Stimulus timing accuracy in PsychoPy – an update, and an example of open science in action

A few days ago I reported on a new paper that tested the timing accuracy of experimental software packages. The paper suggested that PsychoPy  had some issues with presenting very fast stimuli that only lasted one (or a few) screen refreshes.

However, the creator of PsychoPy (Jon Peirce, of Nottingham University) has already responded to the data in the paper. The authors of the paper made all the data and (importantly) the program scripts used to generate it available on the Open Science Framework site. As a result it was possible for Jon to examine the scripts and see what the issue was. It turns out the authors used an older version of PsychoPy, where the ability to set a stimulus duration in frames (or screen refreshes) wasn’t available. Up-to-date versions have this feature, and as a result are now much more reliable. Those who need the full story should read Jon’s comment (and the response by the authors) on the PLoS comments page for the article.

So, great news if, like me, you’re a fan of PsychoPy. However, there’s a wider picture that’s revealed by this little episode, and a very interesting one I think. As a result of the authors posting their code up to the OSF website, and the fact that PLoS One allows comments to be posted on its articles, the issue could be readily identified and clarified, in a completely open and public manner, and within a matter of days. This is one of the best examples I’ve seen of the power of an open approach to science, and the ability of post-publication review to have a real impact.

Interesting times, fo’ shiz.

A new paper on timing errors in experimental software

A new paper just out in PLoS One (thanks to Neuroskeptic for pointing it out on Twitter) shows the results of some tests conducted on three common pieces of software used in psychology and neuroscience research: DMDX, E-Prime, and PsychoPy. The paper is open-access, and you can read it here. The aim was to test the timing accuracy of the software when presenting simple visual stimuli (alternating black and white screens). As I’ve written about before, accurate timing in experiments can often be of great importance and is by no means guaranteed, so it’s good to see some objective work on this, conducted using modern hardware and software.

The authors followed a pretty standard procedure for this kind of thing, which is to use a piece of external recording equipment (in this case the Black Box Tool Kit) connected to a photo-diode. The photodiode is placed on the screen of the computer being tested and detects every black-to-white or white-to-black transition, and the data is logged by the BBTK device. This provides objective information about when exactly the visual stimulus was displayed on the screen (as opposed to when the software running the display thinks it was displayed on the screen, which is not necessarily the same thing). In this paper the authors tested this flickering black/white stimulus at various speeds from 1000ms, down to 16.6667ms (one screen refresh or ‘tick’ on a standard 60Hz monitor).

It’s a nice little paper and I would urge anyone interested to read it in full; the introduction has a really nice review of some of the issues involved, particularly about different display hardware.  At first glance the results are a little disappointing though, particularly if (like me) you’re a fan of PsychoPy. All three bits of software were highly accurate at the slower black/white switching times, but as the stimulus got faster (and was therefore more demanding on the hardware) errors began to creep in. At times less than 100ms, errors (in the form of dropped, or added frames) begin to creep in to E-Prime and PsychoPy; DMDX on the other hand just keeps on truckin’, and is highly accurate even at the fastest-switch conditions. PsychoPy is particularly poor under the most demanding conditions, with only about a third of ‘trials’ being presented ‘correctly’ under the fastest condition.

Why does this happen? The authors suggest that DMDX is so accurate because it uses Microsoft’s DirectX graphics libraries, which are highly optimised for accurate performance on Windows. Likewise, E-Prime uses other features of Windows to optimise its timing. PsychoPy on the other hand is platform-agnostic (it will run natively on Windows, Mac OS X, and various flavours of Linux) and therefore uses a fairly high-level language (Python). In simple terms, PsychoPy can’t get quite as close to the hardware as the others, because it’s designed to work on any operating system; there are more layers of software abstraction between a PsychoPy script and the hardware.

Is this a problem? Yes, and no. Because of the way that the PsychoPy ‘coder’ interface is designed, advanced users who require highly accurate timing have the opportunity to optimise their code, based on the hardware that they happen to be using. There’s no reason why a  Python script couldn’t take advantage of the timing features in Windows that make E-Prime accurate too – they’re just not included in PsychoPy by default, because it’s designed to work on unix-based systems too. For most applications, dropping/adding a couple of frames in a 100ms stimulus presentation is nothing in particular to worry about anyway; certainly not for the applications I mostly use it for (e.g. fMRI experiments where, of course, timing is important in many ways, but the variability in the haemodynamic response function tends to render a lot of experimental precision somewhat moot). The authors of this paper agree, and conclude that all three systems are suitable for the majority of experimental paradigms used in cognitive research. For me, the benefits of PsychoPy (cross-platform, free licensing, user-friendly interface) far outweigh the (potential) compromise in accuracy. I haven’t noticed any timing issues with PsychoPy under general usage, but I’ve never had a need to push it as hard as these authors did for their testing purposes. It’s worth noting that all the testing in this paper was done using a single hardware platform; possibly other hardware would give very different results.

