Football fever grips the globe as we reach the final stages of the 2010 FIFA World Cup in South Africa. Alongside the traditional game where one winning team takes all, leaving 31 losing teams to go home earlier than expected, there is another competition running in parallel. Which losing team can come up with the [...]... Read more »
Lucifora, C., & Simmons, R. (2003) Superstar Effects in Sport: Evidence From Italian Soccer. Journal Of Sports Economics, 4(1), 35-55. DOI: 10.1177/1527002502239657
Zak, P., Kurzban, R., Ahmadi, S., Swerdloff, R., Park, J., Efremidze, L., Redwine, K., Morgan, K., & Matzner, W. (2009) Testosterone Administration Decreases Generosity in the Ultimatum Game. PLoS ONE, 4(12). DOI: 10.1371/journal.pone.0008330
Elmar Bittner, Andreas Nussbaumer, Wolfhard Janke, & Martin Weigel. (2006) Football fever: goal distributions and non-Gaussian statistics. Eur. Phys. J. B 67, 459 (2009). arXiv: physics/0606016v1
Goff, J., & Carré, M. (2010) Soccer ball lift coefficients via trajectory analysis. European Journal of Physics, 31(4), 775-784. DOI: 10.1088/0143-0807/31/4/007
Abell, J. (2010) ‘They seem to think “We're better than you”’: Framing football support as a matter of ‘national identity’ in Scotland and England. British Journal of Social Psychology. DOI: 10.1348/014466610X514200
Wayne C. Naidoo, & Jules R. Tapamo. (2006) Soccer video analysis by ball, player and referee tracking. SAICSIT '06: Proceedings of the 2006 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries. DOI: 10.1145/1216262.1216268
In 2001, Reg Watson and Daniel Pauly published a paper in Nature (“Systematic distortions in world fisheries catch trends”) that showed anomalous trends for China’s marine fisheries catch trends. They took fisheries catch data within various countries’ EEZs and saw the difference between the predicted catch and the observed catch. China apparently had catches exponentially [...]... Read more »
Watson, R., & Pauly, D. (2001) Systematic distortions in world fisheries catch trends. Nature, 414(6863), 534-536. DOI: 10.1038/35107050
A zombie is another name for The Walking Dead -- those who are lifeless, apathetic, or totally lacking in independent judgment. But in an ecological sense, a zombie species no longer fulfills its ecological function because it is becoming extinct... Read more »
Shultz, S., Baral, H., Charman, S., Cunningham, A., Das, D., Ghalsasi, G., Goudar, M., Green, R., Jones, A., Nighot, P.... (2004) Diclofenac poisoning is widespread in declining vulture populations across the Indian subcontinent. Proceedings of the Royal Society B: Biological Sciences, 271(Suppl_6). DOI: 10.1098/rsbl.2004.0223
Lemus, J., & Blanco, G. (2009) Cellular and humoral immunodepression in vultures feeding upon medicated livestock carrion. Proceedings of the Royal Society B: Biological Sciences, 276(1665), 2307-2313. DOI: 10.1098/rspb.2009.0071
Naidoo, V., Wolter, K., Cromarty, D., Diekmann, M., Duncan, N., Meharg, A., Taggart, M., Venter, L., & Cuthbert, R. (2009) Toxicity of non-steroidal anti-inflammatory drugs to Gyps vultures: a new threat from ketoprofen. Biology Letters, 6(3), 339-341. DOI: 10.1098/rsbl.2009.0818
Jackson, A., Ruxton, G., & Houston, D. (2008) The effect of social facilitation on foraging success in vultures: a modelling study. Biology Letters, 4(3), 311-313. DOI: 10.1098/rsbl.2008.0038
Swan, G., Cuthbert, R., Quevedo, M., Green, R., Pain, D., Bartels, P., Cunningham, A., Duncan, N., Meharg, A., Lindsay Oaks, J.... (2006) Toxicity of diclofenac to Gyps vultures. Biology Letters, 2(2), 279-282. DOI: 10.1098/rsbl.2005.0425
Cuthbert, R., Parry-Jones, J., Green, R., & Pain, D. (2007) NSAIDs and scavenging birds: potential impacts beyond Asia's critically endangered vultures. Biology Letters, 3(1), 90-93. DOI: 10.1098/rsbl.2006.0554
OMG ZOMBIE POST!!!
