Evidence and Error

Can you spot the predator?

noreply@blogger.com (Patrick) — Wed, 27 Jun 2012 03:16:00 +0000

I'll give you a hint: the predator in question is an arthropod, but not an insect.

If you can't spot it, don't be too hard on yourself... neither could the ant in the center of this blanketflower.

The predator I refer to is a spider from the family Thomisidae, commonly referred to as 'crab spiders'. (If you still haven't spotted it, the spider is on the left side of the central reddish disk). Most crab spiders, this one included, employ an ambush hunting strategy. They simply sit tight on or beside a flower and wait for insects to come to them.

Our predator should be more obvious in this one....

Truth in advertising: Dove's "Followers" video

noreply@blogger.com (Patrick) — Sun, 27 May 2012 22:40:00 +0000

I came across this ad by Dove while watching an episode of The Office.

The video, which is part of the Dove Movement for Self-Esteem, lists the number of Twitter followers (as of March 8, 2012) associated with "Today's top female..."

Reality Star -- 4,553,457
Fashion Model -- 13,710,211
Pop Star -- 20,268,261
Scientist -- 7

and concludes with the message

Dove wants to introduce your daughter to some new role models. Because strong female role models build stronger self-esteem.

Alright, it's a clever video and improving self-esteem is a worthwhile goal. But 7! Today's top female scientist has only 7 followers on Twitter? I don't know about you, but reading that statistic set off like all of my bullshit detectors. Of course, I don't doubt that scientists (regardless of gender) generally have far fewer Twitter followers than reality stars, models, and pop stars. I just don't think the difference is quite that large.

Initially, I figured it would be easy to disprove the '7 followers' statistic, but then something occurred to me... who the hell is "today's top female scientist"? The word 'top' is both ambiguous and subjective. Identifying "today's top female scientist" is kind of like identifying "the world's best city".

Given this ambiguity, there is really no way to test the claims made in Dove's video (well played, Dove). Nonetheless, I think if we can find some prominent female scientists with a lot more than 7 Twitter followers, it will demonstrate that Dove's video is somewhat misleading.

Here's what Google turned up (note my search was haphazard and certainly not exhaustive):

Number of Twitter followers for select female research scientists:
Dr. Kate Clancy	Anthropologist	3,093
Dr. Rachael Dunlop	Medical Researcher	6,337
Dr. Chris Gunter	Geneticist; Senior Editor for Nature (2002-2008)	4,706
Dr. Hopi Hoekstra	Evolutionary Biologist	279
Dr. Karen James	Geneticist; Director of Science, HMCS Beagle Project	7,044
Dr. Carolyn Porco	Planetary Scientist; Leader of the Cassini Imaging Team	9,124
Dr. Lisa Randall	Theoretical Physicist; Elected Member of the NAS	4,204
Dr. Jennifer Rohn	Cell Biologist	3,366
Dr. Tara Smith	Epidemiologist	3,334

Number of Twitter followers for select female science enthusiasts, journalists, etc.:
Dr. Deborah Berebichez	Physicist; Science Communicator	6,573
Dr. Sylvia Earle	Oceanographer; Nat Geo Explorer-In-Residence	7,090
Jane Goodall Institute	Conservation Organization Founded by Jane Goodall	120,493
Maggie Koerth-Baker	Science Editor for boingboing.net	8,506
Dr. Emily Lakdawalla	Former Planetary Geologist; Science Writer	14,792
Joanne Manaster	Biology Lecturer; Science Communicator	13,311
Jennifer Oullette	Science Writer	5,638
Dr. Kirsten Sanford	Former Neurophysiologist; Science Broadcaster	119,639
Nicole Stott	Astronaut	94,045

Happily, the situation is not quite as gloomy as Dove's video would seem to imply. There are female research scientists with almost 10,000 Twitter followers and a few female science communicators with around 100,000 followers. Two scientists in particular, Drs. Carolyn Porco and Lisa Randall, probably are contenders for the title of "today's top scientist" and happen to have far more than 7 followers (to the tune of about 3 orders of magnitude). Drs. Porco and Randall have each co-authored more than 100 peer-reviewed publications and have received numerous awards, honours, and prizes in their respective fields.

So was Dove lying?

