Your mileage will vary. There are so many parameters and interactions in the brain that any of them could be the bottleneck or responsible pathway, and one could fall prey to the common U-shaped dose-response curve (eg. Yerkes-Dodson law; see also “Chemistry of the adaptive mind”& de Jongh et al 2007) which may imply that the smartest are those who benefit least but ultimately they all cash out in a very few subjective assessments like ‘energetic’ or ‘motivated’, with even apparently precise descriptions like ‘working memory’ or ‘verbal fluency’ not telling you much about what the nootropic actually did. It’s tempting to list the nootropics that worked for you and tell everyone to go use them, but that is merely generalizing from one example (and the more nootropics - or meditation styles, or self-help books, or “getting things done” systems - you try, the stronger the temptation is to evangelize). The best you can do is read all the testimonials and studies and use that to prioritize your list of nootropics to try. You don’t know in advance which ones will pay off and which will be wasted. You can’t know in advance. And wasted some must be; to coin a Umeshism: if all your experiments work, you’re just fooling yourself. (And the corollary - if someone else’s experiments always work, they’re not telling you everything.)

The above are all reasons to expect that even if I do excellent single-subject design self-experiments, there will still be the old problem of “internal validity” versus “external validity”: an experiment may be wrong or erroneous or unlucky in some way (lack of internal validity) or be right but not matter to anyone else (lack of external validity). For example, alcohol makes me sad & depressed; I could run the perfect blind randomized experiment for hundreds of trials and be extremely sure that alcohol makes me less happy, but would that prove that alcohol makes everyone sad or unhappy? Of course not, and as far as I know, for a lot of people alcohol has the opposite effect. So my hypothetical alcohol experiment might have tremendous internal validity (it does prove that I am sadder after inebriating), and zero external validity (someone who has never tried alcohol learns nothing about whether they will be depressed after imbibing). Keep this in mind if you are minded to take the experiments too seriously.

Somewhat ironically given the stereotypes, while I was in college I dabbled very little in nootropics, sticking to melatonin and tea. Since then I have come to find nootropics useful, and intellectually interesting: they shed light on issues in philosophy of biology & evolution, argue against naive psychological dualism and for materialism, offer cases in point on the history of technology & civilization or recent psychology theories about addiction & willpower, challenge our understanding of the validity of statistics and psychology - where they don’t offer nifty little problems in statistics and economics themselves, and are excellent fodder for the young Quantified Self movement; modafinil itself demonstrates the little-known fact that sleep has no accepted evolutionary explanation. (The hard drugs also have more ramifications than one might expect: how can one understand the history of Southeast Asia and the Vietnamese War without reference to heroin, or more contemporaneously, how can one understand the lasting appeal of the Taliban in Afghanistan and the unpopularity & corruption of the central government without reference to the Taliban’s frequent anti-drug campaigns or the drug-funded warlords of the Northern Alliance?)

No effects, alone or mixed with choline+piracetam. This is pretty much as expected from reports about ALCAR (Examine.com), but I had still been hoping for energy boosts or something. (Bought from Smart Powders.)

Adderall is a mix of 4 amphetamine salts (FDA adverse events), and not much better than the others (but perhaps less addictive); as such, like caffeine or methamphetamine, it is not strictly a nootropic but a cognitive enhancer and can be tricky to use right (for how one should use stimulants, see “How To Take Ritalin Correctly”). I ordered 10x10mg Adderall IR off Silk Road (Wikipedia). On the 4th day after confirmation from seller, the package arrived. It was a harmless looking little padded mailer. Adderall as promised: 10 blue pills with markings, in a double ziplock baggy (reasonable, it’s not cocaine or anything). They matched pretty much exactly the descriptions of the generic I had found online. (Surprisingly, apparently both the brand name and the generic are manufactured by the same pharmacorp.)

I took the first pill at 12:48 pm. 1:18, still nothing really - head is a little foggy if anything. later noticed a steady sort of mental energy lasting for hours (got a good deal of reading and programming done) until my midnight walk, when I still felt alert, and had trouble sleeping. (Zeo reported a ZQ of 100, but a full 18 minutes awake, 2 or 3 times the usual amount.)

At this point, I began thinking about what I was doing. Black-market Adderall is fairly expensive; $4-10 a pill vs prescription prices which run more like $60 for 120 20mg pills. It would be a bad idea to become a fan without being quite sure that it is delivering bang for the buck. Now, why the piracetam mix as the placebo as opposed to my other available powder, creatine powder, which has much smaller mental effects? Because the question for me is not whether the Adderall works (I am quite sure that the amphetamines have effects!) but whether it works better for me than my cheap legal standbys (piracetam & caffeine)? (Does Adderall have marginal advantage for me?) Hence, I want to know whether Adderall is better than my piracetam mix. People frequently underestimate the power of placebo effects, so it’s worth testing. (Unfortunately, it seems that there is experimental evidence that people on Adderall know they are on Adderall and also believe they have improved performance, when they do not. So the blind testing does not buy me as much as it could.)

The adrafinil/Olmifon (bought simultaneously with the hydergine from Anti-Aging Systems, now Antiaging Central) was a disappointment. Almost as expensive as actual modafinil, with the risk of liver problems, but did nothing whatsoever that I noticed. It is supposed to be subtler than modafinil, but that’s a little ridiculous.

The advantage of adrafinil is that it is legal & over-the-counter in the USA, so one removes the small legal risk of ordering & possessing modafinil without a prescription, and the retailers may be more reliable because they are not operating in a niche of dubious legality. Based on comments from others, the liver problem may have been overblown, and modafinil vendors post-2012 seem to have become more unstable, so I may give adrafinil (from another source than Antiaging Central) a shot when my modafinil/armodafinil run out.

Very expensive; I noticed minimal improvements when combined with sulbutiamine & piracetam+choline. Definitely not worthwhile for me.

Bacopa monnieri (Examine, nootropics survey, Reddit discussions) is an herb prominent for its use in Ayurvedic medicine. Taken chronically over long time-periods, Bacopa may improve cognition and help with stress, but (like some other Ayurvedic remedies) has a potential drawback in the form of heavy metal contamination.

The psychology & medical literature is sporadic and studies are somewhat confounded by the issues of dosage and dose length, but overall the evidence for improvements is better than that for most nootropics. “The Cognitive-Enhancing Effects of Bacopa monnieri: A Systematic Review of Randomized, Controlled Human Clinical Trials” found evidence of benefits to memory in several >=12-week-long RCTs. “Cognitive effects of two nutraceuticals Ginseng and Bacopa benchmarked against modafinil: a review and comparison of effect sizes” reviewed mostly the same studies. “Meta-analysis of randomized controlled trials on cognitive effects of Bacopa monnieri extract” attempted to meta-analyze the studies (specifically: Stough et al 2001, Roodenrys et al 2002, Raghav et al 2006, Barbhaiya et al 2008, Calabrese et al 2008, Stough et al 2008, Peth-Nui et al 2012, & Sathyanarayanan et al 2013) and found that of the 21 possible metrics/tests, the trail-making and reaction-time improvements had low heterogeneity between studies & also reached statistical-significance; unfortunately, heterogeneity was very high in most metrics, few studies examined the same metrics, and the low study count renders impossible any estimate of how bad the publication bias is (typically severe) or other checks of quality/reliability, so the results are not as impressive as they might appear. Overall, it seems like Bacopa likely has some useful effects.

Practically, I have 2 concerns about Bacopa:

1. obtaining a heavy-metal-free product for long-term consumption (what good is it to benefit from Bacopa if you are simultaneously building up a near-permanent heavy metal burden?)

   CoAs are the first line of defense but availability differs: Ayur Sante, Nutrigold, Indigo Herbs, Banyan Botanical & Nutricargo reportedly provide CoAs as does NootropicsDepot; while Planetary Herbals, Himalaya, & Mind Nutrition have stonewalled requests.

   Probably the best bet is to go with Bacognize: NootropicsDepot’s CoA& an independent lab test confirms that the heavy metal levels are acceptably low in that standardized branded source. (May cost more, though.)

2. how hard self-experimenting is.

   Self-experimenting is both easy and hard: it’s easy because there should be effects on memory recall which can be easily extracted from spaced repetition software like Mnemosyne, but hard because onset requires many weeks so a self-experiment must either have few blocks (limiting comparability) or be lengthy (to allow for multiple transitions between Bacopa and placebo conditions). I may have to settle for a lesser analysis like a before-after comparison, and only then if the results are positive consider the expense & difficulty of a full RCT over a number of months.

Bacopa can be bought from a number of retailers. There are several capsule sellers on Amazon, and NootropicsDepot sells both capsules& powder. More specifically (as of 11 September 2014, assuming using Amazon’s free shipping & NootropicsDepot’s regular S&H):

The cheapest Bacognize, if one doesn’t want to cap one’s own pills (as one probably does since the powder reportedly tastes bad), would seem to be the Swanson. (The Nutrigold is substantially cheaper, but also comes with a different branded source and is half-generic, and if one wanted to roll the dice, there are probably much cheaper sources of Bacopa on Amazon.) At 90x250mg, the dose per pill is a little inconveniently low for daily usage (>=300mg would be better) but still usable, so the daily cost would be $0.13, and it’d cover 90 days - conveniently, the same time period used in a number of studies such as Stough et al 2008.

I ordered 2 bottles of the Swanson on 14 September, and began taking one pill in the morning with olive oil on 26 September 2014. I intend to do an ABA trial, checking for correlated changes in my Mnemosyne scores, sleep (changes are often reported anecdotally), and possibly other things such as my daily self-ratings or extracted factors.

Based on this H+ article/advertisement, I gave a PEA supplement a try. Noticed nothing. Critical commentators pointed out that PEA was notoriously degraded by the digestive system and has essentially no effect on its own, though Neurvana’s ‘pro’ supplement claimed to avoid that. I guess it doesn’t.

Discussions of PEA mention that it’s almost useless without a MAOI to pave the way; hence, when I decided to get deprenyl and noticed that deprenyl is a MAOI, I decided to also give PEA a second chance in conjunction with deprenyl. Unfortunately, in part due to my own shenanigans, Nubrain canceled the deprenyl order and so I have 20g of PEA sitting around. Well, it’ll keep until such time as I do get a MAOI.

Caffeine (Examine.com; FDA adverse events) is of course the most famous stimulant around. But consuming 200mg or more a day, I have discovered the downside: it is addictive and has a nasty withdrawal - headaches, decreased motivation, apathy, and general unhappiness. (It’s a little amusing to read academic descriptions of caffeine addiction; if caffeine were a new drug, I wonder what Schedule it would be in and if people might be even more leery of it than modafinil.) Further, in some ways, aside from the ubiquitous placebo effect, caffeine combines a mix of weak performance benefits (Lorist & Snel 2008, Nehlig 2010) with some possible decrements, anecdotally and scientifically:

1. slows memory retrieval for unprimed memories (although it speeds retrieval for related/primed memories)
2. the usual U-curve applies to caffeine doses: eg while a small dose of caffeine in energy drinks substantially improves reaction-time in the cued go/no-go task, higher doses improve reaction-time less and are much closer to baseline (their optimal tested dose is, for my weight of 93kg, ~100mg)
3. caffeine damages sleep (necessary for memory and alertness), even 6 hours before sleep
4. very low doses (9mg) of caffeine can still have negative effects
5. did I mention that it correlates with changed estrogen levels in women?
6. in rats, it inhibits memory formation in the hippocampusandinmice, although other mice saw mental benefits with improvement to “long-term memory when tested with object recognition”

Finally, it’s not clear that caffeine results in performance gains after long-term use; homeostasis/tolerance is a concern for all stimulants, but especially for caffeine. It is plausible that all caffeine consumption does for the long-term chronic user is restore performance to baseline. (Imagine someone waking up and drinking coffee, and their performance improves - well, so would the performance of a non-addict who is also slowly waking up!) See for example, James & Rogers 2005, Sigmon et al 2009, and Rogers et al 2010. A cross-section of thousands of participants in the Cambridge brain-training study found “caffeine intake showed negligible effect sizes for mean and component scores” (participants were not told to use caffeine, but the training was recreational & difficult, so one expects some difference).

This research is in contrast to the other substances I like, such as piracetam or fish oil. I knew about withdrawal of course, but it was not so bad when I was drinking only tea. And the side-effects like jitteriness are worse on caffeine without tea; I chalk this up to the lack of theanine. (My later experiences with theanine seems to confirm this.) These negative effects mean that caffeine doesn’t satisfy the strictest definition of ‘nootropic’ (having no negative effects), but is merely a ‘cognitive enhancer’ (with both benefits & costs). One might wonder why I use caffeine anyway if I am so concerned with mental ability.

My answer is that this is not a lot of research or very good research (not nearly as good as the research on nicotine, eg.), and assuming it’s true, I don’t value long-term memory that much because LTM is something that is easily assisted or replaced (personal archives, and spaced repetition). For me, my problems tend to be more about akrasia and energy and not getting things done, so even if a stimulant comes with a little cost to long-term memory, it’s still useful for me. I’m going continue to use the caffeine. It’s not so bad in conjunction with tea, is very cheap, and I’m already addicted, so why not? Caffeine is extremely cheap, addictive, has minimal effects on health (and may be beneficial, from the various epidemiological associations with tea/coffee/chocolate & longevity), and costs extra to remove from drinks popular regardless of their caffeine content (coffee and tea again). What would be the point of carefully investigating it? Suppose there was conclusive evidence on the topic, the value of this evidence to me would be roughly $0 or since ignorance is bliss, negative money - because unless the negative effects were drastic (which current studies rule out, although tea has other issues like fluoride or metal contents), I would not change anything about my life. Why? I enjoy my tea too much. My usual tea seller doesn’t even have decaffeinated oolong in general, much less various varieties I might want to drink, apparently because de-caffeinating is so expensive it’s not worthwhile. What am I supposed to do, give up my tea and caffeine just to save on the cost of caffeine? Buy de-caffeinating machines (which I couldn’t even find any prices for, googling)? This also holds true for people who drink coffee or caffeinated soda. (As opposed to a drug like modafinil which is expensive, and so the value of a definitive answer is substantial and would justify some more extensive calculating of cost-benefit.)

I ordered 400g of ‘anhydrous caffeine’ from Smart Powders. Apparently my oolong tea doesn’t contain very much caffeine, so adding a fraction of a gram wakes me up a bit. Surprisingly for something with ‘anhydrous’ in its name, it doesn’t seem to dissolve very well.

I ultimately mixed it in with the 3kg of piracetam and included it in that batch of pills. I mixed it very thoroughly, one ingredient at a time, so I’m not very worried about ‘hot spots’. But if you are, one clever way to get accurate caffeine measurements is to measure out a large quantity & dissolve it since it’s easier to measure water than powder, and dissolving guarantees even distribution. This can be important because caffeine is, like nicotine, an alkaloid poison which - “the dose makes the poison” - can kill in high doses, and concentrated powder makes it easy to take too much, as one inept Englishman discovered the hard way. (This dissolving trick is applicable to anything else that dissolves nicely.)

Does little alone, but absolutely necessary in conjunction with piracetam. (Bought from Smart Powders.) When turning my 3kg of piracetam into pills, I decided to avoid the fishy-smelling choline and go with 500g of DMAE (Examine.com); it seemed to work well when I used it before with oxiracetam& piracetam, since I had no ‘piracetam headaches’, and be considerably less bulky.

In the future, I might try Alpha-GPC instead of the regular cholines; that supposedly has better bio-availability.

Chocolate or cocoa powder (Examine.com), contains the stimulants caffeine and the caffeine metabolite theobromine, so it’s not necessarily surprising if cocoa powder was a weak stimulant. It’s also a witch’s brew of chemicals such as polyphenols and flavonoids some of which have been fingered as helpful, which all adds up to an unclear impact on health (once you control for eating a lot of sugar).

Googling, you sometimes see correlational studies like “Intake of Flavonoid-Rich Wine, Tea, and Chocolate by Elderly Men and Women Is Associated with Better Cognitive Test Performance”; in this one, the correlated performance increase from eating chocolate was generally fairly modest (say, <10%), and the maximum effects were at 10g/day of what was probably milk chocolate, which generally has 10-40% chocolate liquor in it, suggesting any experiment use 1-4g. More interesting is the blind RCT experiment “Consumption of cocoa flavanols results in acute improvements in mood and cognitive performance during sustained mental effort”, which found improvements at ~1g; the most dramatic improvement of the 4 tasks (on the “Threes correct”) saw a difference of 2 to 6 at the end of the hour of testing, while several of the other tests converged by the end or saw the controls winning (“Sevens correct”). Crews et al 2008 found no cognitive benefit, and an fMRI experiment found the change in brain oxygen levels it wanted but no improvement to reaction times.

It’s not clear that there is much of an effect at all. This makes it hard to design a self-experiment - how big an effect on, say, dual n-back should I be expecting? Do I need an arduous long trial or an easy short one? This would principally determine the “value of information” too; chocolate seems like a net benefit even if it does not affect the mind, but it’s also fairly costly, especially if one likes (as I do) dark chocolate. Given the mixed research, I don’t think cocoa powder is worth investigating further as a nootropic.

Coconut oil was recommended by Pontus Granström on the Dual N-Back mailing list for boosting energy & mental clarity. It is fairly cheap (~$13 for 30 ounces) and tastes surprisingly good; it has a very bad reputation in some parts, but seems to be in the middle of a rehabilitation. Seth Robert’s Buttermind experiment found no mental benefits to coconut oil (and benefits to eating butter), but I wonder.

The first night I was eating some coconut oil, I did my n-backing past 11 PM; normally that damages my scores, but instead I got 66/66/75/88/77% (▁▁▂▇▃) on D4B and did not feel mentally exhausted by the end. The next day, I performed well on the Cambridge mental rotations test. An anecdote, of course, and it may be due to the vitamin D I simultaneously started. Or another day, I was slumped under apathy after a promising start to the day; a dose of fish & coconut oil, and 1 last vitamin D, and I was back to feeling chipper and optimist. Unfortunately I haven’t been testing out coconut oil & vitamin D separately, so who knows which is to thank. But still interesting.

After several weeks of regularly consuming coconut oil and using up the first jar of 15oz, I’m no longer particularly convinced it was doing anything. (I’ve found it’s good for frying eggs, though.) Several days after using up the second jar, I notice no real difference in mood or energy or DNB scores.

One of the most obscure -racetams around, coluracetam (Smarter Nootropics, Ceretropic, Isochroma) acts in a different way from piracetam - piracetam apparently attacks the breakdown of acetylcholine while coluracetam instead increases how much choline can be turned into useful acetylcholine. This apparently is a unique mechanism. A crazy Longecity user, ScienceGuy ponied up $16,000 (!) for a custom synthesis of 500g; he was experimenting with 10-80mg sublingual doses (the ranges in the original anti-depressive trials) and reported a laundry list of effects (as does Isochroma): primarily that it was anxiolytic and increased work stamina. Unfortunately for my stack, he claims it combines poorly with piracetam. He offered free 2g samples for regulars to test his claims. I asked & received some.

Experiment design is complicated by his lack of use of any kind of objective tests, but 3 metrics seem worthwhile:

1. dual n-back: testing his claims about concentration, increased energy & stamina, and increased alertness & lucidity.

2. daily Mnemosyne flashcard scores: testing his claim about short & medium-term memory, viz.

   I have personally found that with respect to the NOOTROPIC effect(s) of all the RACETAMS, whilst I have experienced improvements in concentration and working capacity / productivity, I have never experienced a noticeable ongoing improvement in memory. COLURACETAM is the only RACETAM that I have taken wherein I noticed an improvement in MEMORY, both with regards to SHORT-TERM and MEDIUM-TERM MEMORY. To put matters into perspective, the memory improvement has been mild, yet still significant; whereas I have experienced no such improvement at all with the other RACETAMS.

3. daily mood/productivity log (1-5): for the anxiolytic and working claims.

(In all 3, higher = better, so a multivariate result is easily interpreted..)

He recommends a 10mg dose, but sublingually. He mentions “COLURACETAM’s taste is more akin to that of PRAMIRACETAM than OXIRACETAM, in that it tastes absolutely vile” (not a surprise), so it is impossible to double-blind a sublingual administration - even if I knew of an inactive equally-vile-tasting substitute, I’m not sure I would subject myself to it. To compensate for ingesting the coluracetam, it would make sense to double the dose to 20mg (turning the 2g into <100 doses). Whether the effects persist over multiple days is not clear; I’ll assume it does not until someone says it does, since this makes things much easier.

Creatine (Examine.com) monohydrate was another early essay of mine - cheap (because it’s so popular with the bodybuilder types), and with a very good safety record. I bought some from Bulk Powders and combined it with my then-current regimen (piracetam+choline).

I’m not a bodybuilder, but my interest was sparked by several studies, some showing benefits and others not - usually in subpopulations like vegetarians or old people.

As I am not any of the latter, I didn’t really expect a mental benefit. As it happens, I observed nothing. What surprised me was something I had forgotten about: its physical benefits. My performance in Taekwondo classes suddenly improved - specifically, my endurance increased substantially. Before, classes had left me nearly prostrate at the end, but after, I was weary yet fairly alert and happy. (I have done Taekwondo since I was 7, and I have a pretty good sense of what is and is not normal performance for my body. This was not anything as simple as failing to notice increasing fitness or something.) This was driven home to me one day when in a flurry before class, I prepared my customary tea with piracetam, choline & creatine; by the middle of the class, I was feeling faint & tired, had to take a break, and suddenly, thunderstruck, realized that I had absentmindedly forgot to actually drink it! This made me a believer.

After I ran out of creatine, I noticed the increased difficulty, and resolved to buy it again at some point; many months later, there was a Smart Powders sale so bought it in my batch order, $12 for 1000g. As before, it made Taekwondo classes a bit easier. I paid closer attention this second time around and noticed that as one would expect, it only helped with muscular fatigue and did nothing for my aerobic issues. (I hate aerobic exercise, so it’s always been a weak point.) I eventually capped it as part of a sulbutiamine-DMAE-creatine-theanine mix. This ran out 1 May 2013. In March 2014, I spent $19 for 1kg of micronized creatine monohydrate to resume creatine use and also to use it as a placebo in a honey-sleep experiment testing Seth Roberts’s claim that a few grams of honey before bedtime would improve sleep quality: my usual flour placebo being unusable because the mechanism might be through simple sugars, which flour would digest into. (I did not do the experiment: it was going to be a fair amount of messy work capping the honey and creatine, and I didn’t believe Roberts’s claims for a second - my only reason to do it would be to prove the claim wrong but he’d just ignore me and no one else cares.) I didn’t try measuring out exact doses but just put a spoonful in my tea each morning (creatine is tasteless). The 1kg lasted from 25 March to 18 September or 178 days, so ~5.6g & $0.11 per day.

