposits are assigned DOIs and can represent a wide variety of digital research objects, including articles, preprints, datasets, code, preregistrations, tests, and multimedia content. Metadata record fields support detailed descriptions of authorship, affiliations, object type, publication status, licensing, keywords, and links between related research outputs from the same study. Because PsychArchives is embedded in the ZPID ecosystem, metadata are also optimized for indexing by services such as PsychPorta, Google Scholar, and other scholarly discovery systems. The repository's emphasis is not only on describing objects individually, but also on connecting related outputs into a coherent research record. Once again, you are not as excited about this as you should be, but knowing a dataset belongs to a supplement that belongs to a paper is something we all want even if we do not realize it.

ResearchBox takes a somewhat different approach to metadata, prioritizing usability and transparency of research materials by people who browse the website over highly formalized archival description. Public deposits are organized into standardized "Bingo Tables" that indicate which research components are available, such as preregistrations, materials, datasets, code, and manuscripts. ResearchBox automatically generates dataset codebooks and supports searching within datasets, variable descriptions, and code files, making metadata partly file-derived rather than solely author-supplied (this is very good, and something other repositories can learn from – I strongly believe that if we ever have FAIR metadata, it is because most of it is automatically generated). As there is no API, this metadata can only be searched manually on the website, which is the biggest limitation of ResearchBox.

4. Anonymous Peer Review

Many journals still value and require anonymous peer review. PsychArchives, ResearchBox, and the OSF make it possible to share an anonymous link to the datafiles for peer review. GitHub repositories can be made anonymous for blind peer review through https://anonymous.4open.science. Zenodo allows users to create a new upload in Zenodo, keep it private, fill in the metadata while not disclosing any personal information, and without publishing the record, click Share, Links, and create a secret link with the permission that people with the link "Can preview draft". This allows reviewers to access the unpublished draft, while the files remain invisible to the public. Dataverse offers a similar but more automated solution: from an unpublished draft dataset, selecting Edit, then Preview URL, offers a choice between a "General Preview URL" (a secret link giving access to the draft, comparable to Zenodo's) and an "Anonymous Preview URL". The anonymous option automatically strips author names and other identifying information from the citation, version history, and metadata fields before showing the page to anyone holding the link, rather than relying on the depositor to avoid entering identifying information in the first place.

All platforms allow anonymous access to files, but ResearchBox, OSF, and PsychArchives make it really easy. Dataverse accommodates it well, it takes some getting used to on Zenodo, and it is hacky but possible on GitHub.

5. Usability

When it comes to data sharing platforms, they need to excel at two things. First, it should be easy to upload and share data, and second, others should be able to find and download the data. One of the continuing complaints about the Open Science Framework was that the user experience got in the way of data sharing and re-use. When it comes to sharing your data, ResearchBox and PsychArchives offer a much smoother experience for users who want to manually upload files, and especially ResearchBox cares a lot about ease of use. Dataverse and Zenodo are also quite intuitive, but a bit more bare-bones and it has some peculiarities we will need to get used to. The biggest is probably that Zenodo does not support folders – all files are uploaded in a flat structure – so users need to learn how to use file names or zip files to organize the files they upload. Using GitHub probably requires a short course, and the biggest difference with other platforms is that you are not supposed to interact with it directly. Instead, you use software (or, if you are a hardcore programmer, the terminal) to push and pull files from the platform.

When it comes to finding files, ResearchBox has the most useful search, as it allows you to search not just for filenames, but also inside data and code files. However, the lack of an API means you can only perform these searches manually, and machines can’t access the file content. This is a major limitation. Dataverse also allows you to search inside tabular data. This means you can search for the word ‘PANAS’ and try to find datasets that have data from the positive and negative affect schedule measure. Dataverse can be searched through an API. All other platforms only allow users to search variable names and metadata.