Not exactly, but they did use ambiguous language and data to exaggerate their claim. In all likelihood, the majority of today's 'top' scientists (regardless of gender and how one defines 'top') would have 0 (or more accurately, N/A) Twitter followers because relatively few scientists use Twitter (especially the most accomplished scientists who are generally a bit older). I spent about 45 minutes checking whether any female Nobel Laureates, Fellows of the Royal Society, or National Medal of Science recipients were on Twitter, but I couldn't find a single one (again, my search was not exhaustive). Of course, admitting that "today's top female scientist" doesn't use Twitter would have made for a much less effective, but probably more accurate, video.

Again, I don't doubt the general claim made in Dove's video. I'm simply a big fan of truth in advertising.

What DO scientists look like?

noreply@blogger.com (Patrick) — Sun, 27 May 2012 00:57:00 +0000

This Is What A Scientist Looks Like is a really cool project aimed at myth-busting the traditional scientist stereotype. Here's a snippet from their website:

Have you ever searched the Internet for an image of a scientist? Of the first 20 images in a Google image search, only two are women, all are white, and most have either too much or too little hair. Oh and most are sporting spectacles ...

[This Is What A Scientist Looks Like] is dedicated to changing the overwhelming stereotype that science is conducted behind closed doors by unapproachable old, white men.

The website features photographs of real scientists doing everyday things like dancing, surfing, playing with pets, rocking out on the guitar, posing with their wedding party, or pretending to be a walrus. It turns out that scientists are just as diverse and multidimensional as any other group of people, and they are even allowed to have fun!

0.999... = 1

noreply@blogger.com (Patrick) — Thu, 24 May 2012 00:32:00 +0000

Maybe this mathematical curiosity is more widely appreciated than I realize. However, I only recently became aware of it, so I will share with you the madness that I've discovered.

It turns out that 0.999... (the “...” means that the 9s go on forever) is equal to 1. And I don't just mean that the two numbers are extremely close to one another or that they are equal by convention. Rather, I'm telling you that the symbols 0.999... and 1 represent the exact same real number and that this equality can be proven.

If you're learning of this for the very first time, I can guess how you are feeling:

I was feeling the exact same way, and I knew what had to be done. Research mode engaged.

After about 20 minutes of intense research (e.g. Wikipedia, various math blogs, etc.), I was entirely satisfied that 0.999… = 1.

Preliminaries

First off, Wikipedia has an entire page on 0.999… = 1. If that doesn’t settle the matter, then I don’t know what will.

Next stop WolframAlpha, a “computational knowledge engine” that knows pretty much everything. For example, WolframAlpha knows the estimated number of atoms in the sun (9 x 10^56), the average undergraduate tuition at Harvard ($33,700 US), the population of Bahrain in 1962 (161,000 people), and even the phase of the moon on the night that Leonardo da Vinci was born (waning crescent). WolframAlpha also knows that 0.999… is equal to 1.

Evidence

Of course, the above arguments are really only appeals to authority, not actual evidence. So let’s get down to business.

Hopefully we can all agree that

1/9 = 0.111…

If so, we can also agree that

9 * 1/9 = 9 * 0.111...

We’ve simply multiplied both sides of the equation by 9, so the equality still holds true.

If we now simplify both sides of the equation, we are left with

1 = 0.999…

Crazy, right?! Another way to think about this is to ask: is there any number that can fit between 0.999… and 1? If two numbers are truly different, then at least one other number should fit in between them. But nothing fits between 0.999... and 1. For example, if we subtract 0.999... from 1, we get

1 - 0.999... = 0.000...1

The “...” on the right side of the equation represents an infinite number of 0s, so the 1 at the end is irrelevant. Subtracting 0.999... from 1 leaves us with nothing – a big fat goose egg.

You might have guessed that this strange phenomenon is not limited to 0.999… and 1. For the same reasons described above

1.999... = 2
0.42999... = 0.43
99.999... = 100, etc.

In fact, every nonzero number that ends in an infinite number of 0s (e.g. 1 can also be written as 1.000…) has a counterpart that ends in an infinite number of 9s.

See here and here for more details on this mathemagical weirdness.

Jay Ingram on Canada's Experimental Lakes Area

noreply@blogger.com (Patrick) — Mon, 21 May 2012 22:13:00 +0000

I came across a great (and rather old) video today describing some of the research that has been done at Canada's Experimental Lakes Area.

There are some scientific experiments that are so big, they cannot be done in a lab. That is why a small corner of Northwestern Ontario has been set aside for the scientists with those big ideas. It is called the experimental lakes area. Fisheries and Oceans scientists are trying to replicate what happens on a much larger scale elsewhere, for example acidification of lakes or flooding of peat bogs and dry forest. Their findings are now used to help mitigate the environmental impact of projects such a hydro electric dams.

