Category Archives: Cool new tech
This may not look like much, but it’s actually pretty cool. It’s a new five-button response-box being built for our MRI scanner by one of the technicians where I work. The cool thing is that the main chassis has been custom-designed, and then fabricated using a polyethylene-extruding 3D printer. The micro-switches for the four fingers and thumb and the wired connections for each have obviously been added afterwards.
3D printing in plastic is a great way of creating hardware for use in the MRI environment, as, well… it’s plastic, and it can create almost any kind of structure you can think of. We sometimes need to build custom bits of hardware for use in some of our experiments, and previously we’d usually build things by cutting plastic sheets or blocks to the required shape, and holding them together with plastic screws. Using a 3D-printer means we can produce solid objects which are much stronger and more robust, and they can be produced much more quickly and easily too. I love living in the future.
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.
It’s always exciting when you find a new piece of cool software to play with, and even more so when what you’ve found is totally free, open-source, and available on all platforms. So it is with MakeHuman – an utterly awesome bit of kit. I wrote a piece before about FaceGen, which is also pretty cool, but MakeHuman takes it to the next level, by modelling all kinds of body characteristics as well as faces, and, of course, doing it all for free.
I’ve just downloaded it and played with it for a few minutes, but I’m already impressed by the range of options available. Through a very simple slider and radio-button based interface you have very fine control over all kinds of variables, including gender, weight, age, height, and many more, with endless fine tweak-ability possible of body and face if you dig through the options. There are also basic libraries of clothes and poses included. Here’s a well, a human, I made in just a couple of minutes:
And here’s a close-up of the face, after I added some hair and gave him a nasty expression:
Pretty cool indeed. This could potentially be a massively useful tool for people interested in face/body perception – using this, one could generate a large number of highly-controlled experimental stimuli that just differ in one aspect (say, weight, or race… whatever) very easily and quickly. Download it and have a play around!
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.
Forensic and criminal psychology are somewhat odd disciplines; they sit at the cross-roads between abnormal psychology, law, criminology, and sociology. Students seem to love forensic psychology courses, and the number of books, movies, and TV shows which feature psychologists cooperating with police (usually in some kind of offender-profiling manner) attests to the fascination that the general public have for it too. Within hours of the Newtown, CT shooting spree last December, ‘expert’ psychologists were being recruited by the news media to deliver soundbites attesting to the probable mental state of the perpetrator. Whether this kind of armchair diagnosis is appropriate or useful (hint: it’s really not), it’s a testament to the acceptance of such ideas within society at large.
Back in the late 80s and early 90s there were two opposing approaches to offender profiling, rather neatly personified by American and British practitioners. A ‘top-down’ (or deductive) approach was developed by the FBI Behavioral Sciences Unit, and involved interviewing convicted offenders, attempting to derive (somewhat subjective) general principles in order to ‘think like a criminal’. By contrast, the British approach (developed principally by David Canter and colleagues) took a much more ‘bottom-up’ (or inductive) approach focused on empirical research, and more precisely quantifiable aspects of criminal behaviour.
Interestingly, the latter approach was ideally suited to standardised analysis methods, and duly spawned a number of computer-based tools. The most prominent among them was a spatial/geographical profiling tool, developed by Canter’s Centre for Investigative Psychology, and named ‘Dragnet’. The idea behind it was relatively simple – that the most likely location of the residence of a perpetrator of a number of similar crimes could be deduced from the locations of the crimes themselves. For example, a burglar doesn’t tend to rob his next-door neighbours, nor does he tend to travel too far from familiar locations to ply his trade – he commits burglaries at a medium distance from home, and generally roughly the same distance. Also general caution might prevent him from returning to the same exact location twice, so an idealised pattern of burglary might include a central point (the perpetrators home) with a number of crime locations forming the points of a circle around it. For an investigator, of course the location of the central point isn’t known a priori, however it can easily be deduced simply by looking at the size and shape of the circle.
In practice of course, it’s never this neat, but modern techniques incorporate various other features (terrain, social geography, etc.) to build statistical models and have met with some success. Ex-police officer Kim Rossmo has been the leading figure in geographic profiling in recent years, and founded the Center for Geospatial Intelligence and Investigation at Texas State university.