Personally, although I appreciate ‘first-order’ direct manual usability of a platform, I care a lot more about ‘second-order’ indirect programmatic access to a platform. We will always have different data repositories. If I want to search for datasets that contain the ‘PANAS’ I do not expect that I will visit all repositories one by one, but I will use a search engine that covers all existing databases. Indeed, I am pretty sure that these search databases will have downloaded and processed all files on data platforms using algorithms that extract all data they can from the files. Searches will in the future be performed on a custom database. I also think that we will rely less on the user interface of a website. One thing my master students have recently taught me is that nowadays you ask an AI to create the user interface you want, instead of manually using a website. An example of this for the OSF is BetterOSF by Gilad Feldman. Making a custom interface on top of an existing data repository (or on top of multiple data repositories) is something AI can code for you relatively easily, but it requires the data repository to have a good API.

6. GDPR

As a scientist in the EU, I have to follow the General Data Protection Regulation (GDPR). For fully anonymized data, the GDPR does not apply, and I can store the data anywhere in the world. However, most of my data is pseudonymous. There is a participant ID in the data I share that links the participant to a datafile that is not public, but that is stored in the data archive our department uses to store research data. Even if the shared data has no personally identifiable information, the fact that it is possible to link it to an email address or IP address through the participant number makes the data pseudonymized, and I need to follow the GDPR.

As far as I understand the GDPR, it suggests (as they say, #NotALawyer) that there are routes to sharing pseudonymized data outside of the EU, but either the data repository needs to be Data Privacy Framework certified, or there should be a Standard Contractual Clauses agreement, or I would need to document the procedure for pseudonymization, and formally ask for approval from national supervisory authority, and inform participants about it. Sharing anonymized data on Harvard Dataverse or ResearchBox is easy – sharing pseudonymized data is not easy.

Zenodo is an interesting case, and I learned something new and important while preparing this blog. Cern is located in Geneva, so you might think this makes data storage on Zenodo simple from a GDPR standpoint. But Cern is an international organization with special legal status under international law. It is not subject to the GDPR. CERN instead processes personal data under its own internal framework, which it and Zenodo describe as built to the same standard as GDPR, without being a legal GDPR obligation. This means it is legally not possible to share pseudonymized data with them under the GDPR. They explicitly state they can’t sign data processing agreements. They explicitly say other repositories might be better for data that originated from or contains/ed sensitive personal data. Even though I was not aware of this, and I think I have often heard it said the Zenodo is GDPR compliant, it can’t be, as the GDPR does not apply. This is a weird legal loophole that applies to all International Organizations (this is an interesting discussion). It seems that the EU has a new set of guidelines open for consultation until October 30th to treat what is 'anonymized' more flexibly, which would possibly make Zenodo compliant with the GDPR. 

The easiest way to comply with the GDPR for pseudonymized data is to use DataverseNL (which has signed an agreement to process data with participating institutions) – or other local repositories you might have access to (for my own universities, there is the 4TU.Research.Data repository). PsychArchives only accepts anonymized data. The downside of this is that research data might become distributed over a large number of local repositories, each with their own rules and standards, which will make it more difficult to automatically find and integrate all data in a field.

Conclusion

We live in a time where you get to pick which service to use to freely store your research data. This is truly amazing, and I am immensely grateful to all the people who have built and maintained these infrastructures. It also means there is something to pick, and that platforms can learn from each other. This is also why ResearchBox gets so many things right – as the newest addition to the data repository landscape, it could learn from and improve on existing repositories. However, they really need an API. Without one, it is difficult for me to recommend the platform, even though it scores at least as well as other platforms on the other dimensions. PsychArchives similarly needs to document their API, and allow programmatic access to files shared under restrictions (some files are publicly available under license restrictions, but can only be downloaded after manually clicking a box).