Unfortunately, in science as in life, all good things must come to an end... generally under a Conservative government :-P

Phosphorus, detergent, and Canada's Experimental Lakes

noreply@blogger.com (Patrick) — Mon, 21 May 2012 02:32:00 +0000

I'm angry at the people who decided that phosphate was growing algae. I'm not sure that I believe that. –Sue Wright, Texas

Sue Wright, quoted above, was upset because in 2010, sixteen American states banned the sale of dishwashing detergent containing high levels of phosphorus, an aquatic pollutant that sometimes causes eutrophication (algal blooms). Unfortunately, phosphorus is a rather effective component of detergent, so phosphorus-free dishwashing detergents did not immediately perform quite as well as their predecessors. This led some consumers (like our pal Sue) to complain to detergent manufacturers, state governments, consumer protection agencies, and the media.

What I like most about Sue’s complaint is that her anger was directed toward “the people who decided that phosphate was growing algae” rather than the policymakers who drafted and enacted the legislation. Her implied logic is exquisite – a factual claim has resulted in legislation that negatively affects some aspect of my life, therefore I don’t believe this factual claim and furthermore am angry at those who made it!

So, who specifically should Sue have directed her anger toward? Which jackass scientist “decided that phosphate was growing algae”?

The answer, unsurprisingly, is that many independent studies (involving various research groups) have demonstrated that phosphorus pollution, under some conditions, will stimulate algal growth and lead to eutrophication (see Schindler 2006 for a review). Here, I will focus on just one of these studies, perhaps the most influential.

My real motivation for discussing this particular paper is the recent announcement that the Canadian Government is discontinuing its operation of the Experimental Lakes Area (ELA), a collection of 58 pristine lakes that for over 40 years have been set aside for long-term ecosystem monitoring and ecosystem-scale experiments (more on the ELA later).

Green sludge

In the 1960s and 70s, many North American rivers and lakes, especially the Great Lakes, were experiencing rapid declines in water quality (see here and here). Industrial and municipal effluents were stimulating the growth of algae and other aquatic plants (termed ‘eutrophication’) leading to unsightly mats of green sludge, oxygen depletion, massive die-offs of fish and other aquatic life, and problems with the taste and odour of municipal drinking water.

The August 1969 issue of Time Magazine describes the then deteriorating state of Lake Erie:

Each day, Detroit, Cleveland and 120 other municipalities fill Erie with 1.5 billion gallons of inadequately treated wastes, including nitrates and phosphates. These chemicals act as fertilizer for growths of algae that suck oxygen from the lower depths and rise to the surface as odoriferous green scum. Commercial and game fish … have nearly vanished ... Weeds proliferate, turning water frontage into swamp. In short, Lake Erie is in danger of dying by suffocation.

The public, industry, and all levels of government agreed that something had to be done to curb the declining state of North American waterways. However, there was disagreement over the most effective course of regulatory action because at the time, scientists and policymakers were still debating which nutrients were responsible for eutrophication. Was algal growth primarily limited by carbon, nitrogen, or phosphorus?

Schindler 1974

Experiments are the best way to establish causation, but are not always feasible. For example, the best way to test the anthropogenic climate change hypothesis would be to release copious quantities of greenhouse gas into the atmospheres of a random sample of earth-like planets, leave another randomly-chosen bunch of planets untouched, and then compare change in climate across the two groups of planets. Clearly this is not feasible, and clearly we can’t experimentally pollute a bunch of lakes just for the sake of science. Right? Wrong. Well, wrong to the second assertion at least.

The aforementioned Experimental Lakes Area is (was) a wonderful place where scientists could manipulate whole lakes to test hypotheses on the scale of entire ecosystems. In the late 1960s and early 70s, David Schindler – a Canadian limnologist who at the time was director of the ELA – oversaw a number of whole-lake experiments designed to determine which nutrient (out of carbon, nitrogen, and phosphorus) was primarily responsible for eutrophication.

In an initial experiment, Schindler et al. added copious amounts of nitrogen and phosphorus to Lake 227 which naturally had an extremely low concentration of dissolved carbon. If algal growth was primarily limited by carbon (and not nitrogen or phosphorus), then the N + P treatment should not stimulate the growth of algae. However, this was not the case. Within weeks of the treatment, Schindler et al. observed that Lake 227 “was transformed into a teeming, green soup” with algal concentrations up to two orders of magnitude higher than nearby untreated lakes. Clearly, low levels of carbon had not been limiting the growth of algae.