Ryan Carey tracked creatine consumption vs some tests with ambiguous results.

Fish oil (Examine.com, buyer’s guide) provides benefits relating to general mood (eg. inflammation& anxiety; see later on anxiety) and anti-schizophrenia; it is one of the better supplements one can take. (The known risks are a higher rate of prostate cancer and internal bleeding, but are outweighed by the cardiac benefits - assuming those benefits exist, anyway, which may not be true.) The benefits of omega acids are well-researched.

It is at the top of the supplement snake oil list thanks to tons of correlations; for a review, see Luchtman & Song 2013 but some specifics include “Teenage Boys Who Eat Fish At Least Once A Week Achieve Higher Intelligence Scores”, anti-inflammatory properties (see “Fish Oil: What the Prescriber Needs to Know” on arthritis), and others - “Fish oil can head off first psychotic episodes” (study; Seth Roberts commentary), “Fish Oil May Fight Breast Cancer”, “Fatty Fish May Cut Prostate Cancer Risk”& “Walnuts slow prostate cancer”, “Benefits of omega-3 fatty acids tally up”, “Serum Phospholipid Docosahexaenonic Acid Is Associated with Cognitive Functioning during Middle Adulthood”endlessanecdotes.

But like any other supplement, there are some safetyconcerns negative studies like “Fish oil fails to hold off heart arrhythmia” or “other reports cast doubt on a protective effect against dementia” or “Fish Oil Use in Pregnancy Didn’t Make Babies Smart” (WSJ) (an earlypromise but one that fadedabitlater) or “…Supplementation with DHA compared with placebo did not slow the rate of cognitive and functional decline in patients with mild to moderate Alzheimer disease.”.

As far as anxiety goes, psychiatrist Emily Deans has an overview of why the Kiecolt-Glaser et al 2011 study is nice; she also discusses why fish oil seems like a good idea from an evolutionary perspective. There was also a weaker earlier 2005 study also using healthy young people, which showed reduced anger/anxiety/depression plus slightly faster reactions. The anti-stress/anxiolytic may be related to the possible cardiovascular benefits (Carter et al 2013).

pwr.t.test(d=3.55,type="paired",power=0.75,alternative="greater",sig.level=0.05)

     Paired t test power calculation

              n =2.269155

              NOTE:n is number of *pairs*

pwr.t.test(d=0.28,type="paired",power=0.75,alternative="greater",sig.level=0.05)

     Paired t test power calculation

              n =69.98612

pwr.t.test(d=0.28,type="paired",alternative="greater",sig.level=0.05,n=9)

          power =0.1908962pwr.t.test(d=0.5,type="paired",alternative="greater",sig.level=0.05,n=9)

          power =0.3927739

The chemical Huperzine-A (Examine.com) is extracted from a moss. It is an acetylcholinesterase inhibitor (instead of forcing out more acetylcholine like the -racetams, it prevents acetylcholine from breaking down). My experience report: One for the ‘null hypothesis’ files - Huperzine-A did nothing for me. Unlike piracetam or fish oil, after a full bottle (Source Naturals, 120 pills at 200μg each), I noticed no side-effects, no mental improvements of any kind, and no changes in DNB scores from straight Huperzine-A.

Possible confounding factors:

• youth: I am considerably younger than the other poster who uses HA
• I only tested a few days with choline+H-A (but I didn’t notice anything beyond the choline there).
• counterfeiting? Source Naturals is supposed to be trustworthy, but rare herbal products are most susceptible to fake goods.

It’s really too bad. H-A is cheap, compact, doesn’t taste at all, and in general is much easier to take than fish oil (and much easier to swallow than piracetam or choline!). But if it doesn’t deliver, it doesn’t deliver.

Hydergine (FDA adverse events) was another disappointment (like the adrafinil, purchased from Anti-Aging Systems/Antiaging Central). I noticed little to nothing that couldn’t be normal daily variation.

As discussed in my iodine essay (FDA adverse events), iodine is a powerful health intervention as it eliminates cretinism and improves average IQ by a shocking magnitude. If this effect were possible for non-fetuses in general, it would be the best nootropic ever discovered, and so I looked at it very closely. Unfortunately, after going through ~20 experiments looking for ones which intervened with iodine post-birth and took measures of cognitive function, my meta-analysis concludes that: the effect is small and driven mostly by one outlier study. Once you are born, it’s too late. But the results could be wrong, and iodine might be cheap enough to take anyway, or take for non-IQ reasons. (This possibility was further weakened for me by an August 2013 blood test of TSH which put me at 3.71 uIU/ml, comfortably within the reference range of 0.27-4.20.)

library(pwr)pwr.t.test(power=0.75, sig.level=0.05, n=22)

     Two-sample t test power calculation

              n =22
              d =0.8130347

pwr.t.test(type="two.sample",power=0.75,alternative="greater",n=102)

     Two-sample t test power calculation

              n =102
              d =0.325867

pwr.t.test(type="paired",d=0.325867,power=0.75,alternative="greater")

     Paired t test power calculation

              n =52.03677

pwr.t.test(type="paired",d=0.1,power=0.75,alternative="greater")

     Paired t test power calculation

              n =539.2906

R>before <-c(27,31,18,26,22,29,20,13,18,31,27) # I split the 11 questions into how many picked,
R>after  <-c(24,20,33,25,29,22,31,38,33,20,24) # for it, before vs after
R>summary(before); summary(after)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.13.019.026.023.828.031.0
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.20.023.025.027.232.038.0
R>
R>chisq.test(before, after, simulate.p.value=TRUE)

    Pearson`s Chi-squared test with simulated p-valuedata:  before and afterX-squared = 77, df = NA, p-value = 0.000135R> wilcox.test(before, after)    Wilcoxon rank sum test with continuity correctiondata:  before and afterW = 43, p-value = 0.2624alternative hypothesis: true location shift is not equal to 0R> binom.test(c(sum(before), sum(after)))    Exact binomial testdata:  c(sum(before), sum(after))number of successes = 262, number of trials = 561, p-value = 0.1285alternative hypothesis: true probability of success is not equal to 0.595% confidence interval: 0.4251 0.5093sample estimates:probability of success                 0.467

Kratom (Erowid, Reddit) is a tree leaf from Southeast Asia; it’s addictive to some degree (like caffeine and nicotine), and so it is regulated/banned in Thailand, Malaysia, Myanmar, and Bhutan among others - but not the USA. (One might think that kratom’s common use there indicates how very addictive it must be, except it literally grows on trees so it can’t be too hard to get.) Kratom is not particularly well-studied (and what has been studied is not necessarily relevant - I’m not addicted to any opiates!), and it suffers the usual herbal problem of being an endlessly variable food product and not a specific chemical with the fun risks of perhaps being poisonous, but in my reading it doesn’t seem to be particularly dangerous or have serious side-effects.

A LessWronger found that it worked well for him as far as motivation and getting things done went, as did another LessWronger who sells it online (terming it “a reasonable productivity enhancer”) as did one of his customers, a pickup artist oddly enough. The former was curious whether it would work for me too and sent me Speciosa Pro’s “Starter Pack: Test Drive” (a sampler of 14 packets of powder and a cute little wooden spoon). In SE Asia, kratom’s apparently chewed, but the powders are brewed as a tea.

1. I started with the 10g of ‘Vitality Enhanced Blend’, a sort of tan dust. Used 2 little-spoonfuls (dust tastes a fair bit like green/oolong tea dust) into the tea mug and then some boiling water. A minute of steeping and… bleh. Tastes sort of musty and sour. (I see why people recommended sweetening it with honey.) The effects? While I might’ve been more motivated - I hadn’t had caffeine that day and was a tad under the weather, a feeling which seemed to go away perhaps half an hour after starting - I can’t say I experienced any nausea or very noticeable effects. (At least the flavor is no longer quite so offensive.)
2. 3 days later, I’m fairly miserable (slept poorly, had a hair-raising incident, and a big project was not received as well as I had hoped), so well before dinner (and after a nap) I brew up 2 wooden-spoons of ‘Malaysia Green’ (olive-color dust). I drank it down; tasted slightly better than the first. I was feeling better after the nap, and the kratom didn’t seem to change that.
3. The next day was somewhat similar, so at 2:40 I tried out 3 spoonfuls of ‘sm00th’ (?), a straight tan powder. Like the Malaysia Green, not so bad tasting. By the second cup, my stomach is growling a little. No particular motivation.
4. A week later: ‘Golden Sumatran’, 3 spoonfuls, a more yellowish powder. (I combined it with some tea dregs to hopefully cut the flavor a bit.) Had a paper to review that night. No (subjectively noticeable) effect on energy or productivity. I tried 4 spoonfuls at noon the next day; nothing except a little mental tension, for lack of a better word. I think that was just the harbinger of what my runny nose that day and the day before was, a head cold that laid me low during the evening.
5. 4 spoons of ‘Thai Red Vein’ at 1:30 PM; cold hasn’t gone away but the acetaminophen was making it bearable.
6. 4 spoons of ‘Enriched Thai’ (brown) at 8PM. Steeped 15 minutes, drank; no effect - I have to take a break to watch 3 Mobile Suit Gundam episodes before I even feel like working.
7. 5 spoons of ‘Enriched Sumatran’ (tannish-brown) at 3:10 PM; especially sludgy this time, the Sumatran powder must be finer than the other.
8. 4 spoons ‘Synergy’ (a “Premium Whole Leaf Blend”) at 11:20 AM; by 12:30 PM I feel quite tired and like I need to take a nap (previous night’s sleep was slightly above average, 96 ZQ).
9. 5 spoons ‘Essential Indo’ (olive green) at 1:50 PM; no apparent effect except perhaps some energy for writing (but then a vague headache).

At dose #9, I’ve decided to give up on kratom. It is possible that it is helping me in some way that careful testing (eg. dual n-back over weeks) would reveal, but I don’t have a strong belief that kratom would help me (I seem to benefit more from stimulants, and I’m not clear on how an opiate-bearer like kratom could stimulate me). So I have no reason to do careful testing. Oh well.

Hericium erinaceus (Examine.com) was recommended strongly by several on the ImmInst.org forums for its long-term benefits to learning, apparently linked to Nerve growth factor. Highly speculative stuff, and it’s unclear whether the mushroom powder I bought was the right form to take (ImmInst.org discussions seem to universally assume one is taking an alcohol or hotwater extract). It tasted nice, though, and I mixed it into my sleeping pills (which contain melatonin & tryptophan). I’ll probably never know whether the $30 for 0.5lb was well-spent or not.

(I was more than a little nonplussed when the mushroom seller included a little pamphlet educating one about how papaya leaves can cure cancer, and how I’m shortening my life by decades by not eating many raw fruits & vegetables. There were some studies cited, but usually for points disconnected from any actual curing or longevity-inducing results.)

Lithium (review; FDA adverse events) is a pretty unusual substance and like caffeine and the amphetamines, questionably classified as a nootropic. As a metal, lithium is dangerous at many doses. It’s famously used for manic-depression and some other disorders, but the doses are large and verge on the point where ‘the cure is worse than the disease’. Most lithium research focuses on these larger doses, so one has to parse citations carefully to see whether it is telling one something useful about the low levels one might use for supplements or just reinforcing what one already knew (‘large doses are double-edged swords’).

So, on the positive side:

• Discover’s “The Metal Marvel That Has Mended Brains for 50 Years” covers some of the neurogenesis effects of lithium (see also Shiotsuki 2008), which may be related to its possible antioxidant effect
• lithium has a connection to DHA and thus fish oil: “How lithium works in the brain”

• in mice, lithium helps prevent Parkinson’s

  There has been speculationabout anti-Alzheimer’s properties due to correlations, but an experimental null result in Hampel et al 2009.

  The results on dementia are also mixed: Terao et al 2006 found patients prescribed lithium had less, while Dunn et al 2005 had found the exact opposite.
• Tsaltas et al 2008 (see also Vo et al 2015), is a review of lithium studies. There may be some long-term benefits related to the neuroprotective effects, which counterbalance the negative or null effects discussed.

• natural drinking water levels have been correlated with

  • decreased crime^{[A closely-related Texas study found similar inverse correlations for mental hospitals, but I haven’t been able to find fulltext: “The relationship of tap water and physiological levels of lithium to mental hospital admission and homicide in Texas”, Dawson, in Lithium in Biology and Medicine 1991. An odd result is decreased lithium levels in autistic children and also their mothers, Adams et al 2006.]}[A mouse study found lithium reduced aggression in one mouse breed: “Effects of nutritional lithium deficiency on behavior in rats”, Klemfuss & Schirauzer 1995], & Giotakos et al 2015; but Dawson’s data was criticized as confounded with distance by Pokorny et al 1972, and lithium/suicide seems related to & possibly confounded by altitude (Helbich et al 2013 found lithium decreased with altitude in Austria and affected suicide rates; but Huber et al 2014 finds in the USA that lithium increases with altitude) and altitude itself has suggested to cause suicide/mental illness. Gonzalez et al 2008, as expected, found r=-0.35, but its sample (1 year of violent crime in 11 counties) was too small to make that correlation statistically-significant; the larger Servello 2008 and Blüml et al 2013 tried to correct for the criticisms & found a statistically-significant reduction in Texan suicides. This is especially interesting given reduced lithium in criminals These studies are reviewed in Mauer et al 2014, Vita et al 2014, & Mischley 2014 (media op-ed).

  • decreased suicides in Japan: Ohgami et al 2009 (see discussion in Terao et al 2009), Sugawara et al 2013 (criticized in Chandra & Babu 2009; see also Schöpfer & Schrauzer 2011’s finding of unmeasurably low lithium levels in many Tokyoites’ hair) & Ishii et al 2015& Shiotsuki et al 2016, along with, a decrease replicated in Austria by Kapusta et al 2011/Helbich et al 2012/Helbich et al 2015& in Greece, but not quite in England& Italy& possibly Portugal.

    This correlation would make sense given that lithium prevents suicide in patients with mood disorders
  • increased longevity
  • on the other hand, a 1975 survey of Maryland counties found no correlation between lithium & mood

• causes improved mood in former drug-users and visitors to Japanese springs (Shiotsuki 2008); a lithium-water experiment in 2012 was terminated for logistical reasons.

One of the main problems with inferring that lithium causes these reductions is that it seems difficult to reconcile with how large the doses must be to treat mental illness:

1. most dose-response relationships tend to have relatively simple curves which look like U-curves or V-curves or straight lines, where the effect diminishes fast when you move away from the best dose;
2. the psychiatricly-useful doses are something like 100x the higher groundwater doses;
3. so for most such curves, if the peak is at X mg, then a dose at X/100 or X/1000 mg will do little;
4. any effects in the population should be ~0, and thus nearly impossible to detect,
5. but the correlates are often found, and if causal, would be large reductions in crime/suicide/mental-illness rates / large effects in the population;
6. 4 & 5 seem to be contradictory.

The best responses seem to be that either lithium’s effects diminish quite gradually so that small groundwater doses can still have a meaningful population effect (negate #1), that groundwater doses are more effective than one would expect comparing to psychiatric doses of lithium carbonate (perhaps due to chronic lifelong exposure; negate relevance of #2/3), or that lithium may have multiple mechanisms one of which kicks in at psychiatric dose levels and the other at groundwater levels (somewhat supported by some psychiatric observations that depressives seem to benefit from lower doses but in different ways; negate #1 in a different way).

Ken Gillman, echoing the earlier criticisms of the Ohgami et al 2009 correlation by Chandra & Babu 2009, criticizes the correlations as generally invalid due to the smallness of the drinking water dose compared to the dietary doses of lithium; I disagree inasmuch as lithium doses are cumulative, Schrauzer 2002 reports an FDA estimate of daily American lithium consumption 1mg, points out that natural levels can reach as high as 0.34mg via drinking water, Dawson 1991 finds Texans’ lithium excretion to “vary inversely with rainfall, reflecting the dilution of drinking water supplies” (see also Dawson 1970), and Dawson et al 1972 directly compared lithium levels in county waters with urine lithium measurements and found a clean linear relationship as expected. These points suggest strongly that Gillman is wrong to think that consumption of bottled water or imported vegetables would swamp any contribution from drinking water - random noise cancels out, and small correlations can be detected using very large samples like state or nation level samples. (One commenter has suggested that the darkness caused by rain is what increases suicide rates, not the dilution of natural lithium in the drinking water; this seems unlikely as it would be inconsistent with the known peaking of suicide rates in spring when there is little darkness, and with the lack of correlation of life satisfaction with daily weather.)

The criticisms of the trace lithium correlation seem weak to me, and even keeping in mind the meta results that the overwhelming majority of correlations disappear when experimentally tested (a 1% chance is a reasonable guess at the true a priori odds), the potential benefits seem so overwhelming that I am puzzled that, in the 42+ years since the correlation was first noted, no one has done a simple experiment of randomizing some counties and increasing their trace lithium concentrations within the normal range of natural variations in trace lithium concentrations. A quick estimate for what I mean. In Dawson et al 1972, the counties in the highest lithium category had 30% of the mental hospital admissions that the lowest lithium counties did; the USA spends something like $60b annually on mental health issues (the NYT quotes $150b government expenditure & $500b society-wide costs). If the reduction in admissions was equivalent to a reduction in the underlying disorders and expenditures scale exactly with number of disorders, then lithizing the USA would reap gains of something like $35b annually (NPV at 5%: ~$615b); however, this is only the direct expenditures, arguably the field is underfunded (reportedly, less than half the sufferers of mental illness may be treated), and of course the losses to society is far larger than that as peoples’ lives are destroyed, crime increases (with its massive negative externalities), careers abandoned, etc. At 1% prior odds and $615b total payoff, the expected value is ~$600m; a conclusive experiment ought to be trivially cheap to run (a few millions?), since it requires only supplementation of lithium at centralized water facilities, presents minimal ethical concerns due to remaining strictly within natural variation & regulatory limits (and vastly less than people voluntarily consume in bottled mineral waters), and can be analyzed using only statistics already being collected by police or health departments.

(The potential gain is large enough that, even if one objected that we don’t know for sure that psychiatric lithium does not impede creativity and so it is hypothetically possible that lithization would reduce societal creativity, the benefit probably outweighs the costs. An example: suppose lithization cost us one Nikola Tesla a year; as it happens, an academic researcher can be funded for life with ~$4m (fully loaded cost: ~$200k I’ve read (some relevant figures), and a ~20y career - assuming no outside earnings or grants or payments), and using the previous savings of $30b, one could fully fund 7500 people each year to research and work on whatever they want for life; is it plausible that one potential Tesla outweighs 7500 independent effectively-tenured researchers?)