Those who are into doing very accurate experiments with very short display times (e.g. research on sub-conscious priming, or visual psychophysics) tend to use pretty specialised and highly-optimised hardware and software, anyway. If I ever had a need for such accuracy, I’d definitely undertake some extensive testing of the kind that these authors performed, no matter what hardware/software I ended up using. As always, the really important thing is to be aware of the potential issues with your experimental set-up, and do the required testing before collecting your data. Never take anything for granted; careful testing, piloting, examination of log files etc. is a potential life-saver.

TTFN.

 

Towards open-source psychology research

A couple of interesting things have come along recently which have got me thinking about the ways in which research is conducted, and how software is used in psychology research.

The first is some recent publicity around the Many Labs replication project  – a fantastic effort to try and perform replications of some key psychological effects, with large samples, and in labs spread around the world. Ed Yong has written a really great piece on it here for those who are interested. The Many Labs project is part of the Open Science Framework – a free service for archiving and sharing research materials (data, experimental designs, papers, whatever).

The second was a recent paper by Tom Stafford and Mike Dewar in Psychological Science. This is a really impressive piece of research from a very large sample of participants (854,064!) who played an online game. Data from the game was analysed to provide metrics of perception, attention and motor skills, and to see how these improved with training (i.e. more time spent playing the game). The original paper is here (paywalled, unfortunately), but Tom has also written about it on the Mind Hacks site and on his academic blog. The latter piece is interesting (for me anyway) as Tom says that he found his normal approach to analysis just wouldn’t work with this large a dataset and he was obliged to learn Python in order to analyse the data. Python FTW!

Anyway, the other really nice thing about this piece of work is that the authors have made all the data, and the code used to analyse it, publicly available in a GitHub repository here. This is a great thing to do, particularly for a large, probably very rich dataset like this – potentially there are a lot of other analyses that could be run on these data, and making it available enables other researchers to use it.

These two things crystallised an important realisation for me: It’s now possible, and even I would argue preferential, for the majority of not-particularly-technically-minded psychology researchers to perform their research in a completely open manner. Solid, free, user-friendly cross-platform software now exists to facilitate pretty much every stage of the research process, from conception to analysis.

Some examples: PsychoPy is (in my opinion) one of the best pieces of experiment-building software around at the moment, and it’s completely free, cross-platform, and open-source. The R language for statistical computing is getting to be extremely popular, and is likewise free, cross-platform, etc. For analysis of neuroimaging studies, there are several open-source options, including FSL and NiPype. It’s not hard to envision a scenario where researchers who use these kinds of tools could upload all their experimental files (experimental stimulus programs, resulting data files, and analysis code) to GitHub or a similar service. This would enable anyone else in the world who had suitable (now utterly ubiquitous) hardware to perform a near-as-dammit exact replication of the experiment, or (more likely) tweak the experiment in an interesting way (with minimal effort) in order to run their own version. This could potentially really help accelerate the pace of research, and the issue of poorly-described and ambiguous methods in papers would become a thing of the past, as anyone who was interested could simply download and demo the experiment themselves in order to understand what was done. There are some issues with uploading very large datasets (e.g. fMRI or MEG data) but initiatives are springing up, and the problem seems like it should be a very tractable one.

The benefit for researchers should hopefully be greater visibility and awareness of their work (indexed in whatever manner; citations, downloads, page-views etc.). Clearly some researchers (like the authors of the above-mentioned paper) have taken the initiative and are already doing this kind of thing. They should be applauded for taking the lead, but they’ll likely remain a minority unless researchers can be persuaded that this is a good idea. One obvious prod would be if journals started encouraging this kind of open sharing of data and code in order to accept papers for publication.

One of the general tenets of the open-source movement (that open software benefits everyone, including the developers) is doubly true of open science. I look forward to a time when the majority of research code, data, and results are made public in this way and the research community as a whole can benefit from it.

How to hack conditional branching in the PsychoPy builder

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.

Some Python resources

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.

Toodles.

Back to school special

Unimatrix-0 High School has really excellent attendance and discipline statistics

So, another academic year is about to hove into view over the horizon, and what better time to take stock of your situation, make sure your gear is fit for purpose, and think about levelling-up your geek skills to cope with the rigours of the next year of academic life. If you need any hardware, Engadget’s Back to School review guides are a great place to start, and have reviews of all kinds of things from smartphones to gaming systems, all arranged helpfully in several price categories.