In a second experiment, Schindler et al. divided another lake, Lake 226, into two equal halves using a large vinyl curtain that was sealed into the sediment and surrounding bedrock. The team added an equivalent amount of carbon and nitrogen to both halves of the lake, but added phosphorus to only one side. This manipulation resulted in what James Elser at Arizona State University has called “the single most powerful image in the history of limnology”.

Figure 1. Lake 226 following fertilization with carbon, nitrogen, and phosphorus (below divider) versus carbon and nitrogen only (above divider).

Just a few months after the nutrient additions began, the side of the lake receiving C + N + P was completely covered by a bloom of blue-green algae whereas algae levels on the C + N side were essentially unchanged from when the nutrient additions began. It was abundantly clear that phosphorus had been limiting the growth of algae in Lake 226.

In a final experiment, Schindler et al. manipulated a third lake, Lake 304, to test whether, and how quickly, a lake could recover from phosphorus-induced eutrophication. The team measured the concentration of algae in Lake 304 at approximately monthly intervals over the course of five years, between 1969 and 1973. For three of those years, 1971–1973, the lake received additions of carbon and nitrogen, and for two years, 1971–1972, also received phosphorus. The experiment therefore mimicked what might happen if governments took steps to limit the amount of phosphorus entering a polluted water body. The general finding was that summertime algal concentrations increased dramatically in 1971 and 1972 when the lake was being fertilized with C + N + P, but returned to near baseline levels in 1973 after phosphorus fertilization was discontinued.

Figure 2. Chlorophyll a concentrations (a proxy for algal growth) in Lake 304 from 1969–1973. Boxplots are based on data extracted from Figure 2 of Schindler 1974 and only include samples from mid-summer; between June and September.

This result again demonstrated that algal growth was limited by phosphorus, and furthermore showed that reducing the amount of phosphorus entering a polluted lake could lead to rapid recovery.

This series of experiments led by Schindler was instrumental in convincing scientists, governments, and the public that phosphorus played a significant role in eutrophication and should therefore be regulated. Throughout the 1970s and 80s, the Canadian government and many American states enacted legislation banning or limiting the use of phosphorus in laundry detergents and other cleaning products.

The Experimental Lakes Area

Schindler’s work on eutrophication represents just a small fraction of the world-class research conducted at the Experimental Lakes Area. Over the past 40 years, research carried out at the ELA has led to 676 peer-reviewed publications including 8 papers in the journal Nature and 15 in Science (the most prestigious scientific journals). In addition, 116 graduate theses and 158 technical reports have been based on research at the ELA.

Much of the research carried out at the ELA has been highly relevant to public policy. Scientists with the Department of Fisheries and Oceans and researchers from universities across Canada and the world have used the ELA to determine how aquatic ecosystems are impacted by things like synthetic hormones from birth control pills, acid rain, aquaculture, common forms of habitat destruction such as the removal of aquatic vegetation, hydroelectric reservoirs, eutrophication, and the accumulation of heavy metals and organic toxicants. A news article in the journal Science refers to the ELA as “Ecology’s supercollider” and James Elser of Arizona State University suggests that “it’s hard to overstate the impact [the ELA] has had”.

Tragically, the Canadian government feels that $600,000 per year (the ELA’s estimated operating budget) is too high a price to pay for world-class environmental science. In case it isn’t clear, I emphatically disagree.

____________________________________

Schindler, D. (1974). Eutrophication and Recovery in Experimental Lakes: Implications for Lake Management Science, 184 (4139), 897-899 DOI: 10.1126/science.184.4139.897

c²… c² run

noreply@blogger.com (Patrick) — Wed, 14 Dec 2011 20:21:00 +0000

a² + b² = c², and that is a fact! At least I think it is. That’s what I learned in grade 8 anyway, so it’s probably true, especially since my grade 8 teacher taught the theorem to our class via song and interpretive dance. People rarely tell lies through interpretive dance, so I had no reason way back in grade 8 to doubt the validity and universality of the Pythagorean theorem.

In my old age, however, I am crusty and skeptical and disinclined to take Pythagoras’ (and Mr. Baumgartner’s) word as established truth. I recently decided to embark on a quest to figure out whether there is any evidence in support of the Pythagorean theorem. What follows, in brief, is what I found…

Is there any evidence to support the Pythagorean theorem?