Dataverse and PsychArchives support metadata in the most systematic manner. Although very few people want to learn about DublinCore metadata standards, people who create data repositories should, and repositories that support these metadata standards will turn out to be more useful a decade from now than platforms that do not. However, as I personally prefer metadata to live as a standalone .JSON file that is shared alongside data (similar to how we now recommend a readme file) I think it is not that problematic if repositories do not follow a metadata standard.

The best choice for a repository also depends on your computer skills. If you interact with the internet by pointing and clicking, you will feel more comfortable on ResearchBox and PsychArchives than on GitHub. But if you interact with the internet through R or Python, you will need to use Zenodo, Dataverse, or GitHub.

If you have pseudonymized data, formally Zenodo can’t be GDPR compliant (you either make data anonymous, or switch to another repository). Either make the data fully anonymous (e.g., delete any identifying information, but this makes data provenance more difficult, and is undesirable) or use GDPR compliant data repositories (e.g., DataverseNL).

It is clear to me that everyone will need to make their own decisions about the repository that is the best fit for their use-case. In my case, I will likely use Zenodo for anonymized data and projects that have code (probably by creating a GitHub release and linking it to Zenodo), and – surprisingly, as I did not expect it when I started this blog post – the local 4TU.Research.Data repository for personal data that needs to adhere to the General Data Protection Regulation.

Coda

When we compare the Open Science Framework against the alternatives, it is clear we are losing an important player in the data repository landscape. It does well in terms of GDPR, has a decent API, and facilitated anonymous peer review. The usability could be better, but for small projects, it worked well enough for me (at least until the last redesign). Big alternative platforms such as Zenodo, GitHub, and Dataverse were around at the same time, but it was the Open Science Framework that managed to convince researchers in the social sciences to share data, code, and materials. That is a huge accomplishment, and if users move on to these other platforms, it is largely because the Open Science Framework got people started in practicing open science.

Evaluating Dr. Cuddy’s Claim that the Debunking of Power Posing is a Myth

In this blog post I will analyse the arguments that Dr. Amy Cuddy provided in a LinkedIn post “The "Power Posing Was Debunked" Myth: What the Research Actually Shows — and Why Scientific Discourse Matters” on February 26. You can find the LinkedIn post here:

https://www.linkedin.com/pulse/power-posing-debunked-myth-what-research-actually-shows-amy-cuddy-t6lnc

In the post, Cuddy says she was “effectively silenced” by an “attempt to shut down this line” of research. She credits “the courage of the individual scientists who kept going despite enormous pressure not to” for the fact that she can still summarize “what the evidence now shows”.

Power posing has two categories of claimed effects. The first effect is on self-reported feelings. For example, if we instruct people to stand in a constricted versus an expanded posture, they will self-report feeling more powerful. There is an ongoing debate about whether, or how much, this effect is caused by a demand effect (i.e., people report what they think the investigator wants them to say, not what they actually feel). A meta-analysis has shown this self-report effect is larger in within-subject designs, and in studies without a cover story (Körner et al., 2022). The second effect is on physiological or behavioral outcomes. This is the contested area, and the research outcome that Cuddy is mainly trying to defend in her blog post. If you want to explore a meta-analysis on these two categories of effects, you can do so at https://metaanalyses.shinyapps.io/bodypositions/ (made by Körner et al., 2022). I would especially recommend exploring the QRP/Publication bias tab for the physiological and behavioral outcomes.

At the end of the post, Cuddy writes that she is thankful that not everyone stopped doing research on power poses, because then: “We would not know what we now know — which is that these effects are real, that they matter, and that the story people were told was wrong.”  She concludes with: “The evidence is there. It has been there for years. All I am asking is that people look at it.”

I am happy to do so. Let’s go.

