Regular readers will know that I’m a big fan of PsychoPy, which (for non-regular readers; *tsk*) is a piece of free, open-source software for designing and programming experiments, built on the Python language. I’ve been using it a lot recently, and I’m happy to report my initial ardour for it is still lambently undimmed.
The PsychoPy ‘builder’ interface (a generally brilliant, friendly, GUI front-end) does have one pretty substantial drawback though; it doesn’t support conditional branching. In programming logic, a ‘branch’ is a point in a program which causes the computer to start executing a different set of instructions. A ‘conditional branch’ is where the computer decides what to do out of two of more alternatives (i.e. which branch to follow) based on some value or ‘condition’. Essentially the program says, ‘if A is true: do X, otherwise (or ‘else’ in programming jargon) if B is true; do Y’. One common use of conditional branching in psychology experiments is to repeat trials that the subject got incorrect; for instance, one might want one’s subjects to achieve 90% correct on a block of trials before they continue to the next one, so the program would have something in it which said ‘if (correct trials > 90%); then continue to the next block, else if (correct trials < 90%); repeat the incorrect trials’.
At the bottom of the PsychoPy builder is a time-line graphic (the ‘flow panel’) which shows the parts of the experiment:
The experiment proceeds from left to right, and each part of the flow panel is executed in turn. The loops around parts of the flow panel indicate that the bits inside the loops are run multiple times (i.e. they’re the trial blocks). This is an extremely powerful interface, but there’s no option to ‘skip’ part of the flow diagram – everything is run in the order in which it appears from left-to-right.
This is a slight issue for programming fMRI experiments that use block-designs. In block-design experiments, typically two (or more) blocks of about 15-20 seconds are alternated. They might be a ‘rest’ block (no stimuli) alternated with a visual stimulus, or two different kinds of stimuli, say, household objects vs. faces. For a simple two-condition-alternating experiment one could just produce routines for two conditions, and throw a loop around them for as many repeats as needed. The problem arises when there are more than two block-types in your experiment, and you want to randomise them (i.e. have a sequence which goes ABCBCACAB…etc.). There’s no easy way of doing this in the builder. In an experiment lasting 10 minutes one might have 40 15-second blocks, and the only way to produce the (psuedo-random) sequence you want is with 40 separate elements in the flow panel that all executed one-by-one (with no loops). Building such a task would be very tedious, and more importantly, crashingly inelegant. Furthermore, you probably wouldn’t want to use the same sequence for every participant, so you’d have to laboriously build different versions with different sequences of the blocks. There’s a good reason for why this kind of functionality hasn’t been implemented; it would make the builder interface much more complicated and the PsychoPy developers are (rightly) concerned with keeping the builder as clean and simple as possible.
Fortunately, there’s an easy little hack which was actually suggested by Jon Peirce (and others) on the PsychoPy users forum. You can in fact get PsychoPy to ‘skip’ routines in the flow panel, by the use of loops, and a tiny bit of coding magic. I thought it was worth elaborating the solution on here somewhat, and I even created a simple little demo program which you can download and peruse/modify.
So, this is how it works. I’ve set up my flow panel like this:
So, there are two blocks, each of which have their own loop, a ‘blockSelect’ routine, and a ‘blockSelectLoop’ enclosing the whole thing. The two blocks can contain any kind of (different) stimulus element; one could have pictures, and one could have sounds, for instance – I’ve just put some simple text in each one for demo purposes. The two block-level loops have no condition files associated with them, but in the ‘nReps’ field of their properties box I’ve put a variable ‘nRepsblock1’ for block1 and ‘nRepsblock2’ for block2. This tells the program how many times to go around that loop. The values of these variables are set by the blockSelect routine which contains a code element, which looks like this:
The full code in the ‘Begin Routine’ box above is this:
if selectBlock==1: nRepsblock1=1 nRepsblock2=0 elif selectBlock==2: nRepsblock1=0 nRepsblock2=1
This is a conditional branching statement which says ‘if selectBlock=1, do X, else if selectBlock=2, do Y’. The variable ‘selectBlock’ is derived from the conditions file (an excel workbook) for the blockSelectLoop, which is very simple and looks like this:
So, at the beginning of the experiment I define the two variables for the number of repetitions for the two blocks, then on every go around the big blockSelectLoop, the code in the blockSelect routine sets one of the number of repetitions of each of the small block-level loops to 0, and the other to 1. Setting the number of repetitions for a loop to 0 basically means ‘skip that loop’, so one is always skipped, and the other one is always executed. The blockSelectLoop sequentially executes the conditions in the excel file, so the upshot is that this program runs block1, then block2, then block1 again, then block2 again. Now all I have to do if I want to create a different sequence of blocks is to edit the column in the conditions excel file, to produce any kind of sequence I want.