Let's all pause and contemplate how awesome I look as a zombie. I DO love brains. Very much. OM NOM NOM.
So Sci was thinking about what to post for Zombie Day. She thought about wondering if dogs could sniff early stage zombie infection and thus help with quarantine. She thought about whether or not grocery stores would be a good place to hide, but Evil assured her that Costco is better (everything is better when you buy IN BULK!). She then thought about maybe finding a disease or mental problem that made people crave human flesh.
And then she went, holy crap that is AWESOME.
And then I abandoned that one paper I was going to write about wasps, which is ALSO awesome, but will have to happen another time.
Because we usually think of a zombie epidemic as being something that would occur via a viral or bacterial infection, which would then cause the victim to become undead and then go about seeking human flesh (or brains, but apparently the fixation on brains alone is a relatively new phenomenon in the zombie mythos).
But what about the cannibalism itself, the whole seeking after human flesh bit? What if a lust for human flesh could arise from...eating human flesh? As, say, in a scenario where starvation was forcing people to cannibalism, and thus the massive social taboos against cannibalism are relaxed? And then...all you'd need is a disease that spread VIA cannibalism. Like kuru, only this would involve MOAR BRAINZ.
So this post has two aspects to it, the prospect for spread, and the prospect of a way to eliminate the zombie menace.
Rudolf and Antonovics. "Disease transmission by cannibalism: rare event or common occurrence?" Proceedings of Biological Science, 2007.
I would also like to note that killing people and eating them apparently sounds a lot more scientific as "interspecific necrophagy". Read the rest of this post... | Read the comments on this post...... Read more »
OMG ZOMBIE POST!!! Let’s all pause and contemplate how awesome I look as a zombie. I DO love brains. Very much. OM NOM NOM. So Sci was thinking about what to post for Zombie Day. She thought about wondering if dogs could sniff early stage zombie infection and thus help with quarantine. She thought about [...]... Read more »
Ever since I first looked at this NYT visualization by Amanda Cox, I’ve always wanted to reproduce this in R. This is a plot that stacks multiple time series onto one another, with the width of the river/ribbon/hourglass representing the strength at each time. The NYT article used box office revenue as the width of the river. It’s also an interactive web app. thanks to some help from graphic designers.... Read more »
Havre, S., Hetzler, E., Whitney, P., & Nowell, L. (2002) ThemeRiver: visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics, 8(1), 9-20. DOI: 10.1109/2945.981848
Remember the voodoo correlations and double-dipping controversies that rocked the world of fMRI last year? Well, the guys responsible have teamed up and written a new paper together. They are...The paper is Everything you never wanted to know about circular analysis, but were afraid to ask. Our all-star team of voodoo-hunters - Ed "Hannibal" Vul (now styled Professor Vul), Nikolaus "Howling Mad" Kriegeskorte, Russell "B. A. Baracus" Poldrack - provide a good overview of the various issues and offer their opinions on how the field should move forward.For those who've forgotten, the fuss concerns a statistical trap that it's easy for neuroimaging researchers, and certain other scientists, to fall into. Suppose you have a large set of data - like a scan of the brain, which is a set of perhaps 40,000 little cubes called voxels - and you search it for data points where there is a statistically significant effect of some kind.Because you're searching in so many places, in order to avoid getting lots of false positives you set the threshold for significance very high. That's fine in itself, but a problem arises if you find some significant effects and then take those significant data points and use them as a measure of the size of the effects - because you have specifically selected your data points on the basis that they show the very biggest effects out of all your data. This is called the non-independence error and it can make small effects seem much bigger.The latest paper offers little that's new in terms of theory, but it's a good read and it's interesting to get the authors' expert opinion on some hot topics. Here's what they have to say about the question of whether it's acceptable to present results that suffer from the non-independence error just to "illustrate" your statistically valid findings:Q: Are visualizations of non-independent data helpful to illustrate the claims of a paper?A: Although helpful for exploration and story telling, circular data plots are misleading when presented as though they constitute empirical evidence unaffected by