Yes. Boatloads. There are hundreds of published mathematical proofs of the Pythagorean theorem. Check out this excellent page for 92 of them.

What exactly does the Pythagorean theorem state?

The theorem states that for any right triangle, the squared length of the hypotenuse is equal to the sum of the squared lengths of the other two sides, commonly written as: a² + b² = c².

This equality can be thought of in terms of areas of squares build on each side of a right triangle, as illustrated in the figure below. Because the area of a square is simply the squared length of any of its sides, the Pythagorean theorem essentially states that the area of the square on the hypotenuse (yellow square) is equal to the sum of the areas of the squares on the other two sides (blue and pink squares).

Prove it...

Here is one proof that involves simple geometry and just a splash of algebra:

Start with 4 identical copies of a right triangle.

Rotate the triangles to 0°, 90°, 180°, and 270°, respectively.

Form these triangles into a square such that each side of the large square is the hypotenuse (C) of the original triangles. Note that the area of this large square will be C².

The large square has a square hole inside of it. The sides of this small square are equal to A - B.

The area of the large square (C²) must equal the summed areas of the constituent shapes – the 4 triangles and the small inner square.

The area of each triangle (½base • height) is ½AB, and the area of the small inner square is (A-B)².

So we have:

C² = 4(½AB) + (A - B)²

C² = 2AB + (A² - 2AB + B²)

C² = A² + B²

Hurray!

Was Scarecrow correct?

No. In The Wizard of Oz (1939), Scarecrow demonstrates his ‘knowledge’ by claiming “The sum of the square roots of any two sides of an isosceles triangle is equal to the square root of the remaining side. Oh joy! Rapture! I’ve got a brain!”

Not even close, Scarecrow. You can watch this epic math fail here via YouTube.

Facial metrics predict unethical behaviour

noreply@blogger.com (Patrick) — Wed, 14 Dec 2011 03:53:00 +0000

At the sight of that skull, I seemed to see all of a sudden ... the nature of the criminal – an atavistic being who reproduces in his person the ferocious instincts of primitive humanity and the inferior animals. Thus were explained anatomically the enormous jaws, high cheek-bones, prominent superciliary arches ... found in criminals, savages, and apes ... and the irresistible craving for evil for its own sake.
–Cesare Lombroso

The idea that facial metrics can provide information about a person’s propensity for unethical behaviour is making something of a comeback in the scientific literature. Recent research has correlated facial width-to-height ratios with:

(a) time spent in the penalty box among Canadian hockey players (Carré and McCormick 2008),
(b) propensity to punish an opponent at a cost to oneself (Carré & McCormick 2008), and
(c) the propensity to selfishly exploit an opponent’s trust in a game context (Stirrat & Perrett 2010).

Not only can these “unethical” or “aggressive” tendencies be correlated to facial metrics via meticulous research – it turns out that casual observers are also able to predict a complete stranger’s propensity for aggression with surprising accuracy based only on a picture of that person’s face (Carré and McCormick 2009). The ethical implications of these findings are interesting and perhaps controversial (think racial and criminal profiling), but I will resist the temptation to go down that road at the moment, and instead focus on a recent study by Michael Haselhuhn and Elaine Wong titled Bad to the bone: facial structure predicts unethical behaviour.

Three things in particular attracted me to this paper: (i) the author’s use of the term ‘unethical’ which in my mind is subjective and imprecise; (ii) their reference to William March’s classic novel The Bad Seed and its conclusion that some people are just ‘born evil’; and (iii) the final sentence of the discussion “Perhaps some men truly are bad to the bone.” In truth, it was ridiculousness that drew me in, but nonetheless, let us proceed with the science!

Haselhuhn and Wong summarize their main result as follows:

...we show that genetically determined physical traits can serve as reliable predictors of unethical behaviour ... Speciﬁcally, we identify a key physical attribute, the facial width-to-height ratio, which predicts unethical behaviour in men.

In a first experiment, the authors arranged for 96 pairs of Business Admin students to participate in a negotiation exercise (conducted via email). Each pair consisted of a ‘buyer’ and a ‘seller’. Sellers were told that the property they were selling must not be commercially developed whereas buyers were instructed to obtain the property specifically for commercial development. The researchers quantified ‘unethical behaviour’ based on whether or not a buyer explicitly misstated his or her intentions at any point during the email negotiations.