Hopefully it should be clear how to extend this very simple example to use three (or more) block/trial types. I’ve actually used this technique to program a rapid event-related experiment based on this paper, that includes about 10 different trial types, randomly presented, and it works well. I also hope that this little program is a good example of what can be achieved by using code-snippets in the builder interface; this is a tremendously powerful feature, and really extends the capabilities of the builder well beyond what’s achievable through the GUI. It’s a really good halfway step between relying completely on the builder GUI and the scaryness of working with ‘raw’ python code in the coder interface too.
If you want to download this code, run it yourself and poke it with a stick a little bit, I’ve made it available to download as a zip file with everything you need here. Annoyingly, WordPress doesn’t allow the upload of zip files to its blogs, so I had to change the file extension to .pdf; just download (right-click the link and ‘Save link as…’) and then rename the .pdf bit to .zip and it should work fine. Of course, you’ll also need to have PsychoPy installed as well. Your mileage may vary, any disasters that occur as a result of you using this program are your own fault, etc. etc. blah blah.
Happy coding! TTFN.
Since I’ve switched to using PsychoPy for programming my behavioural and fMRI experiments (and if you spend time coding experiments, I strongly suggest you check it out too, it’s brilliant) I’ve been slowly getting up to speed with the Python programming language and syntax. Even though the PsychoPy GUI ‘Builder’ interface is very powerful and user-friendly, one inevitably needs to start learning to use a bit of code in order to get the best out of the system.
Before, when people occasionally asked me questions like “What programming language should I learn?” I used to give a somewhat vague answer, and say that it depended largely on what exactly they wanted to achieve. Nowadays, I’m happy to recommend that people learn Python, for practically any purpose. There are an incredible number of libraries available that enable you to do almost anything with it, and it’s flexible and powerful enough to fit a wide variety of use cases. Many people are now using it as a free alternative to Matlab, and even using it for ‘standard’ statistical analyses too. The syntax is incredibly straight-forward and sensible; even if an individual then goes on to use a different language, I think Python is a great place to start with programming for a novice. Python seems to have been rapidly adopted by scientists, and there are some terrific resources out there for learning Python in general, and its scientific applications in particular.
For those getting started there are a number of good introductory resources. This ‘Crash Course in Python for Scientists’ is a great and fairly brief introduction which starts from first principles and doesn’t assume any prior knowledge. ‘A Non-Programmers Tutorial for Python 2.6′ is similarly introductory, but covers a bit more material. ‘Learn Python the Hard Way’ is also a well-regarded introductory course which is free to view online, but has a paid option ($29.95) which gives you access to additional PDFs and video material. The ‘official’ Python documentation is also pretty useful, and very comprehensive, and starts off at a basic level. Yet another good option is Google’s Python Classes.
For those who prefer a more interactive experience, CodeAcademy has a fantastic set of interactive tutorials which guide you through from the complete beginning, up to fairly advanced topics. PythonMonk and TryPython.org also have similar systems, and all three are completely free to access – well worth checking out.
For Neuroimagers, there are some interesting Python tools out there, or currently under development. The NIPY (Neuroimaging in Python) community site is well worth a browse. Most interestingly (to me, anyway) is the nipype package, which is a tool that provides a standard interface and workflow for several fMRI analysis packages (FSL, SPM and FreeSurfer) and facilitates interaction between them – very cool. fMRI people might also be very interested in the PyMVPA project which has implemented various Multivariate Pattern Analysis algorithms.