selection. Disclaimers and graphical indications of circularity should accompany such visualizations.Now an awful lot of people - and I confess that I've been among them - do this without the appropriate disclaimers. Indeed, it is routine. Why? Because it can be useful illustration - although the size of the effects appears to be inflated in such graphs, on a qualitative level they provide a useful impression of the direction and nature of the effects.But the A Team are right. Such figures are misleading - they mislead about the size of the effect, even if only inadvertently. We should use disclaimers, or ideally, avoid using misleading graphs. Of course, this is a self-appointed committee: no-one has to listen to them. We really should though, because what they're saying is common sense once you understand the issues.It's really not that scary - as I said on this blog at the outset, this is not going to bring the whole of fMRI crashing down and end everyone's careers; it's a technical issue, but it is a serious one, and we have no excuse for not dealing with it.Kriegeskorte, N., Lindquist, M., Nichols, T., Poldrack, R., & Vul, E. (2010). Everything you never wanted to know about circular analysis, but were afraid to ask Journal of Cerebral Blood Flow & Metabolism DOI: 10.1038/jcbfm.2010.86... Read more »
Kriegeskorte, N., Lindquist, M., Nichols, T., Poldrack, R., & Vul, E. (2010) Everything you never wanted to know about circular analysis, but were afraid to ask. Journal of Cerebral Blood Flow . DOI: 10.1038/jcbfm.2010.86
You might expect a paper whose title starts with “Neural control” to include neurons.
This new paper by Liden and collegues doesn’t. It’s straight behaviour paper in the style of classic neuroethology. It starts by explicitly trying to tie itself to a hot new field: neuroeconomics. Neuroeconomics is about value assessment and decision making in humans. In many cases, this means doing brains scans of people while they play with experimenter’s money.
Liden and company argue that humans are far too complicated (which is true), and the way to go about understanding how you make decisions is by looking at an escape system. Escape systems are reasonably simple in terms of neurons, and they definitely control a decision: Fight or flight? Or, in the case of crayfish, freeze or tailflip?
When crayfish see a large dark object looming over them, they can freeze, or perform an escape tailflip. The experimenters put crayfish in a tank, and passed shadows over them. They did record from the giant pair of escape neurons that trigger visual escape responses, but they way they used those recordings was as a marker for whether an escape tailflip occurred.
Not surprisingly, when you change the stimulus the crayfish gets, the behaviour changes. Speed the shadow up, and the crayfish is more likely to freeze. But if the crayfish does tailflip, it “decides” to do so in less time.
The big finding that has the authors tooting the neuroeconomics horn is that if you put a strong smell of food in the water, the animal is more likely to freeze than if there’s very little smell of food. This, the researchers argue, means that the crayfish moderating the use of its escape behaviour due to the presence of the food, because the escape would be costly in that it would take the animal away from the food it wants.
But. This effect happens only at one of two shadow speeds that they tested. The effect does not appear to be very robust.
There are two problems with integrating this sort of study into the fold of neuroeconomics: The neurons and the economics.
As for the neurons, the title of this paper implies that it’s found how these neurons make decisions, but this paper suggests what to look for. It doesn’t give new discoveries at the neuronal level. There are no new neurons or synapses or neurotransmitters described here.
And the search isn’t going to be easy. The main neurons that make the escape decision in these cases are medial giant (MG) neurons. While the MG axons are well known, the bit where all the interesting integration is going on – the place where sensory neurons and interneurons are connecting with the MGs – are nearly a complete mystery, tucked away inside the crayfish’s brain. A while ago, I went looking in the scientific literature for any good picture or diagram of the MG cell bodies and dendrites in the brain – and came up empty handed.
I’m glad that the authors are working with the MG-mediated escape responses; they’re overdue for attention. But make no mistake, the low level of information about them is a bug, not a feature, for crayfish escape as a neuroeconomics model.
As for the economics, it seems difficult to assign values in this situation. Human economics is pretty easy: $20 is better than $10, but not as good as $50. Having a standardized interval scale makes some of the most interesting neuroeconomics research possible.