In a second study, 103 undergraduates completed an online survey designed to assess their own ‘sense of power’ and then were allowed to enter a lottery for a chance to win a $50 gift card. The number of times that a student could enter the lottery was to be determined by a single roll of two dice (simulated at random.org). Because there was no oversight when students inputted the result of their dice roll at the end of the survey, there was nothing stopping participants from cheating and entering a higher number than was actually rolled (thereby increasing their chance of winning the gift card). Furthermore, because the dice roll was random, researchers were able to quantify cheating (at the group level, but not the individual level) based on deviations from the expected average roll of 7. For both experiments, two research assistants measured facial width-to-height ratios (hereafter ‘facial WHR’) of all participants based on school photographs (inter-rater agreement was high, r = 0.758).

So, what happened? In the first study, 18/96 buyers engaged in explicit deception during the email negotiation. Based on logistic regression, the probability of deception significantly increased with increasing facial WHR for men, while women’s facial WHR was not significantly related to deception. In the second study, the average reported dice roll from 103 participants was 7.76, significantly greater than what would be expected by random chance (i.e. 7.00), and therefore indicative of cheating. The reported dice roll (and therefore the incidence of cheating) did not differ between men and women. Just as before, ‘unethical behaviour’ (reported dice roll) was significantly and positively related to facial WHR for men, but not significantly influenced by facial WHR for women.

Recall that in the second study, participants also completed a survey about their own sense of power. It turns out that sense of power was positively related to both facial WHR and reported dice roll for men but unrelated to both variables among women. Some statistical voodoo (similar to path analysis) allowed the researchers to conclude that sense of power mediated the relationship between facial WHR and cheating behaviour among men.

Holy eff, right? I’m pretty amazed by these data. Before reading the paper, I expected that I would take issue with their analyses or conclusions, but the study was in fact well done. The only issue I take is with the authors’ claim (referring to the second experiment) that

...our approach introduces potential noise to the data as, for example, men with smaller facial WHRs may legitimately roll and report higher dice totals. Thus, testing for cheating behaviour using this paradigm represents a conservative test of our hypotheses.

This is simply untrue. It is equally likely that men with larger facial WHRs might legitimately roll and report higher dice totals. Their approach is imprecise, but not ‘conservative’. A more precise way to test the relationship between facial WHR and cheating would be to use the difference between reported dice rolls and actual dice rolls as a dependent variable. This would require an experimental modification allowing the researchers to know with certainty what each participant actually rolled (e.g. using cameras, software tracking, etc.). Nonetheless, the results seem robust.

So then, how could this relationship have evolved? Intuition would suggest that physical signals that reliably predict unethical behaviour should be selected against. If you’re trying to deceive or cheat someone, you don’t want to tell them up front. Haselhuhn and Wong suggest that a relationship between certain facial features and unethical behaviour could evolve through pleiotropic associations with sexually selected traits such as aggression and dominance. If females like to mate with dominant males, and dominance is correlated both with certain facial features and unethical behaviour, than unethical behaviour may come to be associated with those same facial features.

Importantly, the correlation between facial WHR and unethical behaviour demonstrated by Haselhuhn and Wong does not necessarily imply that some people are ‘born evil’. For starters the effect sizes they reported were fairly small – facial WHR explained a relatively small proportion of the variance in propensity to cheat and deceive. Second, as the authors point out

...it is important to recognize that other developmental processes may play an important part in forming these links ... one possibility is that men with greater facial WHRs are perceived and treated by others in ways that encourage unethical action (i.e. a self-fulfilling prophecy).

Again, although the reported relationships seem to be robust, I see no data here or elsewhere supporting the idea that some men are “bad to the bone”.

____________________________________

Haselhuhn, M., & Wong, E. (2011). Bad to the bone: facial structure predicts unethical behaviour Proceedings of the Royal Society B: Biological Sciences DOI: 10.1098/rspb.2011.1193

Too sexy for my smile

noreply@blogger.com (Patrick) — Mon, 12 Dec 2011 19:36:00 +0000

CBC News recently reported on a study published in the journal Emotion that attempted to determine how body language influences perceived sexual attractiveness. I take issue with some of the methods and interpretations. You can read the actual paper here, and the CBC article here, but the gist of it is as follows:

A large sample of men and women were shown photographs of members of the opposite sex and asked to rate their sexual attractiveness.
Each photo depicted a ‘model’ displaying one of four emotions – happiness, pride, shame, or neutral.
In the first study, all participants were asked to rate a single photograph. All male participants rated the same female model in one of the four possible poses. Likewise, all female participants rated the same male model in one of the four poses.

