The findings of the third, laboratory-based, study further illuminate the relationship between the use of devices and the potential for distraction. The researchers in this study set out to test whether undergraduates who are “heavy media multi-taskers” might have an improved ability, relative to peers who are “light media multi-taskers,” to filter out distracting information. The researchers defined “media multi-tasking” or simultaneously engaging with different media—including print, television, computer-based video, music, text messaging, instant messaging, web-surfing, email. Their findings were precisely the opposite of what they had expected to find: heavy media multi-tasking was related to a reduced ability to ignore distractions and focus on pertinent information—even after accounting for potential differences in academic aptitude, personality and performance on standard creativity and memory tasks.

This study suggests that students who frequently switch their attention back and forth—from listening to a lecture or answering questions to activities such as texting or updating social networking sites, for example—may actually be less able to filter out irrelevant distractions from pertinent information—even when they are not texting or social networking.

The first paragraph in the above quote neatly summarizes the study; the second paragraph is extrapolating the very artificial laboratory results to the classroom, which is certainly a reasonable speculation, but not something demonstrated by the study. Nevertheless, reading these results (which I did after following a link in an email which I was checking when I should have been finishing that paper on DNA binding sites I had started reading…) leaves me with a strong urge to improve my own task discipline.

Here’s the study:

Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583–15587.

Chronic media multitasking is quickly becoming ubiquitous, although processing multiple incoming streams of information is considered a challenge for human cognition. A series of experiments addressed whether there are systematic differences in information processing styles between chronically heavy and light media multitaskers. A trait media multitasking index was developed to identify groups of heavy and light media multitaskers. These two groups were then compared along established cognitive control dimensions. Results showed that heavy media multitaskers are more susceptible to interference from irrelevant environmental stimuli and from irrelevant representations in memory. This led to the surprising result that heavy media multitaskers performed worse on a test of task-switching ability, likely due to reduced ability to filter out interference from the irrelevant task set. These results demonstrate that media multitasking, a rapidly growing societal trend, is associated with a distinct approach to fundamental information processing.

MMS treatments cause side effects like discomfort, fever, diarrhea, and vomiting (these are the one’s its proponents advertise) with no evidence that it is an effective treatment for anything other than being comfortable and not having diarrhea. Fortunately, there is something you can do to help. Emily Willingham has started a petition to ban the sale and use of MMS as a treatment for autism (read the whole petition below).

The claims made by MMS are duping concerned parents into torturing their children, who are not in a position to understand why mommy and daddy are hurting them.

Sign the petition. Feel good about yourself for at least the rest of the day, because you are a good person, mostly. Ok, I don’t really know about your personal life, but this would mean you’re at least a little bit good.

Block peddling of bleach enemas as “cure” for autism in children

Greetings,

I just signed the following petition addressed to the US Food and Drug Administration, US Department of Health and Human Services, and the Federal Trade Commission.

Order cease and desist on selling, recommending, or administering Miracle Mineral Supplement, also known as MMS or sodium dichlorite solution (industrial strength bleach), as “curative” for children with autism when used orally, in baths, or in repeatedly administered enemas.

Here is what one autistic child underwent with this “treatment”–A parent writes: “He is nonverbal and fairly low-functioning, so I don’t get any feedback from him as to how he is feeling. Last week, I started him on 1 drop of MMS (bleach solution) then upped the dose to 1 drop, 2x a day this week. After about 4 days at 2 drops/day, he vomited once and had diarrhea all day. I am assuming it is the MMS. His gut tends to be very sensitive to anything I give him.”

A full account of the history of this product is available here: http://www.sciencebasedmedicine.org/index.php/bleaching-away-what-ails-you. It recently was featured at an Autism One conference: http://www.autismone.org/

In her Autism One presentation (here: http://www.livestream.com/autismone/video?clipId=pla_a7e0b96e-deb1-4399-9131-d1f6d0a23157), MMS peddler Kerri Rivera references effects such as diarrhea and fever during application. There is no medical indication for this compound, either orally or as a bath or enema, and no indications for its use or efficacy in autism. Among the recommendations for application of this substance in unconsenting, autistic children is the so-called 72-2 protocol, involving application of this bleach compound every 2 hours for 72 hours “every possible weekend.” More information about that is on Rivera’s Website, http://www.autismo2.com/mms.html.