Trying to find the references

I tried to look up the references cited by Cuddy in her post. However, this reference:

Andolfi, V. R., & Antonietti, A. (2020). Contractive vs. expansive body posture effects on convergent-integrative thinking tasks. Journal of Creative Behavior, 54(4), 871–880.

does not exist in literature databases, and the authors (who do exist) do not list this paper on their own websites. An inspection of the journal’s website shows that a different article was published in volume 54, issue 4 on these pages. This raises questions about how this reference was generated, with generation by AI being a plausible candidate (also in view of the 4 malformed references I will point out below). The reference appears in the following sentence in Cuddy’s LinkedIn post:

Andolfi and Antonietti (2020, Journal of Creative Behavior) provided further evidence that contractive postures specifically benefited convergent-integrative thinking tasks. That level of specificity — where the direction of the effect depends on the type of cognitive task — is exactly the kind of finding that emerges when a field matures.

When Cuddy says ‘The evidence is there’, this is not correct for the Andolfi and Antonietti article, which does not seems to exist in the scholarly record.

There are an additional 4 references that suggest that the literature review may have in part been generated by automated tools, but for these 4 references, there are papers that match the content discussed in the literature review in the LinkedIn post.

 

Reference in LinkedIn post	Actual Reference
Michinov, E., & Michinov, N. (2020). Creativity connected with body posture: The effects of expansive and contractive postures on creative performance. Psychology of Aesthetics, Creativity, and the Arts, 14(1), 116–127	Michinov, N., & Michinov, E. (2022). Do open or closed postures boost creative performance? The effects of postural feedback on divergent and convergent thinking. Psychology of Aesthetics, Creativity, and the Arts, 16(3), 504–518. https://doi.org/10.1037/aca0000306
Wainio-Theberge, S., Bhatt, M., Bhattacharyya, K., et al. (2025). Neural correlates of power-related postures and their behavioural consequences: A preliminary electrophysiological investigation. Social Cognitive and Affective Neuroscience, 20(1), nsaf03	Wainio-Theberge, S., & Armony, J. L. (2025). Neural correlates of power-related postures and their behavioural consequences: A preliminary electrophysiological investigation. Social Cognitive and Affective Neuroscience, 20(1), nsaf036. https://doi.org/10.1093/scan/nsaf036
Elkjær, E., Mikkelsen, M. B., Michalak, J., Mennin, D. S., & O'Toole, M. S. (2023). Using bodily displays to facilitate approach action outcomes within the context of a personally relevant task. Frontiers in Psychology, 14, 1147printing	Elkjær, E., Mikkelsen, M. B., Tramm, G., Michalak, J., Mennin, D. S., & O’Toole, M. S. (2022). Using bodily displays to facilitating approach action outcomes within the context of a personally relevant task. Brain and Behavior, 13(1), e2855. https://doi.org/10.1002/brb3.2855
Körner, R., Köhler, H., & Schütz, A. (2020). Powerful and confident children through expansive body postures? A preregistered test of the effects of power posing on children. School Psychology International, 41(4), 315–330.	Körner, R., Köhler, H., & Schütz, A. (2020). Powerful and confident children through expansive body postures? A preregistered study of fourth graders. School Psychology International, 41(4), 315–330. https://doi.org/10.1177/0143034320912306

 

We see all these references that are incorrect refer to the later literature, and summarize the research of the people who ‘kept going’. These references are at the core of the argument Cuddy is making.

Evaluating the evidence: Three examples

Cuddy wrote a narrative review, which requires that the validity of the conclusions, and the strength of the evidence, needs to be evaluated for every study. Let’s carefully examine some of the papers she cited and evaluate the evidence. Cuddy writes about a first study:

Wainio-Theberge and colleagues (2025, Social Cognitive and Affective Neuroscience) published the first EEG study of power posing, finding significant effects on arousal and valence, with suggestive differences in frontal brain activity between expansive and contractive postures. A new neural methodology for a question people said was already answered.