On the null and negative sides:

• although the lithium-Parkinson’s research with its relatively low doses is a reminder to avoid lithium doses anywhere near what is used for psychiatric disorders: “In at least two known cases, toxic levels of the drug have actually caused Parkinson’s.”
• In one small human trial (149 experimental, 447 total selected from >1000) on PatientsLikeMe investigating a 2008 paper’s finding that lithium might delay ALS, only the null effect was found.
• There are serious negative effects to taking a lot of lithium - 2-4 grams will trash your long-term memory and similar doses have been linked with many cognitive issues.
• Tsaltas et al 2008 says many studies can’t be generalized to healthy populations; for every study finding damage to performance or memory, there seems to be a study finding the null result. But whichever is true, it is not encouraging.
• Of 22 Alzheimer’s patients taking 100mg of lithium carbonate, 3 stopped due to side-effects (Macdonald et al 2008); but 100mg carbonate is substantially more than 5mg orotate, and one might guess that old ill people would report more side-effects in general
• Broberg et al 2011 documents extreme daily consumption of 2-30mg from water & food in Andean villages, correlating with changes in some thyroid-related biomarkers, suggestive of potential thyroid issues
• Aprahamian et al 2014 was an RCT of 61 patients with “mild cognitive impairment” given ~150mg of lithium carbonate (targeting blood level of 0.25-0.5 mmol/L); it found minimal damage to liver function, but a few worrisome secondary outcomes such as somewhat higher complaints of side-effects (4.07 vs 4.98 symptoms)

Lithium orotate is sold commercially in low-doses; I purchased 200 pills with 5mg of lithium each. To put this dosage in comparison, the therapeutic doses of lithium are roughly 100x higher (around 500mg). The pills are small and tasteless, and not at all hard to take.

lithium <-read.csv("lithium.csv")
lithium$Date <-as.Date(lithium$Date)rm(lithium$X)

mp <-read.csv("mp.csv")
mp$Date <-as.Date(mp$Date)

mnemosyne <-read.csv("gwern-mnemosyne.csv", header=FALSE,col.names =c("Timestamp", "Easiness", "Grade"),colClasses=c("integer",   "numeric",  "integer"))
mnemosyne$Date <-as.Date(as.POSIXct(mnemosyne$Timestamp, origin ="1970-01-01", tz ="EST"))
mnemosyne <-mnemosyne[mnemosyne$Date>as.Date("2012-05-11") &mnemosyne$Date<as.Date("2013-11-02"),]
mnemosyne <-aggregate(mnemosyne$Grade, by=list(mnemosyne$Date), FUN=function (x) { mean(as.vector(x));})colnames(mnemosyne) <-c("Date", "Mnemosyne.grade")

zeo <-read.csv("http://www.gwern.net/docs/zeo/gwern-zeodata.csv")
zeo$Sleep.Date <-as.Date(zeo$Sleep.Date, format="%m/%d/%Y")colnames(zeo)[1]  <- "Date"
zeo <-zeo[zeo$Date>as.Date("2012-05-11") &zeo$Date<as.Date("2013-11-02"),]
zeo <-zeo[,c(1:10, 23)]

zeo$Start.of.Night <-sapply(strsplit(as.character(zeo$Start.of.Night), " "), function(x) { x[[2]] })
## convert "06:45" to 24300
interval <-function(x) { if (!is.na(x)) { if (grepl(" s",x)) as.integer(sub(" s","",x))
                                           else { y <-unlist(strsplit(x, ":"));as.integer(y[[1]])*60 +as.integer(y[[2]]); }
                                                  }
                          else NA
                          }
zeo$Start.of.Night <-sapply(zeo$Start.of.Night, interval)
## the night 'wraps around' at ~800, so let's take 0-400 and add +800 to reconstruct 'late at night'
zeo[zeo$Start.of.Night<400,]$Start.of.Night <-(zeo[zeo$Start.of.Night<400,]$Start.of.Night +800)

arbtt1 <-read.csv("2012-2013-arbtt.csv")
arbtt2 <-read.csv("2013-2014-arbtt.csv")
arbtt <-rbind(arbtt1, arbtt2)
arbtt <-arbtt[as.Date(arbtt$Day)>=as.Date("2012-05-11") &as.Date(arbtt$Day)<=as.Date("2013-11-02"),]
## rename Day -> Date, delete Percentage
arbtt <-with(arbtt, data.frame(Date=Day, Tag=Tag, Time=Time))
## Convert time-lengths to second-counts: "0:16:40" to 1000 (seconds); "7:57:30" to 28650 (seconds) etc.
## We prefer units of seconds since arbtt has sub-minute resolution and not all categories
## will have a lot of time each day.
interval <-function(x) { if (!is.na(x)) { if (grepl(" s",x)) as.integer(sub(" s","",x))
                                           else { y <-unlist(strsplit(x, ":"));as.integer(y[[1]])*3600 +as.integer(y[[2]])*60 +as.integer(y[[3]]);
                                                 }
                                          }
                          else NA
                          }
arbtt$Time <-sapply(as.character(arbtt$Time), interval)library(reshape)
arbtt <-reshape(arbtt, v.names="Time", timevar="Tag", idvar="Date", direction="wide")
arbtt[is.na(arbtt)] <-0
arbtt$Date <-as.Date(arbtt$Date)

patches <-read.csv("patchlog.txt")
patches$Date <-as.Date(patches$Date)

## merge all the previous data into a single data-frame:
lithiumExperiment <-merge(merge(merge(merge(merge(lithium, mp), mnemosyne, all=TRUE),
                            patches, all=TRUE), arbtt, all=TRUE), zeo, all=TRUE)
## no patches recorded for a day == 0 patches that day
lithiumExperiment[is.na(lithiumExperiment$Gwern.net.patches),]$Gwern.net.patches  <-0
## NA=I didn't do SRS that day; but that is bad and should be penalized!
lithiumExperiment[is.na(lithiumExperiment$Mnemosyne.grade),]$Mnemosyne.grade  <-0


productivity <-lithiumExperiment[,c(3,5:22)]library(psych) ## for factor analysisnfactors(productivity)# VSS complexity 1 achieves a maximimum of 0.58  with  14  factors# VSS complexity 2 achieves a maximimum of 0.67  with  14  factors# The Velicer MAP achieves a minimum of 0.02  with  1  factors# Empirical BIC achieves a minimum of  -304.3  with  4  factors# Sample Size adjusted BIC achieves a minimum of  -97.84  with  7  factors## Statistics by number of factors#    vss1 vss2   map dof   chisq     prob sqresid  fit RMSEA    BIC SABIC complex  eChisq    eRMS# 1  0.16 0.00 0.016 152 1.3e+03 2.6e-190    20.4 0.16 0.122  389.4 871.9     1.0 2.1e+03 1.1e-01# 2  0.27 0.31 0.022 134 7.8e+02  1.9e-91    16.7 0.31 0.095  -65.2 360.1     1.3 1.1e+03 7.9e-02# 3  0.30 0.40 0.021 117 4.9e+02  5.2e-47    14.3 0.41 0.078 -247.2 124.2     1.6 7.0e+02 6.2e-02# 4  0.39 0.47 0.024 101 2.5e+02  4.1e-14    12.1 0.50 0.052 -389.8 -69.2     1.7 3.4e+02 4.3e-02# 5  0.39 0.51 0.028  86 1.9e+02  2.5e-10    11.2 0.54 0.049 -347.4 -74.4     1.7 2.4e+02 3.6e-02# 6  0.41 0.53 0.034  72 1.4e+02  7.9e-06    10.3 0.57 0.041 -317.3 -88.8     1.6 1.7e+02 3.1e-02# 7  0.44 0.54 0.041  59 8.6e+01  1.2e-02     9.6 0.60 0.030 -285.1 -97.8     1.8 1.1e+02 2.5e-02# 8  0.40 0.52 0.050  47 1.1e+02  1.4e-07     9.9 0.59 0.053 -181.2 -32.0     2.0 2.0e+02 3.3e-02# 9  0.48 0.57 0.063  36 4.6e+01  1.1e-01     8.3 0.66 0.024 -180.2 -65.9     1.7 6.0e+01 1.8e-02# 10 0.51 0.62 0.079  26 1.9e+01  8.3e-01     7.2 0.70 0.000 -144.6 -62.1     1.6 1.9e+01 1.0e-02# 11 0.52 0.62 0.098  17 1.4e+01  6.8e-01     6.7 0.72 0.000  -93.2 -39.3     1.7 1.5e+01 9.0e-03# 12 0.52 0.61 0.124   9 1.1e+01  3.1e-01     6.7 0.72 0.020  -46.1 -17.5     1.6 1.3e+01 8.3e-03# 13 0.48 0.61 0.163   2 4.9e+00  8.6e-02     6.3 0.74 0.053   -7.7  -1.3     1.8 6.2e+00 5.8e-03# 14 0.58 0.67 0.210  -4 7.5e-03       NA     4.9 0.80    NA     NA    NA     1.8 9.0e-03 2.2e-04# 15 0.56 0.64 0.293  -9 4.6e-06       NA     5.3 0.78    NA     NA    NA     2.0 6.1e-06 5.7e-06# 16 0.53 0.62 0.465 -13 8.7e-07       NA     5.5 0.77    NA     NA    NA     2.1 8.6e-07 2.2e-06# 17 0.51 0.61 0.540 -16 9.3e-12       NA     5.6 0.77    NA     NA    NA     2.1 1.1e-11 7.8e-09# 18 0.51 0.61 1.000 -18 7.0e-10       NA     5.6 0.77    NA     NA    NA     2.1 7.8e-10 6.5e-08# 19 0.51 0.61    NA -19 0.0e+00       NA     5.6 0.77    NA     NA    NA     2.1 6.2e-25 1.8e-15#    eCRMS   eBIC# 1  0.112 1107.9# 2  0.089  303.3# 3  0.075  -31.6# 4  0.055 -300.5# 5  0.050 -304.3# 6  0.047 -280.3# 7  0.042 -257.4# 8  0.062  -97.2# 9  0.039 -167.1# 10 0.026 -144.7# 11 0.028  -92.1# 12 0.036  -44.0# 13 0.054   -6.4# 14    NA     NA# 15    NA     NA# 16    NA     NA# 17    NA     NA# 18    NA     NA# 19    NA     NA

factorization <-fa(productivity, nfactors=4); factorization# Standardized loadings (pattern matrix) based upon correlation matrix#                     MR3   MR1   MR2   MR4     h2    u2 com# MP                 0.05  0.01 -0.02  0.34 0.1241 0.876 1.1# Gwern.net.patches -0.04  0.01  0.01  0.48 0.2241 0.776 1.0# Time.WWW           0.98 -0.04 -0.10  0.02 0.9778 0.022 1.0# Time.X             0.49  0.29  0.47 -0.03 0.5801 0.420 2.6# Time.IRC           0.35 -0.06 -0.14  0.16 0.1918 0.808 1.8# Time.Writing       0.04 -0.01  0.04  0.69 0.4752 0.525 1.0# Time.Stats         0.42 -0.10  0.30  0.01 0.2504 0.750 1.9# Time.PDF          -0.09 -0.05  0.98  0.00 0.9791 0.021 1.0# Time.Music         0.10 -0.10  0.02  0.03 0.0196 0.980 2.2# Time.Rec           0.03  0.99 -0.03 -0.02 0.9950 0.005 1.0# Time.SRS           0.06 -0.06  0.07  0.10 0.0209 0.979 3.4# Time.Sysadmin      0.22  0.13 -0.04  0.13 0.0953 0.905 2.4# Time.DNB          -0.04 -0.05 -0.06  0.07 0.0149 0.985 3.3# Time.Bitcoin       0.15 -0.07 -0.07 -0.04 0.0306 0.969 2.1# Time.Blackmarkets  0.18 -0.09 -0.08  0.02 0.0470 0.953 1.9# Time.Programming  -0.04  0.05 -0.04  0.43 0.1850 0.815 1.1# Time.Backups      -0.09  0.06 -0.01  0.04 0.0114 0.989 2.4# Time.Umineko      -0.16  0.71 -0.03  0.06 0.5000 0.500 1.1# Time.Typing       -0.03 -0.04  0.02 -0.01 0.0034 0.997 2.4##                        MR3  MR1  MR2  MR4# SS loadings           1.67 1.64 1.33 1.08# Proportion Var        0.09 0.09 0.07 0.06# Cumulative Var        0.09 0.17 0.24 0.30# Proportion Explained  0.29 0.29 0.23 0.19# Cumulative Proportion 0.29 0.58 0.81 1.00##  With factor correlations of#       MR3   MR1   MR2   MR4# MR3  1.00  0.12 -0.05  0.10# MR1  0.12  1.00  0.07 -0.08# MR2 -0.05  0.07  1.00 -0.08# MR4  0.10 -0.08 -0.08  1.00## Mean item complexity =  1.8# Test of the hypothesis that 4 factors are sufficient.## The degrees of freedom for the null model are 171# and the objective function was 3.08 with Chi Square of 1645# The degrees of freedom for the model are 101  and the objective function was  0.46## The root mean square of the residuals (RMSR) is  0.04# The df corrected root mean square of the residuals is  0.06## The harmonic number of observations is  538 with the empirical chi square  332.7  with prob <  1.6e-26# The total number of observations was  542  with MLE Chi Square =  246  with prob <  4.1e-14## Tucker Lewis Index of factoring reliability =  0.832# RMSEA index =  0.052  and the 90 % confidence intervals are  0.043 0.06# BIC =  -389.8# Fit based upon off diagonal values = 0.88# Measures of factor score adequacy#                                                 MR3  MR1  MR2  MR4# Correlation of scores with factors             0.99 1.00 0.99 0.79# Multiple R square of scores with factors       0.98 0.99 0.98 0.63# Minimum correlation of possible factor scores  0.95 0.99 0.96 0.25

## I interpret MR3=Internet+Stats usage; MR1=goofing off; MR2=reading/stats; MR4=writing
## I don't care about MR1, so we'll look for effects on 3/2/4:
lithiumExperiment$MR3 <-predict(factorization, data=productivity)[,1]
lithiumExperiment$MR2 <-predict(factorization, data=productivity)[,3]
lithiumExperiment$MR4 <-predict(factorization, data=productivity)[,4]write.csv(lithiumExperiment, file="2012-lithium-experiment.csv", row.names=FALSE)

lithiumExperiment <-read.csv("http://www.gwern.net/docs/lithium/2012-lithium-experiment.csv")
l1 <-lm(cbind(MP, Mnemosyne.grade, Gwern.net.patches, ZQ, MR3, MR2, MR4) ~Lithium, data=lithiumExperiment)summary(l1)# Response MP :## Coefficients:#             Estimate Std. Error t value Pr(>|t|)# (Intercept)   3.0613     0.0591    51.8   <2e-16# Lithium      -0.0425     0.0841    -0.5     0.61## Residual standard error: 0.755 on 320 degrees of freedom#   (220 observations deleted due to missingness)# Multiple R-squared:  0.000796,    Adjusted R-squared:  -0.00233# F-statistic: 0.255 on 1 and 320 DF,  p-value: 0.614### Response Mnemosyne.grade :## Coefficients:#             Estimate Std. Error t value Pr(>|t|)# (Intercept)    3.158      0.120   26.41   <2e-16# Lithium       -0.141      0.170   -0.83     0.41## Residual standard error: 1.53 on 320 degrees of freedom#   (220 observations deleted due to missingness)# Multiple R-squared:  0.00214, Adjusted R-squared:  -0.000975# F-statistic: 0.687 on 1 and 320 DF,  p-value: 0.408### Response Gwern.net.patches :## Coefficients:#             Estimate Std. Error t value Pr(>|t|)# (Intercept)   3.8712     0.3271   11.83   <2e-16# Lithium       0.0345     0.4655    0.07     0.94## Residual standard error: 4.18 on 320 degrees of freedom#   (220 observations deleted due to missingness)# Multiple R-squared:  1.72e-05,    Adjusted R-squared:  -0.00311# F-statistic: 0.00549 on 1 and 320 DF,  p-value: 0.941### Response ZQ :## Coefficients:#             Estimate Std. Error t value Pr(>|t|)# (Intercept)   91.773      1.024   89.66   <2e-16# Lithium        0.523      1.457    0.36     0.72## Residual standard error: 13.1 on 320 degrees of freedom#   (220 observations deleted due to missingness)# Multiple R-squared:  0.000402,    Adjusted R-squared:  -0.00272# F-statistic: 0.129 on 1 and 320 DF,  p-value: 0.72### Response MR3 :## Coefficients:#             Estimate Std. Error t value Pr(>|t|)# (Intercept)  -0.0258     0.0691   -0.37     0.71# Lithium       0.0657     0.0983    0.67     0.50## Residual standard error: 0.882 on 320 degrees of freedom#   (220 observations deleted due to missingness)# Multiple R-squared:  0.00139, Adjusted R-squared:  -0.00173# F-statistic: 0.447 on 1 and 320 DF,  p-value: 0.504### Response MR2 :## Coefficients:#             Estimate Std. Error t value Pr(>|t|)# (Intercept)   0.0187     0.0788    0.24     0.81# Lithium       0.0435     0.1121    0.39     0.70## Residual standard error: 1.01 on 320 degrees of freedom#   (220 observations deleted due to missingness)# Multiple R-squared:  0.00047, Adjusted R-squared:  -0.00265# F-statistic: 0.15 on 1 and 320 DF,  p-value: 0.698### Response MR4 :## Coefficients:#              Estimate Std. Error t value Pr(>|t|)# (Intercept)  0.000209   0.052772    0.00     1.00# Lithium     -0.073464   0.075099   -0.98     0.33## Residual standard error: 0.674 on 320 degrees of freedom#   (220 observations deleted due to missingness)# Multiple R-squared:  0.00298, Adjusted R-squared:  -0.000134# F-statistic: 0.957 on 1 and 320 DF,  p-value: 0.329summary(manova(l1))#            Df      Pillai  approx F num Df den Df  Pr(>F)# Lithium     1 0.009477169 0.4291862      7    314 0.88373# Residuals 320

An unusual intervention is infrared/near-infrared light of particular wavelengths (LLLT), theorized to assist mitochondrial respiration and yielding a variety of therapeutic benefits. Some have suggested it may have cognitive benefits. LLLT sounds strange but it’s simple, easy, cheap, and just plausible enough it might work. I tried out LLLT treatment on a sporadic basis 2013-2014, and statistically, usage correlated strongly & statistically-significantly with increases in my daily self-ratings, and not with any sleep disturbances. Excited by that result, I did a randomized self-experiment 2014-2015 with the same procedure, only to find that the causal effect was weak or non-existent. I have stopped using LLLT as likely not worth the inconvenience.

Low level laser therapy (LLLT) is a curious treatment based on the application of a few minutes of weak light in specific near-infrared wavelengths (the name is a bit of a misnomer as LEDs seem to be employed more these days, due to the laser aspect being unnecessary and LEDs much cheaper). Unlike most kinds of light therapy, it doesn’t seem to have anything to do with circadian rhythms or zeitgebers. Proponents claim efficacy in treating physical injuries, back pain, and numerous other ailments, recently extending it to case studies of mental issues like brain fog. (It’s applied to injured parts; for the brain, it’s typically applied to points on the skull like F₃ or F₄.) And LLLT is, naturally, completely safe without any side effects or risk of injury.

To say that this all sounds dubious would be an understatement. (My first reaction was that LLLT and lostfalco’s other proposals were probably the stupidest thing I’d seen all month.)

The research literature, while copious, is messy and varied: methodologies and devices vary substantially, sample sizes are tiny, the study designs vary from paper to paper, metrics are sometimes comically limited (one study measured speed of finishing a RAPM IQ test but not scores), blinding is rare and unclear how successful, etc. Relevant papers include Chung et al 2012, Rojas & Gonzalez-Lima 2013, & Gonzalez-Lima & Barrett 2014. Another Longecity user ran a self-experiment, with some design advice from me, where he performed a few cognitive tests over several periods of LLLT usage (the blocks turned out to be ABBA), using his father and towels to try to blind himself as to condition. I analyzed his data, and his scores did seem to improve, but his scores improved so much in the last part of the self-experiment I found myself dubious as to what was going on - possibly a failure of randomness given too few blocks and an temoral exogenous factor in the last quarter which was responsible for the improvement.

While the mechanism is largely unknown, one commonly mechanism possibility is that light of the relevant wavelengths is preferentially absorbed by the protein cytochrome c oxidase, which is a key protein in mitochondrial metabolism and production of ATP, substantially increasing output, and this extra output presumably can be useful for cellular activities like healing or higher performance.

I was contacted by the Longecity user lostfalco, and read through some of his writings on the topic. I had never heard of LLLT before, but the mitochondria mechanism didn’t sound impossible (although I wondered whether it made sense at a quantity level), and there was at least some research backing it; more importantly, lostfalco had discovered that devices for LLLT could be obtained as cheap as $15. (Clearly no one will be getting rich off LLLT or affiliate revenue any time soon.) Nor could I think of any way the LLLT could be easily harmful: there were no drugs involved, physical contact was unnecessary, power output was too low to directly damage through heating, and if it had no LLLT-style effect but some sort of circadian effect through hitting photoreceptors, using it in the morning wouldn’t seem to interfere with sleep.

Since LLLT was so cheap, seemed safe, was interesting, just trying it would involve minimal effort, and it would be a favor to lostfalco, I decided to try it. I purchased off eBay a $13 “48 LED illuminator light IR Infrared Night Vision+Power Supply For CCTV. Auto Power-On Sensor, only turn-on when the surrounding is dark. IR LED wavelength: 850nm. Powered by DC 12V 500mA adaptor.” It arrived in 4 days, on 7 September 2013. It fits handily in my palm. My cellphone camera verified it worked and emitted infrared - important because there’s no visible light at all (except in complete darkness I can make out a faint red light), no noise, no apparent heat (it took about 30 minutes before the lens or body warmed up noticeably when I left it on a table). This was good since I worried that there would be heat or noise which made blinding impossible; all I had to do was figure out how to randomly turn the power on and I could run blinded self-experiments with it.

My first time was relatively short: 10 minutes around the F₃/F₄ points, with another 5 minutes to the forehead. Awkward holding it up against one’s head, and I see why people talk of “LED helmets”, it’s boring waiting. No initial impressions except maybe feeling a bit mentally cloudy, but that goes away within 20 minutes of finishing when I took a nap outside in the sunlight. Lostfalco says “Expectations: You will be tired after the first time for 2 to 24 hours. It’s perfectly normal.”, but I’m not sure - my dog woke me up very early and disturbed my sleep, so maybe that’s why I felt suddenly tired. On the second day, I escalated to 30 minutes on the forehead, and tried an hour on my finger joints. No particular observations except less tiredness than before and perhaps less joint ache. Third day: skipped forehead stimulation, exclusively knee & ankle. Fourth day: forehead at various spots for 30 minutes; tiredness 5/6/7/8th day (11/12/13/4): skipped. Ninth: forehead, 20 minutes. No noticeable effects.

Pilot

At this point I began to get bored with it and the lack of apparent effects, so I began a pilot trial: I’d use the LED set for 10 minutes every few days before 2PM, record, and in a few months look for a correlation with my daily self-ratings of mood/productivity (for 2.5 years I’ve asked myself at the end of each day whether I did more, the usual, or less work done that day than average, so 2=below-average, 3=average, 4=above-average; it’s ad hoc, but in some factor analyses I’ve been playing with, it seems to load on a lot of other variables I’ve measured, so I think it’s meaningful).

On 15 March 2014, I disabled light sensor: the complete absence of subjective effects since the first sessions made me wonder if the LED device was even turning on - a little bit of ambient light seems to disable it thanks to the light sensor. So I stuffed the sensor full of putty, verified it was now always-on with the cellphone camera, and began again; this time it seemed to warm up much faster, making me wonder if all the previous sessions’ sense of warmth was simply heat from my hand holding the LEDs

In late July 2014, I was cleaning up my rooms and was tired of LLLT, so I decided to chuck the LED device. But before I did that, I might as well analyze the data.

That left me with 329 days of data. The results are that (correcting for the magnesium citrate self-experiment I was running during the time period which did not turn out too great) days on which I happened to use my LED device for LLLT were much better than regular days. Below is a graph showing the entire MP dataseries with LOESS-smoothed lines showing LLLT vs non-LLLT days:

LLLT pilot analysis

The correlation of LLLT usage with higher MP self-rating is fairly large (r=0.19 / d=0.455) and statistically-significant (p=0.0006).

I have no particularly compelling story for why this might be a correlation and not causation. It could be placebo, but I wasn’t expecting that. It could be selection effect (days on which I bothered to use the annoying LED set are better days) but then I’d expect the off-days to be below-average and compared to the 2 years of trendline before, there doesn’t seem like much of a fall.

