Astral Codex Ten Podcast

In 2016, I wrote Ketamine Research In A New Light, which discussed the emerging consensus that, contra existing theory, ketamine's rapid-acting antidepressant effects had nothing to do with NMDA at all. I discussed some experiments which suggested they might actually be due to a related receptor, AMPA. The latest development is Attenuation of Antidepressant Effects of Ketamine by Opioid Receptor Antagonism, which finds that the opioid-blocker naltrexone prevents ketamine's antidepressant effects. Naltrexone does not prevent dissociation or any of the other weird hallucinatory effects of ketamine, which are probably genuinely NMDA-related. This suggests it's just a coincidence that NMDA antagonism and some secondary antidepressant effect exist in the same drug. If you can prevent an effect from working by blocking the opiate system, a natural assumption is that the effect works on the opiate system, and the authors suggest this is probably true. (unexpected national news tie-in: Kavanaugh accuser Christine Blasey Ford is one of the authors of this paper) In retrospect, there were warnings. The other study to have found an exciting rapid-acting antidepressant effect for an ordinary drug was Ultra-Low-Dose Buprenorphine As A Time-Limited Treatment For Severe Suicidal Ideation. It finds that buprenorphine (the active ingredient in suboxone), an opiate painkiller also used in treating addictions to other opiates, can quickly relieve the distress of acutely suicidal patients. This didn't make as big a splash as the ketamine results, for two reasons. First, everyone knows opiates feel good, and so maybe this got interpreted as just a natural extension of that truth (the Scientific American article on the discovery focused on an analogy where "mental pain" was the same as "physical pain" and so could be treated with painkillers). Second, we're currently fighting a War On Opiates, and discovering new reasons to prescribe them seems kind of like giving aid and comfort to the enemy.

Four years ago I examined the claim that SSRIs are little better than placebo. Since then, some of my thinking on this question has changed. First, we got Cipriani et al's meta-analysis of anti-depressants. It avoids some of the pitfalls of Kirsch and comes to about the same conclusion. This knocks down a few of the lines of argument in my part 4 about how the effect size might look more like 0.5 than 0.3. The effect size is probably about 0.3. Second, I've seen enough to realize that the anomalously low effect size of SSRIs in studies should be viewed not as an SSRI-specific phenomenon, but as part of a general trend towards much lower-than-expected effect sizes for every psychiatric medication (every medication full stop?). I wrote about this in my post on melatonin: The consensus stresses that melatonin is a very weak hypnotic. The Buscemi meta-analysis cites this as their reason for declaring negative results despite a statistically significant effect – the supplement only made people get to sleep about ten minutes faster. "Ten minutes" sounds pretty pathetic, but we need to think of this in context. Even the strongest sleep medications, like Ambien, only show up in studies as getting you to sleep ten or twenty minutes faster; this NYT article says that "viewed as a group, [newer sleeping pills like Ambien, Lunesta, and Sonata] reduced the average time to go to sleep 12.8 minutes compared with fake pills, and increased total sleep time 11.4 minutes." I don't know of any statistically-principled comparison between melatonin and Ambien, but the difference is hardly (pun not intended) day and night. Rather than say "melatonin is crap", I would argue that all sleeping pills have measurable effects that vastly underperform their subjective effects. Or take benzodiazepines, a class of anxiety drugs including things like Xanax, Ativan, and Klonopin. Everyone knows these are effective (at least at first, before patients develop tolerance or become addicted). The studies find them to have about equal efficacy as SSRIs. You could almost convince me that SSRIs don't have a detectable effect in the real world; you will never convince me that benzos don't. Even morphine for pain gets an effect size of 0.4, little better than SSRI's 0.3 and not enough to meet anyone's criteria for "clinically significant". Leucht 2012provides similarly grim statistics for everything else. I don't know whether this means that we should conclude "nothing works" or "we need to reconsider how we think about effect sizes".