From the description in Cuddy’s blog, you might assume the “significant effects on arousal and valence, with suggestive differences in frontal brain activity between expansive and contractive postures” would support the hypothesis. But this is not the case. The significant effects were actually in the opposite direction of the hypothesis. This is not mentioned in the abstract of the Wainio-Theberge et al. article, and one would need to read the paper to get this information:

We found no significant posture differences in the EEG spectral exponent (t(101) = 1.01, P = .32). In contrast, a significant posture effect was observed for frontal asymmetry (t(101) = −2.63, P = .01); however, post hoc t-tests in each group separately (‘Models 1c and 1e’) revealed that the effect was in the opposite direction as hypothesized (see Discussion). Namely, we observed a significant right-lateralized frontal alpha asymmetry (FAA) in the contractive group (t(45) = 2.17, P = .04) and a left-lateralized FAA in the expansive one which failed to reach significance (t(55) = −1.63, P = .11).

Cuddy writes in the blog that she responded to journalists skeptical about power posing: “I spent more than ten hours responding — reviewing the literature, pulling citations, writing carefully, anticipating distortions” In this case, her review of the literature presented a finding as providing support for power posing, when in fact the effect was in the opposite direction of the hypothesis.

As a second paper, let’s take Barel and colleagues (2024). First, I want to thank the authors for sharing their data, after I tried to access it by clicking the google drive link in the article. All numbers were reproducible. Cuddy cites the paper as follows:

“As other researchers began testing that broader construct, using different measures in different populations, they found effects consistently: action orientation (Huang et al., 2011, Psychological Science), […], and risk-taking itself, partially (Barel et al., 2024, BMC Psychology).

It is unclear what is meant by 'partially', as the authors are clear that they found that power posing did not affect risk-taking: "There was no statistically significant distribution in risk-taking between high and low power conditions [χ2 = 0.00, p > 0.99]." The risk-taking outcome that Cuddy cites the study for is a clear null result.

The basis for "partially" is presumably a separate analysis reported in the paper: in a logistic regression predicting risk-taking, the authors found a significant interaction between power condition and cortisol change and write that they "did partially replicate an effect of changes in cortisol levels on risk-taking." But note that they claim an effect of cortisol changes on risk, not of power posing on risk. For power posing to affect risk through cortisol, power posing would first have to change cortisol, and it did not: the authors report no main effects of time or power on cortisol. With that first link missing, the high-power participants whose cortisol fell are not a subgroup of people for whom the power pose worked, as their cortisol would have moved the same way without any pose. The significant effect is a within-group association between two measures, which can't be attributed to the power posing manipulation.

To their credit, the authors themselves never claim power posing affected risk-taking. This framing comes from Cuddy, who presents the paper as a partial replication of a risk-taking effect after a power posing manipulation, which the study did not support.

When discussing a third paper, Cuddy writes: “Körner, Köhler, and Schütz (2020, School Psychology International) conducted a preregistered study of 108 German fourth graders — children — and found that expansive postures increased self-esteem, positive feelings, feelings of power, and even children's perceptions of their relationship with their teacher. The strongest effects were on school-related self-esteem. This is exactly the kind of applied, developmentally informed research that matters — taking findings from the lab and asking whether they help real children in real classrooms.”

The Körner et al study was preregistered: https://aspredicted.org/blind.php?x=sn4su9 with 4 t-tests to examine 4 dependent variables of interest. Of the 4 tests, 2 are significant (p = 0.04 and p = 0.013), but neither survive a correction for multiple comparisons (0.05/4 = 0.0125) which was necessary in this analysis.

The blog by Cuddy states “The strongest effects were on school-related self-esteem.” But the biggest effect is actually on the student-teacher relationship:

Finally, there was a significant difference between the two groups regarding the pictures related to the student–teacher relationship: high power posers more frequently chose the picture showing a good student–teacher relationship than low power posers, Χ²(1) = 11.181, p = .001, φ = –.322.