People who want to do some 3D programming for game-like interfaces or experimental tasks will also want to check out VPython (“3D Programming for ordinary mortals”!).
Finally, those readers who are invested in the Apple ecosystem and own an iPhone/iPad will definitely want to check out Pythonista – a full featured development environment for iOS, with a lot of cool features, including exporting directly to XCode (thanks to @aechase for pointing this one out on Twitter). There looks to be a similar app called QPython for Android, though it’s probably not as full-featured; if you’re an Android user, you’re probably fairly used to dealing with that kind of disappointment though. ;o)
Anything I’ve missed? Let me know in the comments and I’ll update the post.
I’ve mentioned OpenSesame briefly on here before, but for those of you who weren’t keeping up, it’s a pretty awesome, free psychology experiment-developing application, built using the Python programming language, and it has a lot in common with PsychoPy (which is also awesome).
The recently-released new version of OpenSesame has just taken an important step, in that it now supports the Android mobile operating system, meaning that it can run natively on Android tablets and smartphones. As far as I’m aware, this is the first time that a psychology-experimental application has been compiled (and released to the masses) for a mobile OS.
This is cool for lots of reasons. It’s an interesting technical achievement; Android is a very different implementation to a desktop OS, being focused heavily on touch interfaces. Such interfaces are now ubiquitous, and are much more accessible, in the sense that people who may struggle with a traditional mouse/keyboard can use them relatively easily. Running psychology experiments on touch-tablets may enable the study of populations (e.g., the very young, very old, or various patient groups) that would be very difficult with a more ‘traditional’ system. Similarly, conducting ‘field’ studies might be much more effective; I can imagine handing a participant a tablet for them to complete some kind of task in the street, or in a shopping mall, for instance. Also, it may open up the possibility of using the variety of sensors in modern mobile devices (light, proximity, accelerometers, magnetometers) in interesting and creative ways. Finally, the hardware is relatively cheap, and (of course) portable.
I’m itching to try this out, but unfortunately don’t have an Android tablet. I love my iPad mini for lots of reasons, but the more restricted nature of Apple’s OS means that it’s unlikely we’ll see a similar system on iOS anytime soon.
So, very exciting times. Here’s a brief demo video of OpenSesame running on a Google Nexus 7 tablet (in the demo the tablet is actually running a version of Ubuntu Linux, but with the new version of OpenSesame it shouldn’t be necessary to replace the Android OS). Let me know in the comments if you have any experience with tablet-experiments, or if you can think of any other creative ways they could be used.
Very exciting news here: I’ve just been invited to the first British Psychological Society (Maths, Statistics and Computing Section) Psychology open textbook hackathon!
Inspired by this event (where people got together and wrote an open-source maths textbook in a weekend) the day aims to raise awareness and skills, as well as perhaps produce some usable output.
The organisers are Thom Baguley of Nottingham Trent University (and the Serious Stats blog and book) and Sol Nte of Manchester University. They’ve very kindly invited me as a guest, so I’ll be hanging out and learning some new tricks myself, I’m sure.
Here’s the flyer for the event, with sign-up details etc. It’s free, but strictly limited to 20 places – if you’re keen, best be quick… (click the pic below for a bigger version):
A quick post to point you towards a great website with a lot of really cool content (if you’re into that kind of thing, which if you’re reading this blog, then I assume you probably are… anyway, I digress; I apologise, it was my lab’s Christmas party last night and I’m in a somewhat rambling mood. Anyway, back to the point).
So, the website is called cogsci.nl, and is run by a post-doc at the University of Aix-Marseille called Sebastiaan Mathôt. It’s notable in that it’s the homepage of OpenSesame – a very nice-looking, Python-based graphical experiment builder that I’ve mentioned before on these very pages. There’s a lot of other cool stuff on the site though, including more software (featuring a really cool online tool for instantly creating Gabor patch stimuli), a list of links to stimulus sets, and a selection of really-cool optical illusions. Really worth spending 20 minutes of your time poking around a little and seeing what’s there.