Given the effects that even the people selling it cite–fever, clear discomfort, vomiting, diarrhea–and the use of it in minor children who often are unable to verbalize their experience, we ask that the relevant authorities turn immediate attention to Kerri Rivera, organizers of the Autism One conference where she presented this information, and anyone who is selling or recommending this product for use in children with autism.

Thank you for your time.

Joshua T. Witten, PhD

Extrapolation is not fact. It is estimate. And the accuracy is in the eye of the beholder. So if they [the North Carolina legislature] want to legislate HOW to estimate, it is far less controversial than you make it sound. You base estimates on past experience, not models, which is what climate change is really based on, not fact.

This person is lacking a coherent notion of what extrapolation, estimate, model, and fact mean in science. Reading the comment in context, this person seems to be defending the idea that a linear fit to your data which you use to make predictions is “extrapolation” from past experience, not a model, and is a more reliable way to do science than using a model. To be fair, this confusion is common, and in my experience the role of models in science is not generally taught well in schools. So let’s talk about the role of models in science.

1) Science is based on models. That includes all science. Models express our beliefs about causal relationships in the systems we study. Any time you deal with causal relationships you generally use a model to capture those relationships in some organized form: the attraction and repulsion between atoms, relationships between reactant concentrations and reaction rates, the diffusion of perfume in a room, the relationships between temperature, ice sheet area, and sea level, the gravitational attraction between masses, all of these are dealt with using models. Those pretty pictures of protein and DNA structures you see in science magazines are models, based on approximations of the behavior of chemical bonds and data from X-ray diffraction experiments. Supply and demand. Volcanoes. The carbon cycle. We use models to study them.

2) Generally when you apply mathematical equations to your data, those equations are your model.* Most (all?) relationships that you’re taught to think of as ‘fundamental laws’ are models, but they happen to be extremely general and typically highly exact, and they usually have a stellar evidential track record. But they are models nonetheless, including Newton’s and Maxwell’s laws. Fundamental laws don’t perfectly express exact fundamental relationships; they are human constructs that represent our best but inexact understanding of what we believe are fundamental relationships.

Scientists make predictions from models. Any extrapolation from your data is based on a model; a linear model like y = mx + b is no less a model than a non-linear model. There is no reason to assume that linear models are a better, a more honest, or a more accurate representation of the real relationship between your variables. In fact, given how non-linear the world tends to be, linear models are usually a less than optimal choice, but scientists like to work with them because they are much more mathematically tractable than non-linear models.

Models very often involve simplification. The real world is complex, but many complexities can be conveniently ignored. (This is particularly true of fundamental laws, which are very idealized representations of the world, particularly the macroscopic world.) The most informative models capture the most relevant, important, and interesting causal relationships of your system of interest. Models that try to capture every complexity become unwieldy and less useful.

3) Models are not the same thing as computer simulations. I occasionally hear climate science derisively denounced because it is based on “computer simulations.” You can use a computer to simulate the behavior of your model, but not all simulations are based on good models. I could build an awesome game physics engine (well, actually I couldn’t, but someone else could), one that accurately simulates the behavior of exploding objects and shattering glass on a modern urban battlefield, without basing the internal workings of that engine on anything resembling our known understanding of physics. In that case, I would have created a cool simulation, but not a genuine physical model.

Scientific simulations however, are based on models, on quantitative expressions of what we believe to be the key causal relationships. Often our models get so complex that we can’t wrap our minds around them just by looking at the equations, and so we use computers to see how those models behave. Such simulations are not inherently less valid than simple linear models, but in practice it is harder to successfully find good model parameters for complex models. That means the predictions of complex models can be more uncertain. (On the other hand, you can also run into some pretty serious problems with parameter estimation for linear models when you use them to represent complex, non-linear phenomena.)