In a second study, three large groups of participants rated a bunch of photographs that were viewed online. Again the photographs displayed a member of the opposite sex expressing one of the four emotions. This time, however, the photographs (over 400 of them) were obtained online (e.g. from Google Images) and sorted into their respective categories (2 genders • 4 emotions = 8 categories) by trained assistants according to published guidelines. So, unlike the first study, each category here contained pictures of many models, and different models were used to depict each emotion.

The general result that held across both studies was that males expressing happiness were rated the least attractive and males expressing pride were the most attractive. The trend was essentially reversed for female models, such that happy females were rated the most attractive whereas females expressing pride were among the least attractive.
There were other interesting results and many details I have left out for the sake of brevity. Check out the original paper for more information.

So what are the shortcomings of this study? My problem with the first study (which in fairness the authors do acknowledge) is that the sample size for each gender is one. All female participants rated the same male model, and all male participants rated the same female model. This study provides great evidence that this particular woman and this particular man are respectively more and less attractive when smiling, but we have no evidence that this trend exists in the population at large. It is entirely plausible that for different subjects the trend would be reversed.

To really hammer this point home, consider the question – are songs in the key of C minor more enjoyable than those played in the key of D minor? What the authors have essentially done is asked the London Philharmonic to record two versions of Beethoven’s Symphony No. 5 – one version in the original C minor, and the other transposed into D minor. They then asked 184 participants to rate the enjoyability of one of the versions, and concluded that songs in C minor are more enjoyable than songs in D minor because participants on average gave the C minor version of Beethoven’s Symphony No. 5 a higher enjoyability score. Crazy, right!? Maybe Beethoven’s other symphonies actually sound better in D minor, or maybe his symphonies sound better in C minor but his sonatas are more enjoyable in D minor, or maybe Beethoven’s compositions are generally more enjoyable in C but Bach’s are consistently more enjoyable in D, etc. Point is, you can’t make generalizations with a sample size of one. Again, the authors do actually acknowledge this point, and claim that the second study addresses this shortcoming.

Problem number deux. In the second study, where photographs were obtained from the internet and many models were used in each category, I believe there were systematic differences between categories apart from just emotional expression. Admirably, the authors have posted all of the photos used in their study here. There are a few trends that really stuck out for me. One is that photographs in the pride samples were mostly comprised of athletes in their race or match apparel, whereas few or no athletes appeared in the other three categories. Another trend is that most neutral photographs tended to show only the face and sometimes shoulders, whereas hands, upper bodies, and even full bodies appeared in the other categories. A third issue is that neutral faces were almost always facing directly toward the camera with no angle or tilt, whereas faces and bodies in other categories were much more likely to be angled. There also seem to be differing proportions of professional-looking photographs between the different categories (the authors did partially control for the number of models that appeared to be professional models, but only in two of the three samples). In sample A, all of the shame photographs appear to be professionally taken, whereas most of the neutral photographs appear to have been taken by a kid at the DMV.

Going back to the music analogy, the authors have essentially downloaded a bunch of songs from iTunes, half in C minor and half in D minor, but for whatever reason most of their C minor songs happen to fall into the Hip-Hop & Reggae genre, and most of the songs in D minor happen to belong to the Country & Western genre. Even if we have a large and random sample of the population rating the enjoyability of these different songs, any average differences observed between songs in C and D minor are not necessarily due to the different key signatures, but could just as easily be due to any of the myriad differences that (on average) distinguish Hip-Hop music from Country music. Of course the same is true for the different sets of photographs in the study described above, except the confounding variables in this case were photograph quality, angle of head from camera, proportion of body appearing in the photograph, clothing and location of the model, etc.

To conclude (finally!), I don’t really doubt the claims made in this study, I just don’t think they necessarily follow from the obtained results. There are logistical limitations to any study, and we can rarely design studies that will definitively test a hypothesis of interest while controlling for every possible confounding factor. I do however think that it is reasonable and possible to more conclusively and meticulously test the hypothesis that emotional expression influences perceived attractiveness by members of the opposite sex.

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Tracy, J. L., & Beall, A. T. (2011). Happy guys finish last: the impact of emotion expressions on sexual attraction. Emotion 11:1379-1387. DOI: 10.1037/a0022902

Senescence: an example

noreply@blogger.com (Patrick) — Mon, 12 Dec 2011 06:26:00 +0000

Most of us mere mortals realize that the older we get, the more likely it is that we will die. Here are some data in support of that claim...