I’ll leave you with a video of Sebastiaan demonstrating an experimental program, written in his OpenSesame system, running on a Google Nexus 7 Tablet (using Ubuntu linux as an OS). The future! It’s here!
I wrote a tiny post about PsychoPy a little while ago and it’s something I’ve been meaning to come back to since then. I’ve recently been tasked with an interesting problem; I need an experimental task for a bunch of undergrads to use in a ‘field study’ – something that they can run on their personal laptops, and test people in naturalistic environments (i.e. the participants’ homes). The task is based on a recent paper (Rezlescu et al., 2012) in PLoS One, and involves presenting face stimuli that vary in facial characteristics associated with trustworthiness, in a ‘game’ where the participant plays the role of an investor and has to decide how much money they would invest in each person’s business. I was actually given a version of the experiment programmed (by someone else) in Matlab using the Psychtoolbox system. However, using this version seemed impractical for a number of reasons; firstly Matlab licences are expensive and getting a licenced version of Matlab on every student’s computer would have blown the budget available. Secondly, in my (admittedly, limited) experience with Matlab and Psychtoolbox, I’ve always found it to be a little… sensitive. What I mean is that whenever I’ve tried to transfer a (working) program onto another computer, I’ve generally run into trouble. Either the timing goes to hell, or a different version of Matlab/Psychtoolbox is needed, or (in the worst cases) the program just crashes and needs debugging all over again. I could foresee getting this Matlab code working well on every single students’ laptop would be fraught with issues – some of them might be using OS X, and some might be using various versions of Windows – this is definitely going to cause problems.*
Somewhat counterintuitively therefore, I decided that the easiest thing to do was start from scratch and re-create the experiment using something else entirely. Since PsychoPy is a) entirely free, b) cross-platform (meaning it should work on any OS), and c) something I’d been meaning to look at seriously for a while anyway, it seemed like a good idea to try it out.
I’m happy to report it’s generally worked out pretty well. Despite being a complete novice with PsychoPy, and indeed the Python programming language, I managed to knock something reasonably decent together within a few hours. At times it was frustrating, but that’s always the case when programming experiments (at least, it’s always the case for a pretty rubbish programmer like me, anyway).
So, there are two separate modules to PsychoPy – the ‘Builder’ and the ‘Coder’. Since I’m a complete novice with Python, I steered clear of the Coder view, and pretty much used the Builder, which is a really nice graphical interface where experiments can be built up from modules (or ‘routines’) and flow parameters (i.e. ‘loop through X number of trials’) can be added. Here’s a view of the Builder with the main components labelled (clicky for bigness):
At the bottom is the Flow panel, where you add new routines or loops into your program. The large main Routine panel shows a set of tabs (one for each of your routines) where the events that occur in each of the routines can be defined on a timeline-style layout. At the right is a panel containing a list of stimuli (pictures, videos, random-dot-kinematograms, gratings etc.) and response types (keyboard, mouse, rating scales) that can be added to the routines. Once a stimulus or response is added to a routine, a properties box pops up which allows you to modify basic (e.g. position, size, and colour of text) and some advanced (through the ‘modify everything’ field in some of the dialog boxes) characteristics.
It seems like it would be perfectly possible to build some basic kinds of experiments (e.g. a Stroop task) through the builder without ever having to look at any Python code. However, one of the really powerful features of the Builder interface is the ability to insert custom code snippets (using the ‘code’ component). These can be set to execute at the beginning or end of the experiment, routine, or on every frame. This aspect of the Builder really extends its capabilities and makes it a much more flexible, general-purpose tool. Even though I’m not that familiar with Python syntax, I was fairly easily able to get some if/else functions incorporating random number generation that calculated the amount returned to the investor on a trial, and to use those variables to display post-trial feedback. Clearly a bit of familiarity with the basics of programming logic is important to use these functions though.
