Archive for the ‘science’ Category

In this entry, unless otherwise noted, humanism will refer to the belief that humans have special status (i.e., superiority) among species (in the same spirit as the way sexism refers to views about the sexes, and racism refers to views about races).

Science has gradually chipped away at humanism.  Evidence for heliocentrism, evolution, the cognitive map of bees, super organisms, the evolution of culture, and evidence against dualism and free will, to name some examples, have had a big impact.   However, humanism still persists in various ways throughout our culture.

Consider language.  Here are some humanist words/concepts:

  • natural‘ – If humans build a skyscraper it’s unnatural, but if bees build a beehive it’s natural.  If humans clean a new environment with antibacterial soap, it’s unnatural, but if Jewel Wasps do it it’s natural (note: ants also make antibiotics).  And so on.
    All living and non-living things affect the environment around them.  Humans have their own niches in that regard (in terms of how we do it), but so does everything else.
  • ‘humanist’ / ‘humanism’ – Sometimes people use the word ‘humanism’ as a synonym for being nice.  That definition of humanism is itself humanist (the bad kind), because it suggests that humans have some special ability for kindness.
  • ‘animals’ – The word ‘animals’ often implies only non-human animals.

Humanist thinking also includes greatly overestimating how many things are uniquely human.

It’s great to see people like Neil Shubin trying to get people to see the evolution of living things as a small part of the evolution of the universe.  Humanism will be difficult to defeat, however, because we have egos interacting with paradigm shift resistance.

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At the end of a year, people like to make lists of top movies, books, etc.  What I plan to do instead is write about the things I learned each year. So, here are some brief highlights of things I learned in 2011:

  • Epigenetics, toolkit genes, genetic switches and how most conversations about heritability are flawed.  I learned a lot about imprinted genes from Charlene Lewis (especially BDNF), about toolkit genes from reading Sean Carroll’s Endless Forms Most Beautiful (which I highly recommend) and about all of these topics from (some of) Robert Sapolsky’s lectures on human behavioral biology (which are fantastic, and free on youtube and itunes).
  • Social belonging sits atop the hierarchy of needs.  Sister Y introduced this idea with her blog here: “the need for social belonging is more pressing than the need for food.”  I have noticed that people are far more likely to want to kill (themselves or someone else) when they have been socially shamed, rejected, or ostracized.  NYU Psychology Professor James Gilligan noted:”The emotional cause that I have found just universal among people who commit serious violence, lethal violence is the phenomenon of feeling overwhelmed by feelings of shame and humiliation. I’ve worked with the most violent people our society produces who tend to wind up in our prisons. I’ve been astonished by how almost always I get the same answer when I ask the question—why did you assault or even kill that person? And the answer I would get back in one set of words or another but almost always meaning exactly the same thing would be, ‘Because he disrespected me,’ or ‘He disrespected my mother,’ or my wife, my girlfriend, whatever.”

    In the same program, Pieter Spierenburg pointed out that murder in defense of your reputation used to be viewed as a pretty minor offense: “Originally around 1300 the regular punishment for an honourable killing would be a fine or perhaps a banishment, whereas punishment for a treacherous murder would be execution.”

  • Evidence in favor of our promiscuous past, the most interesting of which is sperm competition.  I was introduced to this topic in Sex at Dawn.
  • Life cycles of parasites.  I learned about this from Robert Sapolsky and This Week in Parasitism.  I particularly love Toxoplasma and fish tapeworm.
  • Lead and crime.  There are a lot of theories about why crime has declined since the 1990s.  These theories include:  legalization of abortion, tougher sentencing, end of crack epidemic, etc.  But I think the most interesting one is the reduction in lead exposure.  Total lead exposure was a non-decreasing function  from 1900 to 1970.  Lead exposure from gasoline increased sharply from 1930 to 1970.   We know that lead exposure, especially chronic exposure, has neurotoxic effects.  It can be particularly damaging to the frontal lobe.  Thus, we would expect that kids who were exposed to lead would be more likely to engage in impulse crimes when they are young adults.   Jessica Reyes documented the link between lead exposure and crime in the US in this paper.   The graph below, taken from her paper, overlays the lead exposure curve and crime rate curve (with a 22 year lag for lead exposure, because 22 is the average age at which violent crimes are committed, so we would expect childhood exposure to lead to have the largest impact approximately 20 years later):