Figure 1. Age- and sex-specific mortality rates in Canada from 1995–1997. Data from Statistics Canada.

Each dot in the above figure represents the percentage of Canadians that achieved a given age between the years 1995–1997, but died before reaching their next birthday. For example, 0.4% of Canadian males that celebrated their 50th birthday sometime between 1-Jan-1995 and 1-Jan-1997, died before celebrating their 51st birthday. For 95-year-old males, this mortality rate jumps to 28%. In general, these data demonstrate that among Canadians, mortality rates increase with chronological age, a phenomenon termed senescence.

Presumably, senescence occurs because our bodies deteriorate over time. But why has natural selection not eliminated this fitness-limiting phenomenon? All else being the same, shouldn't individuals that live longer have higher lifetime fitness and inject more of their genes into the population's gene pool than shorter-lived individuals??

So WTF is going on... why can't I haz immortality? Does this mean that I was intelligently designed by a supernatural father-figure whose first creations accidentally committed the original sin thus condemning all of humanity to a miserably mortal existence dominated by ignorance and concupiscence?

Well, uhh, I'll leave that for you to decide. I will simply note that there are good evolutionary reasons for the occurrence of senescence, which I will discuss in a future post.

Opercula are super neat

noreply@blogger.com (Patrick) — Sat, 10 Dec 2011 20:08:00 +0000

WTF are opercula, you ask? Opercula are bony structures that cover and protect the gills of teleost (bony) fish. Wikipedia’s disambiguation page informs me that the word is also used to describe various 'lid-like' structures in other plants and animals.

What’s super neat about opercula (at least fish opercula) is that they can often be used to determine an individual's age. The bones (including opercula) of many fish species contain annual growth rings (similar to the cross section of a tree trunk) that form because bone is deposited at different rates throughout the year, depending on the season. The rate of bone deposition more or less tracks growth rate, so any environmental factor that influences an individual's growth rate can also influence bone deposition rates.

Figure 1. Operculum of 12-year-old walleye (Sander vitreus) taken from a lake in Northwestern Ontario. From Cooley and Franzin (1995)

Opercula are part of the reason that I’m not jealous of biologists that study fish in tropical areas… tropical fish can be difficult to age via bony structures because tropical fish don't usually exhibit consistent variation in bone deposition rates over fixed time periods. There are exceptions to this rule due to climatic and hydrological cycles that occur in the tropics, but in general, fish that occur in temperate areas (i.e. areas with distinct seasons, like Canda!) are much easier to age.

What’s so great about knowing an animal’s age? Well, age is obviously important when we want to determine whether an animal is legally allowed to vote, buy porn, or rent a car for a reasonable price (I just want to be 25 damn it!). It is also essential for the determination of individual growth rates, population growth rates, and sustainable harvest rates.

And now you know.

Introduction

noreply@blogger.com (Patrick) — Fri, 09 Dec 2011 18:18:00 +0000

Name: Patrick

Occupation: student, aspiring biologist?

Interests: science, biology, everything

Favourite organism: water bear

I'm currently working toward a Ph.D. in Evolutionary Biology. My thesis research is on the evolution of senescence (the increase in mortality rate and decrease in reproductive output that accompany increasing chronological age). Srsly, why has selection not taken care of that? (I'll tell you in a future post!)

WHY BLOG?

Science outreach:

I’ve become interested in science outreach and education over the past few years, largely through work I have done with Let’s Talk Science. I feel as though science education can sometimes be too focused on the content of science, and that this overemphasis might come at the expense of understanding the process of science. To me, knowing that earth revolves around the sun is far less important than knowing how a person could test this hypothesis, or what type of data could shed light on such a question (honestly, I don’t have a clue… I should probably dedicate a future post to the evidence for heliocentrism). As such, I hope to use this blog to discuss the process of science, and give a behind-the-scenes tour of scientific research for those not familiar with the goings-on in academia.

Learning through writing:

I have a terrible memory. Information passing through my jumble of neurons is quickly lost to me if not immediately transferred to a more reliable and permanent media format (e.g. paper, stone tablets, Google’s servers, etc). Aside from creating a more permanent record of the information once known to me, the process of writing also seems to help solidify ideas and concepts in my mind.

Open access:

Scientific research is largely funded through the public purse, yet most citizens do not have access to the results of the research that they are funding. As an aspiring researcher, I feel responsible for the dissemination of my own research to any and all interested parties.