This brings me to the Coder view – at any point the ‘Compile Script’ button in the toolbar can be pushed, which opens up the Coder view and displays a script derived from the current Builder view. The experiment can then be run either from the Builder or the Coder. I have to admit, I didn’t quite understand the relationship between the two at first – I was under the impression that these were two views of the same set of underlying data, and changes in either one would be reflected in the other (a bit like the dual-view mode of HTML editors like Dreamweaver) but it turns out that’s not the case, and in fact, once I thought about it, that would be very difficult to implement with a ‘proper’ compiled language like Python. So, a script can be generated from the Builder, and the experiment can then be run from that script, however, changes made to it can not be propagated back to the Builder view. This means that unless you’re a serious Python ninja, you’re probably going to be doing most of the work in the Builder view. The Coder view is really good for debugging and working out how things fit together though – Python is (rightly) regarded as one of the most easily human-readable languages and if you’ve got a bit of experience with almost any other language, you shouldn’t find it too much of a problem to work out what’s going on.
Another nice feature is the ability of the ‘loop’ functions to read in the data it needs for each repeat of the loop (e.g. condition codes, text to be presented, picture filenames, etc.) from a plain text (comma separated) file or Excel sheet. Column headers in the input file become variables in the program and can then be referenced from other components. Data is also saved by default in the same two file formats – .csv and .xls. Finally, the PsychoPy installation comes with a set of nine pre-built demo experiments which range from the basic (Stroop) to more advanced ones (BART) which involve a few custom code elements.
There’s a couple of features that it doesn’t have which I think would be really useful – in particular in the Builder view it would be great if individual components could be copied and pasted between different routines. I found myself adding in a number of text elements and it was a bit laborious to go through them all and change the font, size, position etc. on each one so they were all the same. Of course ‘proper’ programmers working in the Coder view would be able to copy/paste these things very easily…
So, I like PsychoPy; I really do. I liked it even more when I transferred my program (written on a MacBook Air running OS X 10.8) onto a creaky old Windows XP desktop and it ran absolutely perfectly, first time. Amazing! I’m having a little bit of trouble getting it running well on a Windows Vista laptop (the program runs slowly and has some odd-looking artefacts on some of the pictures) but I’m pretty sure that’s an issue with the drivers for the graphics card and can be relatively easily fixed. Of course, Vista sucks, that could be the reason too.
So, I’d recommend PsychoPy to pretty much anybody – the Builder view makes it easy for novices to get started, and the code components and Coder view means it should keep seasoned code-warriors happy too. Plus, the holy trinity of being totally free, open-source, and cross-platform are huge advantages. I will definitely be using it again in future projects, and recommending it to students who want to learn this kind of thing.
Happy experimenting! TTFN.
*I don’t mean to unduly knock Matlab and/or Psychtoolbox – they’re both fantastically powerful and useful for some applications.
PsychoPy is something which I’ve been meaning to write something substantive on for a while. Briefly though, it’s a system for designing and running experiments, programmed in the Python language, with a nice GUI front-end to it. I’ve only flirted with it briefly, but the open-source and cross-platform nature of it makes it a very attractive package for programming experiments, in my opinion. If I was learning this stuff for the first time, it’s definitely the system I’d use.
The purpose of this post was just to publicise a YouTube video, put up by the creator of PsychoPy – Jon Peirce, of Nottingham University. The video is a great little starter-tutorial for PsychoPy and gently walks the viewer through creating a simple experiment – great stuff.
Happy experimenting! Here’s the vid:
My lovely, lovely friends at TheNeuronClub have just put up a new post, where they say some very kind things about this blog – thanks guys! However, this post is not just about a bit of self-back-slappery – they also mention a few really great-looking resources to help you get started with programming using Python – if you’re interested in coding, this would be a great place to start. Check out their piece here. You can also (*ahem* self-promotion *ahem*), if you felt so inclined, read my previous guest piece on their site (on whether to average functionally or anatomically in fMRI) here.