Software like this seems like it should be useful, but by and large has failed to deliver on its promises in a major way. At one point it was thought that the future police service would incorporate these tools (and others) routinely in order to solve, and perhaps even predict, crimes. With the sheer amount and richness of data available on the general populace (through online search histories, social networking sites, insurance company/credit card databases, CCTV images, mobile-phone histories, licence-plate-reading traffic cameras, etc. etc.) and on urban environments (e.g. Google maps) that crime-solving software would now be highly developed, and use all these sources of information. However, it seems to have largely stalled in recent years; the Centre for Investigative Psychology’s website has clearly not been updated in several years, and it seems no-one has even bothered producing versions of their software for modern operating systems.
Some others seem to be pursuing similar ideas with more modern methods (e.g. this company), yet still we’re nowhere near any kind of system like the (fictional) one portrayed in the TV series ‘Person of Interest‘, which can predict crimes by analysis of CCTV footage and behaviour patterns derived therefrom. Whether or not this will ever be possible, there is certainly relevant data out there, freely accessible to law-enforcement agencies; the issue is building the right kind of data-mining algorithms to make sense of it all – clearly, not a trivial endeavour.
Something that will undoubtedly help, is the fairly recent development of pretty sophisticated facial recognition technology. Crude face-recognition technology is now embedded in most modern digital cameras, can be used as ID-verification (i.e. instead of a passcode) to unlock smartphones, and is used for ‘tagging’ pictures on websites like Facebook and Flickr. Researchers have been rapidly refining the techniques, including some very impressive methods of generating interpolated high-resolution images from low-quality sources (this paper describes an impressive ‘face hallucination’ method; PDF here). These advancements, while impressive, are essentially a somewhat dry problem in computer vision; there’s no real ‘psychology’ involved here.
One other ‘growth area’ in criminal/legal psychology over the last few years has been in fMRI lie-detection. Two companies (the stupidly-or-maybe-ingeniously-named No Lie MRI, and Cephos) have been aggressively pushing for their lie-detection procedures to be introduced as admissible evidence in US courts. So far they’ve only had minor success, but frankly, it’s only a matter of time. Most serious commentators (e.g. this bunch of imaging heavy-hitters) still strike an extremely cautious tone on such technologies, but they may be fighting a losing battle.
Despite these two very technical areas then, in general, the early promise of a systematic scientific approach to forensic psychology that could be instantiated in formal systems has not been fulfilled. I’m not sure if this is because of a lack of investment, expertise, interest, or just because the problem turned out to be substantively harder to address than people originally supposed. There is an alternative explanation of course – that governments and law enforcement agencies have indeed developed sophisticated software that ties together all the major databases of personal information, integrates it with CCTV and traffic-camera footage, and produces robust models of the behaviour of the general public, both as a whole, and at an individual level. A conspiracy theorist might suppose that if such a system existed, information about it would have to be suppressed, and that’s the likely reason for the apparent lack of development in this area in recent years. Far-fetched? Maybe.
TTFN, and remember – they’re probably (not?) watching you…
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!
Another very quick link-out post I’m afraid ladies and gentleman – all this damn pushing back of the boundaries of science I’ve been doing lately has left me absolutely no time at all, plus have you ever tried pushing back a boundary? It’s bloody exhausting.
Anyway, following on from my recent post about the auditory gorillas experiment, where I talked a little about audio editing, I spotted a fantastic little piece over on Engadget about the basics of digital audio, covering things like sampling rates, bit-rate, file-formats and loads of other useful/mildly-nerdy stuff. In fact their whole ‘primed’ series (where they dissect common bits of computer technology from first principles) is well worth checking out.
Have a good weekend!
A very minimal post merely to point any interested readers towards an interesting discussion going on in the comments section of a post on Engadget here. A reader asked for suggestions for a tablet and/or apps for his developmentally-delayed daughter, and a large number of people have contributed some useful ideas and links. Just try to ignore the (inevitable *sigh*) Android vs. iOS fan-boy squabbling.