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Ole Rogeberg

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Adolescent cannabis use, intelligence and selection effects

Don't know anything about this study apart from what's reported here, but some interesting quotes (disregard the headline, which does not reflect the article):

The research, from the Avon Longitudinal Study of Parents and Children, tracked more than 2,000 teenagers to examine links between cannabis use and poorer educational performance.

The research looked at intelligence tests taken by the young people when they were aged eight and then compared the results with tests at the age of 15 and GCSE exams a year later.


Young people using cannabis were "associated with decreased intellectual performance".

But these teenagers were also likely to be involved in other types of behaviour, such as drinking alcohol or taking other types of drugs.

Once these other risk factors were taken into account, there was no discernible impact on intelligence from occasional cannabis use.

But for heavy cannabis users, there were slightly poorer exam results at age 16, even when other factors such as alcohol use were taken into account.

The study, launched at the European College of Neuropsychopharmacology annual congress in Berlin, raises questions about public health messages, suggests Ms Mokrysz, from UCL's Clinical Psychopharmacology Unit.

"The belief that cannabis is particularly harmful may detract focus from an awareness of other potentially harmful behaviours," she said.

"People often believe that using cannabis can be very damaging to intellectual ability in the long-term, but it is extremely difficult to separate the direct effects of cannabis from other potential explanations."

She said that a loss of "cognitive performance" could often reflect the lifestyle accompanying cannabis use, rather than the cannabis itself.

The chair of the annual congress, Guy Goodwin, from the University of Oxford, said: "This is a potentially important study because it suggests that the current focus on the alleged harms of cannabis may be obscuring the fact that its use is often correlated with that of other even more freely available drugs and possibly lifestyle factors.

"These may be as or more important than cannabis itself."
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1965 - Drones appear in Fantastic Four

Doctor Doom finds an early drone prototype in the Fantastic Four armory and instantly sees the appeal....
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Daniel Dennet disappoints on free will

Daniel Dennet wrote a looong review of Sam Harris's short book on free will where Dennet "praises" Harris for making errors so clearly that Dennet can explain to the world why so many scientists are wrong about free will. After slogging through his review I found little to justify his arrogance. Harris's reply gets at some of the reasons, while Daniel Miessler picks apart several of the weaker and disappointing arguments made by Dennet.

Dennet's review: Harris's reply:
Daniel Miessler's comments on Dennet:
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What is God selecting for?

If God is truly going to judge the living and dead according to the criteria of, say, the Bible - what does this tell us about God and what he is up to? And what does he want with the kind of people he will be picking up?

Assume that there is a God, and that one (and only one) of the different religions is true. Assume further that there will be a "day of judgment" (either sequentially as we die, or that we'll be put in stasis until we're all judged simultaneously), and that those who have lived according to the true religion's tenets will be allowed to live on in this or some other sphere. Finally, assume that God is an intentional and intelligent being.

Does this make it possible to "reverse engineer" what God is trying to do?

What I mean is: The universe basically becomes a "people-generating machine," whose output is run through a "judgment filter" and retained to the extent that the judged individuals had discovered and successfully pursued the one true religion amongst all the false ones.

Based on this, we can describe the kind of people he will be picking up by studying empirically the traits of religious people and how these vary with adherence to the tenets of their faith. According to the wikipedia article on religiosity, this will tend to be people with particular sets of genes (44% of the variation in adult religiosity seems to be related to genetic factors), living in countries with low welfare spending, with lower intelligence (ON AVERAGE!) and a belief that the world is just (despite appearances to the contrary).

I'm less clear on why you would go to the trouble of creating a universe to grow and identify such individuals, though. Any suggestions?
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Why drug laws only get stricter over time?

A new "debate-article" in the journal Addiction suggests that there is a "drug policy ratchet" that tightens regulations over time, which they connect to the oft-quoted "law" from MacCoun that "Scientific research on drugs cannot motivate a change from tough law to lenient law, but it can motivate a change in the opposite direction"

The authors suggest that this is driven by "totemic" policies that are meant to show "the government to be tough in its quest to protect the public from harmful people and substances" - with danger identified in practice through three types of "guilt by association":

Guilty by deviant association - substances used predominantly by groups "considered as deviant from the perspective of the white, Protestant elite who focused on the perceived threat from minority ethnic migrants and their 'alien' traditions"

Guilt by lunatic association - substances banned "when it is thought that its use produces mental illness in people who were not previously ill; perhaps especially when this illness is observed in the children of powerful social groups, as exemplified in some of the media coverage that preceded the 2009 re-re-classification of cannabis"

Guilt by molecular association "motivated by a presumption of harm through a pharmacological comparison with other controlled substances, their relative potency and effect"

The article does not really answer the question it poses (Why do sanctions typically go up?) except by describing it. Why do these three types of guilt and this desire from policy makers to "appear tough" play such a strong role in this context relative to others?
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Does the DEA actively help shore up the monopoly power of drug cartels?

Profit-oriented businesses love it when something takes out their competition. Here's a case where it seems like the DEA did the dirty work for a drugs cartel by gratefully accepting information on their competitors and working to take them out. Large drug seizures for the DEA - larger profits for the remaining drug cartel. Win-win for both parties in the drug war?