But what is the value of the smell of food? What is the cost of tailflipping? An escape tailflip is very brief, and it doesn’t take the animal very far from the food. The authors point out that crayfish who win fights gain access to food – which is true – to argue how important food resources are to crayfish. What they don’t mention is that will crayfish fight in the complete absence of food or any other resource. The advantages that crayfish gain through fighting are subtle enough that it took several decades of research before people figured out that there were any resource advantages gained by winning fights.
It’s also worth noting that these experiments are very similar to some theoretical models about the regulation of crayfish behaviour. Don Edwards (a former supervisor of senior author Jens Herberholz) wrote a paper discussing how a crayfish might choose between different behaviours (getting food, escaping a predator) back in 1991. The results of that computer model feel very much like the results presented here.
Indeed, thinking back to the 1990s, there were a lot of important papers on how crayfish escape responses were modulated via changes to the lateral giant (LG) interneuron circuit (e.g., Yeh et al. 1996). The changes were caused by social interactions, so this was pitched as a model for... human aggression!
All these little moments of “spin” in this paper seem to have carried through to how the paper has been promoted. This press release, from the interesting (albeit sometimes dodgy) Science Direct is a testament to good marketing. There’s another press release here. Here’s a snippet from one:
(A) new line of research that may help unravel the cellular brain activity involved in human decisions.
Yeah, and if I don’t clean out my fridge, I may help develop a new antibiotic.
This is good research. But to say that it’s going to help us understand human decision making? Not yet. Not by a long shot.
P.S. – I have to bust the authors and reviewers’ and editor’s chops for letting this get into the paper:
(D)ifferences were not statistically significant although only marginally(.)
A p value is either significant, or it is not. It is not legitimate to treat the p value as some sort of direct indication of the “reality” or the size of an effect; see Schmidt (2010) for more.
Shameless self-promotion! For those who might want more information on the crayfish escape system, I wrote a review on this, focusing on the evolution and diversity of the behaviour and the underlying neurons responsible for it (Faulkes 2008).
Edwards DH. 1991. Mutual inhibition among neural command systems as a possible mechanism for behavioral choice in crayfish. The Journal of Neuroscience 11: 1210-1223. http://www.jneurosci.org/cgi/content/abstract/11/5/1210
Faulkes Z. 2008. Turning loss into opportunity: The key deletion of an escape circuit in decapod crustaceans. Brain, Behavior and Evolution 72(4): 351-361. http://dx.doi.org/10.1159/000171488
Liden, William H., Phillips, Mary L., & Herberholz, Jens (2010). Neural control of behavioural choice in juvenile crayfish Proceedings of the Royal Society B: In press. 10.1098/rspb.2010.1000
... Read more »
Liden, William H., Phillips, Mary L., & Herberholz, Jens. (2010) Neural control of behavioural choice in juvenile crayfish. Proceedings of the Royal Society B. info:/10.1098/rspb.2010.1000
Yeh, S., Fricke, R., & Edwards, D. (1996) The Effect of Social Experience on Serotonergic Modulation of the Escape Circuit of Crayfish. Science, 271(5247), 366-369. DOI: 10.1126/science.271.5247.366
I do not think I have ever blogged the paper that played an important role in my thesis (doi:10.1021/ci990038z); research of one of the papers in my thesis, started with the hypothesis proposed therein. The paper had a really good idea; but, unfortunately, it did not contain the data to support the hypothesis. That gets me to one important lesson I learned: a QSAR data set of less than 100 molecules is not enough to make untargeted statistical models.
The paper reads quite nicely, and the results are clear: by combining spectral types, the RMSEP goes down. Good! Lower prediction errors; that's what we all want. So, a M.Sc. student of mine set off, but after about half a year, he was still unable to make statistically good models. He used bootstrapping to 'prove' it was not his fault: there was not enough data for the method to learn the underlying patters. Hence my above lesson. My student went on with larger data sets, and laid out the foundation of what later became the paper on using NMR spectra in QSPR modeling (doi:10.1021/ci050282s). Now you understand why QSAR is missing.