If you really want to be ahead of the game this year though, you’ll need to put in a bit of extra time and effort, and learn some new skills. Here are my recommendations for what computing skills psychology students should be learning, for each year of a standard UK BSc in Psychology.*

If you’re starting your 1st year…

A big part of the first year is about learning basic skills like academic writing, synthesising information, referencing etc. Take a look at my computer skills checklist for psychology students and see how you measure up. Then, the first thing you need to do, on day one, is start using a reference manager. This is an application that will help you organise journal articles and other important sources for your whole degree, and will even do your essay referencing for you. I like Mendeley, but Zotero is really good as well. Both are totally free. Download one of them right now. This is honestly the best bit of advice I can possibly give to any student. Do it. I just can’t emphasise this enough. Really. OK. Moving on.

Next you need to register for a Google account, if you don’t have one already. Here’s why. Then use your new Google username to sign up for Feedly and start following some psychology and neuroscience blogs. Here and here are some good lists to get you started. If you’re a real social-media fiend, sign up for Twitter and start following some of these people.

You may want to use the 5GB of free storage you get with Google Drive as a cloud back-up space for important documents, or you may want to sign up for a Dropbox account as well. Use one or the other, or preferably both, because none of your data is safe. Ever.

You’ll want to start getting to know how to use online literature databases. Google Scholar is a good start, but you’ll also need to get familiar with PubMed, PsycInfo and Web of Knowledge too.

If you’re really keen and want to learn some general skills that will likely help you out in the future, learn how to create a website with WordPress or Github Pages.  Or maybe download Gimp and get busy with learning some picture editing.

If you’re starting your 2nd year…

This is when things get more serious and you probably can’t expect to turn up to tutorials with an epically massive hangover and still understand everything that’s going on. Similarly, you need to step it up a level with the geekery as well.

You probably learned some SPSS in your statistics course in the first year. That’s fine, but you probably don’t have a licence that allows you to play with it on your own computer. PSPP is the answer – it’s a free application that’s made to look and work just like SPSS – it even runs SPSS syntax code. Awesomes. Speaking of which, if you’re not using the syntax capabilities of SPSS and doing it all through the GUI, you’re doing it wrong. 

If you really want to impress, you’ll start using R for your lab reports. The seriously hardcore will just use the base R package, but don’t feel bad if you want to use R-Commander or Deducer to make life a bit easier. Start with the tutorials here.

If you’re starting your 3rd year…

This is the year when you’ll probably have to do either a dissertation, a research project, or maybe both. If you’re not using a reference manager already, trying to do a dissertation without one is utter lunacy – start now.

For your research project, try and do as much of it as you can yourself. If you’re doing some kind of survey project, think about doing it online using Google Forms, or LimeSurvey. If you’re doing a computer-based task, then try and program it yourself using PsychoPy. Nothing will impress your project supervisor more than if you volunteer to do the task/survey set-up yourself. Then of course you can analyse the data using the mad statz skillz you learned in your second year. Make some pretty looking figures for your final report using  the free, open-source Veusz.

Learning this stuff might all sound like a lot to ask when you also have essays to write, tutorials to prepare for, and parties to attend. However, all these things are really valuable CV-boosting skills which might come to be invaluable after you graduate. If you want to continue studying at Masters or PhD level, potential supervisors will be looking for applicants with these kinds of skills, and solid computer knowledge can also help to distinguish you from all the other psychology graduates when applying for ‘normal’ jobs too. It really is the best thing you can learn, aside from your course material, naturally.

Have I missed anything important? Let me know in the comments!

Good luck!

* I realise US colleges and other countries have a different structure, but I think these recommendations will still broadly apply.

Open-Source software for psychology and neuroscience

Researchers typically use a lot of different pieces of software in the course of their work; it’s part of what makes the job so varied. Separate packages might be used for creating experimental stimuli, programming an experiment, logging data, statistical analysis, and preparing work for publication or conferences. Until fairly recently there was little option but to use commercial software in at least some of these roles. For example, SPSS is the de facto analysis tool in many departments for statistics, and the viable alternatives were also commercial – there was little choice but to fork over the money. Fortunately, there are now pretty viable alternatives for cash-strapped departments and individual researchers. There’s a lot of politics around the open-source movement, but for most people the important aspect is that the software is provided for free, and (generally) it’s cross-platform (or can be compiled to be so). All that’s required is to throw off the shackles of the evil capitalist oppressors, or something. 

So, there’s a lot of software listed on my Links page but I thought I’d pick out my favourite bits of open-source software, that are most useful for researchers and students in psychology.

First up – general office-type software; there are a couple of good options here. The Open Office suite has been around for 20 years, and contains all the usual tools (word processor, presentation-maker, spreadsheet tool, and more). It’s a solid, well-designed system that can pretty seamlessly read and write the Microsoft Office XML-based (.docx, .pptx) file formats. The other option is Libre Office, which has the same roots as Open Office, and similar features. Plans are apparently underway to port Libre Office to iOS and Android – nice. The other free popular options for presentations is, of course, Prezi.

