Psychology | Reality Bending Lab

About signal complexity

Mon, 05 Dec 2022 00:00:00 +0000

The signals recorded from the brain or the body are rich in information, and there are many ways to analyze them. For instance, for EEG, one can focus on Event Related Potentials (ERP), time-frequency analyses, microstates, etc.

An alternative framework, used to characterize the general characteristics of the signal, relies on the extraction of indices of “complexity” (a general term for constructs such as entropy, chaos, fractal dimension, predictability). However, that field is quite complex (no pun intended), drawing heavily onto mathematical concepts that psychologists or neuroscientists might not be familiar with.

In order to better understand the world of complexity indices as applied to neurophysiology, we have done some groundwork to help us make better decisions in our future usage of this type of analysis.

A gentle introduction to complexity indices applied to neuroscience.
A indices selection guide in which we compare how different indices relate to one another.

Additionally, we also provide an easy way to compute them in Python in our NeuroKit package (see here for the list of functions and here for an EEG application).

NeuroKit2 0.2.0 is out 🎉

Wed, 18 May 2022 00:00:00 +0000

NeuroKit2 0.2.0 is out! 🎉

What was supposed to be a small release turned out in a massive update. A big thanks - and a warm welcome - to An Shu and Max, the newest member of the Reality Bending Team, and thus maintainers of NeuroKit. They worked massively to update all of the examples and docstrings. New features include:

A BRAND NEW WEBSITE with a revamped documentation, now hopefully much more useful to navigate. Check-it out: https://neuropsychology.github.io/NeuroKit/ and let us know what you think!!
An overhaul of the Complexity Indices: With more than a 100 indices, NeuroKit is now the most comprehensive package to quantify chaos, entropy and fractal dimension of signals.
Tons of other improvements and fixes ☺️

Once again, a big thanks to all the contributors for their help in making NeuroKit an awesome open-source software for physiological signal processing!

NeuroKit2 0.1.5 'Complexity' is out 🎉

Fri, 12 Nov 2021 00:00:00 +0000

NeuroKit2 0.1.5 is out! 🎉

In the latest 0.1.5 release of NeuroKit2, our team has fixed several bugs in existing functionalities and in particular, overhauled the support for computing complexity measures of neurophysiological signals. We added a ton of new indices of entropy and fractal dimensions, including:

Petrosian’s, Katz’s and Sevcik fractal dimension
Differential, Permutation, Spectral, SVD entropy
Fisher information
Hjorth’s and Lempel-Ziv’s complexity
Relative Roughness
Hurst and Lyapunov exponent(s)
Detrended Fluctuation Analysis (as well as MFDFA)
…

You can compute them all using the new nk.complexity() function!

import neurokit2 as nk

signal = nk.signal_simulate(frequency=[5, 6], noise=0.5)

results, info = nk.complexity(signal, which="fast")
results

  DiffEn       FI    Hjorth       KFD  PEn  ...      PFD        RR       SFD
1.536573  0.01524  1.355543  4.720953  1.0  ... 1.017423  1.638357  1.691036

To understand more about complexity science, we recommend reading our preprint, which introduces the theoretical (and mathematical) meanings of complexity and reviews the existing studies of complexity analysis across multiple fields of psychology.

Of course, the NeuroKit2 Python package also includes tons of other useful features for physiological signal processing (see this quick example)!

Don’t forget to watch our repo to keep a look out for more complexity functionalities coming up! 👀

What is Reality Bending?

Mon, 28 Sep 2020 00:00:00 +0000

As you know, reality bending is my primary research direction. However, it is not (yet) a well-established scientific topic, nor is it clearly defined. In fact, it is not defined at all, hence the purpose of this article.

But what does it refer too? Is it some kind of Avatar: The Last Airbender thing? Or some Avengers-style superpower? Well… I sure wish it was 😁

Essentially, reality bending refers to the study of the internal and external determinants of subjective reality. In other words, we seek to understand the processes that modulate our conscious experience of reality. The word “bending” encapsulates the active nature of the mechanisms at stake. Indeed, being anything but stable, our perception of reality can be quite easily influenced, whether voluntarily or not, sometimes to extreme degrees of alteration.

Daumier, H. (1925), Don Quixote attacking the windmills.

Reality benders seek to unravel the structure and mechanisms of the sense of reality by studying natural instances of its distortion, or by directly inducing them through a variety of means.