But there is a problem with this finding. Students spent months building a relationship with their teacher. Then, as part of the experiment, the students posed for 60 seconds and self-reported on that relationship, without any further interaction with the teacher.  There is no possible causal mechanism for the power pose to impact the relationship with teachers. Although unintended, this question is an excellent probe for demand effects. As the power pose can’t change history and impact the actual relationship between students and teachers, the observed effect can only be caused by a demand effect. Neither Cuddy nor the original authors realized this. Cuddy instead concludes: “This is exactly the kind of applied, developmentally informed research that matters — taking findings from the lab and asking whether they help real children in real classrooms.”

Evaluating the Research Line

Evaluating evidence is effortful and messy. Single studies always have weaknesses, and the reader might reasonably wonder whether I’m cherry-picking a few bad apples from an otherwise strong set. I don’t think I am, and I will explain the more general pattern I observed when reading all the cited papers.

Exploratory claims

The Körner et al (2020) study above was preregistered, and therefore we were able to evaluate that the claims were not severely tested, as they would not survive the required correction for multiple comparisons (Lakens, 2019). But most claims in the papers that Cuddy cites are based on exploratory analyses. The studies all have many dependent variables, and a large number of tests can be performed. These studies observe a mix of significant and non-significant results, but the significant results have a high probability of being Type 1 errors and can’t be presented as evidence. If researchers in this field would perform more direct replication studies, and would preregister their studies more, they could address this problem. Some preregistered their studies, which is excellent, but some don't, even though they work in a highly contested research area, and the significant results primarily come from exploratory analyses.

Researchers in the field are often honest about this, but especially in a narrative summary, it is easy to lose track of the fact that most of the authors of studies cited by Cuddy do not consider their own findings to be strong evidence. For example, Metzler et al (2023) write “Finally, it is important to transparently report on the level of evidence this study provides for power pose effects on low-level social behavior. This requires mentioning its exploratory nature [...] we are convinced that the medium effect sizes, given our sample size, would require replication before strong conclusions can be drawn”. I would say this is especially important given that the main result was a 3-way interaction with a p-value of 0.03: “the predicted three-fold interaction suggested that this effect of emotion on action choices (more avoidance for anger than fear) changed between sessions as a function of adopted pose (OR = 1.19, 95% CI[1.02, 1.38], z = 2.18, p = .029)”.

Another example comes from Elkjær et al (2022). The main finding is: “Concerning approach tendencies, the 2 × 3 interaction analysis on DAT “approach threat 1” was significant (F(1, 87) = 3.27, p = .043, ηp2 = .07). Regarding DAT avoid threat (1 + 2), the overall 2 × 3 interaction analysis was significant (F(1, 87) = 6.39, p = .003, η_p² = .13).” The study was preregistered (https://aspredicted.org/blind.php?x=9j3b38) which allows us to see that the preregistered predictions are not supported. The authors predicted significant effects for the expansive condition compared to both the constricted condition and the control condition. However, they did not find effects compared to the control condition. Such patterns of mixed results are present in many studies in the literature. On the one hand, this is part of normal research, especially early on in research lines, when researchers have not figured out how to reliably produce the effect they are examining. On the other hand, power posing has been studied since 2010, and a research line can never get a strong basis if it does not move beyond a literature where all significant results are based on exploratory partial confirmations.

If you want to see the exploration of data in action, I would recommend looking at the OSF repository related to the paper by Michinov and Michinov (2024): www.osf.io/c9mzh, and see which variables and ways of computing variables are reported in the final paper, and which are not.

Underpowered studies and selection for significance

The sample sizes in the studies cited by Cuddy are often small – especially for key sub-group analyses, when the total sample size might be distributed across cells in a 2x3 design. This would not be problematic if the effects of power posing were known to be large. But even the self-report effect where participants indicate they feel more or less powerful has a rather small effect size of only g = 0.37 (see https://metaanalyses.shinyapps.io/bodypositions/). Less direct effects, for example on behavior, are likely to have a much smaller effects (unless researchers can propose strong theoretical arguments why more indirect effects would be larger, see Anvari et al., 2023). In one-tailed independent t-tests, 80% power would require 184 participants (92 per condition), but none of the studies are close to achieving such sample sizes.