The R code:

lllt <-read.csv("http://www.gwern.net/docs/nootropics/2014-08-03-lllt-correlation.csv")
l <-lm(MP ~LLLT +as.logical(Magnesium.citrate) +as.integer(Date) +as.logical(Magnesium.citrate):as.integer(Date),data=lllt); summary(l)# ...Coefficients:#                                                        Estimate   Std. Error  t value   Pr(>|t|)# (Intercept)                                         4.037702597  0.616058589  6.55409 5.0282e-10# LLLTTRUE                                            0.330923350  0.095939634  3.44929 0.00069087# as.logical(Magnesium.citrate)TRUE                   0.963379487  0.842463568  1.14353 0.25424378# as.integer(Date)                                   -0.001269089  0.000880949 -1.44059 0.15132856# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.001765953  0.001213804 -1.45489 0.147332120.330923350 /sd(lllt$MP, na.rm=TRUE)# [1] 0.455278787cor.test(lllt$MP, as.integer(lllt$LLLT))##   Pearson`s product-moment correlation## data:  lllt$MP and as.integer(lllt$LLLT)# t = 3.4043, df = 327, p-value = 0.0007458# alternative hypothesis: true correlation is not equal to 0# 95% confidence interval:#  0.0784517682 0.2873891665# sample estimates:#         cor# 0.185010342

## check whether there's odd about non-LLLT days by expanding to include baseline
llltImputed <-lllt
llltImputed[is.na(llltImputed)] <-0
llltImputed[llltImputed$MP ==0,]$MP <-3# clean up an outlier using mediansummary(lm(MP ~LLLT +as.logical(Magnesium.citrate) +as.integer(Date) +as.logical(Magnesium.citrate):as.integer(Date),data=llltImputed))# ...Coefficients:#                                                        Estimate   Std. Error  t value   Pr(>|t|)# (Intercept)                                         2.959172295  0.049016571 60.37085 < 2.22e-16# LLLT                                                0.336886970  0.083731179  4.02344 6.2212e-05# as.logical(Magnesium.citrate)TRUE                   2.155586397  0.619675529  3.47857 0.00052845# as.integer(Date)                                    0.000181441  0.000103582  1.75166 0.08017565# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.003373682  0.000904342 -3.73054 0.00020314power.t.test(power=0.8,delta=(0.336886970 /sd(lllt$MP, na.rm=TRUE)),type="paired",alternative="one.sided")##      Paired t test power calculation##               n = 30.1804294#           delta = 0.463483435#              sd = 1#       sig.level = 0.05#           power = 0.8#     alternative = one.sided## NOTE: n is number of *pairs*, sd is std.dev. of *differences* within pairslibrary(ggplot2)
llltImputed$Date <-as.Date(llltImputed$Date)ggplot(data = llltImputed, aes(x=Date, y=MP, col=as.logical(llltImputed$LLLT))) +geom_point(size=I(3)) +stat_smooth() +scale_colour_manual(values=c("gray49", "green"),name ="LLLT")

So, I have started a randomized experiment; should take 2 months, given the size of the correlation. If that turns out to be successful too, I’ll have to look into methods of blinding - for example, some sort of electronic doohickey which turns on randomly half the time and which records whether it’s on somewhere one can’t see. (Then for the experiment, one hooks up the LED, turns the doohickey ‘on’, and applies directly to forehead, checking the next morning to see whether it was really on or off).

Sleep

One reader notes that for her, the first weeks of LLLT usage seemed to be accompanied by sleeping longer than usual. Did I experience anything similar? There doesn’t appear to be any particular effect on total sleep or other sleep variables:

lllt <-read.csv("http://www.gwern.net/docs/nootropics/2014-08-03-lllt-correlation.csv")
zeo <-read.csv("http://www.gwern.net/docs/zeo/gwern-zeodata.csv")
lllt$Date <-as.Date(lllt$Date)
zeo$Date <-as.Date(zeo$Sleep.Date, format="%m/%d/%Y")

sleepLLLT <-merge(lllt, zeo, all=TRUE)
l <-lm(cbind(Start.of.Night, Time.to.Z, Time.in.Wake, Awakenings, Time.in.REM, Time.in.Light, Time.in.Deep, Total.Z, ZQ, Morning.Feel) ~LLLT, data=sleepLLLT)summary(manova(l))
##            Df     Pillai approx F num Df den Df  Pr(>F)
## LLLT        1 0.04853568 1.617066     10    317 0.10051
## Residuals 326library(ggplot2)qplot( sleepLLLT$Date, sleepLLLT$Total.Z, color=sleepLLLT$LLLT)

LLLT pilot factor analysis

Factor-analyzing several other personal datasets into 8 factors while omitting the previous MP variable, I find LLLT correlates with personal-productivity-related factors, but less convincingly than MP, suggesting the previous result is not quite as good as it seems.

My worry about the MP variable is that, plausible or not, it does seem relatively weak against manipulation; other variables I could look at, like arbtt window-tracking of how I spend my computer time, # or size of edits to my files, or spaced repetition performance, would be harder to manipulate. If it’s all due to MP, then if I remove the MP and LLLT variables, and summarize all the other variables with factor analysis into 2 or 3 variables, then I should see no increases in them when I put LLLT back in and look for a correlation between the factors & LLLT with a multivariate regression.

Preparation of data:

lllt <-read.csv("~/wiki/docs/nootropics/2014-08-03-lllt-correlation.csv",colClasses=c("Date",rep("integer", 4), "logical"))
lllt <-data.frame(Date=lllt$Date, LLLT=lllt$LLLT)
mp <-read.csv("~/selfexperiment/mp.csv", colClasses=c("Date", "integer"))
creativity <-read.csv("~/selfexperiment/dailytodo-marchjunecreativity.csv",colClasses=c("Date", "integer"))
mnemosyne <-read.csv("~/selfexperiment/mnemosyne.csv", header=FALSE,col.names =c("Timestamp", "Easiness", "Grade"),colClasses=c("integer",   "numeric",  "integer"))
mnemosyne$Timestamp <-as.POSIXct(mnemosyne$Timestamp, origin ="1970-01-01", tz ="EST")
mnemosyne$Date    <-as.Date(mnemosyne$Timestamp)
mnemosyne <-aggregate(Grade ~Date, mnemosyne, mean)
mnemosyne$Average.Spaced.repetition.score <-mnemosyne$Graderm(mnemosyne$Grade)

dnb <-read.csv("~/doc/brainworkshop/data/stats.txt", header=FALSE)
dnb$V1 <-as.POSIXct(dnb$V1, format="%Y-%m-%d %R:%S")
dnb <-dnb[!is.na(dnb$V1),]
dnb <-with(dnb, data.frame(Timestamp=V1, Nback.type=V2, Percentage=V3))
dnb$Date <-as.Date(dnb$Timestamp)
dnbDaily <-aggregate(Percentage ~Date +Nback.type, dnb, mean)

arbtt1 <-read.csv("~/selfexperiment/2012-2013-arbtt.txt")
arbtt2 <-read.csv("~/selfexperiment/2013-2014-arbtt.txt")
arbtt <-rbind(arbtt1, arbtt2)rm(arbtt$Percentage)
interval <-function(x) { if (!is.na(x)) { if (grepl(" s",x)) as.integer(sub(" s","",x))
                                          else { y <-unlist(strsplit(x, ":"));as.integer(y[[1]])*3600 +as.integer(y[[2]])*60 +as.integer(y[[3]]);
                                                }
                                          }
                         else NA
                         }
arbtt$Time <-sapply(as.character(arbtt$Time), interval)library(reshape)
arbtt <-reshape(arbtt, v.names="Time", timevar="Tag", idvar="Day", direction="wide")
arbtt$Date <-as.Date(arbtt$Day)rm(arbtt$Day)
arbtt[is.na(arbtt)] <-0

patches <-read.csv("~/selfexperiment/patchlog-gwern.net.txt", colClasses=c("integer", "Date"))
patches$Gwern.net.patches.log <-log1p(patches$Gwern.net.patches)# modified lines per day is much harder: state machine to sum lines until it hits the next date
patchCount <-scan(file="~/selfexperiment/patchlog-linecount-gwern.net.txt", character(), sep ="\n")
patchLines <-new.env()
for (i in 1:length(patchCount)) {
 if (grepl("\t", patchCount[i])) { patchLines[[date]] <-patchLines[[date]] +sum(sapply(strsplit(patchCount[i], "\t"), as.integer))
                                  }
  else { date <-patchCount[i]
   patchLines[[date]] <-0 }
   }
patchLines <-as.list(patchLines)
patchLines <-data.frame(Date =rep(names(patchLines), lapply(patchLines, length)),Gwern.net.linecount=unlist(patchLines))rm(row.names(patchLines))
patchLines$Date <-as.Date(patchLines$Date)
patchLines$Gwern.net.linecount.log <-log1p(patchLines$Gwern.net.linecount)

firstDay <-patches$Date[1]; lastDay <-patches$Date[nrow(patches)]
patches <-merge(merge(patchLines, patches, all=TRUE), data.frame(Date=seq(firstDay, lastDay, by="day")), all=TRUE)# if entries are missing, they == 0
patches[is.na(patches)] <-0# combine all the data:
llltData <-merge(merge(merge(merge(merge(lllt, mp, all=TRUE), creativity, all=TRUE), dnbDaily, all=TRUE), arbtt, all=TRUE), patches, all=TRUE)write.csv(llltData, file="2014-08-08-lllt-correlation-factoranalysis.csv", row.names=FALSE)

Factor analysis. The strategy: read in the data, drop unnecessary data, impute missing variables (data is too heterogeneous and collected starting at varying intervals to be clean), estimate how many factors would fit best, factor analyze, pick the ones which look like they match best my ideas of what ‘productive’ is, extract per-day estimates, and finally regress LLLT usage on the selected factors to look for increases.

lllt <-read.csv("http://www.gwern.net/docs/nootropics/2014-08-08-lllt-correlation-factoranalysis.csv")# the log transforms are more useful:rm(lllt$Date, lllt$Nback.type, lllt$Gwern.net.linecount, lllt$Gwern.net.patches)# https://stats.stackexchange.com/questions/28576/filling-nas-in-a-dataset-with-column-medians-in-r
imputeColumnAsMedian <-function(x){
   x[is.na(x)] <-median(x, na.rm=TRUE) #convert the item with NA to median value from the column
   x #display the column
}
llltI  <-data.frame(apply(lllt, 2, imputeColumnAsMedian))library(psych)nfactors(llltI[-c(1,2)])# VSS complexity 1 achieves a maximimum of 0.56  with  16  factors# VSS complexity 2 achieves a maximimum of 0.66  with  16  factors# The Velicer MAP achieves a minimum of 0.01  with  1  factors# Empirical BIC achieves a minimum of  -280.23  with  8  factors# Sample Size adjusted BIC achieves a minimum of  -135.77  with  9  factorsfa.parallel(llltI[-c(1,2)], n.iter=2000)# Parallel analysis suggests that the number of factors =  7  and the number of components =  7
## split the difference between sample-size adjusted BIC and parallel analysis with 8:
factorization <-fa(llltI[-c(1,2)], nfactors=8); factorization# Standardized loadings (pattern matrix) based upon correlation matrix#                           MR6   MR1   MR2   MR4   MR3   MR5   MR7   MR8     h2    u2 com# Creativity.self.rating   0.22  0.06 -0.04  0.08 -0.04  0.02 -0.05 -0.14 0.0658 0.934 2.5# Percentage              -0.05 -0.02  0.01  0.01  0.00 -0.42  0.02  0.02 0.1684 0.832 1.0# Time.X                  -0.04  0.11  0.04  0.88 -0.02  0.01  0.01  0.02 0.8282 0.172 1.0# Time.PDF                 0.02  0.99 -0.02  0.04  0.02  0.00 -0.01 -0.01 0.9950 0.005 1.0# Time.Stats              -0.10  0.21  0.12  0.16 -0.04  0.04  0.12  0.25 0.2310 0.769 4.3# Time.IRC                 0.01 -0.02  0.99  0.02  0.02  0.01  0.00 -0.01 0.9950 0.005 1.0# Time.Writing             0.01 -0.02  0.01  0.04 -0.01 -0.03  0.68  0.04 0.4720 0.528 1.0# Time.Rec                 0.20 -0.12 -0.06  0.42  0.62 -0.02 -0.07 -0.01 0.8501 0.150 2.2# Time.Music              -0.05  0.05  0.02  0.02 -0.04  0.22  0.02  0.13 0.0909 0.909 2.0# Time.SRS                -0.07  0.09  0.08  0.00  0.00  0.08  0.06  0.16 0.0702 0.930 3.6# Time.Sysadmin            0.05 -0.09 -0.04  0.15  0.07  0.01  0.14  0.42 0.2542 0.746 1.7# Time.Bitcoin             0.45  0.02  0.25 -0.07 -0.03 -0.09 -0.04  0.11 0.3581 0.642 1.9# Time.Backups             0.22  0.10 -0.08 -0.19  0.12  0.13  0.02  0.27 0.1809 0.819 4.3# Time.Blackmarkets        0.62 -0.01  0.06 -0.02 -0.09 -0.01 -0.04  0.15 0.4442 0.556 1.2# Time.Programming         0.06 -0.01 -0.01 -0.04  0.07  0.08  0.41 -0.07 0.1790 0.821 1.3# Time.DNB                -0.01 -0.01  0.02  0.01 -0.01  0.76 -0.01  0.00 0.5800 0.420 1.0# Time.Typing             -0.04  0.05  0.02  0.01 -0.02 -0.01  0.00 -0.01 0.0054 0.995 2.9# Time.Umineko            -0.10  0.08  0.06 -0.15  0.77 -0.01  0.03  0.02 0.5082 0.492 1.2# Gwern.net.linecount.log  0.65  0.03  0.00 -0.03  0.04  0.00  0.10 -0.13 0.4223 0.578 1.1# Gwern.net.patches.log    0.11  0.02 -0.01  0.02  0.00  0.06  0.29 -0.06 0.1001 0.900 1.5##                        MR6  MR1  MR2  MR4  MR3  MR5  MR7  MR8# SS loadings           1.24 1.12 1.13 1.13 1.05 0.86 0.80 0.48# Proportion Var        0.06 0.06 0.06 0.06 0.05 0.04 0.04 0.02# Cumulative Var        0.06 0.12 0.17 0.23 0.28 0.33 0.37 0.39# Proportion Explained  0.16 0.14 0.14 0.14 0.13 0.11 0.10 0.06# Cumulative Proportion 0.16 0.30 0.45 0.59 0.73 0.84 0.94 1.00##  With factor correlations of#       MR6   MR1   MR2   MR4   MR3   MR5   MR7  MR8# MR6  1.00 -0.13  0.26  0.15  0.22 -0.09  0.03 0.16# MR1 -0.13  1.00 -0.12  0.25 -0.05  0.06  0.12 0.11# MR2  0.26 -0.12  1.00  0.04 -0.04  0.10  0.20 0.19# MR4  0.15  0.25  0.04  1.00  0.32  0.01 -0.05 0.10# MR3  0.22 -0.05 -0.04  0.32  1.00 -0.04 -0.07 0.00# MR5 -0.09  0.06  0.10  0.01 -0.04  1.00  0.11 0.11# MR7  0.03  0.12  0.20 -0.05 -0.07  0.11  1.00 0.20# MR8  0.16  0.11  0.19  0.10  0.00  0.11  0.20 1.00## Mean item complexity =  1.9# Test of the hypothesis that 8 factors are sufficient.## The degrees of freedom for the null model are  190# and the objective function was  2.46 with Chi Square of  5344.68# The degrees of freedom for the model are 58  and the objective function was  0.07## The root mean square of the residuals (RMSR) is  0.02# The df corrected root mean square of the residuals is  0.03## The harmonic number of observations is  2178 with the empirical chi square  190.08  with prob <  5.9e-16# The total number of observations was  2178  with MLE Chi Square =  149.65  with prob <  4.9e-10## Tucker Lewis Index of factoring reliability =  0.942# RMSEA index =  0.027  and the 90 % confidence intervals are  0.022 0.032# BIC =  -296.15# Fit based upon off diagonal values = 0.98# Measures of factor score adequacy#                                                 MR6  MR1  MR2  MR4  MR3  MR5  MR7   MR8# Correlation of scores with factors             0.84 1.00 1.00 0.93 0.89 0.80 0.76  0.64# Multiple R square of scores with factors       0.70 0.99 0.99 0.86 0.79 0.63 0.58  0.41# Minimum correlation of possible factor scores  0.40 0.99 0.99 0.71 0.58 0.27 0.16 -0.19

The important factors seem to be: #1/MR6 (Creativity.self.rating, Time.Bitcoin, Time.Backups, Time.Blackmarkets, Gwern.net.linecount.log), #2/MR1 (Time.PDF, Time.Stats), #7/MR7 (Time.Writing, Time.Sysadmin, Time.Programming, Gwern.net.patches.log), and #8/MR8 (Time.States, Time.SRS, Time.Sysadmin, Time.Backups, Time.Blackmarkets). The rest seem to be time-wasting or reflect dual n-back/DNB usage (which is not relevant in the LLLT time period).

So we want to extract and look at factors #1/2/7/8 (MR6/1/7/8):

lllt$MR6 <-predict(factorization, data=llltI[-c(1,2)])[,1]
lllt$MR1 <-predict(factorization, data=llltI[-c(1,2)])[,2]
lllt$MR7 <-predict(factorization, data=llltI[-c(1,2)])[,7]
lllt$MR8 <-predict(factorization, data=llltI[-c(1,2)])[,8]
l <-lm(cbind(MR6, MR1, MR7, MR8) ~LLLT, data=lllt); summary(l)# Response MR6 :# Coefficients:#              Estimate Std. Error  t value Pr(>|t|)# (Intercept) 1.5307773  0.0736275 20.79085  < 2e-16# LLLTTRUE    0.1319675  0.1349040  0.97823  0.32868## Response MR1 :# Coefficients:#               Estimate Std. Error  t value  Pr(>|t|)# (Intercept) -0.1675241  0.0609841 -2.74701 0.0063473# LLLTTRUE     0.0317851  0.1117381  0.28446 0.7762378## Response MR7 :# Coefficients:#               Estimate Std. Error  t value Pr(>|t|)# (Intercept) -0.0924052  0.0709438 -1.30251 0.193658# LLLTTRUE     0.2556655  0.1299869  1.96686 0.050045## Response MR8 :# Coefficients:#              Estimate Std. Error t value Pr(>|t|)# (Intercept) 0.0741850  0.0687618 1.07887  0.28144# LLLTTRUE    0.1380131  0.1259889 1.09544  0.274130.2556655 /sd(lllt$MR7)# [1] 0.335510445summary(manova(l))#            Df     Pillai approx F num Df den Df  Pr(>F)# LLLT        1 0.01372527 1.127218      4    324 0.34355# Residuals 327

All of the coefficients are positive, as one would hope, and one specific factor (MR7) squeaks in at d=0.34 (p=0.05). The graph is much less impressive than the graph for just MP, suggesting that the correlation may be spread out over a lot of factors, the current dataset isn’t doing a good job of capturing the effect compared to the MP self-rating, or it really was a placebo effect:

library(ggplot2)
llltRecent$index <-1:nrow(llltRecent)qplot(index, MR7, color=LLLT, data=llltRecent) +geom_point(size=I(3)) +stat_smooth() +scale_colour_manual(values=c("gray49", "green"),name ="LLLT")

The concentration in one factor leaves me a bit dubious. We’ll see what the experiment turns up.

Experiment

A randomized non-blind self-experiment of LLLT 2014-2015 yields a causal effect which is several times smaller than a correlative analysis and non-statistically-significant/very weak Bayesian evidence for a positive effect. This suggests that the earlier result had been driven primarily by reverse causation, and that my LLLT usage has little or no benefits.

Following up on the promising but unrandomized pilot, I began randomizing my LLLT usage since I worried that more productive days were causing use rather than vice-versa. I began on 2 August 2014, and the last day was 3 March 2015 (n=167); this was twice the sample size I thought I needed, and I stopped, as before, as part of cleaning up (I wanted to know whether to get rid of it or not). The procedure was simple: by noon, I flipped a bit and either did or did not use my LED device; if I was distracted or didn’t get around to randomization by noon, I skipped the day. This was an unblinded experiment because finding a randomized on/off switch is tricky/expensive and it was easier to just start the experiment already. The question is simple too: controlling for the simultaneous blind magnesium experiment& my rare nicotine use (I did not use modafinil during this period or anything else I expect to have major influence), is the pilot correlation of d=0.455 on my daily self-ratings borne out by the experiment?

llltRandom <-read.csv("http://www.gwern.net/docs/nootropics/2015-lllt-random.csv",colClasses=c("Date", "logical", "integer", "logical", "logical"))# impute magnesium data: that randomized experiment started a month later
llltRandom[is.na(llltRandom$Magnesium.random),]$Magnesium.random <-0
l <-lm(MP ~LLLT.random +Nicotine +Magnesium.random, data=llltRandom); summary(l); confint(l)# ...Coefficients:#                    Estimate Std. Error  t value Pr(>|t|)# (Intercept)      3.28148626 0.06856553 47.85912  < 2e-16# LLLT.randomTRUE  0.04099628 0.09108322  0.45010  0.65324# NicotineTRUE     0.21152245 0.26673557  0.79300  0.42893# Magnesium.random 0.10299190 0.09312616  1.10594  0.27038## Residual standard error: 0.5809214 on 163 degrees of freedom#   (47 observations deleted due to missingness)# Multiple R-squared:  0.01519483,  Adjusted R-squared:  -0.002930415# F-statistic: 0.8383241 on 3 and 163 DF,  p-value: 0.474678##                           2.5 %       97.5 %# (Intercept)       3.14609507948 3.4168774481# LLLT.randomTRUE  -0.13885889747 0.2208514560# NicotineTRUE     -0.31518017129 0.7382250752# Magnesium.random -0.08089731164 0.28688110340.04099628 /sd(llltRandom$MP)# [1] 0.0701653002library(ggplot2)ggplot(data = llltRandom, aes(x=Date, y=MP, col=as.logical(llltRandom$LLLT.random))) +geom_point(size=I(3)) +stat_smooth() +scale_colour_manual(values=c("gray49", "blue"),name ="LLLT")

The estimate of the causal effect of LLLT+placebo is not statistically-significant, and the effect size of +0.04 / d=0.07 is much smaller than d=0.455 (15%) and the original pilot’s point estimate of +0.33 is excluded by the new confidence interval (95% CI: -0.13 - +0.22).

