HENRY

Ruth Walker in Verbal Energy:

We (editors) know to say ‘21 large green tables’ but why not ‘green large 21 tables’? or ‘21 green large tables’? Is there a rule for this?

From Xinhua:

Chinese archaeologists say they have found more than 2,000 pictographs dating back 7,000 to 8,000 years, about 3,000 years before other texts that are believed to be the origin of modern Chinese characters

Kai Sand-Jensen has a great paper in Oikos (of all places?) where he asks the great question why are scientific publications boring?, and comes up with ten recommendations how to write boring papers. I’ve taken his list and paraphrased them below:

1) Avoid Focus: Far too often the main results are hidden after paragraphs of densely layered questions, ideas and arguments. Please be clear and directly state your main hypothesis.

2) Avoid originality and personality: Please present your studies with originality and be excited about them! If they’re not exciting, why are you bothering to publish them?

3) Write l-o-n-g contributions – Short papers are good. Long papers may show off your deep understanding of the nuances of your topic, but you’ll be the only one reading it (Some fields are worse than others here – I won’t mention any names though!).

4) Remove implications and speculation: These are the fun bits of science. Removing them makes your paper pointless. Sand-Jensen appropriately quotes one of the most gorgeous lines in biology here:

It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material. (Watson & Crick 1953)

5) Leave out illustrations: Pictures are truly worth a thousand words. Besides, most of the time, they’re the only thing anyone looks at. I’d argue that there’s a corollary to this: you should leave out useless figures (cough median-joining networks cough). Only keep the ones that explain your study and results beautifully

6) Omit necessary steps of reasoning: This one hits home – I’ve been working on replicating an analysis done a year ago, but the details are just not available in the paper for me to work out what’s going on. A flowchart would have been wonderful.

7) Use many abbreviations and terms: Whilst this shows how learned you are, it immediately makes your paper inaccessible to anyone interested.

8 ) Suppress humor and flowery language: I can’t put this better than Kai Sand-Jensen did:

Naming a new species Cafeteria, or for that matter calling a delicate, transparent medusa Lizzia blondina, shows lack of respect and will prevent us from ever forgetting the names. I highly discourage creating these
kinds of clever names, because science writing should remain a puritanical, serious and reputable business.

9) Degrade Biology to Statistics: That is, do not “reduce all species to numbers or statistical elements without considering any interesting biological aspects of adaptation, behavior and evolution”. I may be guilty of this one myself preferring the big-picture approach, but I do try to remember why I find the topic interesting at all – and that’s got nothing to do with, oh, Bayes Factors.

10) Quote numerous papers for trivial statements: Not everything needs a reference, especially not the trivial points. I know you spent hours finding and reading those boring papers, but let it go…

In short: reading science should be fun, and writing science should be fun… and now I’m back to writing up a boring paper.

Around 40,000 years ago, Homo sapiens took over most of Europe, and drove out Neanderthals out of everywhere except the Iberian peninsula in Spain. A new paper by Sepulchre et al shows that the climatic changes caused major desertification of the peninsula, and led to modern humans avoiding the region, allowing the poor old Neanderthals to eke out a few more centuries

The Royal Society Prize for the best science book has gone to Daniel Gilbert’s Stumbling on Happiness. So what’s it about? Malcolm Gladwell’s review at amazon says:

Stumbling on Happiness is a book about a very simple but powerful idea. What distinguishes us as human beings from other animals is our ability to predict the future—or rather, our interest in predicting the future. We spend a great deal of our waking life imagining what it would be like to be this way or that way, or to do this or that, or taste or buy or experience some state or feeling or thing. We do that for good reasons: it is what allows us to shape our life. And it is by trying to exert some control over our futures that we attempt to be happy. But by any objective measure, we are really bad at that predictive function. We’re terrible at knowing how we will feel a day or a month or year from now, and even worse at knowing what will and will not bring us that cherished happiness. Gilbert sets out to figure what that’s so: why we are so terrible at something that would seem to be so extraordinarily important

There’s also a good review at the New York Times…

What would have happened if, at the end of “Casablanca,” Ingrid Bergman had stayed with Humphrey Bogart in Morocco, rather than boarding the plane to Lisbon with her Nazi-fighting husband? Would she have regretted it? Or did she end up lamenting the decision she did make? According to Daniel Gilbert, odds are that either decision would have made her equally happy in the long run.

More from The Guardian, more on other nominees here

A new issue of PLoS Biology is out, and it’s easy to see why this is fast becoming my favorite journal.

First up is Cetaceans Have Complex Brains for Complex Cognition which reviews the evidence for complex cognition in cetaceans (Whales and Dolphins and the like).

This is a response to claims (e.g. Manger 2006) that cetacean brains are large only because they’ve got a lot of thermogenic glial cells to counteract heat loss from ocean temperatures during the Eocene-Oligocene transition around 30–40 million years ago.

The authors, a whos-who of animal intelligence researchers conclude that:

Evidence from various domains of research demonstrates that cetacean brains underwent elaboration and reorganization during their evolution with resulting expansion of the neocortex. Cortical evolution, however, proceeded along very different lines than in primates and other large mammals. Despite this divergence, many cetaceans evince some of the most sophisticated cognitive abilities among all mammals and exhibit striking cognitive convergences with primates, including humans.

In many ways, it is because of the evolution of similar levels of cognitive complexity via an alternative neuroanatomical path that comparative studies of cetacean brains and primate brains are so interesting. They are examples of convergent evolution of function largely in response, it appears, to similar societal demands.

Up next is an interesting paper by John Whitfield about thermodynamics and evolution, which opens with the oddest metaphor for entropy that I’ve seen:

Most glaringly, the second law states that over time, any system will tend to the maximum level of entropy, meaning the minimum level of order and useful energy. Open a bottle of perfume in a closed room, and eventually the pool of scent will become a smelly cloud. Organisms do their damnedest to avoid the smelly cloud of equilibrium, otherwise known as death, and a common argument of anti-evolutionists is that the universe’s tendency toward disorder means that natural selection cannot make living things more complex. The usual counter to this argument is that organisms maintain internal order and build complexity by exporting entropy—importing energy in one form, and radiating it out in another, higher-entropy form…

But recently, some physicists have gone beyond this and argued that living things belong to a whole class of complex and orderly systems that exist not despite the second law of thermodynamics, but because of it.

Enjoy!

Bill Softky at El Reg has a chat to Franz Och from google about their automatic language translation:

What is clearly missing from this approach is any form of “understanding”. The machine has no idea that “walk” is an action using “feet,” except when its statistics tell it the text strings “walk” and “feet” sometimes show up together. Nor does it know the subtle differences between “to boycott” and “not to attend.” Och emphasized that the system does not even represent nouns, verbs, modifiers, or any of the grammatical building blocks we think of as language. In fact, he says, “linguists think our structures are weird” – but he demurred on actually describing them. His machine contains only statistical correlations and relationships, no more or less than “what is in the data.” Each word and phrase in the source votes for various phrases in the output, and the final result is a kind of tallying of those myriad votes.

Fascinating stuff, but unfortunately a bit light on the technical details.