The research area of power posing is also characterized by the selective reporting of significant results. This combination of underpowered studies and selection for significance leads to highly inflated effect sizes. We can see these effects in Andolfi et al (2017):

The effect sizes of an open or closed posture simply can’t be in the range of d = 1.22, or even d = 0.69 (for examples of realistic effect sizes to expect based on group differences, see DataColada 18). The effects are inflated, and there is no way of knowing what the true effect sizes are. They might be zero, as many replication studies of exactly such implausibly large effects based on studies with tiny samples have turned out to be.

The study by Michinov and Michinov similarly shows effects for significant tests that are too large. Adopting a posture for a few minutes can’t plausibly influence creative tasks with effects such as d = 0.634. When you evaluate evidence, thinking about selective reporting and inflated effects should be part of the evaluation.

 

Quality of the design and analysis

I could not help noticing that there is a lot of room to improve the quality of the study design and analysis, as reported in papers in this literature. This in itself does not mean that the evidence is unreliable, but it does not make it easier for a research field to generate high quality evidence. For example, Elkjær et al (2022) report the following power analysis:

“Based on a priori power calculations, using a repeated-measures ANOVA interaction analysis, 2 (time; before vs. after the manipulation) × 3 (condition; EXP, CON, N), 90 participants were required to detect a small effect size (d = 0.34), with an alpha of .05 and a beta of .20.”

At first sight, this looks like best practice. They acknowledge power posing effects are small (d = 0.34 is very much in line with the meta-analysis they published in the same year). Regrettably, what the authors actually did was enter an f = -.34, not a d, as you can see in the screenshot below, which leads to a sample size that is much lower than what they would actually have needed to achieve high power, according to their own meta-analytic effect size estimate:

This means that despite the power analysis, the study was still massively underpowered. The sample size justifications in all studies cited by Cuddy are problematic. This is probably true for many research lines, but it is especially problematic for a research line where researchers are still trying to establish if the basic effect exists or not.

While reading the articles, I also noticed many of the issues that we often see in other literatures when research teams lack statistical expertise. There are often small inconsistencies in the correct degrees of freedom, incorrectly performed statistical tests, an overreliance on p-values despite underpowered studies, and misinterpretations of non-significant results. I don’t want to single out more examples, but it would probably be good for the field if researchers would enlist some methodological and statistical expertise if they want to generate reliable evidence.  

Tools to evaluate claims

Cuddy writes: “When people are told that research is fake — without being given the tools to evaluate that claim — it doesn't just affect one researcher or one line of work. It feeds a broader cynicism: that science can't be trusted, that findings are arbitrary, that expertise is performance.” I strongly agree. This is why I have created a free textbook, Improving Your Statistical Inferences, to learn how to evaluate the actual evidence in scientific papers. Here are three decent heuristics to follow when you evaluate the evidence in a research line:

1. If a finding shows what you want to be true, be extra skeptical.
2. If you have a strong conflict of interest, be extra skeptical.
3. Studies with low power due to too small sample sizes, lack of preregistration, no direct replications, strong indications of selective reporting, low methodological quality, repeating limitations in discussion sections without addressing them, implausibly large effect sizes, a lack of impact on other research areas, significant claims that mainly come from exploratory analyses, continued uncertainty about the basic effect after more than a decade and dozens of studies, and the research community disengaging with a literature are all signs of a lack of evidence.

According to Cuddy, she “live[s] inside a false narrative” where power posing is incorrectly believed to be a ‘myth’, and she believes that “none of this would have happened if the methods guys, and the journalists who trusted them without doing proper research, hadn't created the conditions that made it happen.”