I have strong priors about the possible effects of LLLT, nicotine & magnesium (specifically, I know from experience that they tend to be small), so a Bayesian linear model using JAGS is useful for letting me take that into account and also producing more meaningful results (probabilities, rather than p-values):

# JAGS won't automatically drop rows with missing variables like `lm` does by default
llltClean <-llltRandom[!is.na(llltRandom$LLLT.random),]library(rjags)library(R2jags)
model1<-"model {    for (i in 1:n) {        MP[i] ~ dnorm(MP.hat[i], tau)        MP.hat[i] <- a + b1*LLLT.random[i] + b2*Nicotine[i] + b3*Magnesium.random[i]    }    # intercept    a  ~ dnorm(3, 4) # prec 4 ~= 0.5^-2 ~= SD 0.5, the historical SD of my MPs    # coefficients    ## informative prior: effects should be <0.5 usually, and >0.3 is unusual    b1 ~ dnorm(0, 13) # prec 13 ~= SD 0.3    b2 ~ dnorm(0, 13)    b3 ~ dnorm(0, 13)    # informative prior: 2-5 doesn't allow for much variance    sigma ~ dunif(0, 1)    # convert SD to 'precision' unit that JAGS's distributions use instead    tau <- pow(sigma, -2)}"
j1 <-with(llltClean, jags(data=list(n=nrow(llltClean), MP=MP, LLLT.random=LLLT.random,Nicotine=Nicotine, Magnesium.random=Magnesium.random),parameters.to.save=c("b1", "b2", "b3"),model.file=textConnection(model1),n.chains=getOption("mc.cores"), n.iter=1000000))print(j1, intervals=c(0.0001, 0.5, 0.9999))# Inference for Bugs model at "4", fit using jags,#  4 chains, each with 1e+06 iterations (first 5e+05 discarded), n.thin = 500#  n.sims = 4000 iterations saved#          mu.vect sd.vect   0.01%     50%  99.99%  Rhat n.eff# b1         0.042   0.087  -0.276   0.041   0.326 1.002  2100# b2         0.114   0.194  -0.533   0.114   0.745 1.001  4000# b3         0.100   0.088  -0.266   0.100   0.412 1.001  4000# deviance 293.023   2.864 288.567 292.420 314.947 1.001  4000## For each parameter, n.eff is a crude measure of effective sample size,# and Rhat is the potential scale reduction factor (at convergence, Rhat=1).## DIC info (using the rule, pD = var(deviance)/2)# pD = 4.1 and DIC = 297.1# DIC is an estimate of expected predictive error (lower deviance is better).

This analysis suggests that there’s a 95% probability the effect is somewhere between -0.129 & 0.208 (d=-0.22 - d=0.35), similar to the original linear model’s CI. More relevantly: there is only a 70% probability that the effect is >0 (albeit probably tiny), and >99.99% probability it’s not as big as the pilot data had claimed.

At small effects like d=0.07, a nontrivial chance of negative effects, and an unknown level of placebo effects (this was non-blinded, which could account for any residual effects), this strongly implies that LLLT is not doing anything for me worth bothering with. I was pretty skeptical of LLLT in the first place, and if 167 days can’t turn up anything noticeable, I don’t think I’ll be continuing with LLLT usage and will be giving away my LED set. (Should any experimental studies of LLLT for cognitive enhancement in healthy people surface with large quantitative effects - as opposed to a handful of qualitative case studies about brain-damaged people - and I decide to give LLLT another try, I can always just buy another set of LEDs: it’s only ~$15, after all.)

For the full writeup of background, methodology, data, and statistical analysis, please see the LSD microdosing page.

Intrigued by old scientific results & many positive anecdotes since, I experimented with “microdosing” LSD - taking doses ~10μg, far below the level at which it causes its famous effects. At this level, the anecdotes claim the usual broad spectrum of positive effects on mood, depression, ability to do work, etc. After researching the matter a bit, I discovered that as far as I could tell, since the original experiment in the 1960s, no one had ever done a blind or even a randomized self-experiment on it.

The self-experiment was simple: I ordered two tabs off Silk Road, dissolved one in distilled water, put the solution in one jar & tap water in the other, and took them in pairs of 3-day blocks.

The results of my pre-specified analysis on a well-powered randomized blind self-experiment:

1. Sleep:

2. Mnemosyne flashcard scores: none (p=0.52)
3. Mood/productivity: none (d=-0.18; p=0.86)

4. Creativity: none (d=-0.19; p=0.87)

I concluded that if anything, the LSD microdosing may done the opposite of what I wanted.

The metal magnesium (Examine.com), like potassium (which didn’t help me), plays many biological roles and has an RDA for me of 400mg which is higher than I likely get (most people apparently get less, with 68% of American adults <RDA; and while I frequently eat oats, milk, peanut butter, and whole-wheat bread, I don’t eat many leafy greens and my tap water is very soft). The anecdotes are the usual positive effects: general benefits, life improvements, higher affect; the interesting bits are the claims that magnesium is anxiolytic and affects sleep (positively, if you don’t mind the increase in dreaming, which makes me wonder if the benefits ascribed to float tanks might not be due to absorbing magnesium via the Epsom salts which provide the buoyancy).

Citrate

Encouraged by TruBrain’s magnesium & my magnesium l-threonate use, I design and run a blind random self-experiment to see whether magnesium citrate supplementation would improve my mood or productivity. I collected ~200 days of data at two dose levels. The analysis finds that the net effect was negative, but a more detailed look shows time-varying effects with a large initial benefit negated by an increasingly-negative effect. Combined with my expectations, the long half-life, and the higher-than-intended dosage, I infer that I overdosed on the magnesium. To verify this, I will be running a followup experiment with a much smaller dose.

The original magnesium l-threonate caused me no apparent problems by the time I finished off the powder and usage correlated with better days, further supporting the hypothesis that magnesium helps it. But l-threonate would be difficult to cap (and hence blind self-experiment) and is ruinously expensive on a per-dose basis. So I looked around for alternatives for the followup; one of the most common compounds suggested was the citrate form because it is reasonably well-absorbed and causes fewer digestive problems, so I could just take that. Magnesium oxide is widely available it looks cheap, but the absorption/bioavailabilty problem makes it unattractive: at a 3:5 ratio, an estimate of 4% absorption, a ZMA formulation of an impressive-sounding 500mg would be $500\times \frac{3}{5}\times 0.04=12$mg or a small fraction of RDAs for male adults like 400mg elemental. (Calcium shouldn’t be a problem since I get 220mg of calcium from my multivitamin and I enjoy dairy products daily.)

Finding a usable product on Amazon caused me some difficulties. I wanted a 500mg magnesium-citrate-only product at <$20 for 120 doses, but I discovered most of the selection for “magnesium citrate” had sub-500mg doses, involved calcium citrate or other substances like zinc (not necessarily a bad thing, but would confound an experiment), were mostly magnesium oxide rather than citrate, or some still other problem. Ultimately I settled on Solgar’s $13 120x400mg magnesium citrate as acceptable. (To compare with the bulkiness of the LEF vitamin D+l-threonate powder, the Office of Dietary Supplements says magnesium citrate is 16% magnesium, so to get 400mg of magnesium as claimed, would take 2.5g of material, rather than 7g for 200mg; even if l-threonate is absorbed 100% and citrate 50%, the citrate is ahead. The pills turn out to be wider and longer than my 00 pills; if I want to get them into my gel capsules, I have to crush them into fine powder. The powder from one pill turns out to take up 2 00 pills.)

My impression after the first two days (2 doses of 400mg each, one with breakfast & then lunch) was positive. I did not have the rumored digestion problems, and the first day went excellently: I was up until 1:30AM working and even then didn’t feel like going to bed - and I probably should have since I then slept abominably, which made the second day merely a good day. The third day I took none and it was an ordinary day. This is consistent with what I expected from the LEF l-threonate & TruBrain glycinate/lycinate, and so it is worth investigating with a self-experiment.

Experiment

The basic idea is to remedy a deficiency (not look for acute stimulant effects) and magnesium has a slow excretion rate, so week-long blocks seem appropriate. I can reuse the same methodology as the lithium self-experiment. The response variables will be the usual mood/productivity self-rating and, since I was originally interested in magnesium for possible sleep quality improvements, a standardized score of sleep latency + # of awakenings + time spent awake (the same variable as my potassium sleep experiment).

Since each 400mg pill takes up 2 00 pills, that’s 4 gel caps a day to reach 800mg magnesium citrate (ie. 136mg elemental magnesium), or 224 gel caps (2x120) for the first batch of Solgar magnesium pills. Turning the Solgar tablets into gel capsules was difficult enough that I switched to NOW Food’s 227g magnesium citrate powder for the second batch.

Power

Reusing the magnesium correlation from the first Noopept self-experiment and using the t-test as an approximation

pwr.t.test(d = (0.27 /sd(npt$MP)), power =0.8, type="paired", alternative="greater")# Paired t test power calculation## n = 50.61# ...

50 pairs of active/placebos or 100 days. With 120 tablets and 4 tablets used up, that leaves me 58 doses. That might seem adequate except the paired t-test approximation is overly-optimistic, and I also expect the non-randomized non-blinded correlation is too high which means that is overly-optimistic as well. The power would be lower than I’d prefer. I decided to simply order another bottle of Solgar’s & double the sample size to be safe.

Is 200 enough? There are no canned power functions for the ordinal logistic regression I would be using, so the standard advice is to estimate power by simulation: generating thousands of new datasets where we know by construction that the binary magnesium variable increases MP by 0.27 (such as by bootstrapping the original Noopept experiment’s data), and seeing how often in this collection the cutoff of statistical-significance is passed when the usual analysis is done (background: CrossValidated or “Power Analysis and Sample Size Estimation using Bootstrap”). In this case, we leave alpha at 0.05, reuse the Noopept experiment’s data with its Magtein correlation, and ask for the power when n=200

library(boot)library(rms)
npt <-read.csv("http://www.gwern.net/docs/nootropics/2013-gwern-noopept.csv")
n <-200
magteinPower <-function(dt, indices) {
    d <-dt[sample(nrow(dt), n, replace=TRUE), ] # new dataset, possibly larger than the original
    lmodel <-lrm(MP ~Noopept +Magtein, data = d)return(anova(lmodel)[8])
}
bs <-boot(data=npt, statistic=magteinPower, R=100000, parallel="multicore", ncpus=4)
alpha <-0.05print(sum(bs$t<=alpha)/length(bs$t))# [1] 0.7132

So the power will be ~71%.

Data

1. 27 August - 2 September: 0

   3 September - 9 September: 1

2. 10 September - 15 September: 0

   16 September - 21 September: 1

3. 22 September - 28 September: 0

   29 September - 5 October: 1

4. 6 October - 12 October: 0

13 October - 19 October: 1 5. 21 October - 27 October: 1

28 October - 2 November: 0

6. 5 November - 11 November: 1 (skipped 3/4 November)

   During 11 November, I accidentally unblinded myself while cleaning my room. Hence, I refilled the active jar and began a fresh pair of blocks.

7. 12 - 17 November: 0

   18 - 24 November: 1
8. 25 - 29 November: 0; on 30 November, I again unblinded myself and started over later.

9. 2 - 8 December: 1

   9 - 15 December: 0

10. 16 - 17 December: 0

    18 - 19 December: 1

    At this point, I discovered I had run out of magnesium pills and had forgotten to order the magnesium citrate powder I’d intended to. I still had a lot of Noopept pills for the concurrently running second Noopept self-experiment, but since I wanted to wrap up some other experiments with a big analysis at the end of the year, I decided to halt and resume in January 2014.

11. 25 - 31 January 2014: 0

    1 February - 7 February: 1

    For this batch, I tried out “NOW Foods Magnesium Citrate Powder” ($7 for 227g); the powder was still a bit sticky but much easier to work with than the Solgar pills, and the 227g made 249 gel capsule pills. The package estimates 119 serving of 315mg elemental magnesium, so a ratio of 0.315g magnesium for 1.9g magnesium citrate, implying that each gel cap pill then contains 0.152g magnesium ($\frac{(119\times 315)}{249}=150$) and since I want a total dose of 0.8g, I need 5 of the gel cap pills a day or 35 per block.

12. 9 - 15 February: 1 (skipped 8 February)

    16 - 22 February: 0

13. 23 - 1 March: 1

    2 - 8 March: 0

14. 9 - 15 March: 1

    16 - 22 March: 0

15. 23 - 29 March: 0

    30 March - 5 April: 1

16. 6 - 12 April: 0

    13 - 19 April: 1

17. 20 - 26 April: 0

    27 April - 3 May: 1

Subjectively, I have no particular comments, other than that (like the threonate), I noticed no diarrhea.

Analysis

Some prep:

magnesium <-read.csv("magnesium.csv")
mp <-read.csv("~/selfexperiment/mp.csv")
mp$MP <-as.integer(as.character(mp$MP))rm(magnesium$MP)
magnesium <-merge(mp, magnesium, all=TRUE)write.csv(magnesium, file="magnesium.csv", row.names=FALSE)

The basic question: did the magnesium citrate increase MP?

magnesium <-read.csv("http://www.gwern.net/docs/nootropics/2013-2014-magnesium.csv")summary(lm(MP ~Magnesium.citrate, data=magnesium))# Coefficients:#                    Estimate Std. Error t value Pr(>|t|)# (Intercept)        3.276515   0.056677   57.81   <2e-16# Magnesium.citrate -0.000543   0.000144   -3.79    2e-04## Residual standard error: 0.671 on 206 degrees of freedom#   (678 observations deleted due to missingness)# Multiple R-squared:  0.065,   Adjusted R-squared:  0.0605# F-statistic: 14.3 on 1 and 206 DF,  p-value: 0.000201mean(-0.000543 *c(136, 800))# [1] -0.2541

The initial results are a shock: the mean effect of the magnesium citrate comes in at almost the exact same magnitude (-0.25) as had been estimated for the Magtein back in the original Noopept analysis (0.26), except the estimated average effect is negative, as in, the magnesium citrate was harmful, and statistically-significantly so. Huh?

This was so unexpected that I wondered if I had somehow accidentally put the magnesium pills into the placebo pill baggie or had swapped values while typing up the data into a spreadsheet, and checked into that. The spreadsheet accorded with the log above, which rules out data entry mistakes; and looking over the log, I discovered that some earlier slip-ups were able to rule out the pill-swap: I had carelessly put in some placebo pills made using rice, in order to get rid of them, and that led to me being unblinded twice before I became irritated enough to pick them all out of the bag of placebos - but how could that happen if I had swapped the groups of pills?

So I began looking further into the data to see just what had happened (perhaps horrendous bad luck on a few days), and turned to a plot:

library(ggplot2)
magnesium$Date <-as.Date(magnesium$Date)with(magnesium[559:nrow(magnesium),],qplot(Date, MP, color=as.factor(Magnesium.citrate), legend="Magnesium citrate", size=I(7)) +scale_colour_manual(values=c("gray49", "red1", "red3"), name ="Magnesium"))

One thing I notice looking at the data is that the red magnesium-free days seem to dominate the upper ranks towards the end, and blues appear mostly at the bottom, although this is a little hard to see because good days in general start to become sparse towards the end. Now, why would days start to be worse towards the end, and magnesium-dose days in particular? The grim surmise is: an accumulating overdose - no immediate acute effect, but the magnesium builds up, dragging down all days, but especially magnesium-dose days. The generally recognized symptoms of hypermagnesemia don’t include effect on mood or cognition, aside from “muscle weakness, confusion, and decreased reflexes…poor appetite that does not improve”, but it seems plausible that below medically-recognizable levels of distress like hypermagnesemia might still cause mental changes, and I wouldn’t expect any psychological research to have been done on this topic.

A picture is worth a thousand words, particularly in this case where there seems to be temporal effects, different trends for the conditions, and general confusion. So, I drag up 2.5 years of MP data (for context), plot all the data, color by magnesium/non-magnesium, and fit different LOESS lines to each as a sort of smoothed average (since categorical data is hard to interpret as a bunch of dots), which yields:

magnesium[is.na(magnesium$Magnesium.citrate),]$Magnesium.citrate <--1ggplot(data = magnesium, aes(x=Date, y=MP, col=as.factor(magnesium$Magnesium.citrate))) +geom_point(size=I(4)) +stat_smooth() +scale_colour_manual(values=c("gray49", "grey35", "red1", "red3" ),name ="Magnesium")

That really says it all: there’s an initial spike in MP, which reads like the promised stimulative effects possibly due to fixing a deficiency (a spike which doesn’t seem to have any counterparts in the previous history of MP), followed by a drastic plunge in the magnesium days but not so much the control days (indicating an acute effect when overloaded with magnesium), a partial recovery during the non-experimental Christmas break, another plunge, and finally recovery after the experiment has ended.

We can verify the negative correlation with the date & an interaction between magnesium and the date; I don’t know whether to treat the magnesium dose as a linear, categorical, or logical, so I’ll try all of them:

magnesium <-read.csv("http://www.gwern.net/docs/nootropics/2013-2014-magnesium.csv")
magnesium$Date <-as.integer(magnesium$Date)
slm <-step(lm(MP ~Magnesium.citrate *as.logical(Magnesium.citrate) *as.factor(Magnesium.citrate) *Date *Noopept, data=magnesium))# MP ~ as.logical(Magnesium.citrate) + Date + as.logical(Magnesium.citrate):Datesummary(slm)# Coefficients:#                                         Estimate Std. Error t value Pr(>|t|)# (Intercept)                             3.942875   0.571166    6.90  6.3e-11# as.logical(Magnesium.citrate)TRUE       1.217860   0.806212    1.51    0.132# Date                                   -0.000992   0.000830   -1.20    0.233# as.logical(Magnesium.citrate)TRUE:Date -0.002105   0.001173   -1.79    0.074## Residual standard error: 0.664 on 204 degrees of freedom#   (678 observations deleted due to missingness)# Multiple R-squared:  0.0933,  Adjusted R-squared:  0.08# F-statistic:    7 on 3 and 204 DF,  p-value: 0.000167

As feared and consistent with the accumulating overdose hypothesis, scores decrease over time and they decrease if magnesium is used that day. (The interaction isn’t statistically-significant, but I am not surprised: I powered this self-experiment to detect one main effect, not two main effects and an interaction.)

But at least initially, the magnesium seemed to be remarkably useful. The crossover point, using this linear model, would have been somewhere around 20 days of the early small magnesium doses:

# Extract estimated MP for continuous small magnesium dosing, then for no magnesium dosing;# compare them pair-wise to see which is bigger, and count how many days favor magnesium dosing.with(magnesium[!is.na(magnesium$Magnesium.citrate),], sum(predict(slm, newdata=data.frame(Date=Date, Magnesium.citrate=136)) >predict(slm, newdata=data.frame(Date=Date, Magnesium.citrate=0))))
[1] 20

The final question is: since I was taking an overdose, how did I mess up? I thought I was making sure I got at least the right RDA of elemental magnesium by aiming for 800mg of elemental magnesium and carefully converting from raw powder weight. So I went back to the original references, and scrutinizing them closely, they really were talking about elemental magnesium and indicating I should be getting 400mg elemental a day, but I did notice something: I got the dose wrong for the Solgar pills, it wasn’t 800mg elemental, it was 800mg of citrate - I misread the label. So I went from taking ~130mg of elemental magnesium in the first period to ~800mg in the second; I don’t think it is an accident that the second period seems to have been much worse (between the plot and the time trend).

I find this very troubling. The magnesium supplementation was harmful enough to do a lot of cumulative damage over the months involved (I could have done a lot of writing September 2013 - June 2014), but not so blatantly harmful enough as to be noticeable without a randomized blind self-experiment or at least systematic data collection - neither of which are common among people who would be supplementing magnesium I would much prefer it if my magnesium overdose had come with visible harm (such as waking up in the middle of the night after a nightmare soaked in sweat), since then I’d know quickly and surely, as would anyone else taking magnesium. But the harm I observed in my data? For all I know, that could be affecting every user of magnesium supplements! How would we know otherwise?

Sleep

I reused the magnesium data with my Zeo data and looked at the effects. The result were ambiguous: only a few effects survive multiple correction, which were a mix of good and bad ones, and I would guess that some of the bad effects are due to too much magnesium (although there is no time trend as blatant).

Conclusion

The interpretation which seems to best resolve everything I know about magnesium with the data from my experiment is that magnesium supplementation does indeed help me a large amount, but I was taking way too much.

Experiment 2

This is not 100% clear from the data and just blindly using a plausible amount carries the risk of the negative effects, so I intend to run another large experiment. I will reuse the “NOW Foods Magnesium Citrate Powder”, but this time, I will use longer blocks (to make cumulative overdosing more evident) and try to avoid any doses >150mg of elemental magnesium.

I spent 2.5 hours making gel capsules:

• 10x24 Bisquick placebo
• 10x24 magnesium citrate
• 480 total

The powder totals 227g of magnesium citrate, hence there is ~0.945g per magnesium citrate pill. The nutritional information states that it contains 119 servings of 0.315g magnesium elemental = 37.485g elemental, as expected, and so likewise there is 0.156g elemental magnesium per pill. This is the same dosage as the second half of the first magnesium citrate experiment (249 gel capsules there, 240 here), where the overdose effect seemed to also happen; so to avoid the overdosage, I will take one pill every other day to halve the dose to an average of ~0.078g/78mg elemental per day (piggybacking on the morning-caffeine experiment to make compliance easier).

At 1 pill every other day, 14 doses, so pairs of 28-day blocks. The total time to use up all pills will then be ~960 days; this is a long time and excessively-powered, so I may stop early or possibly do a sequential analysis.

The benefit of sequential analysis here is being able to stop early, conserving pills, and letting me test another dosage: if I see another pattern of initial benefits followed by decline, I can then try cutting the dose by taking one pill every 3 days; or, if there is a benefit and no decline, then I can try tweaking the dose up a bit (maybe 3 days out of 5?). Since I don’t have a good idea what dose I want and the optimal dose seems like it could be valuable (and the wrong dose harmful!), I can’t afford to spend a lot of time on a single definitive experiment.

Power

Since I didn’t take any 78mg elemental doses and the effects were time-varying, it’s more difficult to estimate the expected effect-size and hence power.

If I assume that the coefficient of +1.22 for as.logical(Magnesium.citrate)TRUE’s effect on MP in the previous analysis represents the true causal effect of 0.156g elemental magnesium without any overdose involved and that magnesium would have a linear increase (up until overdose), then one might argue that optimistically 0.078 would cause an increase of ~0.61. Or one could eyeball the graph and note that the LOESS lines look like at the magnesium peak improved by <+0.5 over the long-run baseline of ~3 Then one could do a power estimate with those 2 estimates.