So, if those results are so clear, then why does it not work? As said, the data set was too small for pattern recognition methods to see what was going on. The RMSEP numbers just came out nicely; however, if we had only made the below plot, if would have warned. But I failed to do that at the time. Lesson learned: do not just look at the data, but also look at the model. And look really means looking with your eyes at graphical representations of that model. The plot:
The numbers in this plot are hidden in tables in the paper. The RMSEP values earlier mentioned are calculated from those. From the plot, you can see that the test data consisted of 5 compounds; the training set contained 37 compounds; all are congenerics, and do not span a high diversity. Now, the plot shows five models: black is COMFA; orange is based on experimental IR spectra; red, green, and blue are models where two types of representations are combined. From the RMSEP values it can be seen that combining representation improves the RMSEP values. That's what you want, and sort of makes sense.
Now, I did not make this plot until I started writing up the paper, and tried to figure out why the QSAR data set did not work. My eyes opened wide when I saw the orange dots! Anti-correlation! WTF?!?! I mean, we are looking at a plot visualizing the predicted versus the experimental activity... Actually, the others are not really convincing either, are they? Looking at the predictions for compounds with experimental values around 1.0-1.5 (if you really want to know the unit, read the article), the pattern is pretty much anti-correlated too. Thinking about it, it seems the RMSEP is mainly reflecting the error of the left most compound, the one with a experimental activity of about 0.3.
Clearly, the orange model is hopeless, but the others are not really better. Now, the paper actually makes statements comparing the various combinations of representations, but, in retrospect and looking at this plot, I wonder if the green model is really different from the blue or red models.
Since then, I always make these kind of plots, just to see what my model is like. Since then, I distrust papers that only show RMSEP, Q2, or other quality statistics. Now, the tricky part is, you need those statistics if you want to automate model selection; the variance on those model quality statistics is actually so high (see also my other post today), that you must carefully validate that model selection too, visually of course.
I have been long thinking what to do with these observations. I did not dare publish them in my thesis; I did not dare write a letter to the editor. Perhaps I should. But even writing up this blog makes me feel uncomfortable. Besides the fact that I might be wrong, I also do not like to point out mistakes (IMHO); particularly, when those are published in a respectable journal. I was fooled by the statistics too (and was already well trained), so I cannot comment on the authors overlooking the issue. Or the reviewers! Or the community at large. Also, I do not know what the fate of this paper should be. The idea is quite interesting, even though the published results do not support it. Not shown here, but the bootstrapping results show that the apparent slight improvement is merely a numerical artifact, just happening by chance, based on luckily selecting the test compounds; the data is just insufficient in size to draw any conclusion.
Comparative Spectra Analysis (CoSA): Spectra as Three-Dimensional Molecular Descriptors for the Prediction of Biological Activities Journal of Chemical Information and Modeling, 1999, 39 (5), 861-867 DOI: 10.1021/ci990038z
Willighagen, E., Denissen, H., Wehrens, R., & Buydens, L. (2006). On the Use of 1H and 13C 1D NMR Spectra as QSPR Descriptors Journal of Chemical Information and Modeling, 46 (2), 487-494 DOI: 10.1021/ci050282s... Read more »
Bursi, R., Dao, T., van Wijk, T., de Gooyer, M., Kellenbach, E., & Verwer, P. (1999) Comparative Spectra Analysis (CoSA): Spectra as Three-Dimensional Molecular Descriptors for the Prediction of Biological Activities. Journal of Chemical Information and Modeling, 39(5), 861-867. DOI: 10.1021/ci990038z
Willighagen, E., Denissen, H., Wehrens, R., & Buydens, L. (2006) On the Use of H and C 1D NMR Spectra as QSPR Descriptors . Journal of Chemical Information and Modeling, 46(2), 487-494. DOI: 10.1021/ci050282s
Sometimes I am able to write posts on Friday. Sometimes not.
At least part of the reason for this is that I spend my Thursday afternoons and Fridays (or at least parts thereof) research for and writing the SMC weekly newsletter. Which is awesome.
And you should sign up! Why? Because it’s well interesting, of course. Peter [...]