 

Scientific criticism is a cornerstone of a healthy science

When I read Cuddy’s LinkedIn post, I was highly skeptical of the claim that there was evidence for effects of power posing on measures other than self-report, and that the debunking was a 'myth'. But my first response was to ignore the post. I did not want to examine the evidence behind the claims Cuddy made, because I am clearly one of the “method guys” who, according to Cuddy “manufactured the "debunked" narrative and aimed it, with great precision, at a single researcher”. If I would criticize her post, would I be seen as contributing to “the bullying I was subjected to”, as Cuddy writes?

But I care about criticism in science. And I think it is important that we can criticize scientific claims. My decision to not follow up on examining the claims in the blog post kept nagging me. Cuddy has 900,000 followers on LinkedIn who have read the very strong statement that it is a “myth” that power posing was debunked. If the evidence she presented was overstated – as I feared – scientific criticism would be needed to correct the record. I think it is essential to increase social safety in academia, while being able to criticize each other. I do not want bullying and scientific criticism to become conflated. Scientific criticism is too important for a healthy science to shy away from it, for fear of being called a bully. 

I think scientific criticism is a cornerstone of a reliable science. We have a responsibility to criticize public claims that we believe to be incorrect (either because they are AI generated, miscitations, or overstate the evidence). When I asked whether criticism like this should be voiced publicly (here, here, here, and here), most of the people in my network remarked that such criticisms should be voiced publicly. Others thought I should share these issues privately. In a way, I always have found it comforting to do things which you know will upset some scientists either way. It makes it easier to act on my own principles. And I believe it is essential for a science that aims to contribute to society to maintain a healthy culture of public scientific criticism.

 

 

Thanks to Nina, Sajedeh, Nick and Lisa for feedback on this blog post.

 

 

References

Andolfi, V. R., Di Nuzzo, C., & Antonietti, A. (2017). Opening the mind through the body: The effects of posture on creative processes. Thinking Skills and Creativity, 24, 20–28. https://doi.org/10.1016/j.tsc.2017.02.012

Anvari, F., Kievit, R., Lakens, D., Pennington, C. R., Przybylski, A. K., Tiokhin, L., Wiernik, B. M., & Orben, A. (2023). Not All Effects Are Indispensable: Psychological Science Requires Verifiable Lines of Reasoning for Whether an Effect Matters. Perspectives on Psychological Science, 18(2), 503–507. https://doi.org/10.1177/17456916221091565

Barel, E., Shahrabani, S., Mahagna, L., Massalha, R., Colodner, R., & Tzischinsky, O. (2024). The effects of power posing on neuroendocrine levels and risk-taking. BMC Psychology, 12(1), 726. https://doi.org/10.1186/s40359-024-02194-7

Elkjær, E., Mikkelsen, M. B., Tramm, G., Michalak, J., Mennin, D. S., & O’Toole, M. S. (2022). Using bodily displays to facilitating approach action outcomes within the context of a personally relevant task. Brain and Behavior, 13(1), e2855. https://doi.org/10.1002/brb3.2855

Körner, R., Röseler, L., Schütz, A., & Bushman, B. J. (2022). Dominance and prestige: Meta-analytic review of experimentally induced body position effects on behavioral, self-report, and physiological dependent variables. Psychological Bulletin, 148(1–2), 67–85. https://doi.org/10.1037/bul0000356

Lakens, D. (2019). The value of preregistration for psychological science: A conceptual analysis. Japanese Psychological Review, 62(3), 221–230. https://doi.org/10.24602/sjpr.62.3_221

Metzler, H., Vilarem, E., Petschen, A., & Grèzes, J. (2023). Power pose effects on approach and avoidance decisions in response to social threat. PLOS ONE, 18(8), e0286904. https://doi.org/10.1371/journal.pone.0286904

Michinov, N., & Michinov, E. (2024). Can Sitting Postures Influence the Creative Mind? Positive Effect of Contractive Posture on Convergent-Integrative Thinking. Creativity Research Journal, 36(1), 58–69. https://doi.org/10.1080/10400419.2022.2072557