# _d_:0.61 /sd(magnesium$MP, na.rm=TRUE)# [1] 0.83795623power.t.test(n =480, delta =0.83)#  Two-sample t test power calculation##            n = 480#        delta = 0.83#           sd = 1#    sig.level = 011.05#        power = 1power.t.test(delta =0.83, power =0.9)# Two-sample t test power calculation##          n = 31.4970227#      delta = 0.83#         sd = 1#  sig.level = 0.05#      power = 0.90.5 /sd(magnesium$MP, na.rm=TRUE)# [1] 0.686849369power.t.test(delta =0.68, power =0.8)# Two-sample t test power calculation##          n = 34.9352817power.t.test(delta =0.5, power =0.8)# Two-sample t test power calculation##          n = 63.7657637

Power considerations suggest I could probably terminate after 4 months

Data

1. 29 August - 27 September 2014: 0

28 September - 2 November: 1 2. 3 November - 4 December: 0

5 December - 11 January 2015: 1 3. 12 January - 18 February: 0

19 February - 5 March: 1 (ended block early so I could have complete data for the LLLT experiment's analysis)

4. 6 March - 4 April: 1

   5 April - 10 May: 0

5. 11 May - 12 June: 0

   13 June - 15 July: 1

6. 16 July - 6 September : 1

   Thought I was done with both blocks so I unblinded myself, only to discover I wasn’t. Oops.

7. 7 September - 7 November: 1 (skipped over half a month due to long trip)

   8 November - 8 January 2016: 0

8. 4 March - 28 April: 0

   29 April: - 25 May: 1

9. 1 June - 28 June: 1

   29 June - 26 July: 0

10. 27 July - 24 August: ?

    27? August: ?

Analysis

Conclusion

See Melatonin for information on effects & cost; I regularly use melatonin to sleep (more to induce sleep than prolong or deepen it), and investigating with my Zeo, it does seem to improve & shorten my sleep. Some research suggests that higher doses are not necessarily better and may be overkill, so each time I’ve run out, I’ve been steadily decreasing the dose from 3mg to 1.5mg to 1mg, without apparently compromising the usefulness.

See Modafinil for background on performance improvements and side-effects; the following sections are about my usage.

Nerve growth factor is a protein involved in exactly what its name suggests. Administration may have effects on neurodegeneration, plasticity, and learning. Its co-discoverer, Nobelist Rita Levi-Montalcini, reportedly took NGF eyedrops daily.

NGF may sound intriguing, but the price is a dealbreaker: at suggested doses of 1-100μg (NGF dosing in humans for benefits is, shall we say, not an exact science), and a cost from sketchy suppliers of $1210/100μg/$470/500μg/$750/1000μg/$1000/1000μg/$1030/1000μg/$235/20μg. (Levi-Montalcini was presumably able to divert some of her lab’s production.) A year’s supply then would be comically expensive: at the lowest doses of 1-10μg using the cheapest sellers (for something one is dumping into one’s eyes?), it could cost anywhere up to $10,000.

As well, the possible effects seem like they would be long-term and difficult to measure or experiment on; so if one could somehow afford NGF eyedrops, one wouldn’t be able to know they were working.

So unless the price of NGF comes down by at least two orders of magnitude, it’s not a viable nootropic.

One of the most popular legal stimulants in the world, nicotine is often conflated with the harmful effects of tobacco; considered on its own, it has performance & possibly health benefits. Nicotine is widely available at moderate prices as long-acting nicotine patches, gums, lozenges, and suspended in water for vaping. While intended for smoking cessation, there is no reason one cannot use a nicotine patch or nicotine gum for its stimulant effects.

Nicotine’s stimulant effects are general and do not come with the same tweakiness and aggression associated with the amphetamines, and subjectively are much ‘cleaner’ with less of a crash. I would say that its stimulant effects are fairly strong, around that of modafinil. Another advantage is that nicotine operates through nicotinic receptors and so doesn’t cross-tolerate with dopaminergic stimulants (hence one could hypothetically cycle through nicotine, modafinil, amphetamines, and caffeine, hitting different receptors each time).

Like caffeine, nicotine tolerates rapidly and addiction can develop, after which the apparent performance boosts may only represent a return to baseline after withdrawal; so nicotine as a stimulant should be used judiciously, perhaps roughly as frequent as modafinil. Another problem is that nicotine has a half-life of merely 1-2 hours, making regular dosing a requirement. There is also some elevated heart-rate/blood-pressure often associated with nicotine, which may be a concern. (Possible alternatives to nicotine include cytisine, 2’-methylnicotine, GTS-21, galantamine, Varenicline, WAY-317,538, EVP-6124, and Wellbutrin, but none have emerged as clearly superior.)

I decided to try it out myself since it would be both boring and hypocritical not to. The stimulant properties are well-established, and after reading up, I didn’t think there was a >3% chance it might lead me to any short or long-term future cigarette use.

So I ordered the most cost-effective batch of chewing gum I could find on Amazon (100 Nicorette 4mg) - and the seller canceled on me! Poor show, “Direct Super center”, very poor show.

In August 2011, after winning the spaced repetition contest and finishing up the Adderall double-blind testing, I decided the time was right to try nicotine again. I had since learned that e-cigarettes use nicotine dissolved in water, and that nicotine-water was a vastly cheaper source of nicotine than either gum or patches. So I ordered 250ml of water at 12mg/ml (total cost: $18.20). A cigarette apparently delivers around 1mg of nicotine, so half a ml would be a solid dose of nicotine, making that ~500 doses. Plenty to experiment with. The question is, besides the stimulant effect, nicotine also causes ‘habit formation’; what habits should I reinforce with nicotine? Exercise, and spaced repetition seem like 2 good targets.

Nicotine gum

So I eventually got around to ordering another thing of nicotine gum, “Habitrol Nicotine Gum, 4mg MINT flavor COATED gum. 96 pieces per box”. Gum should be easier to double-blind myself with than nicotine patches - just buy some mint gum. If 4mg is too much, cut the gum in half or whatever. When it arrived, my hopes were borne out: the gum was rectangular and soft, which made it easy to cut into fourths.

Remembering what Wedrifid told me, I decided to start with a quarter of a piece (~1mg). The gum was pretty tasteless, which ought to make blinding easier. The effects were noticeable around 10 minutes - greater energy verging on jitteriness, much faster typing, and apparent general quickening of thought. Like a more pleasant caffeine. While testing my typing speed in Amphetype, my speed seemed to go up >=5 WPM, even after the time penalties for correcting the increased mistakes; I also did twice the usual number without feeling especially tired. A second dose was similar, and the third dose was at 10 PM before playing Ninja Gaiden II seemed to stop the usual exhaustion I feel after playing through a level or so. (It’s a tough game, which I have yet to master like Ninja Gaiden Black.) Returning to the previous concern about sleep problems, though I went to bed at 11:45 PM, it still took 28 minutes to fall sleep (compared to my more usual 10-20 minute range); the next day I use 2mg from 7-8PM while driving, going to bed at midnight, where my sleep latency is a more reasonable 14 minutes. I then skipped for 3 days to see whether any cravings would pop up (they didn’t). I subsequently used 1mg every few days for driving or Ninja Gaiden II, and while there were no cravings or other side-effects, the stimulation definitely seemed to get weaker - benefits seemed to still exist, but I could no longer describe any considerable energy or jitteriness.

The easiest way to use 2mg was to use half a gum; I tried not chewing it but just holding it in my cheek. The first night I tried, this seemed to work well for motivation; I knocked off a few long-standing to-do items. Subsequently, I began using it for writing, where it has been similarly useful. One difficult night, I wound up using the other half (for a total of 4mg over ~5 hours), and it worked but gave me a fairly mild headache and a faint sensation of nausea; these may have been due to forgetting to eat dinner, but this still indicates 3mg should probably be my personal ceiling until and unless tolerance to lower doses sets in.

Experiment

Design

Blinding stymied me for a few months since the nasty taste was unmistakable and I couldn’t think of any gums with a similar flavor to serve as placebo. (The nasty taste does not seem to be due to the nicotine despite what one might expect; Vaniver plausibly suggested the bad taste might be intended to prevent over-consumption, but nothing in the Habitrol ingredient list seemed to be noted for its bad taste, and a number of ingredients were sweetening sugars of various sorts. So I couldn’t simply flavor some gum.)

I almost resigned myself to buying patches to cut (and let the nicotine evaporate) and hope they would still stick on well enough afterwards to be indistinguishable from a fresh patch, when late one sleepless night I realized that a piece of nicotine gum hanging around on my desktop for a week proved useless when I tried it, and that was the answer: if nicotine evaporates from patches, then it must evaporate from gum as well, and if gum does evaporate, then to make a perfect placebo all I had to do was cut some gum into proper sizes and let the pieces sit out for a while. (A while later, I lost a piece of gum overnight and consumed the full 4mg to no subjective effect.) Google searches led to nothing indicating I might be fooling myself, and suggested that evaporation started within minutes in patches and a patch was useless within a day. Just a day is pushing it (who knows how much is left in a useless patch?), so I decided to build in a very large safety factor and let the gum sit for around a month rather than a single day.

The experiment then is straightforward: cut up a fresh piece of gum, randomly select from it and an equivalent ‘dry’ piece of gum, and do 5 rounds of dual n-back to test attention/energy & WM. (If it turns out to be placebo, I’ll immediately use the remaining active dose: no sense in wasting gum, and this will test whether nigh-daily use renders nicotine gum useless, similar to how caffeine may be useless if taken daily. If there’s 3 pieces of active gum left, then I wrap it very tightly in Saran wrap which is sticky and air-tight.) The dose will be 1mg or 1/4 a gum. I cut up a dozen pieces into 4 pieces for 48 doses and set them out to dry. Per the previous power analyses, 48 groups of DNB rounds likely will be enough for detecting small-medium effects (partly since we will be only looking at one metric - average % right per 5 rounds - with no need for multiple correction). Analysis will be one-tailed, since we’re looking for whether there is a clear performance improvement and hence a reason to keep using nicotine gum (rather than whether nicotine gum might be harmful).

Cost-wise, the gum itself (~$5) is an irrelevant sunk cost and the DNB something I ought to be doing anyway. If the results are negative (which I’ll define as d<0.2), I may well drop nicotine entirely since I have no reason to expect other forms (patches) or higher doses (2mg+) to create new benefits. This would save me an annual expense of ~$40 with a net present value of <$820($$); even if we count the time-value of the 20 minutes for the 5 DNB rounds over 48 days ($0.2\times 48\times 7.25=70$), it’s still a clear profit to run a convincing experiment.

Data

Analysis

First, we’ll check the prediction score (versus a random guesser scoring 0; higher is better):

logBinaryScore = sum . map (\(result,p) ->if result then1+ logBase 2 p else1+ logBase 2 (1-p))
logBinaryScore [(True,0.35),(False,0.40),(False,0.40),(True,0.60),(True,0.35),(False,0.45),(False,0.50),
                (True,0.60),(False,0.30),(True,0.50),(False,0.40),(False,0.30),(False,0.25),(False,0.75),
                (False,0.40),(False,0.40),(False,0.65),(False,0.45),(True,0.50),(False,0.65),(True,0.40),
                (True,0.55),(True,0.40),(False,0.50),(False,0.60),(True,0.40),(False,0.50),(False,0.50),
                (False,0.55),(True,0.55),(False,0.50),(False,0.55),(False,0.45),(True,0.55),(True,0.50),
                (True,0.50),(False,0.55),(True,0.50)]~>-0.58

Ouch, so my guesses were actually worse than random; this isn’t encouraging (if nicotine was helpful, why didn’t I notice? Has 1mg tolerated?) but it does indicate the blinding was successful.

Now we will examine the actual performance. Extracting the individual rounds scores from my Brain Workshop log file, we can average them in groups of 5 to get a daily average; then feed them into BEST (Bayesian equivalent of t-test; see Kruschke 2012):

# individual rounds; the imbalance is unfortunate but the experiment design means nothing can be done
on <-c(36,36,25,27,38,50,34,62,33,22,40,28,37,50,25,42,44,58,47,55,38,35,43,60,47,44,40,33,44,19,58,38,41,52,41,33,47,45,45,55,45,27,35,45,30,30,52,36,28,43,50,27,29,55,45,31,15,47,64,35,33,60,38,28,60,45,64,50,44,38,35,61,56,30,44,41,37,41,43,38)
off <-c(25,34,30,40,57,34,41,51,36,26,37,42,40,45,31,24,38,40,47,35,31,27,66,25,17,43,46,50,36,38,58,50,23,50,31,38,33,66,30,68,42,40,29,69,45,60,37,22,28,40,41,45,37,18,50,20,41,42,47,44,60,31,46,46,55,47,42,35,40,29,47,56,37,50,20,31,42,53,27,45,50,65,33,33,33,40,47,41,25,55,40,31,30,45,50,20,25,30,70,47,47,42,40,35,45,60,37,22,38,36,54,64,25,28,50,42,31,50,30,30)
on2 <-rowMeans(as.data.frame(matrix(on,ncol=5,byrow=TRUE)))
off2 <-rowMeans(as.data.frame(matrix(off,ncol=5,byrow=TRUE)))
on2
 [1] 32.440.236.049.244.636.046.045.036.437.841.238.443.848.245.2
[16] 40.0
off2
 [1] 37.237.639.036.833.242.642.447.045.037.438.238.847.638.642.0
[16] 39.642.841.639.238.441.838.644.236.6source("BEST.R")
mcmc =BESTmcmc(on2, off2); postInfo
           SUMMARY.INFO
PARAMETER         mean     median       mode     HDIlow   HDIhigh pcgtZero
  mu1       41.280812941.281920841.227263638.507812944.032699NA
  mu2       40.198108740.195554340.177703938.681080641.706469NA
  muDiff     1.08270421.08378311.1279921 -2.02924324.24490975.87121
  sigma1     5.25636745.08983544.77686813.33074937.511054NA
  sigma2     3.55137963.48509023.34533792.46550244.782887NA
  sigmaDiff  1.70498791.59178391.3816030 -0.65238174.30069293.36015
  nu        37.794819329.321798913.06643362.275571198.116623NA
  nuLog10    1.44724791.46719061.52044740.76049632.101837NA
  effSz      0.24600610.24500740.2361248 -0.43999590.93657075.87121

The results graphed:

We can read off the results from the table or graph: the nicotine days average 1.1% higher, for an effect size of 0.24; however, the 95% credible interval (equivalent of confidence interval) goes all the way from 0.93 to -0.44, so we cannot exclude 0 effect and certainly not claim confidence the effect size must be >0.1. Specifically, the analysis gives a 66% chance that the effect size is >0.1. (One might wonder if any increase is due purely to a “training effect” - getting better at DNB. Probably not.)

This is disappointing.

One curious thing that leaps out looking at the graphs is that the estimated underlying standard deviations differ: the nicotine days have a strikingly large standard deviation, indicating greater variability in scores - both higher and lower, since the means weren’t very different. The difference in standard deviations is just 6.6% below 0, so the difference almost reaches our usual frequentist levels of confidence too, which we can verify by testing:

var.test(on2, off2, alternative="greater")

    F test to compare two variances

data:on2 and off2
F =1.9823, num df =15, denom df =23, p-value =0.06775
alternative hypothesis:true ratio of variances is greater than 195% confidence interval:0.9314525Inf
sample estimates:
ratio of variances1.982333

We can double-check this by seeing what the variance is for the unaveraged scores: we know the means are only 1.1% different, so the additional standard deviation must be coming from how individual days are good or bad, and if that is so, then unaveraging them out to eliminate most of the observed difference. We re-run BEST:

mcmc =BESTmcmc(on,off); postInfo
           SUMMARY.INFO
PARAMETER          mean      median        mode     HDIlow     HDIhigh pcgtZero
  mu1       41.2270365741.2258227641.1157679238.759167043.7209215NA
  mu2       40.1238608340.1223544940.0458534037.965570342.3037602NA
  muDiff     1.103175741.103020231.13446641 -2.15206804.424601374.52276
  sigma1    10.9196624210.8660305210.741581359.133589712.7962565NA
  sigma2    11.6948420511.6611199011.5756001710.105088513.3605913NA
  sigmaDiff -0.77517964 -0.79214849 -0.85774274 -3.17896801.625253525.70744
  nu        46.8625878238.6527868522.910666685.8159908109.9850644NA
  nuLog10    1.579721511.587180811.608109921.02141822.1234248NA
  effSz      0.097785450.097638230.09931263 -0.18958820.390715674.52276

We see the standard deviation difference go away - now the difference estimate is almost centered on zero with a just 75% estimate the standard deviation differs in the observed direction. And to repeat the frequentist test:

var.test(on, off, alternative="greater")

    F test to compare two variances

data:on and off
F =0.8564, num df =79, denom df =119, p-value =0.7689
alternative hypothesis:true ratio of variances is greater than 195% confidence interval:0.6140736Inf
sample estimates:
ratio of variances0.856387

(So our p-value there went from 0.06 to 0.769 when we disaggregated the days, consistent with the Bayesian results.)

Good days and bad days?

The greatly increased variance, but only somewhat increased mean, is consistent with nicotine operating on me with an inverted U-curve for dosage/performance (or the Yerkes-Dodson law): on good days, 1mg nicotine is too much and degrades performance (perhaps I am overstimulated and find it hard to focus on something as boring as n-back) while on bad days, nicotine is just right and improves n-back performance.

This would be easy to test if I had done something before taking the nicotine gum; then I would simply see if pre-gum scores were higher than post-gum scores on nicotine days, but equal on placebo days. Unfortunately, I didn’t.

The closest data I have is my daily log of productivity/mood (1-5). If nicotine scores are higher than placebo scores on bad days (1-2) and lower on good days (3-4), then I think that would be consistent with an inverted U-curve.

nicotine <-read.table(stdin(),header=TRUE)
day      active mp score201208241335.2201208270537.2201208280337.6201208301337.75201208311237.75201209020236.0201209050536.0201209061537.25201209100549.2201209111336.8201209120344.6201209130538.4201209150543.8201209160239.6201209180349.6201209190438.4201209230536.2201209240545.4201209251343.8201209260436.4201209291343.8201209301336.0201210011346.0201210020445.0201210080234.6201210091345.2201210120537.8201210130437.2201210160440.2201210201339.0201210210341.2201210220342.2201210240540.4201210291241.4201210311338.4201211011543.8201211020348.2201211031540.6summary(nicotine)
      day               active             mp            score
 Min.   :20120824   Min.   :0.0000   Min.   :2.000   Min.   :34.60
 1st Qu.:20120911   1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:37.21
 Median :20120926   Median :0.0000   Median :3.000   Median :39.30
 Mean   :20120954   Mean   :0.3947   Mean   :3.632   Mean   :40.47
 3rd Qu.:20121015   3rd Qu.:1.0000   3rd Qu.:5.000   3rd Qu.:43.80
 Max.   :20121103   Max.   :1.0000   Max.   :5.000   Max.   :49.60cor(nicotine)
              day      active          mp       score
day                0.053319680.074371660.32021554
active                        -0.27754064 -0.05727501
mp                                         0.05238032

Interesting. On days ranked ‘2’ (below-average mood/productivity), nicotine seems to have boosted scores; on days ranked ‘3’, nicotine hurts scores; there aren’t enough ‘4’s to tell, but even ’5’ days seem to see a boost from nicotine, which is not predicted by the theory. But I don’t think much of a conclusion can be drawn: not enough data to make out any simple relationship. Some modeling suggests no relationship in this data either (although also no difference in standard deviations, leading me to wonder if I screwed up the data recording - not all of the DNB scores seem to match the input data in the previous analysis). So although the ‘2’ days in the graph are striking, the theory may not be right.

Conclusion

What should I make of all these results?

• The poor prediction performance, while confirming my belief that my novel strategy for blinding nicotine gum worked well, undermines confidence in the value of nicotine.
• I specified at the beginning that I wanted an effect size of >0.2; I got it, but with it came a very wide credible interval, undermining confidence in the effect size.

• The difference in standard deviations is not, from a theoretical perspective, all that strange a phenomenon: at the very beginning of this page, I covered some basic principles of nootropics and mentioned how many stimulants or supplements follow a inverted U-curve where too much or too little lead to poorer performance (ironically, one of the examples in Kruschke 2012 was a smart drug which did not affect means but increased standard deviations).

  If this is the case, this suggests some thoughtfulness about my use of nicotine: there are times when use of nicotine will not be helpful, but times where it will be helpful. I don’t know what makes the difference, but I can guess it relates to over-stimulation: on some nights during the experiment, I had difficult concentrating on n-backing because it was boring and I was thinking about the other things I was interested in or working on - in retrospect, I wonder if those instances were nicotine nights.

In retrospect, there were 2 parts of the experiment design I probably should have changed:

1. I used 1mg gum, rather than 2mg

   1mg may have too small effects to easily detect, and I may have developed tolerance to 1mg even though I’ve been careful to space out all my gum use. 2mg would have reduced this concern.

2. I used 1mg each day regardless of the randomization

This was to make each day more consistent and avoid wasting a sliced piece of gum (due to evaporation, it’s use-it-or-lose-it). But this plausibly is a source of tolerance, and even #1 was not an issue when the self-experiment began, this could have become an issue.

All things considered, I will probably continue using nicotine gum sparingly.

Related to the famous -racetams but reportedly better (and much less bulky), Noopept is one of the many obscure Russian nootropics. (Further reading: Google Scholar, Examine.com, Reddit, Longecity, Bluelight.ru.) Its advantages seem to be that it’s far more compact than piracetam and doesn’t taste awful so it’s easier to store and consume; doesn’t have the cloud hanging over it that piracetam does due to the FDA letters, so it’s easy to purchase through normal channels; is cheap on a per-dose basis; and it has fans claiming it is better than piracetam.