[Originally to be titled "Marijuana: I Was Wrong", but looking back I was suitably careful about everything, and my reward is not having to say that.] Five years ago, I reviewed the potential costs and benefits of marijuana legalization and concluded that there wasn't enough evidence for a firm conclusion. I found that using some made-up math, the effects looked slightly positive, but this was very sensitive to small changes in how made-up the math was. The only really interesting conclusion was that most of the objective costs or benefits of legalization came from road traffic accidents. Either stoned driving would increase such accidents, killing thousands. Or people using marijuana instead of alcohol would decrease those accidents, saving thousands. I concluded: We should probably stop [emphasizing direct] health effects of marijuana and imprisonment for marijuana-related offenses, and concentrate all of our research and political energy on how marijuana affects driving. Using the best evidence available at the time, I predicted that marijuana legalization would probably decrease road traffic accidents. Now several states have legalized marijuana, data are in, and we have some preliminary evidence on how marijuana affects driving. And I was wrong. A study by the Highway Loss Data Institute in June of last year finds that states that legalized marijuana saw insurance claims for auto accidents increase about 3% over the general national trend for the time. An updated study by the same group finds 6% according to insurance claims, and 5.2% according to police reports.

Kelsey Piper has written an article for Vox: Early Childhood Education Yields Big Benefits – Just Not The Ones You Think. I had previously followed various studies that showed that preschool does not increase academic skill, academic achievement, or IQ, and concluded that it was useless. In fact, this had become a rallying point of movement for evidence-based social interventions; the continuing popular support for preschool proved that people were morons who didn't care about science. I don't think I ever said this aloud, but I believed it in my heart. I talked to Kelsey about some of the research for her article, and independently came to the same conclusion: despite the earlier studies of achievement being accurate, preschools (including the much-maligned Head Start) do seem to help children in subtler ways that only show up years later. Children who have been to preschool seem to stay in school longer, get better jobs, commit less crime, and require less welfare. The thing most of the early studies were looking for – academic ability – is one of the only things it doesn't affect. This suggests that preschool is beneficial not because of the curriculum or because of "teaching young brains how to learn" or anything like that, but for purely social reasons. Kelsey reviews some evidence that it might improve child health, but this doesn't seem to be the biggest part of the effect. Instead, she thinks that it frees low-income parents from childcare duties, lets them get better jobs (or in the case of mothers, sometimes lets them get a job at all), and improves parents' human capital, with all the relevant follow-on effects. More speculatively, if the home environment is unusually bad, it gives the child a little while outside the home environment, and socializes them into a "normal" way of life. I'll discuss a slightly more fleshed-out model of this in an upcoming post. My only caveat in agreeing with this perspective is that Chetty finds the same effect (no academic gains, but large life-outcome gains years later) from children having good rather than bad elementary school teachers. This doesn't make sense in the context of freeing up parents' time to get better jobs, or of getting children out of a bad home environment. It might make sense in terms of socializing them, though I would hate to have to sketch out a model of how that works. But since the teacher data and the Head Start data agree, that gives me more reason to think both are right. I can't remember ever making a post about how Head Start was useless, but I definitely thought that, and to learn otherwise is a big update for me. I've written before about how when you make an update of that scale, it's important to publicly admit error before going on to justify yourself or say why you should be excused as basically right in principle or whatever, so let me say it: I was wrong about Head Start. That having been said, on to the self-justifications and excuses

These are my preliminary choices for California elected positions and ballot initiatives. Some of them are based on Ozy's recommendations and the Berkeley EA and rationalist community's recommendations. I agree with the latter's note that because California ballot propositions are weird superlaws that permanently overrule the legislature unless repealed by voters, in general we should be very cautious about them (though some of them were recommended by the legislature itself, since for complicated reasons it needs voter support to do certain things). I'm giving first-level justifications for my votes (ie "I support this person because she wants higher taxes") but not always second-level justifications ("here's why higher taxes are good"). You can usually find discussion of these on other blog posts. Governor of California is the big one. Democrat Gavin Newsom is a former successful businessman, mayor of San Francisco, and lieutenant governor of California (also second cousin of musician Joanna Newsom). He has stated that if elected, he will let people call him "the Gavinator". Republican John Cox is a former successful businessman, best known for sponsoring a ballot initiative to make legislators wear the logos of their top 10 donors on the State Assembly floor, "much like NASCAR drivers". He also has a fascinating plan to reform politics from the ground up with a 12,000 (!) member legislature. I don't really like Newsom – he led a movement called "Care Not Cash" to restrict giving money to the homeless, and supposedly opposed anti-gay Proposition 8 so incompetently that his statements may have increased support for the measure. He also had an affair with his campaign manager's wife in a scandal that seemed unusually scummy even for a politician. I like John Cox as a person, but he doesn't seem to have any relevant governing experience. And he was anti-Trump until Trump became popular among Republicans, then about-faced and decided Trump was his new best friend, and now he's basically just a Trumpist. I am going with Newsom; God help me, God help California.