Objective and subjective determinants of the sense of reality

Let’s take for example a guy watching some episode of his favourite TV show, Friends. As he swiftly moves from laughing to snivelling, we can confidently say that he is fully immersed in the show. He feels like he’s present in the show, from which the fictional characters feel very real: for a moment, his brain processes the perceived experience almost as if it was real.

What leads to this high sense of reality? First, there are objective characteristics of the experience (or rather, of the external source of the experience), i.e., characteristics of the environment. Here, it’s a realistic stimulus displayed on a flat screen. But one could wonder what would happen if the sensory input was richer (imagine being physically IN the show by means of some super virtual reality setup), or poorer (the same story presented as comic strips with the characters portrayed as stick figures).

However, while such manipulations could indeed be used to manipulate our immersion, there is also a subjective component contributing to our sense of reality, related for instance to the affective response, attentional engagement, or self-relevance, that will cause a stimulus to strum unique strings in each individual, depending on his history and state of mind.

Tell me your reality and I’ll tell you who you are

The fact that the sense of reality is, in the end, a subjective experience, means that is is intrinsically connected to the Self (i.e., our physical and mental identity). As such, aside from studying how our sense of reality is influenced by external and internal factors, but also investigate the reverse relationship, i.e., how the variability of our sense of reality can inform us about oneself.

Note that, although the focus is the subjective aspect of reality, it doesn’t mean that we deny the existence, or downplay the importance, of objective reality. Stating that most of our experience is “made-up” (i.e., is a construction of the brain) does not equate absolute relativism (more on that in another post). Objective truths and facts do exist, and are essential to seek.

Altered states of consciousness

Naturally, states in which our sense of reality is distorted (as compared to the consensual collective experience) are of particular interest as models or study-cases of our ideas and theories. They include long-term affections (e.g., neuropsychiatric disorders such as schizophrenia) or transcient states (induced by psychoactive substances or specific practices like meditation and trance).

Thanks for reading! Don’t forget to join me on X @Dom_Makowski 🤗

Why psychologists should join GitHub

Wed, 27 May 2020 00:00:00 +0000

Disclaimer: I don’t have anything to do with GitHub, aside from being a simple user. This article is not an advertisement for it, but rather a perspective on the role of technical social networks in psychology.

I already mentioned in another post that technical aspects and skills will play an increasing role in psychology. This relationship isn’t by any means new. More than a century ago, pioneering psychologists were demonstrating formidable engineering and craftsmanship skills to build new tools and apparatuses to measure what they were interested in (see for instance Nicolas & Makowski, 2016). But years have gone by, and the digital revolution has happened. As a result, most of the “technical” aspects (a rather vague term covering everything that is not related to the semantic knowledge of psychological theories and facts) are now ultimately tied to software. Critically, your ability to use these softwares will determine the speed and ease at which you can achieve your goals and produce high quality assignments.

As an example, during my studies, most of the statistics course was delivered through the usage of one particular software (Statistica© 🤮). At the exam, your score didn’t much depend on your understanding of what a t-test is, when to use it or anything like that; but rather on your knowledge of the software, and your ability to click on the right buttons faster than your peers. While this is an unfortunate example that can be used to criticize the reliance on tools rather than fundamental knowledge, it also tells us something about the reality of the current world. In research, the better you are for instance at data processing (which involves both the knowledge of how to navigate the software and the knowledge of what to do in general), the faster you will be able to carry it out, and the less stressed you will be, resulting in a cascade of other positive outcomes (increased well-being, work-quality, productivity, opportunities, etc.).

However, one common mistake is to delay learning new stuff (especially things outside our comfort zone) until we have no choice. This is understandable given that in the short term, certain skills may not be absolutely needed (i.e., you can manage without them) and acquiring them can be a steep learning curve (which can be hard, frustrating and effortful). However, you should start investing in your technical skills as soon as possible (as the time devoted to learning will only shrink as you advance) if you don’t want to become very close friends with pressure, stress, hatred, frustration and despair. So stay on the light side of the force and embrace the future.

A psychology researcher realizing that he should have learned programming earlier.

And it’s not just about learning to use some software just for the sake of having things done and getting faster results. A lot of science is done through software, not only with them. People are actively discussing new methods, algorithms and tools that then expand like never before the possibilities of researchers. Flaming debates have been going on with frameworks and workflows clashing with thunderous sparks (for instance, Bayesian vs. frequentist statistics, ANOVAs vs. (mixed) regressions, etc.), and these calls for change find echo because of developments of software, allowing initiated users to test, validate and use new methods.