To sum up, this is how the process works in just about every branch of science:

Step 1: You formulate a model to represent your best understanding of how something works, of the underlying causal relationships. That model doesn’t have to be mathematical, but it often is.

Step 2: You try to match your model to your data, by fitting or training your model on this data. This step lets you plug some real numbers into your model, and you can see if your model behaves at all like the system your are studying.

Step 3: Once you’ve trained your model, you use your model to make predictions or extrapolations. These can be predictions about the future, such as the sea level rise as ice sheets melt, or about areas that haven’t been studied yet, such as the behavior of a genetic variant in a different cell type or treatment outcome of a different patient population.

Step 4: Very important, but too often skipped: check your predictions against reality. If your predictions are about the future, you may have to wait a bit. The success of your predictions will increase your confidence in your model, or cause you to revise it.

Good scientists use models. Bad scientists use models. There are bad models and good models, but all fields of science depend on models. There is no such thing as a model-free science that is somehow less speculative and more close to the data.

* Obvious exceptions are definitions, like ∆G = -RT ln K (if you don’t consider this a definition, then at least accept that it can be derived from the definition of G and K). I would also consider descriptive statistics an exception.

That is, the meter or so of sea level rise predicted for the NC Coastal Resources Commission by a state-appointed board of scientists is extremely inconvenient for counties along the coast. So the NC-20 types have decided that we can escape sea level rise – in North Carolina, anyhow – by making it against the law. Or making MEASURING it against the law, anyhow.

The key language is in section 2, paragraph e, talking about rates of sea level rise: “These rates shall only be determined using historical data, and these data shall be limited to the time period following the year 1900. Rates of seas-level rise may be extrapolated linearly. …” It goes on, but there’s the core: North Carolina legislators have decided that the way to make exponential increases in sea level rise – caused by those inconvenient feedback loops we keep hearing about from scientists – go away is to make it against the law to extrapolate exponential; we can only extrapolate along a line predicted by previous sea level rises.

This is the product of people who have been fooled by their own lies. It reminds me of the classic Feynman quote, written into his appendix to the Space Shuttle Challenger report:

For a successful technology, reality must take precedence over public relations, for nature cannot be fooled.

On the other hand, if this does become law (unlikely), there are all sorts of ways to transform your data so that you can make accurate linear extrapolations.

In now thoroughly refuted 2010 paper in Nature, Wilson and colleagues attempted to overturn much of the modern understanding of natural selection theory and altruism, known as inclusive fitness theory. Wilson’s new book (apparently, I have not been graced with a copy) continues this line of argument. Dawkins got testy with Wilson then and now:

. . .unfortunately one is obliged to wade through many pages of erroneous and downright perverse misunderstandings of evolutionary theory.
-Richard Dawkins

The problem is, as David Sloan Wilson pointed out in 2010, the debate isn’t about the evolutionary theory that experts currently recognize. This debate has less similarity to a rigorous debate between the intellectual giants of their field and more to a couple of old guys arguing whether the Yankees or the Mets are better based on their vague memories of the 1972 season.

EO Wilson is an entomologist. He has used evolutionary theory as a paradigm for explaining the behavior of social insects, especially ants. He has not contributed to the development of evolutionary theory.

Richard Dawkins was trained as an animal behaviorist (properly an ethologist) before moving into professional science communication. He has not contributed to the development of evolutionary theory.

Neither man is an expert in evolutionary biology.

Most of the public arguments around evolutionary theory involve outdated understandings of the theory. Based on the rhetoric, you might be surprised to learn that evolutionary theory has changed quite a bit since Darwin, Fisher, Wright, and Haldane‘s day. Evolutionary theory is still a dynamic field. I look forward to getting old enough that my understanding of the theory is also decades out of date.

Incidentally, the propagation of comments following Dawkins’ review is itself a strong refutation of Dawkins’ conception of memetic evolution by natural selection.

Arc is a new digital quarterly from the makers of New Scientist, exploring the future through the world of science fiction and intriguing, thought-provoking ideas.