[Epistemic status: low. You tell me if you think this works.] Commenter no_bear_so_low has been doing some great work with Google Trends recently – see for example his Internet searches increasingly favour the left over the right wing of politics or Googling habits suggest we are getting a lot more anxious. I wanted to try some similar things, and in the process I learned that this is hard. Existing sites on how to use Google Trends for research don't capture some of the things I learned, so I wanted to go over it here. Suppose I want to measure the level of interest in "psychiatry" over the past few years: Looks like interest is going down. But what if I search for "psychiatrist" instead? Uh oh, now it looks like interest is going up. I guess what I'm really interested in is mental health more generally, what if I put in "suicide"? Now everything else is invisible, and the data are dominated by a spike in August 2016, which as far as I can tell is related to the release of the movie "Suicide Squad". I could try other terms, like "depression" and "anxiety", but no_bear's data already tells us those two are moving in opposite directions. Also, depression has a spike in late 2008, which must be related to the stock market crash and people's expectations of an economic depression. This doesn't seem like a great way to figure out anything. I wondered if averaging a bunch of things might take away some of the noise. I chose nine terms that seemed related to psychiatry in some way: psychiatry, psychiatrist, psychotherapy, mental illness, mental health, suicide, depression, antidepressants, and anxiety. Google won't let you combine that many terms in a single query, but that's okay – I don't want to see them relative to one another, I just want to get standardized data on each. There's a button to download any individual Google Trends query as a spreadsheet:

[Epistemic status: fiction] Thanks for letting me put my story on your blog. Mainstream media is crap and no one would have believed me anyway. This starts in September 2017. I was working for a small online ad startup. You know the ads on Facebook and Twitter? We tell companies how to get them the most clicks. This startup – I won't tell you the name – was going to add deep learning, because investors will throw money at anything that uses the words "deep learning". We train a network to predict how many upvotes something will get on Reddit. Then we ask it how many likes different ads would get. Then we use whatever ad would get the most likes. This guy (who is not me) explains it better. Why Reddit? Because the upvotes and downvotes are simpler than all the different Facebook reacts, plus the subreddits allow demographic targeting, plus there's an archive of 1.7 billion Reddit comments you can download for training data. We trained a network to predict upvotes of Reddit posts based on their titles. Any predictive network doubles as a generative network. If you teach a neural net to recognize dogs, you can run it in reverse to get dog pictures. If you train a network to predict Reddit upvotes, you can run it in reverse to generate titles it predicts will be highly upvoted. We tried this and it was pretty funny. I don't remember the exact wording, but for /r/politics it was something like "Donald Trump is no longer the president. All transgender people are the president." For r/technology it was about Elon Musk saving Net Neutrality. You can also generate titles that will get maximum downvotes, but this is boring: it will just say things that sound like spam about penis pills.

I've gotten a chance to discuss The Whole City Is Center with a few people now. They remain skeptical of the idea that anyone could "deserve" to have bad things happen to them, based on their personality traits or misdeeds. These people tend to imagine the pro-desert faction as going around, actively hoping that lazy people (or criminals, or whoever) suffer. I don't know if this passes an Intellectual Turing Test. When I think of people deserving bad things, I think of them having nominated themselves to get the short end of a tradeoff. Let me give three examples: 1. Imagine an antidepressant that works better than existing antidepressants, one that consistently provides depressed people real relief. If taken as prescribed, there are few side effects and people do well. If ground up, snorted, and taken at ten times the prescribed dose – something nobody could do by accident, something you have to really be trying to get wrong – it acts as a passable heroin substitute, you can get addicted to it, and it will ruin your life. The antidepressant is popular and gets prescribed a lot, but a black market springs up, and however hard the government works to control it, a lot of it gets diverted to abusers. Many people get addicted to it and their lives are ruined. So the government bans the antidepressant, and everyone has to go back to using SSRIs instead. Let's suppose the government is being good utilitarians here: they calculated out the benefit from the drug treating people's depression, and the cost from the drug being abused, and they correctly determined the costs outweighed the benefits. But let's also suppose that nobody abuses the drug by accident. The difference between proper use and abuse is not subtle. Everybody who knows enough to know anything about the drug at all has heard the warnings. Nobody decides to take ten times the recommended dose of antidepressant, crush it, and snort it, through an innocent mistake. And nobody has just never heard the warnings that drugs are bad and can ruin your life.