Open-access software

It might not seem like it when you’re studying it at the university, but science is currently in the middle of a revolution. A massive paradigmatic change, partly fuelled by the growth of open-science, which covers aspects like open-access and open-source. This means, for software, that they are no longer being developed by private companies and sold for money. Instead, they are developed in a public fashion, and everybody is welcome to chime in and provide input, suggestions, report bugs or improve the code.

Open-source development means faster and better software, and more importantly, the creation of a true connection between developers and users. In fact, the former are often first and foremost also the latter, meaning that in a lot of cases (at least, mine 😅), people started writing a software because they needed it for their own personal job.

But the beauty lies in the fact that users can seemingly become developers too, or contributors, at the very least. Sometimes, users end up on a software development page to solve a bug or an issue that they encountered. From there, there can start following the developments, and getting involved. And not necessarily be writing code. It can be by answering to other issues, helping other users, reporting bugs and typos, improving the documentation, giving ideas and suggestions, testing new features and encouraging the developers. There are so many to contribute to the development and, as a result, become an active member of the open-science community. And moreover, doing so is also a great way to learn the theoretical bits. For instance, the most efficient way of learning the complexities of EEG acquisition and processing was to follow the development of an EEG-processing software (namely, MNE-Python). Reading issues that users encountered, and replies from experts and developers, trying to understand what functions do, what are the different parameters, what are the possibilities, the limits and so on.

All in all, engaging in open-source software is a great way to increase your technical expertise and get involved in the community of researchers. And who knows, you might meet cool people, create new connections, and that’s always great!

Help making the first pane true.

Now that you’ve buckled up, ready to engage in open-source software, you might wonder; where does that happen? Ladies and gentlemen, let me introduce GitHub.

Several years ago, when I was an undergrad student, I had to write a lot of stuff, such as for instance reports, project and theses. All of these documents went through several back and forths with supervisors, which made comments and modifications. But I was terribly afraid to remove some paragraph or sentence that I would need later on. As a result, I ended up in a hell in which my tormentors were named project.docx, project_v2.docx, project_v3.docx, project_v3_modifs.docx, project_v3_final.docx, project_v4_comments.docx, project_v4_final.docx, project_v4final2.docx, project_v4_finalfinal.docx. And what if my computer broke _{^{(that was before Dropbox)}}? I could lose everything 😱

This is when I heard about something called version control. Apparently, there was a system out there that allowed you to save all your modifications, and be able to go back at any point in time. This system was called git, and it was super obscure. However, I discovered that this system had a, online interface, in the form of a website, on which you can go and upload files and documents for free. This is how I discovered GitHub. And back in the days, it was really mostly used by programmers (because the nature of code makes it very suited for version control), a world I didn’t belong to.

There several great alternatives go GitHub, like GitLab, BitBucket, etc., that might be just as good, if not better. The reason why I’m mainly talking about GitHub here is because this post is not about the intrinsic quality or features of these platforms for developers, but rather as a social network. An important part of any social network is its popularity and - as of for now - GitHub is the most popular.

I witnessed GitHub growing and becoming a true social network, improving its accessibility and user-friendliness. It is now more like a hub where all kinds of people gather to discuss software and technical bits, than a den for hairy geeks. There are also many users who are new to programming (e.g. researchers who are using software as a means to an end) and if you belong to this category of people, don’t underestimate your contribution to developers! Often it is such users that help developers improve user friendliness and identify code bugs (for example, when running the code on actual data sets). It is also used as a public technical portfolio, in which you can display your achievements, your projects and your interests. And while it was originally centred around programming, it has extended its audience quite a bit, and people are now using GitHub to store data (for instance the COVID-19 data), books, create websites (for instance, this website is stored on GitHub) or write scientific papers!

The reason why I’m writing this is because I know all too many young researchers, grappling with some software, struggling to find help, that are too shy to just contact the developers or the community. Just jump in there, create a public issue (instead of writing an email, so that your question will benefit future users). Most of the developers will be happy to help, and glad to see their software and code actually used by others.

In conclusion, go and dive into the world of open-science and open-source software, you’ll be on the right side of history. 😎

What to do once you’re on GitHub

At the very least, the very first step is to create an account. Even if you don’t use it now, it will be useful in the future (it shows that you are interested in technical stuff, it allows you to post issues and connect to other platforms, and support developers).