    I think this is pretty compelling, and a fascinating story.  The League of Nations banned lead pain in 1922, but the US failed to adopt the measure.  The US didn’t take serious action until the 1970s.  To this day, lead paint exposure is a serious problem for people living in old homes in large cities.  I would love to see the lead exposure / crime link investigated using data from other countries.
  • Religion. I learned about the history of god, its relation to changes in civilization (how transitions from polytheism to monotheism paralleled changes from foraging to farming, egalitarianism to hierarchy), lots of cool, related neuroscience, etc.  This is work in progress.  Hopefully I will have more to say about it next year.
  • I found Sister Y’s views on nature very insightful.

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As discussed previously, participants in randomized trials are typically blinded to treatment assignment.  This differs from the non-trial setting, where blinding patients to treatment would be considered unethical.  It is unclear the extent to which uncertainty about treatment assignment affects outcomes.  Most randomized trials are not designed to deal with this issue.

Informed consent laws prevent researchers from lying to patients about treatment assignment.  However, we can, to a large extent, affect what people believe about treatment assignment via the allocation probability.   For example, if subjects are informed that there is a 50% chance they will receive a placebo, they should believe that they have about a 50% chance of receiving placebo.  Alternatively, if we tell them that 99.999% of subjects will receive the active drug, they should be pretty confident that they will receive the active drug.  In the latter example, we will obtain something pretty close to the counterfactual we want (Y0,100%) on 0.001% of subjects.   Of course, we would need an enormous sample size to observe many people like that.  Thus, there are the usual tradeoffs between bias and efficiency.

My suggestion is to randomize subjects to one of several arms that have different allocation probabilities.  Assuming the causal effects are a smooth function of the allocation probability, we could extrapolate to obtain estimates of E(Y1,100% -Y0,100%).

For details, see here, or email for reprint (nequal1@gmail.com).

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Consider the situation where there are two treatments, T=0 or 1.  Let the variable B denote the subject’s confidence (as a percentage) that they have been assigned treatment T=1.  Finally, let the potential outcome Yt,b be the outcome that would be observed if the subject was actually assigned treatment t and were b% confident that they were assigned T=1.

For example, Y1,100% is the outcome that would be observed if the subject was assigned treatment 1 and was sure that they were assigned treatment 1.  Similarly,   Y0,0% is the outcome that would be observed if the subject was assigned treatment 0 and was sure that they were not assigned treatment 1.

I would argue that the causal effect we are most often interested in is  Y1,100% -Y0,100%   That is, the potential outcome if the subject was assigned treatment 1 and was sure they were assigned treatment 1, minus the potential outcome if the subject was assigned treatment 0 but falsely believed they were assigned treatment 1.

To illustrate the idea, imagine that treatment 1 is an active drug and treatment 0 is a placebo.  We are interested in what would happen if the subject believed they were assigned the active drug and did receive the active drug, versus the case where they were assigned placebo but believe it was the active drug.  The difference in these potential outcomes should tell us the effect of the active drug that is not strictly due to knowing that they are taking an active drug.

Using this notation, we can also formally define the placebo effect as Y0,100% -Y0,0% (the difference in potential outcomes if given a placebo, but on the one hand believe it’s an active drug and on the other had know that it’s a placebo).

The problem is that informed consent laws prevent us from directly observing Y1,0% or  Y0,100%  (because it would require lying to subjects about what treatments they are given).  Typically in randomized trials, only one of the following two potential outcomes is observed for each subject: Y1,50% or Y0,50%.  It is unclear how similar a contrast such as  Y1,50% -Y0,50% will be to the contrast we want, Y1,100% -Y0,100%

Thus, most randomized trials with human subjects are not even designed to obtain the variables that we are most interested in.