[Click on the hyperlinked headline for more of the goodness]... Read more »
Greer, E., Maures, T., Hauswirth, A., Green, E., Leeman, D., Maro, G., Han, S., Banko, M., Gozani, O., & Brunet, A. (2010) Members of the H3K4 trimethylation complex regulate lifespan in a germline-dependent manner in C. elegans. Nature. DOI: 10.1038/nature09195
Verma, M., & McOwan, P. (2010) A semi-automated approach to balancing of bottom-up salience for predicting change detection performance. Journal of Vision, 10(6), 3-3. DOI: 10.1167/10.6.3
OK, so, the title of this article is actually Do not log-transform count data, but, as @ascidacea mentioned, you just can’t resist adding the “bitches” to the end.
If you’re like me, when you learned experimental stats, you were taught to worship at the throne of the Normal Distribution. Always check your data and [...]... Read more »
As I'm sure you're all aware by now, human life expectancy for both young and old in the most developed regions of the world is slowly increasing, and this has been the case for some time. As medical technology advances and our wealth grows, we benefit in ways that lead to less biochemical damage to the complex machinery of our body accumulated over the course of a lifetime - and thus a greater likelihood of living longer. That the medical and research establishments have achieved this ongoing benefit even in advance of any structured, deliberate, large-scale efforts to slow or (more preferably) repair the consequences of aging bodes well for the future. The scientific community should be able to achieve far more impressive results when they are actually trying to directly tackle aging. I noticed an open access paper today (PDF version included) that applies some mathematical wizardry so as to break out the most important structural contributions to increasing longevity. I think you'll find it interesting: The ongoing increase in life expectancy in developed countries is associated with changes in the shape of the survival curve. These changes can be characterized by two main, distinct components: (i) the decline...... Read more »
Rousson, V., & Paccaud, F. (2010) A set of indicators for decomposing the secular increase of life expectancy. Population Health Metrics, 8(1), 18. DOI: 10.1186/1478-7954-8-18
A recently published study suggests that the Deepwater Horizon oil leak may devastate the endangered Atlantic bluefin population, causing it to completely collapse or possibly go extinct.... Read more »
Steven L. H. Teo, & Barbara A. Block. (2010) Comparative Influence of Ocean Conditions on Yellowfin and Atlantic Bluefin Tuna Catch from Longlines in the Gulf of Mexico. PLoS ONE, 5(5). info:/10.1371/journal.pone.0010756
How many species are there here? It’s a beguilingly simple question, and a fundamental area of interest. A moments thought shows that the bigger here is, the more species there will be. So, if we start from a little patch of my lawn, and take successively larger heres until we’ve included the whole world, we [...]... Read more »
O'Dwyer, J., & Green, J. (2010) Field theory for biogeography: a spatially explicit model for predicting patterns of biodiversity. Ecology Letters, 13(1), 87-95. DOI: 10.1111/j.1461-0248.2009.01404.x
Rosindell, J., & Cornell, S. (2009) Species–area curves, neutral models, and long-distance dispersal. Ecology, 90(7), 1743-1750. DOI: 10.1890/08-0661.1
Mathematician Vladimir Arnold, perhaps one of the best known and highly cited Russian scientist, has died yesterday today at the age of 72. He was receiving treatment in France, but his disease was stronger, reports lenta.ru, citing a source close to the family. Arnold was one of the greatest mathematicians of the XX century [...]... Read more »
In particle physics, experimentalists often aim to set limits on certain physical quantities, in part to verify theories. Say a theory predicts that a particle called Gobbledygook has a 10-8 chance of decaying into two Gooks and a chance of decaying into three Gobbles. Often, the ratio between these two decay modes are closely related [...]... Read more »
Imagine this card trick. A statistician divides a regular deck of cards into two sets: one of 20 and one of 32 cards. Then, he lets students prove that in both sets, the proportion of court cards is larger among the black ones than among the red cards. How is this possible and what are the consequences for statistical analyses?... Read more »
Simpson, E.H. (1951) The Interpretation of Interaction in Contingency Tables. Journal of the Royal Statistical Society. Series B (Methodological), 13(2), 238-241. info:/
Could punishing bad behavior be the origin of human cooperation?Humans are one of the most cooperative species on the planet. Our ability to coordinate behavior and work collaboratively with others has allowed us to create the natural world's largest and most densely populated societies, outside of deep sea microbial mats and a few Hymenoptera mega-colonies.