Less clear to what extent they also helped in other ways - such as by not going after this cartel as a quid-pro-quo for valuable intel or by getting them access to guns. Still - having free muscle-help from the world's biggest superpower must have been kind of a scoop for the drug cartel.

"The DEA agents met with members of the cartel in Mexico to obtain information about their rivals and simultaneously built a network of informants who sign drug cooperation agreements, subject to results, to enable them to obtain future benefits, including cancellation of charges in the U.S.," reports El Universal, which also interviewed more than one hundred active and retired police officers as well as prisoners and experts.

Zambada-Niebla's lawyer claimed to the court that in the late 1990s, Castro struck a deal with U.S. agents in which Sinaloa would provide information about rival drug trafficking organizations while the U.S. would dismiss its case against the Sinaloa lawyer and refrain from interfering with Sinaloa drug trafficking activities or actively prosecuting Sinaloa leadership.
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Is there no racial bias precisely because it seems like there is?

I can hardly call myself a blogger when there's a year between posts, but I found it interesting that a similar kind of fact was used as evidence of racial bias by Ezra Klein and evidence against racial bias by an academic paper in a top economics journal.
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Smartphones are an expansion pack for the human brain

People aren't getting dumber from modern technology - people-using-cellphones have superpowers, just like Tony-Stark-using-an-Iron-Man-suit (though a different kind).
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What's missing from the Basis B1 "quantified self" watch

I've now used the B1 watch from  +Basis since August 1 last year. The watch contains a bunch of sensors that track perspiration, pulse, motion and temperature, and it syncs to an online web-service where you can see and "analyze" your data. 

Personally, what I find most lacking is the website. You can choose between a "heat-map" showing how one indicator (e.g., average temperature) varied across 1-hour blocks over a two week period, or you can see a graph showing how a bunch of indicators changed during a day or a subportion of a day. There is - currently - no API and no way to export your data.

I find these "analytic" choices puzzling. They basically let you see "how things look now," whereas the point of such a watch for most users would be to learn something that can be used to improve some specified outcome. Off the bat, I can think of several things I hope to see that might make the watch more useful and exciting than it currently is. Basis is rolling out a new version of the webpage on the 21st of January, so what follows can be thought of as my wishlist (in addition to an API and data export option,  of course):

1. Increased flexibility in terms of defining indicators and determining time-scale of a graph. For instance, I might want to specify a couple of outcome measures (e.g., "average pulse while awake and at rest," or "total hours of sleep during a night") and see how these change over larger timescales such as weeks or months. "I was so stressed at work last October, let's see how that impacted my sleeping patterns and my three-day moving average daytime-pulse-while-at- rest ?"

2. Open and adjustable/shareable aggregations of indicators into outcomes The Basis website does try to aggregate and combine the various measures into more meaningful indicators such as sleep, and "activity" and even "walking" and "cycling" - but the way it does this is hard to understand. My "daily insight" page, for instance, often tells me that I've slept 2 hours - perhaps because interrupted sleep causes some portions of a night's sleep to be allocated to the previous calendar day. I don't know.

I would ideally like to see the "code" or weighting/combination "recipe" that shows how the physiological measures collected are combined to estimate higher level outcomes such as sleep as opposed to waking, or "walking" as opposed to "riding a bike." If these are seen as proprietary trade secrets, it would still be good if users could themselves develop their own such recipes or algorithms and share them amongst themselves. This would also make it possible for a community of users to develop indicators of particular use to people with certain health conditions etc (e.g., number of times heart rate is outside of a specified bound during a day, or variability measures). 

3. Ability to analyze  experiments. The point of a watch such as this is to learn something about yourself. We might want to know, for instance, how we would be affected by quitting coffee. Or a smoker might want to track the rise and fall of "withdrawal" effects as he or she quits. By allowing users to define and manually enter "potentially causal" variables such as coffee use, attendance at exercise classes, "meetings at work" etc., it would be possible to see how these are correlated with various outcome measures we are interested in. (E.g., "how is my sleep improved if I don't drink coffee after 15:00?", "How is my self-scored productivity at work correlated with sleep quality the night before?")

4. Ability to conduct crowdsourced experiments with other users Related to the last point, it would be cool if a bunch of people with similar questions about, e.g., coffee in the afternoon and sleep, or alcohol drinking and various hangover cures, could be recruited and join together to make their own randomized experiment. This could be an "opt-in" thing where you are asked whether you would like to be informed of such initiatives, and if you are, what areas you would like to follow and be informed of. 

5. Info on what Basis users have learned  This will just be correlational, but it could still be fun if Basis could analyze the aggregate data collected from all users and point out surprising correlations. While correlation does not equal causation, causation does often imply correlation - and surprising patterns could trigger interesting hypotheses that users might want to examine using crowdsourced experiments of the kind described in point 4.
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Interesting tool for comparing resting heart rate to population data

Cool and smooth web-page that updates figures and text as you move sliders to set your own resting heart rate. Would have wanted more details on data sources, how representative the data is for general populations, etc. - but interesting nonetheless.
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