Find a package / software that you like. Super biased suggestions include:
- NeuroKit: a Python package for Neurophysiological Signal Processing
- bayestestR: an R package for Bayesian statistics
- correlation: an R package for correlations
- report: an R package to report statistics
- effectsize: an R package for effect sizes
- parameters: an R package for understanding statistical models
- performance: an R package for testing how good your model is
“Watch it” (the button in the top-right corner), so you’ll be notified of its activity
Read the README file (the “front page”), check-out the issues, understand how to navigate the repository
Engage with the developers, create an issue to report bugs or problems, or just to express support - developing a software takes time and effort, and is often done out of passion and for free. Words of encouragement are always appreciated 🤗
Create your own repository
Make your own website

And if you want to learn how to use GitHub to make contributions, check-out our tutorial!

Thanks for reading! Let me know if I forgot something by adding a comment below 👇

R or Python for Psychologists

Fri, 22 May 2020 00:00:00 +0000

Many psychology students or researchers are faced with the challenge - or the opportunity - of learning a programming language. Which one should you learn?

As an ex- psych student and a daily user and developer of some of them, here’s my take on this hot debate.

What has programming to do with psychology?

If you’re a very young psychology student, or a future one, you might wonder: why the heck would I have to learn programming in psychology? “Psychology is kinda like philosophy, it’s just learning how people’s minds work by reading books and overthinking stuff”. If you still think that, you’re in for one hell of a ride!

Psychology is, since its very beginning, a hard and experimental science. The founding fathers of psychology were dedicated to find ways to objectively measure psychological phenomena and uncovering the mathematical laws that govern Human behaviour (one of the fields of psychology is even called psychophysics). True, this sciency nature has been toned down by the booming popularity of pseudo-scientific approaches like psychoanalysis throughout the 20th century, that contributed to the stereotypical public image of the shrink doodling while listening to a neurotic patient. But that’s a distorted and old-fashioned view, not really representative of the future of psychology.

The fact is that psychology is very closely connected with statistics. Many great statistical advances were made by psychologists, and all true psychological discoveries are backed by statistical findings. And this importance of statistics is - and will be - growing further, partly due to the recent realization of some major issues in the field due to improper statistical procedures (coined the “replicability crisis”). Moreover, psychology is more and more relying on advanced data-acquiring methods (smartphone apps, website behaviour data, online surveys, physiological and brain recording devices like EEG, MRI, etc.). And these new formats often require specialized knowledge (web-scraping, database querying, neuroimaging, signal processing, machine learning, …). And with great data-power comes great data-analysis-responsibilities. Even in the most applied kind of clinical or psychotherapeutic specializations, where you’d think you’d be safe, they are starting to use data intensive methods like neuro-feedback, virtual reality, experience sampling, and other forms of smartphone sensing and surveying.

Long story short, no matter which branch of psychology you specialize in, you will be confronted with some technical aspects that won’t be able to solve with Excel. Moreover, these technical skills are the ones that will make the most difference between students, and that will matter a lot if you want to pursue research or want to go work in the private sector. The golden era where people were recruited in research based on their theoretical expertise is over: technical skills are now the golden ticket to enter - and successfully leave - academia.

So, ready to dive into programming? Fear not! It’s not that complicated. Moreover, it’s one of the most rewarding skill you can develop. I can assure you that you won’t regret the time invested in learning it 😊

But where should you start?

Learn both R and Python

This increasing relationship between psychology and statistics on the one hand, and other more general technical aspects on the other, is the reason why R and Python are so popular in psychology. Both languages are free, open-source, suited for beginners, and have a large base of users with a ton of learning material online. What’s the difference between them?

Put simply, R is for statistics, Python is for the rest.

So why is there a virulent debate going on, and a choice to make? It’s true that I, in theory, would agree with some popular recommandations and suggest learning both, as they are complementary and have their own strengths and weaknesses. I myself use both on a daily basis, so why would preach what I practice?

That said, many opinionated people are also arguing in favour of one or the other (usually the only one they know…) will say that learning both is essentially a waste of time. They will put forth a strong argument: you can do whatever you do in R in Python, and vice-versa. In other words, both languages can be used to achieve your goals, so it’s better to specialize in one than have a limited knowledge of both.

Although I do not agree with that statement, I do acknowledge that people have limited time and resources to learn. Saying “just learn both” is easy, but is arguably an unrealistic expectation for the vast majority of people. So why learning both can be a long-term goal (especially if you want to do research), you have to start somewhere. So, what starter language should you select?

Ash choosing his starter programming language. He has the choice between R, Python and Bulbasaur, i.e, Matlab - the one that no one likes.