Their latest issue (the second one) is “Post-human conditions”. The website is frustratingly vague, but you can find more on their tumblr site.

Between this and the New Yorker sci-fi issue, I’ve got some reading to do.

But I’m not here to lecture you about your personal failings. I’m here to recommend that you listen to the most recent episode (Episode 283). Why? Two reasons.

First, the show introduction presents a moving story about Marc deciding to take responsibility to see a stray cat through the end of its life. It’s a story of compassion, not “passing the buck”, and putting consideration for another being before one’s own comfort and ease. This is especially recommended for the folks that leave messes in the laboratory common areas around here.

Second, Marc and his guest, young comedian Bo Burnham, have a long talk about impostor syndrome. I knew impostor syndrome was an epidemic among young scientists and writers, but apparently it is also running rampant among comedians. As a seasoned veteran, Marc not only manages to remember the insecurity of youth¹ (probably because he never stopped being insecure), but also provides Bo with the insight that there is no “jury” that gets to decide if you are an impostor.

That was a great relief to me, until I realized that science has a whole series of “juries” – thesis committees, journal editors, grant review panels, etc., etc. . .

So, maybe just listen to the introduction.

Walking down an unfamiliar street last week, I turned a corner and saw a sign announcing in a 70s art deco font on a rich burnt orange background: LAUNDERETTE.  Squinting against the midday sun, I smiled and thought of this great song from Belle and Sebastians’ 2006 The Life Pursuit. Catchy country-rock hooks bring you in and subtle changes in each repeat of the chorus keep you there. There’s even a great sustained organ chord at the beginning that lends a momentary but endearing Hall and Oates vibe, not unlike the sign at my particular launderette. (This is not even to mention to video, which I love.) Needless to say, walking the rest of the way down the street humming this gem was no hardship.

Problem is: that was a week ago and it’s been on non-stop repeat in my head, even invading my sleep. It came up on a playlist at friend’s last night and I commented that hearing it might finally be the cure. No such luck, so my only choice was to share it with you.

Coincidentally, I’m not the only one thinking about songs that get stuck in our heads. They are apparently scientifically called Involuntary Musical Imagery and are seen to be a type of involuntary cognition. This month, Victoria J. Williamson and her colleagues from Goldsmiths College, University of London published a study attempting to better understand what they are and why they happen.

Episodes of these stuck songs tend to be repetitive (tell me about it), familiar and fragmentary. It’s usually a small piece of known music that seems to cycle through your head over and over. According to some previous studies, it seems to happen more to people who listen to a lot of music and who have had formal musical education. Williamson and colleagues wanted to probe these causes further and gathered earworm and demographic info from a regular BBC Radio 6 listener earworm feature, from a questionnaire on the station’s website, and from their own Earwormery website.

According to their results, my experience is completely normal: words that appear in songs are a common trigger and earworms often happen in low attention states, when you’re not concentrating on anything in particular and during repetitive tasks (or in my case idly walking down the street). Turns out that despite wanting to feel special with my earworm, though, I may not be any more likely than anyone else to experience one under these circumstances. They didn’t find any connections between the reported earworms and demographics: no age differences, no differences by musical preference and none based on musical training. Earworm triggers seemed to be entirely external, happening when people were reminded of a song for some reason and when they were in the right mood or frame of mind. They do note that they probably only have regular music listeners in their participant group so the question is probably worth pursuing a bit further.

So with that deeper understanding, perhaps I have now passed mine on to you. As earworms go it’s a great song, so enjoy. No word yet on whether the 70s art deco font is a particularly strong trigger though.

Williamson, V. J., Jilka, S. R., Fry, J.,  Finkel, S., & Müllensiefen, D. (2012). How do ”earworms” start? Classifying the everyday circumstances of Involuntary Musical Imagery. Psychology of Music, 40, 259-284.

