Choose Paranoia – in praise of Imposter Syndrome

Athene Donald posted recently on imposter syndrome, that feeling that we’re doing something way beyond our capabilities, perhaps due to clerical error or overenthusiastic “brand management”*. As I’ve touched on before, working in an interdisciplinary team exacerbates that. I’ve heard a talented researcher say “but I haven’t studied maths since the nineties”, and mathematicians wondering out loud what the modifiable unit area problem is. Not that I really know myself…

Interdisciplinary work at its best forces people out of their silos and out of their comfort zones. For example, it’s not enough to be a great mathmo if you don’t gain some understanding of the problem you’re applying yourself to – relying on someone else to deal with the nitty-gritty is not a recipe for success. In this world, everyone should feel like an imposter to some degree.

Although expressed as an afterthought in this blogpost, I have recognised in the past a reverse-Dunning-Kruger type attitude in my behaviour. Dunning-Kruger is the tendency of people to overrate their abilities, reverse-Dunning-Kruger is the tendency for competent people to overestimate others’ abilities/underestimate their own**. I recognise the thought process:

“I’m a reasonably intelligent person, but I don’t possess a unique intellect – so anything which I’m good at can’t be too hard to get good at. That person over there – they’re really good at/knowledgable about things I find really hard, and they could probably get good at the things I do quite easily, if they had the time and inclination [note to self: perpetuate the myth that physics is REALLY TOUGH so they never develop the inclination]. Oh look, there’s another person who’s an expert in a whole different difficult field. And another. Gee whiz”.

If you find this yourself: welcome to interdisciplinary research. And if you’re not working in interdisciplinary research: welcome to academia. There are lots of smart, hardworking people here. And if you’re not working in academia: welcome to the world. There are lots of bright people doing cool things.

When you look at it that way, Imposter Syndrome doesn’t seem like such a bad thing. I liked writer Leila Johnston‘s response: “Imposter syndrome is pretty good, I think, because the alternative is a world in which everyone else is as mediocre as you are.” If the choice is between paranoia and mediocrity, let’s choose paranoia.

*(I don’t know how many academics lie on their CVs – I’m assuming very few – but that is almost certainly a problem in the world at large)

**self-identifying as suffering from reverse-Dunning-Kruger might indicate an overestimation of one’s own abilities, but let’s set that aside for the time being. I’m no expert on foward-reverse-Dunning-Kruger.

Sounds of… Tottenham Court Road

Science Sociologist/Policy Academic/Blogger Alice Bell has very kindly invited me to take part in the Sounds of Science event on February 29th at Charles Darwin House – featuring participants from the BBC, Audioboo and the BMJ. To celebrate world radio day, she wrote this blogpost in celebration, in part, of the sounds around us.

In my time as a scientist I’ve worked in labs, offices, clinics and theatres. I have a particularly vivid memory of being involved with a prostate laser treatment where the theatre staff insisted on playing 80s pop on a little CD player while they worked. Sitting between a man’s stirruped legs, waiting for the treatment to finish while listening to Never Going to Give You Up gives a new definition to the phrase “rick-rolled”. But I digress.

My current office is on Tottenham Court Road (aka “TCR”) – one of London’s busiest streets. When we record Global Lab (the CASA research podcast) you can hear the sound of TCR in the background –  we frequently have to stop and let ambulances and police cars race past. But we wanted to make a feature of this – CASA is a department that has a lot of projects about sensing the city, and it’s entirely appropriate that we’re right at the heart of one of the world’s most vibrant, most historic cities. So I went out onto the street with my iPhone and captured a bit of this. This is what TCR sounded like last July:

So far, so noisy. That weird whale song sound you can hear is the noise buses make – I think it might be their brakes, reverbed by some reflections between the parallel buildings of TCR. I started thinking about whether I could make that musical. It has a certain tonality to it, and with a bit of looping, a certain rhythm. A GarageBand file was born, complete with “dance” drums, and a guitar and a bass part recorded straight into the computer and augmented with Apple’s rather passable amp simulators. This is what it sounds like:

And, GarageBand users, this is what it looks like:

Anyway, that’s how I made background noise into the theme tune for a podcast. If you want to hear (even) more interesting stories about sound and science, come along at the end of the month to Sounds of Science.

Twitter data – visualised by our MRes students

This term we’ve been running our Visualisation module as part of the CASA MRes in Advanced Spatial Analysis and Visualisation. The flavour of this module is what you’d expect – finding interesting ways to communicate complex spatio-temporal data through static, animated and interactive tools. I teach every other week, focussing on the use of Processing to programmatically represent data; 3D design whiz and course director Andy Hudson-Smith tends to work with ArcGIS, Lumion and other 3D tools.

CASA student Fabian Neuhaus‘ twitter maps have had quite an impact in the past – showing patterns of geographical twitter usage around London. We challenged our students to take a sample of the same data (collected by Fabian with Steve Gray’s big data tools) and visualise it. The dataset included the date and time of the tweet, its location (only geotagged tweets were considered), and other information like the username, what platform they tweeted from, and language. Here are some examples of what they came up with…

This was my initial (quick and dirty) stab at visualising the data:

One criticism of my initial attempt is that it lacks geographical markers – Alistair Leak tackled that problem by introducing a map. He chose not to blend time or spatial aspects, and with the underlying map this gives perhaps the most accurate representation of the data. The counter to that is that it contains a lot of visual information for the viewer to take in.

Ian Morton took an intermediate approach; a skeletal geographical boundary provides reference points for the viewer. Each tweet persists in time, shrinking and darkening over successive frames. This is a simple and effective visual grammar to provide some “history” or continuity to the vis, whilst retaining a focus on the most recent events.

Robin Edwards and Martin Dittus took a 3D approach, binning over a geographical grid in a KDE-like approach. These elegant 3D visualisations have both considered the problem of interpolation – how to move from one data state to the next. Robin has approached that problem by the bars instantly moving to the current data point, and then (if subsequent data at that point is zero), fading down to zero gradually (like an old-school “graphic equaliser”). Martin has written a smooth transition between subsequent data points – so the bars move smoothly *towards* the latest point. These interpolations enhance the polish of a vis and provide a sense of continuity in a noisy or discontinuous dataset. Martin also added a rich functionality for filtering the tweets by metadata (language, twitter platform, etc) – giving the user of the interactive app control over their view of the data.

Jack Harrison decided to dispense with space, and treat Processing as a component in a more complex workflow. He analysed temporal patterns in R and output the result to Processing to create a “clock”. By saving this as a PDF, he was able to import it into illustrator, allowing him to add the colour scheme and text and create this wonderfully Art Decon rank-clock like vis.

This is my take on the data – I’ll blog about it in more detail, but it’s essentially a Gaussian KDE with some transparency to give smooth blending between different time points as well as spatial blending. As I didn’t give the students an opportunity to feedback on my offering (after we gave significant feedback on theirs) I’m sure they will express their opinions below the line…