During my bachelors in cognitive neuropsychology, we often looked at case studies in class. We might be given descriptions of patients with particular symptoms, and be asked to diagnose these patients. During these classes, our professor would often refer to Oliver Sacks' famous compilation of case studies, The Man Who Mistook His Wife for a Hat. From this moment on, Sacks was, to me, the face of neuropsychology. (Our professor often implied that he and 'Oliver' were close friends. I've always wondered whether this was true; our professor was a boastful man, and he's not mentioned anywhere in On the Move.)
But aside from a few extracts from Hat (Sacks has a one-word title for all of his books), I never read anything by Sacks until midway through my PhD, when I picked up The Island of the Colorblind. In the first story of Island (there are two), Sacks describes his visit to an isolated island where about 10% of the inhabitants are fully colorblind. Sacks set out to investigate this peculiar prevalence of full colorblindness, which, unlike red-green colorblindness, is very rare. In the second story of Island, Sacks describes another isolated island community, this time with a more serious problem: many people in this community develop a neurodegenerative disease that leads to Parkinson-like symptoms and crippling disability. Again, Sacks tries to get to the bottom of this medical mystery. (Spoiler alert: Neither story is solved.)
While reading Island, I fell in love with Sacks' writing. The way he tells medical stories from a first-person perspective makes that some of them read like detective novels. To Sacks, neurological conditions are crimes to be solved. There's also a hint of this in On the Move, when Sacks tells the story of a patient, Frank C., with a mysterious neurological condition.
The Best Illusion of the Year Contest took place last week. As always, there were some cool, new illusions among the finalists—a feast for illusion-afficionados like myself. I particularly like the Honeycomb Illusion by Marco Bertamini and Nicola Bruno. Take a moment to watch (my rendition of) their illusion in its beautiful simplicity (important: for best effect, watch in full screen and HD quality):
This illusion is nothing but a honeycomb grid with little stars (or barbs) at each node. You can see this in close-up on the right.
A close-up of the Honeycomb Grid, with little stars (or barbs) on each node. This is not the illusion itself!
The stars are clearly visible when you look directly at them. But, and this is where things get interesting, you don't see any stars in the parts of the grid that you don't directly look at. In other words, you get the impression that the little stars follow your eyes around, as you scan the grid with your eyes. I personally find this very compelling.
So what's going on here?
The key to this illusion is that, at any one moment, you only have a clear view of a very small part of the world: the part that falls onto the central part of your retina (the fovea). This part is about the size of your thumb at arm's length. Your peripheral vision, the things that you see from the corner of your eye, is much less sharp, and color blind. This, among other things, is why you move your eyes: You successively direct your central vision at things that you want to see, or that grab your attention. (See also this post.)
This partly explains the Honeycomb Illusion: The stars are simply too small to see with your peripheral vision, so you only see them when you look at them directly. But this is not all; the full explanation is, I believe, a bit more subtle. After all, the Honeycomb Illusion gives the impression that things appear and disappear as you move your eyes. And this is not the impression that you usually have when you move your eyes.
Consider what happens when you see a girl from the corner of your eye, and prepare an eye movement to look at her face. Even though you cannot make out any facial features with your peripheral vision, you know what a face looks like: there are two eyes, with one nose below, and one mouth below that. So when you make an eye movement toward a face, you, or rather your brain, makes a coarse prediction of what you should see. And this prediction is usually accurate.
But sometimes it isn't.
I'm kind of a day dreamer, and I don't pay much attention to my surroundings. So when I walk through the city with my girlfriend, I don't always keep track of where she is. I just assume that the nearest person is her. (I'm not in general a neglectful boyfriend though; or at least I don't think so.) And when I look at this person and discover that it's actually someone else, I get a bit of a shock. This shock is, you might say somewhat fancifully, the feeling of my brain's prediction being violated.
And this may be what happens in the Honeycomb Illusion as well. For some reason, your brain assumes that the the grid is a regular honeycomb—without stars. You can see that some nodes, those that you look at, have little stars on them. But your brain doesn't appear to conclude from this that therefore all nodes must have little stars on them. And when you shift your eyes to another node, and see (again, yet unexpectedly) little stars, you get a little shock; your brain didn't see that coming.
So I believe that this illusion tricks your brain into making an incorrect prediction about what the grid looks like. When you make eye movements, this prediction is violated over and over again, which subjectively gives the impression of stars appearing and disappearing.
Or maybe not. But it's a cool illusion in any case.
Ok, stop procrastinating for a moment, and spend your time on something useful. Like finding Boerke in this image:
Boerke, shown at the bottom left, is hiding among his comic-strip buddies. (Source: Stripmuseum Brussels)
While searching for Boerke, you probably scanned the picture in a particular way: You scrutinized small parts of the picture one by one, looking at things that you wouldn't normally look at. Maybe Boerke is behind the ticket counter? (And where's the ticket counter?) No ... Maybe Boerke is climbing the stairs then? No, that's Bobette ... And so on, until you spotted Boerke. (Assuming you have. If not: keep looking!)
Last week, six contestants in a cycling race in Norway were poisoned after drinking laundry detergent. Drinking detergent seems like an exceedingly idiotic thing to do. So how come that six people did this? Well ...
... Omo Aktiv & Sport really looks like a sports drink.
In the beginning of 2006, there were almost one hundred reports of people being poisoned after drinking Fabuloso, a surface cleaner that promises to make dirty floors shine again. Why? Well ...
... Fabuloso really looks like a soft drink.
The reason that manufacturers make their cleaning products look like food is obvious. People like eating better than cleaning, so a cleaning product is more attractive if it looks edible.
Non-foods that look like foods, and non-drinks that look like drinks, are called food-imitating products. They are recognized as a public health risk by the European Union. If a detergent looks like a drink, someone will drink it. The result is a nasty case of poisoning, which, in rare cases, can be deadly. It's a problem.
I'm writing this on my way back from London, where I attended a workshop on publication bias that was organized by the NC3Rs (the British National Center for the Replacement, Refinement, and Reduction of Animals in Research). Publication bias arises when not all scientific studies are published, and when the chance of whether a study is published depends on its outcome. More specifically, studies that show a 'positive' result (e.g. a treatment effect, or something that supports a researcher's hypothesis) are published more often than studies that show a 'negative' result (e.g. no treatment effect, or something that doesn't support a researcher's hypothesis). Publication bias distorts scientific evidence. In most cases, it makes treatments (drugs, therapies, etc.) seem more effective than they are, simply because we only see studies that show positive treatment effects.
Publication bias is increasingly recognized as a severe problem that affects all areas of science. It's not new. It's just that until recently little was done about it. It was therefore great to see this workshop bring researchers, funders, publishers, and people from industry together with the aim of discussing concrete ways of reducing publication bias. In this post I would like to tell you about some of the things that were discussed.
There were many excellent speakers, but I will first highlight the opening talk by Emily Sena. Her talk was partly based on a meta-analysis in which she investigated publication bias in animal research on stroke treatment. Her work nicely shows how you can answer a seemingly unanswerable question: How many studies were never published, and what did these invisible studies find?