A Redditor ordered some Russian brand Noopept, but finding it was unpleasant & not working for him, gave the left-over half to me:

It appeared in reasonably good shape, and closely matched the photographs in the Wikipedia article. I took 2 of the 25 10mg pills on successive days on top of my usual caffeine+piracetam stack, and didn’t notice anything; in particular, I didn’t find it unpleasant like he did.

pwr.t.test(n=23, type="paired", alternative="greater", sig.level=0.05, power=0.8)

     Paired t test power calculation

              n =23
              d =0.5352
...

npt <-read.csv("http://www.gwern.net/docs/nootropics/2013-gwern-noopept.csv")wilcox.test(MP ~Noopept, alternative="less", data = npt)##     Wilcoxon rank sum test with continuity correction## data:  MP by Noopept# W = 343, p-value = 0.2607summary(lm(MP ~Noopept +Magtein, data = npt))# ...Coefficients:#             Estimate Std. Error t value Pr(>|t|)# (Intercept)   2.8038     0.1556   18.02   <2e-16# Noopept       0.0886     0.2098    0.42     0.67# Magtein       0.2673     0.2070    1.29     0.20## Residual standard error: 0.761 on 53 degrees of freedom# Multiple R-squared:  0.0379,    Adjusted R-squared:  0.00164# F-statistic: 1.05 on 2 and 53 DF,  p-value: 0.359

library(rms)
npt$MP <-as.ordered(npt$MP)
lmodel <-lrm(MP ~Noopept +Magtein, data = npt); lmodel# ...#         Coef    S.E.   Wald Z Pr(>|Z|)# y>=3     0.4330 0.4049  1.07  0.2849# y>=4    -1.4625 0.4524 -3.23  0.0012# Noopept  0.2336 0.5114  0.46  0.6479# Magtein  0.6748 0.5098  1.32  0.1856

set.seed(3333)library(boot)
noopeptPower <-function(dt, indices) {
    d <-dt[indices,] # bootstrap's _n_ = original _n_
    lmodel <-lrm(MP ~Noopept +Magtein, data = d)return(anova(lmodel)[7]) # _p_-value for the Noopept coefficient
}
bs <-boot(data=npt, statistic=noopeptPower, R=100000, parallel="multicore", ncpus=4)
alpha <-0.05print(sum(bs$t<=alpha) /length(bs$t))# [1] 0.073

Noopept followup experiment

Noopept is a Russian stimulant sometimes suggested for nootropics use as it may be more effective than piracetam or other -racetams, and its smaller doses make it more convenient & possibly safer. Following up on a pilot study, I ran a well-powered blind randomized self-experiment between September 2013 and August 2014 using doses of 12-60mg Noopept & pairs of 3-day blocks to investigate the impact of Noopept on self-ratings of daily functioning in addition to my existing supplementation regimen involving small-to-moderate doses of piracetam. A linear regression, which included other concurrent experiments as covariates & used multiple imputation for missing data, indicates a small benefit to the lower dose levels and harm from the highest 60mg dose level, but no dose nor Noopept as a whole was statistically-significant. It seems Noopept’s effects are too subtle to easily notice if they exist, but if one uses it, one should probably avoid 60mg+.

Design

In avoiding experimenting with more Russian Noopept pills and using instead the easily-purchased powder form of Noopept, there are two opposing considerations: Russian Noopept is reportedly the best, so we might expect anything I buy online to be weaker or impure or inferior somehow and the effect size smaller than in the pilot experiment; but by buying my own supply & using powder I can double or triple the dose to 20mg or 30mg (to compensate for the original under-dosing of 10mg) and so the effect size larger than in the pilot experiment.

As it happened, Health Supplement Wholesalers (since renamedPowder City) offered me a sample of their products, including their 5g Noopept powder ($13). I’d never used HSW before & they had some issues in the past; but I haven’t seen any recent complaints, so I was willing to try them. My 5g from batch #130830 arrived quickly (photos: packaging, powder contents). I tried some (tastes just slightly unpleasant, like an ultra-weak piracetam), and I set about capping the fluffy white flour-like powder with the hilariously tiny scoop they provide.

It took 4 hours to cap 432 Noopept pills and another 432 flour pills. I tried to allocate the Noopept as evenly as possible (3 little scoops per pill) which the HSW packaging suggested would be 10-30mg; running out after 432 implies I managed to get ~12mg into each ($\frac{5000}{432}=11.6$). At 2 pills a day, the experiment will run under a year.

I don’t want to synchronize with the magnesium or lithium experiments, so I’ll use paired blocks of 3 days randomized 50:50, which will help with the reported tolerance of Noopept setting in after a few days and one needing to ‘cycle’.

To make things more interesting, I think I would like to try randomizing different dosages as well: 12mg, 24mg, and 36mg (1-3 pills); on 5 May 2014, because I wanted to finish up the experiment earlier, I decided to add 2 larger doses of 48 & 60mg (4-5 pills) as options. Then I can include the previous pilot study as 10mg doses, and regress over dose amount.

During this time period, I generally refrained from using any nicotine (I wound up using it only 3x in the experimental period) or modafinil (0x) to avoid adding variation to results. I did use magnesium citrate & LLLT (discussed later). Finally, I was taking a stack like this:

1. 1mg melatonin at bedtime
2. 5000IU vitamin D & multivitamin at morning; an iron supplement every 3 days
3. from 25 March to 18 September 2014, ~5g of creatine monohydrate per day
4. a few times a day, taking a custom gel pill which in total supplies ~1g piracetam & 200mg caffeine

Power

I’ll first assume the effect size is the same. Using the usual alpha, we can find the necessary sample size by a slight variation on the magnesium bootstrap power calculation. Since the 56 days gave a power of 7% while we want closer to 80%, we probably want to start our power estimation much higher, with n in the 300s:

library(boot)library(rms)
npt <-read.csv("http://www.gwern.net/docs/nootropics/2013-gwern-noopept.csv")
newNoopeptPower <-function(dt, indices) {
    d <-dt[sample(nrow(dt), n, replace=TRUE), ] # new dataset, possibly larger than the original
    lmodel <-lrm(MP ~Noopept +Magtein, data = d)return(anova(lmodel)[7])
}
alpha <-0.05
for (n in seq(from =300, to =600, by =30)) {
   bs <-boot(data=npt, statistic=newNoopeptPower, R=10000, parallel="multicore", ncpus=4)print(c(n, sum(bs$t<=alpha)/length(bs$t)))
}# 0.18/0.19/0.21/0.21/0.23/0.25/0.26/0.28/0.29/0.32/0.32

Even at n=600 (nearly 2 years), the estimated power is only 32%. This is absurdly small and such an experiment would be a waste of time.

Suppose we were optimistic and we doubled the effect from 0.23 to 0.47 (this can be done by editing the first two Noopept rows and incrementing the MP variable by 1), and then looked again at power? At n=300, power has reached 60%, and by n=530, we have hit the desired 80%.

npt[1,2] <-npt[1,2] +1
npt[2,2] <-npt[2,2] +1
n <-530
bs <-boot(data=npt, statistic=newNoopeptPower, R=100000, parallel="multicore", ncpus=4)print(c(n, sum(bs$t<=alpha)/length(bs$t)))
[1] 530.00000.8241

530 is more acceptable, albeit I am worried about doubling the effect.

Data

1. 20mg: 15 September - 17 September: 0

   18 - 20 September: 1

2. 30mg: 21 September - 23? September: 1

   24 - 26: 0

3. 20mg: 27 - 29 September: 0

   30 - 2 October: 1

4. 10mg: 3 - 5 October: 1

   6 - 8 October: 0

5. 30mg: 9 - 11 October: 1

   12 - 14 October: 0

6. 10mg: 15 - 17 October: 1

   18 - 20 October: 0

7. 30mg: 22 - 24 October: 1

   25 - 27 October: 0

8. 10mg: 28 - 30 October: 1

   31 - 2 November: 0

9. 30mg: 4 - 6 November: 1

   7 - 9 November: 0

10. 20mg: 11 - 13 Nov: 0

    14 - 16 Nov: 1

11. 30mg: 20 - 22 November: 1

    23 - 25 November: 0

12. 20mg: 26 - 28 November: 1

    29 - 1 December: 0

13. 30mg: 2 - 4 December: 1

    5 - 7 December: 0

14. 10mg: 8 - 10 December: 1

    11 - 13 December: 0

15. 30mg: 14 - 16 December: 0

    17 - 19 December: 1

16. 20mg: 20 - 22 December: 0

    27 - 29 December: 1

17. 10mg: 1 - 3 January 2014: 1

    4 - 6 January 2014: 0

18. 30mg: 7 - 9 January: 1

    10 - 12 January: 0

19. 10mg: 13 - 15 January: 1

    16 - 17 January: 0

20. 20mg: 18 - 20 January: 0

    21 - 23 January: 1

21. 30mg: 25 - 27 January: 0

    28 - 30 January: 1

22. 10mg: 31 January - 2 February: 1

    3 - 5 February: 0

23. 30mg: 8 - 10 February: 1

    11 - 13 February: 1

24. 10mg: 14 - 16 February: 0

    17 - 19 February: 1

25. 30mg: 20 - 22 February: 1

    22 - 25 February: 0

26. 20mg: 26 - 28 February: 0

    1 March - 3 March: 1

27. 10mg: 4 March - 6 March: 1

    7 March - 9 March: 0
28. 30mg: 10 - 11 March: 0; accidentally unblinded & restarted on the 12th (a spare rice placebo, which is visibly different from the flour/Noopept capsules, was mixed in)

29. 30mg: 12 - 14 March : 1

    15 - 17 March: 0

30. 20mg: 18 - 20 March: 0

    21 - 23 March: 1

31. 10mg: 24 - 26 March: 1

    27 - 29 March: 0

32. 20mg: 30 - 1 April: 0

    2 - 4 April: 1

33. 10mg: 5 - 7 April: 1

    8 - 10 April: 0

34. 30mg: 11 - 13 April: 1

    14 - 16 April: 0

35. 20mg: 17 - 19 April: 0

    20 - 22 April: 1

36. 10mg: 23 - 25 April: 1

    26 - 28 April: 0

37. 30mg: 29 - 1 May: 1

    2 - 4 May: 0

38. 48mg: 5 - 7 May: 0

    8 - 10 May: 1

39. 60mg: 11 - 13 May: 0

    14 - 17 May: 1

40. 20mg: 18 - 20 May: 0

    21 - 23 May: 1

41. 48mg: 24 - 26 May: 1

    27 - 29 May: 0

42. 60mg: 30 - 1 June: 0

    2 - 4 June: 1

43. 30mg: 5 - 7 June: 1

    8 - 10 June: 0

44. 5x: 11 - 13 June: 1

    14 - 16 June: 0

45. 3x: 17 - 19 June: 0

    20 - 22 June: 1

46. 5x: 23 - 25 June: 0

    26 - 28 June: 1

47. 4x: 29 June - 1 July: 1

    2 - 4 July: 0

48. 3x: 5 - 7 July: 1

    8 - 9 July: 0

49. 5x: 10 - 12 July: 1

    13 - 15 July: 0

50. 3x: 16 - 18 July: 0

    19 - 21 July: 1

51. 4x: 23 - 25 July: 0

    26 - 28 July: 1

52. 5x: 29 - 31 July: 0

    1 - 3 August: 1

53. 3x: 4 - 6 August: 1

    7 - 9 August: 0

54. 3x: 10 - 12 August: 0

    13 - 15 August: 1

55. 3x: 16 - 18 August: 0

    19 - 21 August: 1

56. 2x: 23 - 25 August: 0

    26 - 28 August: 1

Analysis

Analyzing the results is a little tricky because I was simultaneously running the first magnesium citrate self-experiment, which turned out to cause a quite complex result which looks like a gradually-accumulating overdose negating an initial benefit for net harm, and also toying with LLLT, which turned out to have a strong correlation with benefits. So for the potential small Noopept effect to not be swamped, I need to include those in the analysis. I designed the experiment to try to find the best dose level, so I want to look at an average Noopept effect but also the estimated effect at each dose size in case some are negative (especially in the case of 5-pills/60mg); I included the pilot experiment data as 10mg doses since they were also blind & randomized. Finally, ‘missingness’ affects analysis: because not every variable is recorded for each date (what was the value of the variable for the blind randomized magnesium citrate before and after I finished that experiment? what value do you assign the Magtein variable before I bought it and after I used it all up?), just running a linear regression may not work exactly as one expects as various days get omitted because part of the data was missing.

noopeptSecond <-read.csv("http://www.gwern.net/docs/nootropics/2013-2014-gwern-noopept.csv", colClasses=c("Date","integer","integer","integer","logical"))
l <-lm(MP ~Noopept +LLLT +as.logical(Magnesium.citrate) +as.integer(Date) +as.logical(Magnesium.citrate):as.integer(Date),data=noopeptSecond)summary(l)# Coefficients:#                                                        Estimate   Std. Error  t value   Pr(>|t|)# (Intercept)                                        24.254373177 14.252905125  1.70171 0.09043607# Noopept                                             0.002069507  0.003937337  0.52561 0.59976836# LLLTTRUE                                            0.330112028  0.096133360  3.43390 0.00072963# as.logical(Magnesium.citrate)TRUE                  27.058060337 19.655569654  1.37661 0.17024431# as.integer(Date)                                   -0.001313316  0.000886616 -1.48127 0.14018300# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.001699162  0.001222719 -1.38966 0.16625033## Residual standard error: 0.640741 on 191 degrees of freedom#   (731 observations deleted due to missingness)# Multiple R-squared:  0.154383,    Adjusted R-squared:  0.132246# F-statistic: 6.97411 on 5 and 191 DF,  p-value: 5.23897e-06

As expected since most of the data overlaps with the previous LLLT analysis, the LLLT variable correlates strongly; the individual magnesium variables may look a little more questionable but were justified in the magnesium citrate analysis. The Noopept result looks a little surprising - almost zero effect? Let’s split by dose (which was the point of the whole rigmarole of changing dose levels):

l2 <-lm(MP ~as.factor(Noopept) +LLLT +as.logical(Magnesium.citrate) +as.integer(Date) +as.logical(Magnesium.citrate):as.integer(Date),data=noopeptSecond)summary(l2)# Coefficients:#                                                        Estimate   Std. Error  t value   Pr(>|t|)# (Intercept)                                        27.044709119 14.677995235  1.84253 0.06697191# as.factor(Noopept)10                                0.099920147  0.139287051  0.71737 0.47403711# as.factor(Noopept)15                                0.526389063  0.297940313  1.76676 0.07889108# as.factor(Noopept)20                                0.114943375  0.147994400  0.77667 0.43832733# as.factor(Noopept)30                                0.019029776  0.125504996  0.15163 0.87964479# LLLTTRUE                                            0.329976497  0.096071943  3.43468 0.00072993# as.logical(Magnesium.citrate)TRUE                  25.615810606 20.397271406  1.25584 0.21073068# as.integer(Date)                                   -0.001488184  0.000913563 -1.62899 0.10499001# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.001610059  0.001269219 -1.26854 0.20617256## Residual standard error: 0.639823 on 188 degrees of freedom#   (731 observations deleted due to missingness)# Multiple R-squared:  0.170047,    Adjusted R-squared:  0.13473# F-statistic: 4.81487 on 8 and 188 DF,  p-value: 2.08804e-05

This looks interesting: the Noopept effect is positive for all the dose levels, but it looks like a U-curve - low at 10mg, high at 15mg, lower at 20mg, and even lower at 30mg 48mg and 60mg aren’t estimated because they are hit by the missingness problem: the magnesium citrate variable is unavailable for the days the higher doses were taken on, and so their days are omitted and those levels of the factor are not estimated. One way to fix this is to drop magnesium from the model entirely, at the cost of fitting the data much more poorly and losing a lot of R²:

l3 <-lm(MP ~as.factor(Noopept) +LLLT, data=noopeptSecond)summary(l3)# Coefficients:#                        Estimate Std. Error  t value   Pr(>|t|)# (Intercept)           3.0564075  0.0578283 52.85318 < 2.22e-16# as.factor(Noopept)10  0.1079878  0.1255354  0.86022 0.39031118# as.factor(Noopept)15  0.1835389  0.2848069  0.64443 0.51975512# as.factor(Noopept)20  0.1314225  0.1301826  1.00952 0.31348347# as.factor(Noopept)30  0.0125616  0.1091561  0.11508 0.90845401# as.factor(Noopept)48  0.2302323  0.2050326  1.12291 0.26231647# as.factor(Noopept)60 -0.1714377  0.1794377 -0.95542 0.34008626# LLLTTRUE              0.2801608  0.0829625  3.37696 0.00082304## Residual standard error: 0.685953 on 321 degrees of freedom#   (599 observations deleted due to missingness)# Multiple R-squared:  0.0468695,   Adjusted R-squared:  0.0260848# F-statistic: 2.25499 on 7 and 321 DF,  p-value: 0.0297924

This doesn’t fit the U-curve so well: while 60mg is substantially negative as one would extrapolate from 30mg being ~0, 48mg is actually better than 15mg. But we bought the estimates of 48mg/60mg at a steep price - we ignore the influence of magnesium which we know influences the data a great deal. And the higher doses were added towards the end, so may be influenced by the magnesium starting/stopping. Another fix for the missingness is to “impute” the missing data. In this case, we might argue that the placebo days of the magnesium experiment were identical to taking no magnesium at all and so we can classify each NA as a placebo day, and rerun the desired analysis:

noopeptImputed <-noopeptSecond
noopeptImputed[is.na(noopeptImputed$Magnesium.citrate),]$Magnesium.citrate <-0
li <-lm(MP ~as.factor(Noopept) +LLLT +as.logical(Magnesium.citrate) +as.integer(Date) +as.logical(Magnesium.citrate):as.integer(Date),data=noopeptImputed)summary(li)# Coefficients:#                                                        Estimate   Std. Error  t value  Pr(>|t|)# (Intercept)                                        10.430818153  8.189365582  1.27370 0.2036989# as.factor(Noopept)10                                0.049595514  0.122841008  0.40374 0.6866772# as.factor(Noopept)15                                0.405925320  0.281291053  1.44308 0.1499824# as.factor(Noopept)20                                0.088343999  0.127014107  0.69554 0.4872219# as.factor(Noopept)30                                0.029464990  0.106375169  0.27699 0.7819668# as.factor(Noopept)48                                0.190340419  0.207736878  0.91626 0.3602263# as.factor(Noopept)60                               -0.210638501  0.184357630 -1.14255 0.2540834# LLLTTRUE                                            0.286295998  0.081098102  3.53024 0.0004765# as.logical(Magnesium.citrate)TRUE                  42.273941799 16.288481089  2.59533 0.0098882# as.integer(Date)                                   -0.000451814  0.000507568 -0.89015 0.3740561# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.002647546  0.001012691 -2.61437 0.0093648## Residual standard error: 0.666405 on 318 degrees of freedom#   (599 observations deleted due to missingness)# Multiple R-squared:  0.108827,    Adjusted R-squared:  0.0808031# F-statistic: 3.88332 on 10 and 318 DF,  p-value: 5.4512e-05

The 48mg/60mg coefficients shift downwards as expected. If we plot the coefficients with arm’s coefplot(), and one squints, the confidence intervals/point-values for Noopept look sort of consistent with a U-curve. What if we switch to a quadratic term to try to turn the Noopept values into a curve?

li2 <-lm(MP ~Noopept +I(Noopept^2) +LLLT +as.logical(Magnesium.citrate) +as.integer(Date) +as.logical(Magnesium.citrate):as.integer(Date),data=noopeptImputed)summary(li2)# Coefficients:#                                                        Estimate   Std. Error  t value   Pr(>|t|)# (Intercept)                                         9.172594278  8.112803113  1.13063 0.25905147# Noopept                                             0.008079500  0.006074315  1.33011 0.18442378# I(Noopept^2)                                       -0.000178179  0.000122736 -1.45172 0.14755366# LLLTTRUE                                            0.284419402  0.080959896  3.51309 0.00050627# as.logical(Magnesium.citrate)TRUE                  41.589054331 16.141539488  2.57652 0.01042501# as.integer(Date)                                   -0.000373812  0.000502850 -0.74339 0.45778931# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.002604384  0.001003433 -2.59547 0.00987860## Residual standard error: 0.665408 on 322 degrees of freedom#   (599 observations deleted due to missingness)# Multiple R-squared:  0.100316,    Adjusted R-squared:  0.0835521# F-statistic: 5.98394 on 6 and 322 DF,  p-value: 6.02357e-06

Looks better, but I’m not sure how well it fits. The quadratic $y=0.0080795x+-0.000178179x^2$ has its maximum around 40mg, though, which seems suspiciously high; it seems that in order to fit the negative estimate for 60mg, the ‘top’ of the curve gets pulled over to 48mg since it’s almost as big as 15mg. I don’t find that entirely plausible.

A fancier method of imputation would be “multiple imputation” using, for example, the R library mice (“Multivariate Imputation by Chained Equations”) (guide), which will try to impute all missing values in a way which mimicks the internal structure of the data and provide several ‘possible’ datasets to give us an idea of what the underlying data might have looked like, so we can see how our estimates improve with no missingness & how much of the estimate is now due to the imputation:

library(mice)
## work around apparent error in MICE: can't handle Dates type
## even though no missing-values in that column...?
noopeptSecond$Date <-as.integer(noopeptSecond$Date)
nimp <-mice(noopeptSecond, m=200, maxit=200)
li3 <-with(nimp, lm(MP ~Noopept +I(Noopept^2) +LLLT +as.logical(Magnesium.citrate) +as.integer(Date) +as.logical(Magnesium.citrate):as.integer(Date)))round(summary(pool(li3)), 4)#                                                        est     se       t       df Pr(>|t|)# (Intercept)                                        -8.3369 3.2520 -2.5636 296.1619   0.0109# Noopept                                             0.0073 0.0057  1.2790 521.5756   0.2015# I(Noopept^2)                                       -0.0001 0.0001 -1.2808 583.2136   0.2008# LLLT                                                0.3069 0.0910  3.3737 168.4541   0.0009# as.logical(Magnesium.citrate)TRUE                   7.0763 3.9584  1.7877 298.7476   0.0748# as.integer(Date)                                    0.0007 0.0002  3.4911 299.6728   0.0006# as.logical(Magnesium.citrate)TRUE:as.integer(Date) -0.0005 0.0002 -1.8119 300.7040   0.0710#                                                       lo 95   hi 95 nmis    fmi lambda# (Intercept)                                        -14.7369 -1.9368   NA 0.4974 0.4940# Noopept                                             -0.0039  0.0185  457 0.2852 0.2824# I(Noopept^2)                                        -0.0004  0.0001   NA 0.2411 0.2385# LLLT                                                 0.1273  0.4864  599 0.6954 0.6918# as.logical(Magnesium.citrate)TRUE                   -0.7135 14.8662   NA 0.4942 0.4908# as.integer(Date)                                     0.0003  0.0011   NA 0.4930 0.4897# as.logical(Magnesium.citrate)TRUE:as.integer(Date)  -0.0009  0.0000   NA 0.4918 0.4884

The coefficients & p-values agree, so it seems that it doesn’t make too much difference how we deal with missingness.