[Epistemic status: so, so, so speculative. I do not necessarily endorse taking any of the substances mentioned in this post.] There's been recent interest in "smart drugs" said to enhance learning and memory. For example, from the Washington Post: When aficionados talk about nootropics, they usually refer to substances that have supposedly few side effects and low toxicity. Most often they mean piracetam, which Giurgea first synthesized in 1964 and which is approved for therapeutic use in dozens of countries for use in adults and the elderly. Not so in the United States, however, where officially it can be sold only for research purposes. Piracetam is well studied and is credited by its users with boosting their memory, sharpening their focus, heightening their immune system, even bettering their personalities. Along with piracetam, a few other substances have been credited with these kinds of benefits, including some old friends: "To my knowledge, nicotine is the most reliable cognitive enhancer that we currently have, bizarrely," said Jennifer Rusted, professor of experimental psychology at Sussex University in Britain when we spoke. "The cognitive-enhancing effects of nicotine in a normal population are more robust than you get with any other agent. With Provigil, for instance, the evidence for cognitive benefits is nowhere near as strong as it is for nicotine." But why should there be smart drugs? Popular metaphors speak of drugs fitting into receptors like "a key into a lock" to "flip a switch". But why should there be a locked switch in the brain to shift from THINK WORSE to THINK BETTER? Why not just always stay on the THINK BETTER side? Wouldn't we expect some kind of tradeoff? Piracetam and nicotine have something in common: both activate the brain's acetylcholine system. So do three of the most successful Alzheimers drugs: donepezil, rivastigmine, and galantamine. What is acetylcholine and why does activating it improve memory and cognition?

[I briefly had a different piece up tonight discussing a conference, but the organizers asked me to hold off on writing about it until they've put up their own synopsis. It will be back up eventually; please accept this post instead for now.] In Jewish legend, the Chamber of Guf is a pit where all the proto-souls hang out whispering and murmuring. Whenever a child is born, an angel reaches into the chamber, scoops up a soul, and brings it into the world. In the syncretist mindset where every legend has to be a metaphor for the human mind, I map the Chamber of Guf to all the thoughts that exist below the level of consciousness, fighting for attention. We already know something like this happens for behaviors. From Guyenet's The Hungry Brain: How does the lamprey decide what to do? Within the lamprey basal ganglia lies a key structure called the striatum, which is the portion of the basal ganglia that receives most of the incoming signals from other parts of the brain. The striatum receives "bids" from other brain regions, each of which represents a specific action. A little piece of the lamprey's brain is whispering "mate" to the striatum, while another piece is shouting "flee the predator" and so on. It would be a very bad idea for these movements to occur simultaneously – because a lamprey can't do all of them at the same time – so to prevent simultaneous activation of many different movements, all these regions are held in check by powerful inhibitory connections from the basal ganglia. This means that the basal ganglia keep all behaviors in "off" mode by default. Only once a specific action's bid has been selected do the basal ganglia turn off this inhibitory control, allowing the behavior to occur. You can think of the basal ganglia as a bouncer that chooses which behavior gets access to the muscles and turns away the rest. This fulfills the first key property of a selector: it must be able to pick one option and allow it access to the muscles. So in the process of deciding what behavior to do, the (lamprey) brain subconsciously considers many different plausible behaviors, all of which compete to be enacted. I don't know how this extends to humans, but it would make sense that maybe only the top few candidate behaviors even make it to consciousness, with the rest getting rejected without conscious consideration.