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The primary criticism of observational studies is that there is no way to know the extent of unmeasured confounding.

Randomized controlled trials (RCTs) have their own limitations.  They often exclude patients with co-morbid conditions and select the most adherent patients using a pre-randomization run-in phase.

However, there is another problem with RCTs, one that is not widely recognized.  Quoting myself in a forthcoming paper (link to abstract (email me for reprint)):

In RCTs patients have uncertainty about what treatment they are receiving. A patient receiving an active drug or therapy might falsely believe that they are receiving the placebo or sham therapy. Outside of the RCT environment, a patient who is prescribed a drug by their physician will be sure that they are receiving the active drug. We would expect placebo effects to be stronger if patients were unaware that they might be given a placebo. Similarly, we might expect active treatments to be more effective if there was no uncertainty about treatment receipt. While there has been great emphasis about the importance of concealing treatment assignment, this concealment creates uncertainty within the patient about treatment assignment.

Treatment uncertainty could affect subjects’ behavior (such as adherence) and subjective well being.  Given the evidence about placebo effects, it’s not unreasonable to speculate that these uncertainty effects could be substantial.  Further, treatment uncertainty also might also affect who is willing to participate in the studies.  For example, patients’ who want the newest therapy might be unwilling to risk getting randomized to  placebo.

In the next post, I will formalize these ideas.  In the final post of this series, I will propose a solution.

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Example 1:  Religious folks say “Divorce is a sin.  It’s not part of god’s plan.”  etc.   Then, when someone gets divorced, they are criticized, judged and/or shunned.  This contributes to kids from divorced families not doing as well.  These data are then used as proof that “god’s plan” is best.

Example 2:  Teens are told “Sex before marriage is bad.  The bible says to wait until marriage.”  Then, when these teens have sex, they feel dirty, guilty, impure.  People then say “see, you had sex and you feel bad. You should follow god’s plan.”

Example 3:  People are told that homosexuality is a sin.  This results in gay people not feeling good about who they are.  They might end up depressed or suicidal.  This is used as evidence that homosexuality is wrong:  “they wouldn’t have been depressed if they had followed god’s plan.”

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Some research suggests that people are happier when they have fewer choices (less opportunity for second guessing and regret). For example, see these TED talks.

I think what happens is that whatever it is that we cannot change about our circumstances gets internalized (perhaps subconsciously — see anterograde amnesia paintings experiment) and sets the baseline.   People living in a poor neighborhood with few career opportunities aren’t comparing themselves to Bill Gates and feeling unhappy.  I suspect that they’re comparing themselves to:  (1) themselves in the recent past; (2) their counterfactual selves, living in the same circumstances, who could have made different decisions; (3) their peers (friends/family).  Even people who are abused, oppressed or starving might report being happy.  The key is for those conditions to feel fixed.  If you have always had a place to live but suddenly find yourself homeless, you will probably be quite unhappy for a while (until you’ve been homeless long enough where you have internalized it).

Robustness of subjective well being has benefits.  From a fitness perspective, the ability to feel happy in a wide variety of living conditions gives one more motivation to survive and reproduce.  For example, if someone who lived in very difficult conditions was unhappy, they might not want to bring new people into this world.  Feeling happy, regardless of circumstances, causes one to feel like ‘life is good’ and want to create more life and continue living.  And of course, people would rather be happy, so it benefits people to have this robust ability.

Someone might ask “as long as people feel happy, isn’t that all that matters?”  Not if our subjective well being mechanism is flawed.    The person who has been homeless for years might feel happy (because subjective well being is relative to your own baseline), but they might have been much happier (higher baseline) if circumstances had been different.

If people feel happy even in oppressive conditions, they might be less likely to fight for change.  Similarly, economically privileged folks who see that poor people smile might be less likely to care about disparity.

Thus, while happiness data are useful, the robustness of happiness is a short-sighted adaptation.

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