A key problem when trying to understand the evolution of cooperation has been the issue of cheaters. Individuals in a social group, whether that group is composed of bacteria, cichlids, chimpanzees, or people, often benefit when cooperating with others who reciprocate the favor. But what about those individuals who take advantage of the generosity of others and provide nothing in return? These individuals could well thrive thanks to the group as a whole and end up with greater fitness than everyone else because they didn't have to pay the costs associated with cooperating. For decades the idea that cheaters may in fact prosper has been the greatest difficulty in understanding cooperation as an evolved trait.
However, new research suggests that cooperation is a viable evolutionary strategy when individuals within the group collectively punish cheaters who don't pull their weight. Robert Boyd, Herbert Gintis, and Samuel Bowles have just published a paper in the journal Science with a model showing how, so long as enough individuals work together to punish violators, each cooperative individual in the group can experience enhanced fitness as a result. Read the rest of this post... | Read the comments on this post...... Read more »
Boyd, R., Gintis, H., & Bowles, S. (2010) Coordinated Punishment of Defectors Sustains Cooperation and Can Proliferate When Rare. Science, 328(5978), 617-620. DOI: 10.1126/science.1183665
Mathematical and computational biologists use algorithms to model and understand biological phenomena but as useful as computer systems are to modellers they also represent an example of what biological systems are not: designed. A recent study by researchers in...... Read more »
Yan KK, Fang G, Bhardwaj N, Alexander RP, & Gerstein M. (2010) Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks. Proceedings of the National Academy of Sciences of the United States of America. PMID: 20439753
A couple of days ago Steven Strogatz, the chaos and complexity theorist, wrote a lucid article in the New York Times about the daunting task of teaching probability theory. In particular he dealt with the cerebrally demanding topic of formalizing and analyzing “conditional probability”. What he suggests is quite reasonable, "comporting with human intuition instead of confounding it."Strogatz recommends skipping the mathematical formalism and avoid using Bayes’s theorem, much as his students usually do. The alternative method which is illustrated in the article by providing an example about a positive mammogram and breast cancer is based on the works of Gerd Gigerenzer, a cognitive psychologist at the Max Planck Institute for Human Development in Berlin.The underlying feature of this method is encouraging people “to think in terms of natural frequencies — simple counts of events — rather than the more abstract notions of percentages, odds, or probabilities.” Even though learning and teaching probability theory as it appears in higher education textbooks have to be sophisticated and mathematically formulated, the method of “simple counting” tend to be more in line with "the innate ability of humans."In 2007, Ernő Téglás, Vittorio Girotto, Michel Gonzalez, and Luca L. Bonatti did some fascinating empirical studies on infant cognition in which they tried to shed light on how humans in early age perceive probability. Their subjects were a group of infants who saw four movies (two probable and two improbable) and the result was that the subjects "looked significantly longer when they witnessed the improbable outcome."To rule out that the infants may had used some "heuristics unrelated to probability reasoning" they designed a complementary experiment and compared the reaction time of infants to impossible vs. possible outcomes. The results showed that infants looked longer at the impossible outcome "although it displayed an object from the more probable class, that is, the one less looked at in the first experiment."The two experiments, combined, pointed out that there is an “innate ability” in humans to rationally expect and know what the chances are that something may occur in the future. And taking into account the works and methods of Gigerenzer and Strogatz it seems that the key to the practical and applied understanding, learning, and teaching of probability theory is to follow the same path an infant or child have to take: simple counting without much mathematical formalism and/or sophisticated computation.Teglas, E., Girotto, V., Gonzalez, M., & Bonatti, L. (2007). Intuitions of probabilities shape expectations about the future at 12 months and beyond Proceedings of the National Academy of Sciences, 104 (48), 19156-19159 DOI: 10.1073/pnas.0700271104... Read more »
Teglas, E., Girotto, V., Gonzalez, M., & Bonatti, L. (2007) Intuitions of probabilities shape expectations about the future at 12 months and beyond. Proceedings of the National Academy of Sciences, 104(48), 19156-19159. DOI: 10.1073/pnas.0700271104
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