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Moved from a psychology degree to a PhD in neuroscience - an electrophysiological investigation of the sources of sensory input to dopamine releasing neurons. From there I moved back to psychology with an experimental postdoc looking at perception with sensory substitution devices. I am looking to move back into neuroscience with my next postdoc, but in the meantime I'm blogging on a mixture of psychology and neuroscience.

A layman reading an apprentice’s books

I’ve just been reading a couple of old posts by Carl Zimmer over at The Loom, Death to Obfuscation! and the Index of Banned Words, which give a few hints on how to write more clearly for an audience outside your scientific field. There was a comment that included a quote from the master of science communication Richard Feynman about being a layman trying to read up on a new subject.

The layman searches for book after book in the hope that he will avoid the complexities which ultimately set in, even with the best expositor of this type. He finds as he reads a generally increasing confusion: one complicated statement after another, one difficult-to-understand thing after another, all apparently disconnected from one another. It becomes obscure, and he hopes that maybe in some other book there is some explanation. . . . The author almost made it — maybe another fellow will make it right.

— Richard Feynman, The Character of Physical Law (1964)

That pretty much describes a lot of my experiences trying to teach myself something new. I frequently find myself searching for a more comprehensive but manageable text. Sometimes such a book or website exists, sometimes I end up with a whole range of different introductory materials.

But even if I get a suitable range of material together it’s not always easy – especially if the whole topic is entirely new to me, and I’ve got next to no mental framework to slot the new information into. If even the information from  introductory material isn’t going in, then I think I must be doing something wrong. It frustrates me. If it’s an introductory text I feel like I should be able to pick things up almost the first time I meet them. I should be able to work through all the examples. They must make sense, surely? Occasionally I have flashes of realisation that I’ve been in this situation before, and it takes a bit of effort and a bit more time, and that when the basics have settled in I’ll have a better idea of what’s going on. Often, however, I’m still blinkered and stubborn. Maybe if I stare at the notation for set theory long enough, all the concepts will make sense. Hopefully, writing posts will help me keep that learning curve in mind, and I’ll spend less time stuck trying to figure out how I can take a shortcut.

Sunday Links #9

A links digest! It’s been a while, so some of these might not be as hot off the press as they could be…

Honesty with statistics – An article by Elizabeth Wager and colleagues in PLoS ONE shows that the Journal of the American Medical Association published fewer industry funded studies after they introduced a requirement for independent statistical analysis, whilst more were published in The Lancet and New England Journal of Medicine.

Boys and girls equally good at maths; ‘gap’ is a self-fulfilling prophecy says long term study

…And also at science blogging! After a conversation late last year about the presence of female bloggers, breaks down its blogs by author gender.

Lots of interesting links from Michelle Greene at NeuRealism, including the roulette of paper rejection, and an article on postgraduate researchers and procrastination.

Bradley Voytek at Oscillatory Thoughts has a post on why people writing about connectomics research should be careful not to exaggerate what it hopes to deliver.

Some interesting discussion over at Skepchick with the Afternoon Inquisition: What area of social science most interests you? Will social science(s) ever become methodologically similar to the natural sciences (i.e. make testable predictions, unveil natural laws, etc.)? I always liked the idea that the error bars tend to be larger on social psych research mostly because we haven’t pinned down all the contributing variables. Whether we ever can or will is another matter.

Using Microsoft Excel – a dirty secret

Hi, my name is Craig, and I have an admission. I’ve come to realise that Microsoft Excel isn’t that bad.

I know, I know. For years I’ve treated it as an oversized calculator, good only for storing tables of data, basic mathematics and knocking up a quick graph. If I wanted to do anything more complicated, I’d use dedicated statistical packages, like GraphPad Prism, or recently, R. However, I’ve recently started using a whole range of tools that I didn’t know Excel had, like array maths, and the LOOKUP, IF, COUNTIF, INDEX and MATCH functions. The fact that Excel and similar spreadsheet programs are so widely used should have probably tipped me off to the fact I was only aware of a tiny fraction of Excel’s capabilities, and used a smaller fraction of those. Instead, its many facets remained unknown unknowns – like the built-in functions in the Spike2 language that cut dozens of lines of clumsy code out of my scripts – automatic and easy ways of doing things that were waiting to be discovered by some idle clicking through the manual, or serendipitous choice of search terms.

So I’ve warmed to Excel now, and I’m going to use it as more than just glorified CSV storage. What’s more, now that I’ve discovered Sparklines for Excel, which puts cell sized graphs of data into the spreadsheet, I’m actually pretty happy with it. But perhaps more importantly, I’ve had another reminder of the benefits of idle curiosity.

Welcome to my new home!

I’m officially launched already! I’m behind the times… Welcome to my new home at the blogging network Southern Fried Science – please update your bookmarks and RSS feeds as appropriate. Thanks to all the guys who invited me here, and hopefully it will be the start of something good. I’m looking forward to all the fresh traffic, which will (maybe) motivate me to write a bit more frequently and regularly.

Make yourselves at home!