There are lots of options for graphics programs, however the two best in terms of features are without a doubt GIMP (designed to be a free alternative to Adobe Photoshop) and Inkscape (vector graphics editor – good replacement for Adobe Illustrator). There’s a bit of a steep learning curve for these, but that’s true of their commercial counterparts too.

Programming experiments – if you’re still using a paid system like E-Prime or Presentation, you should consider switching to PsychoPy – it’s user-friendly, genuinely cross-platform, and absolutely free. I briefly reviewed it before, here.  Another excellent option is Open Sesame.

For statistical analysis there are a couple of options. Firstly, if you’re a SPSS-user and pretty comfortable with it (but fed up of the constant hassles of the licensing system), you should check out PSPP; a free stats program designed to look and feel like SPSS, and replicate many of the functions. You can even use your SPSS syntax – awesome. The only serious issue is that it doesn’t contain the SPSS options for complex GLM models (repeated measures ANOVA, etc.). Hopefully these will be added at some future point. The other popular option is the R language for statistical computing. R is really gaining traction at the moment. The command-line interface is a bit of a hurdle for beginners, but that can be mitigated somewhat by IDEs like R-Commander or RStudio.

For neuroscience there’s the NeuroDebian project – not just a software package, but an entire operating system, bundled with a comprehensive suite of neuroscience tools, including FSL, AFNI and PyMVPA, plus lots of others. There really are too many bits of open-source neuro-software to list here, but a good place to find some is NITRC.org.

So, there you are people; go open-source. You have nothing to lose but your over-priced software subscriptions.

TTFN.

Another miscellaneous grab-bag of goodies, links ‘n’ stuff

In lieu of a ‘proper’ post (forgive me, dear readers, the vicious task-masters at my proper job have been wielding the whip with particular alacrity recently) I’m putting together a list of links to cool things that I’ve come across lately.

So, in no particular order:

Tal Yarkoni’s outstanding Neurosynth website has now gone modular and open-source, meaning you can embed the code for the brain-image viewer into any website, and use it to present your own data – this is seriously cool. Check out his blog-post for the details.

An interesting little comment on “Why Google isn’t good enough for academic search”. Google scholar tends to be my first port of call these days, but the points made in this discussion are pretty much bang-on.

A fantastic PNAS paper by Kosinski et al. (2013; PDF) that demonstrates that personal attributes such as sexual orientation, ethnicity, religious and political views, some aspects of personality, intelligence and many others, can be automatically and accurately (to a fairly startling degree, actually) predicted merely from analysis of Facebook ‘Likes’. A fantastic result, that really demonstrates the value of doing research using online data.

Next up is Google Refine – an interesting little idea from Google intended to assist with cleaning up and re-formatting messy data. Looks like it could be promisingly useful.

A really seriously great website on the stats language R, designed to make the transition for SPSS and SAS users as easy as possible – very clear, very nicely explained. Beautiful stuff.

Another cool website called citethisforme.com; you fill in fields (author, title, etc.) for sources you wish to cite, and it creates a perfectly formatted bibliography for you in the style (APA, Harvard etc.) you choose. A cool idea, but in practice, filling out the fields would be incredibly tedious for anything more than a few sources. Good place to learn about how to format things for different types of reference though.

I’ve previously written about the use of U-HID boards for building USB response devices; I’ve just been made aware of a similar product called Labjack, which looks even more powerful and flexible. A Labjack package is included in the standard distribution of PsychoPy too, which is cool. I’m becoming more and more a fan of PsychoPy by the way – I’m now using it on a couple of projects, and it’s working very well indeed for me.

Now a trio of mobile apps to check out. Reference ME is available for both iOS and Android, and creates a citation in a specific style (Harvard, APA, etc.) when you scan the barcode of a book – very handy! The citations can then be emailed to you for pasting into essays or whatever.

The Great Brain Experiment is a free app from the Wellcome Trust (download links for both iOS and Android here) created in collaboration with UCL. The aim is to crowdsource a massive database on memory, impulsivity, risk-taking and other things. Give it a whirl – it’s free!

Lastly Codea is a very cool-looking iPad-only app that uses the Lua programming language to enable the (relatively) easy development and deployment of ‘proper’ code, entirely on the iPad. Very cool – Wired called it ‘the Garage Band of coding’, and while it’s probably not quite that easy to use, it’s definitely worth checking out if you want to use your iPad as a serious development tool.

If you’re still hungry for more internet goodies, I encourage you most heartily to check out my Links page, which is currently in an ongoing phase of rolling development (meaning, whenever I find something cool, I put it up there).

TTFN.

 

Website of the week: Cogsci.nl. OpenSesame, illusions, online experiments, and more.

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!

Programming experiments using PsychoPy – first impressions

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.