What about Matlab?

There was a time when Matlab was the boss. It was used everywhere and had the best functionalities for neuroimaging, signal processing and maths. But that time is over. Matlab is already a zombie language, which burial process will continue in the years to come. Why is it dead? Because it is expensive, closed, ugly, and most importantly because the alternatives (namely R and Python) are now more powerful and featured than Matlab.

Agamemnon reacting to king Priam saying "The city of Matlab will never be conquered".

The truth is, there are only two reasons people still use Matlab: habit (it’s hard to learn a new approach if your old way of doing things still works) and SPM (a toolbox for neuroimaging that is still - for now - the leader in the field).

But seriously, don’t waste time on it if you have limited resources, it’s just not worth it. You will learn an outdated tool that you won’t be able to use in another lab if they don’t agree to pay for an expensive license (unless you’re a pirate ☠️). Whereas with open and free languages like R or Python, you have access to the best tools and can use them freely everywhere. Also, it makes you a supporter of open-science, which is good 😁.

How to decide between R and Python

Time has come to make a decision.

Despite what people say, R and Python are not equivalent. You can argue as much as you want, but doing statistics and data visualization in Python is not as fast, easy and neat as it is in R. And signal processing or neuroimaging is not as powerful in R as compared to Python. Note that both languages are still growing and changing, and they are influencing themselves: for instance, many popular Python modules (e.g., pandas, statsmodels, seaborn, …) have been directly inspired by R. As such, the boundaries between the two languages are fading (and I’m not even mentioning the great advances in interoperability, with tools like reticulate that allow you to use one language directly inside the other).

That being said, Python and R remain very different languages at their core, with a different feel and vibe to it. R was made by statisticians for statistics, and the majority of its users are academics and researchers. On the contrary, Python is a true general-purpose “programming” language, widely used outside of academia, in the private sector.

Here are some things to consider when deciding on what language to learn:

Reasons to choose Python

You have some basic knowledge or familiarity with programming

For instance, you know what a logical loop is. Python being a true programming language, having any prior experience will be useful.

You are good with logic and spatial representation (like imagining shapes in 3D, rotating them, etc.)

In Python, you will have to think with a “programming” mindset. That means perceiving things in terms of logical statements and blocks, understanding data as 2D or 3D tables that you have to slice and recombine.

You are comfortable with maths

In Python, numbers and numbers combinations are used a lot. Paradoxically, you will typically see a lot more maths in Python than in R.

You plan to do signal processing or experimental tasks creation

These are some of the domains where Python is well-established (which doesn’t mean that R doesn’t have some great tools in development).

You are good at googling and don’t mind spending time looking for the right answer

Python has so much material online that it’s sometimes hard to find the appropriate thing. Harder than in R, in my opinion, which has more well-defined “gold-standard” textbooks and tutorials.

Reasons to choose R

You have no experience with programming whatsoever

R is not made to be used as a traditional programming language. It’s more of finding what functions to apply to what, and that makes it easy for beginners.

You are interested in statistical analyses, modelling things, and making inferences from data

R excels at this. You can create powerful models super easily and jump into their understanding and interpretation.

You like making nice figures and plots

R, through the ggplot ecosystem, has hands down the best tools for visualization. Your imagination is the limit, and you can even create art (check-out the artworks by Thomas Lin Pedersen and Danielle Navarro 😍).

You are not so good with stats or maths

You heard it right! To start with R you don’t need to know stats or maths like a boss. R, in fact, will help you to become proficient at it, by slowly opening more and more layers of complexity to you, if you are deemed worthy.

You are interested in joining the academic community

Because most of its users are academics, R has a fantastic community online, for instance on Twitter #rstats. It’s also super inclusive (e.g., the R-Ladies).

Other considerations

What your peers are learning

It’s easier to learn together, so try to discuss it with your class or lab mates if you can.

What your lab is using

It might be easier if you have mentors that can understand what you are doing and guide you. But that should not be a priority, as it can lead to cargo cult-like old habits reproduction (especially if your lab has a tradition of Matlab 🤭). Instead of submitting to the tradition, assess what the goals and objectives are, and pick the best tool to achieve them. And if you have any issue convincing your boss / supervisor about it, ask some help on Twitter, I bet you’ll get a lot of it.

Hands on!

👉 Looking for places to start? Check out this 10-min crash course introduction to Python and this collection of resources for R.

Thanks for reading! 🐦 Don’t forget to join me on X (@Dom_Makowski) and leave a comment below 👇