*This isn’t really the venue, but the social scientist in me would really like to take a moment to commend the authors on the rigor of their analysis. This is a great example of careful and well documented open qualitative coding. Not only are their procedures impeccable, they take the additional (and often overlooked) step of developing their coding scheme with one data set and then independently confirming it with a second. Just in case you were, you know, wondering about that sort of thing.

Eventually I figured out what’s so great about Philip Dick and Cyril Kornbluth, and now I’ve had my Heinlein epiphany. Stranger in a Strange Land was unpersuasive and bloated (I mistakenly read the uncut version), but the free love and naked hippie Martian religion kept it somewhat interesting. Farnham’s Freehold was entertaining and biting in its social satire, but the characters and future society were a little too ridiculous. Orphans in the Sky is an exemplary juvenile Heinlein, but I’m no longer into The Hardy Boys or Encyclopedia Brown, and Harry Potter was good for just one read through the series. The juvenile Orphans was tolerable only for its historical interest.

So it was with some trepidation, despite the cool vintage cover art, that I opened up the story collection The Green Hills of Earth, featuring a set of Heinlein’s Future History stories. My misgivings quickly melted away, because in these stories Heinlein is at his hard SF best.

Heinlein’s Future History tales describe the realization of humanity’s manifest destiny in space. Heinlein works through the social implications of space colonization, and most importantly, he describes the character of those people who carry out the physical acts of exploration and colonization. The kinds of people who colonize the moon, chart out the most navigable space routes, erect the first space stations, and eke out a living from the Venusian soil are men, almost always men, of action and honor, of practical instincts, not fearless but in control of their fear, who are willing and competent to get the job done. (Think Ed Harris, Tom Hanks and Kevin Bacon in Apollo 13, or rather their real-life counterparts, Gene Kranz, Jim Lowell and Jack Swigert.)

From this grand vision spring a set of remarkable hard SF stories about men who don’t lose their cool when the atomic pile goes bad on a space cruiser or when a mail rocket crash traps them in a partially finished tunnel on the surface of the moon. The occasional woman, if she’s competent at what she does, gets Heinlein’s respect, as in “Delilah and the Space Rigger”, a story about the first woman to work on site at the construction of a space station. The deeper layer here is that these people who build humanity’s future in space are exactly the kinds of people Heinlein believes made our modern world possible, the people who first circumnavigated the Earth, who broke trails through uncharted mountain ranges, who colonized new continents, and who built the technological triumphs like the transcontinental railroad and the Golden Gate Bridge. And thus Heinlein’s Future History stories are essentially an optimistic vision of our past, a view of a species that is destined to continually push beyond present limits, as long as we keep producing those hardy souls who just get it done.

These stories are tightly plotted and executed, with a well-imagined technical realism that undoubtedly was helped by Heinlein’s technical background and experience in the Navy. In contrast to the van Vogt-style stories of super-science that were popular in 30′s and 40′s pulps, Heinlein’s stories acknowledge that the practice of science and engineering is the art of the soluble, heavily constrained by the demands of nature, and not a set of magical techniques to make all your problems disappear. This aspect makes Heinlein’s hard SF very compelling, and after reading these stories I’m not surprised that he became one of science fiction’s most influential authors.

Of course there is the incendiary side of Heinlein. Because he has such strong opinions about the characters of those who conquer our frontiers, Heinlein’s characters tend to fall into one of two categories, the go-getters and the shmucks, which is an excessively binary way to classify people. And women are less likely to have what it takes than men – in these stories many women are at their best in the role of the stoic pioneer wife, uncomplaining and not holding their men back despite fears for his safety. (I shouldn’t overstate this issue – Heinlein does feature women with the Right Stuff, but fewer of them.) There isn’t much to say about this: it’s frustrating, but with a few notable exceptions like C.L. Moore, science fiction in the 30’s and 40’s was the preserve of men and boys.

If you’ve never read Heinlein and are looking for an entry point, start with these stories. Go read these stories knowing that Heinlein was well known as a walking contradiction, but one who was capable of rigging up some of the tightest plots in the field, someone who understood what hard science fiction should really be about, and one with an unusually fruitful imagination who never ran short of ideas or words to describe them.