Finally, we can see if some weak priors/regularization changes the picture much by using a Bayesian regression instead:

library(arm)
bl1 <-bayesglm(MP ~as.factor(Noopept) +LLLT +as.logical(Magnesium.citrate) +as.integer(Date) +as.logical(Magnesium.citrate):as.integer(Date),data=noopeptImputed)display(bl1)#                                                    coef.est coef.se# (Intercept)                                        20.86     7.18# as.factor(Noopept)10                                0.06     0.12# as.factor(Noopept)15                                0.32     0.28# as.factor(Noopept)20                                0.10     0.13# as.factor(Noopept)30                                0.04     0.11# as.factor(Noopept)48                                0.26     0.20# as.factor(Noopept)60                               -0.13     0.18# LLLTTRUE                                            0.27     0.08# as.logical(Magnesium.citrate)TRUE                   0.28     1.33# as.integer(Date)                                    0.00     0.00# as.logical(Magnesium.citrate)TRUE:as.integer(Date)  0.00     0.00# ---# n = 329, k = 11# residual deviance = 144.2, null deviance = 158.5 (difference = 14.3)# overdispersion parameter = 0.5# residual sd is sqrt(overdispersion) = 0.67coefplot(bl1)

simulates <-as.data.frame(coef(sim(bl1, n.sims=100000)))sapply(simulates[1:11], function(c) { quantile(c, c(.025, .975)) } )#       (Intercept) as.factor(Noopept)10 as.factor(Noopept)15 as.factor(Noopept)20# 2.5%   6.80794518         -0.179116006         -0.218679929         -0.151787205# 97.5% 34.85995773          0.304713370          0.865894125          0.348912986#       as.factor(Noopept)30 as.factor(Noopept)48 as.factor(Noopept)60    LLLTTRUE# 2.5%          -0.174273139         -0.143056371         -0.490166499 0.114146706# 97.5%          0.247145243          0.660125966          0.221157470 0.433830363#       as.logical(Magnesium.citrate)TRUE as.integer(Date)# 2.5%                        -2.29986917  -0.001966335149# 97.5%                        2.86557048  -0.000227816111#       as.logical(Magnesium.citrate)TRUE:as.integer(Date)# 2.5%                                     -0.000197411805# 97.5%                                     0.000124153915

The 95% credible intervals emphasize that while the mean estimates of the posterior for the Noopept parameters are positive, there’s substantial uncertainty after updating on the data, and the effects are small.

Should I run another followup experiment? No; the implied effect is so small a confirmatory experiment would have to run a miserably long time, it seems:

library(boot)library(rms)
newNoopeptPower <-function(dt, indices) {
    d <-dt[sample(nrow(dt), n, replace=TRUE), ] # new dataset, possibly larger than the original
    lmodel <-lm(MP ~Noopept +I(Noopept^2) +LLLT +as.logical(Magnesium.citrate) +as.integer(Date) +as.logical(Magnesium.citrate):as.integer(Date),data=d)return(anova(lmodel)[1:2,][5]$`Pr(>F)`)
}
alpha <-0.05
for (n in seq(from =100, to =3000, by =200)) {
   bs <-boot(data=noopeptImputed, statistic=newNoopeptPower, R=10000, parallel="multicore", ncpus=4)print(c(n, sum(bs$t<=alpha)/length(bs$t)))
}# [1] 100.0000   0.0817# [1] 300.0000   0.1145# [1] 500.00000   0.15175# [1] 700.00000   0.17825# [1] 900.0000   0.2132# [1] 1100.0000    0.2401# [1] 1300.00000    0.26345# [1] 1500.00000    0.28595# [1] 1700.0000    0.3146# [1] 1900.00000    0.33695# [1] 2100.0000    0.3513# [1] 2300.00000    0.37485# [1] 2500.00000    0.39065# [1] 2700.0000    0.4068# [1] 2900.0000    0.4238

(I am not running an blind random self-experiment for 8 years just to get barely 40% power.)

Conclusion

So on net, I think there may be an effect but it’s small and I don’t know whether the optimal dose would be lower (~10mg) or much higher (~40mg). I don’t find this a particularly good reason to continue taking Noopept: it seems to either not be helpful in a noticeable way or to be redundant with the piracetam.

Oxiracetam is one of the 3 most popular -racetams; less popular than piracetam but seems to be more popular than aniracetam. Prices have come down substantially since the early 2000s, and stand at around 1.2g/$ or roughly 50 cents a dose, which was low enough to experiment with; key question, does it stack with piracetam or is it redundant for me? (Oxiracetam can’t compete on price with my piracetam pile stockpile: the latter is now a sunk cost and hence free.)

I bought 60 grams from Smart Powders and combined it with the DMAE; I couldn’t compare oxiracetam+DMAE vs oxiracetam+choline-bitartrate because I had capped all the choline with the piracetam. One immediate advantage of oxiracetam: it is not unbelievably foul tasting like piracetam, but slightly sweet.

Regardless, while in the absence of piracetam, I did notice some stimulant effects (somewhat negative - more aggressive than usual while driving) and similar effects to piracetam, I did not notice any mental performance beyond piracetam when using them both. The most I can say is that on some nights, I seemed to be less easily tired when writing or editing or n-backing (and I felt less tired than ICON 2011 than ICON 2010), but those were also often nights I was also trying out all the other things I had gotten in that order from Smart Powders, and I am still dis-entangling what was responsible. (Probably the l-theanine or sulbutiamine.)

In other words, for me, the two -racetams did not seem to ‘stack’. The following are a number of n-back scores from before (piracetam only) and after (piracetam and oxiracetam):

1. [28,39,26,48,34]; [34,60]; [37,53,55] (▁▂▁▄▁▁▆▂▄▅▆)
2. [56,66,44,46,30,24,50,56,34,39,34]; [30,50,31,37,41,23]; [53,35,40] (▅▇▃▃▁▁▄▅▁▂▁▁▄▁▂▂▁▄▁▂)

There may be some improvement hidden in there, but nothing jumps out to my eye. Oxiracetam has smaller recommended doses than piracetam, true, but even after taking that into account, oxiracetam is still more expensive per dose. When I finished it off, I decided it hadn’t shown any benefits so there was no point in continuing it.

I bought 500g of piracetam (Examine.com; FDA adverse events) from Smart Powders (piracetam is one of the cheapest nootropics and SP was one of the cheapest suppliers; the others were much more expensive as of October 2010), and I’ve tried it out for several days (started on 7 September 2009, and used it steadily up to mid-December). I’ve varied my dose from 3 grams to 12 grams (at least, I think the little scoop measures in grams), taking them in my tea or bitter fruit juice. Cranberry worked the best, although orange juice masks the taste pretty well; I also accidentally learned that piracetam stings horribly when I got some on a cat scratch. 3 grams (alone) didn’t seem to do much of anything while 12 grams gave me a nasty headache. I also ate 2 or 3 eggs a day.

Subjectively, I didn’t notice drastic changes. Here’s what I did notice:

• My thinking seems a little clearer
• I’m not so easy to tire - I went through a month’s worth of my Wikipedia watchlist with less fatigue than usual, and n-backing doesn’t seem so tiring.

• DNB-wise, eyeballing my stats file seems to indicate a small increase: when I compare peak scores D4B scores, I see mostly 50s and a few 60s before piracetam, and after starting piracetam, a few 70s mixed into the 50s and 60s. Natural increase from training? Dunno - I’ve been stuck on D4B since June, so 5 or 10% in a week or 3 seems a little suspicious. A graph of the score series:

  ▁▅▂▁▅▅▂▄▁▂▁▄▄▁▄▂▁▃▃▂▂▂▁▆▁▂▁▄▃▁▃▄▁▄▁▂▅▅▂▃▁▃▃▂▄▂▄▇▄▄▄▅▃▄▂▄▅▅▁▅▃▃▄▅▅▃▃▂▄▄▃▄▆▃▅▃▄▅ ▃▅▄▄▄▂▄▂▄▃▄▄▃▄▄▂▃▆▂▁

vs

▆▅▆▄▄▅▃▅▁▁▃▄▅▃▁▅▃▅▂▃▄▃▁▄▅▅▂▃▁▁▆▃▁▄▄▃▁▅▄▄▃▃▄▂▅▃▁▄▂▅▃▆▆▂▃▃▆▄▃▃▂▂▂▁▄▃▃▄▄▂

• The other day, I also noticed I was fidgeting less
• After a week or two, I think I noticed better reflexes - both in catching falling cups and the saccading in BW seems slightly easier. But I could be imagining this since I just saw an Erowid report mentioning better reflexes & I may’ve read that one before I started. (Darn those subconscious impressions and memories! :)

After 7 days, I ordered a kg of choline bitartrate from Bulk Powders. Choline is standard among piracetam-users because it is pretty universally supported by anecdotes about “piracetam headaches”, has support in rat/mice experiments, and also some human-related research. So I figured I couldn’t fairly test piracetam without some regular choline - the eggs might not be enough, might be the wrong kind, etc. It has a quite distinctly fishy smell, but the actual taste is more citrus-y, and it seems to neutralize the piracetam taste in tea (which makes things much easier for me).

The first day (22 September) I took ~10g since I was taking 5g of piracetam; I wound up with some diarrhea & farting. Oops.

On the plus side: - I noticed the less-fatigue thing to a greater extent, getting out of my classes much less tired than usual. (Caveat: my sleep schedule recently changed for the saner, so it’s possible that’s responsible. I think it’s more the piracetam+choline, though.) - One thing I wasn’t expecting was a decrease in my appetite - nobody had mentioned that in their reports.I don’t like being bothered by my appetite (I know how to eat fine without it reminding me), so I count this as a plus. - Fidgeting was reduced further

The second day I went with ~6g of choline; much less intestinal distress, but similar effects vis-a-vis fidgeting, loss of appetite, & reduced fatigue. So in general I thought this was a positive experience, but I’m not sure it was worth $40 for ~2 months’ worth, and it was tedious consuming it dissolved.

Fortunately for me, the FDA decided Smart Powder’s advertising was too explicit and ordered its piracetam sales stopped; I was equivocal at the previous price point, but then I saw that between the bulk discount and the fire-sale coupon, 3kg was only $99.99 (shipping was amortized over that, the choline, caffeine, and tryptophan). So I ordered in September 2010. As well, I had decided to cap my own pills, eliminating the inconvenience and bad taste. 3kg goes a very long way so I am nowhere close to running out of my pills; there is nothing to report since, as the pills are simply part of my daily routine.

Piracetam natural experiment

I take my piracetam in the form of capped pills consisting (in descending order) of piracetam, choline bitartrate, anhydrous caffeine, and l-tyrosine. On 8 December 2012, I happened to run out of them and couldn’t fetch more from my stock until 27 December. This forms a sort of (non-randomized, non-blind) short “natural experiment”: did my daily 1-5 mood/productivity ratings fall during 8-27 December compared to November 2012 & January 2013? The graphed data suggests to me a decline:

The BEST results give a small effect size of -0.26 and only partial exclusion of zero effect size (which a one-tailed two-sample test agrees with):

So the answer is yes, M/P did fall as I expected; but also as one would expect given daily variation and the small sample of ‘off’ days (19 days), the result is not very statistically robust (even ignoring the low quality of data from a natural experiment). But it was an easy ‘experiment’ to run and the result had the right sign, as they say.

In the 2011-2012 Quantified Health Prize, potassium (FDA adverse events) came up twice as a recommendation. Potassium is vital to nerve conduction, since nerve impulses are nothing but potassium and sodium rushing around, but it didn’t seem like a priority to investigate since I am not an athlete nor do I sweat a great deal.

A LessWrong user Kevinclaimeditworkedwell for him:

By which I mean that simple potassium is probably the most positively mind altering supplement I’ve ever tried…About 15 minutes after consumption, it manifests as a kind of pressure in the head or temples or eyes, a clearing up of brain fog, increased focus, and the kind of energy that is not jittery but the kind that makes you feel like exercising would be the reasonable and prudent thing to do. I have done no tests, but “feel” smarter from this in a way that seems much stronger than piracetam or any of the conventional weak nootropics. It is not just me – I have been introducing this around my inner social circle and I’m at 7/10 people felt immediately noticeable effects. The 3 that didn’t notice much were vegetarians and less likely to have been deficient. Now that I’m not deficient, it is of course not noticeable as mind altering, but still serves to be energizing, particularly for sustained mental energy as the night goes on…Potassium chloride initially, but since bought some potassium gluconate pills… research indicates you don’t want to consume large amounts of chloride (just moderate amounts).

…The first time I took supplemental potassium (50% US RDA in a lot of water), it was like a brain fog lifted that I never knew I had, and I felt profoundly energized in a way that made me feel exercise was reasonable and prudent, which resulted in me and the roommate that had just supplemented potassium going for an hour long walk at 2AM. Experiences since then have not been quite so profound (which probably was so stark for me as I was likely fixing an acute deficiency), but I can still count on a moderately large amount of potassium to give me a solid, nearly side effect free performance boost for a few hours…I had been doing Bikram yoga on and off, and I think I wasn’t keeping up the practice because I wasn’t able to properly rehydrate myself.

One claim was partially verified in passing by EliezerYudkowsky (“Supplementing potassium (citrate) hasn’t helped me much, but works dramatically for Anna, Kevin, and Vassar…About the same as drinking a cup of coffee - i.e., it works as a perker-upper, somehow. I’m not sure, since it doesn’t do anything for me except possibly mitigate foot cramps.”)

I largely ignored this since the discussions were of sub-RDA doses, and my experience has usually been that RDAs are a poor benchmark and frequently far too low (consider the RDA for vitamin D). This time, I checked the actual RDA - and was immediately shocked and sure I was looking at a bad reference: there was no way the RDA for potassium was seriously 3700-4700mg or 4-5 grams daily, was there? Just as an American, that implied that I was getting less than half my RDA. (How would I get 4g of potassium in the first place? Eat a dozen bananas a day⸮) I am not a vegetarian, nor is my diet that fantastic: I figured I was getting some potassium from the ~2 fresh tomatoes I was eating daily, but otherwise my diet was not rich in potassium sources. I have no blood tests demonstrating deficiency, but given the figures, I cannot see how I could not be deficient.

Potassium is not the safest supplement ever, but it’s reasonably safe (kidneys can filter out overdoses), and between the anecdotes and my sudden realization that I was highly likely deficient, I decided to try it out.

Potassium citrate powder is neither expensive nor cheap: I purchased 453g for $21. The powder is crystalline white, dissolves instantly in water, and largely tasteless (sort of saline & slightly unpleasant). The powder is 37% potassium by weight (the formula is C₆H₅K₃O₇) so 453g is actually 167g of potassium, so 80-160 days’ worth depending on dose.

My first impression of ~1g around 12:30PM was that while I do not feel like running around, within an hour I did feel like the ‘brain fog’ was lighter than before. The effect wasn’t dramatic, so I can’t be very confident. Operationalizing ‘brain fog’ for an experiment might be hard: it doesn’t necessarily feel like I would do better on dual n-back. I took 2 smaller doses 3 and 6 hours later, to no further effect. Over the following weeks and months, I continued to randomly alternate between potassium & non-potassium days. I noticed no effects other than sleep problems.

Selegilineis a somewhat popular (Erowid, r/nootropics, FDA adverse events) stimulant/anti-depressant which affects dopamine.

Dosage is apparently 5-10mg a day. (Prices can be better elsewhere; selegiline is popular for treating dogs with senile dementia, where those 60x5mg will cost $2 rather than $35. One needs a veterinarian’s prescription to purchase from pet-oriented online pharmacies, though.) I ordered it & modafinil from Nubrain.com at $35 for 60x5mg; Nubrain delayed and eventually canceled my order - and my enthusiasm. Between that and realizing how much of a premium I was paying for Nubrain’s deprenyl, I’m tabling deprenyl along with nicotine & modafinil for now. Which is too bad, because I had even ordered 20g of PEA from Smart Powders to try out with the deprenyl. (My later attempt to order some off the Silk Road also failed when the seller canceled the order.)

2 experiences with sulbutiamine (Examine.com) on Reddit moved me to check it out.

My general impression is positive; it does seem to help with endurance and extended the effect of piracetam+choline, but is not as effective as that combo. At $20 for 30g (bought from Smart Powders), I’m not sure it’s worthwhile, but I think at $10-15 it would probably be worthwhile. Sulbutiamine seems to affect my sleep negatively, like caffeine. I bought 2 or 3 canisters for my third batch of pills along with the theanine. For a few nights in a row, I slept terribly and stayed awake thinking until the wee hours of the morning; eventually I realized it was because I was taking the theanine pills along with the sleep-mix pills, and the only ingredient that was a stimulant in the batch was - sulbutiamine. I cut out the theanine pills at night, and my sleep went back to normal. (While very annoying, this, like the creatine & taekwondo example, does tend to prove to me that sulbutiamine was doing something and it is not pure placebo effect.)

It’s worth noting that sulbutiamine reports vary dramatically, and it seems possible that some people are thiamine-deficient and so would disproportionately; SilasBarta noticed little to nothing (like me), but Jimrandomh reports his life was transformed (and he suspects that his diabetes caused or exacerbated a deficiency).

Taurine (Examine.com) was another gamble on my part, based mostly on its inclusion in energy drinks. I didn’t do as much research as I should have: it came as a shock to me when I read in Wikipedia that “taurine has been shown to prevent oxidative stress induced by exercise” and was an antioxidant - oxidative stress is a key part of how exercise creates health benefits and antioxidants inhibit those benefits.

So now I have to be careful about when I take it so it isn’t near a session of exercise or just accept whatever damage taurine does me. I’m not sure what I’ll do with it when I cap my current supply of powders. (It would make little sense to cap it with the creatine since I would often take the creatine before exercise.)

And the effects? Well, if you look through the WP article or other places, you see it justified in part due to supposed long term benefits or effects on blood sugar. I can’t say I’ve noticed any absence of ‘crashes’, taking it on alternate days or alone. (At least it wasn’t too expensive - $9 for 500g.)

The hormone testosterone (Examine.com; FDA adverse events) needs no introduction. This is one of the scariest substances I have considered using: it affects so many bodily systems in so many ways that it seems almost impossible to come up with a net summary, either positive or negative. With testosterone, the problem is not the usual nootropics problem that that there is a lack of human research, the problem is that the summary constitutes a textbook - or two. That said, the 2011 review “The role of testosterone in social interaction” (excerpts) gives me the impression that testosterone does indeed play into risk-taking, motivation, and social status-seeking; some useful links and a representative anecdote:

• Andrew Sullivan:

• “The Manly Molecule”, Steve Sailer 2000

• Wedrifid, 2012:

  While the primary effect of the drug is massive muscle growth the psychological side effects actually improved his sanity by an absurd degree. He went from barely functional to highly productive. When one observes that the decision to not attempt to fulfill one’s CEV at a given moment is a bad decision it follows that all else being equal improved motivation is improved sanity.

  Elaborating on why the psychological side effects of testosterone injection are individual dependent: Not everyone get the same amount of motivation and increased goal seeking from the steroid and most people do not experience periods of chronic avolition. Another psychological effect is a potentially drastic increase in aggression which in turn can have negative social consequences. In the case of counterfactual wedrifid he gets a net improvement in social consequences. He has observed that aggression and anger are a prompt for increased ruthless self-interested goal seeking. Ruthless self-interested goal seeking involves actually bothering to pay attention to social politics. People like people who do social politics well. Most particularly it prevents acting on contempt which is what wedrifid finds prompts the most hostility and resentment in others. Point is, what is a sanity promoting change in one person may not be in another.

As it happens, these are areas I am distinctly lacking in. When I first began reading about testosterone I had no particular reason to think it might be an issue for me, but it increasingly sounded plausible, an aunt independently suggested I might be deficient, a biological uncle turned out to be severely deficient with levels around 90 ng/dl (where the normal range for 20-49yo males is 249-839), and finally my blood test in August 2013 revealed that my actual level was 305 ng/dl; inasmuch as I was 25 and not 49, this is a tad low.

One idea I’ve been musing about is the connections between IQ, Conscientiousness, and testosterone. IQ and Conscientiousness do not correlate to a remarkable degree - even though one would expect IQ to at least somewhat enable a long-term perspective, self-discipline, metacognition, etc! There are indications in studies of gifted youth that they have lower testosterone levels. The studies I’ve read on testosterone indicate no improvements to raw ability. So, could there be a self-sabotaging aspect to human intelligence whereby greater intelligence depends on lack of testosterone, but this same lack also causes lack of correlation with Conscientiousness, vastly undermining the utility of greater intelligence? Could cases of high IQ types who suddenly stop slacking and accomplish great things sometimes be due to changes in testosterone? Studies on the correlations between IQ, testosterone, Conscientiousness, and various measures of accomplishment are confusing and don’t always support this theory, but it’s an idea to keep in mind.

One might suggest just going to the gym or doing other activities which may increase endogenous testosterone secretion. This would be unsatisfying to me as it introduces confounds: the exercise may be doing all the work in any observed effect, and certainly can’t be blinded. And blinding is especially important because the 2011 review discusses how some studies report that the famed influence of testosterone on aggression (eg. Wedrifid’s anecdote above) is a placebo effect caused by the “folk wisdom” that testosterone causes aggression & rage!

I have a needle phobia, so injections are right out; but from the images I have found, it looks like testosterone gels resemble other gels like Vaseline. This suggests an easy experimental procedure: spoon an appropriate dose of testosterone gel into one opaque jar, spoon some Vaseline gel into another, and pick one randomly to apply while not looking. If one gel evaporates but the other doesn’t, or they have some other difference in behavior, the procedure can be expanded to something like “and then half an hour later, take a shower to remove all visible traces of the gel”. Testosterone itself has a fairly short half-life of 2-4 hours, but the gel or effects might linger. (Injections apparently operate on a time-scale of weeks; I’m not clear on whether this is because the oil takes that long to be absorbed by surrounding materials or something else.) Experimental design will depend on the specifics of the obtained substance. As a controlled substance (Schedule III in the US), supplies will be hard to obtain; I may have to resort to the Silk Road.

Power-wise, the effects of testosterone are generally reported to be strong and unmistakable. Even a short experiment should work. I would want to measure DNB scores & Mnemosyne review averages as usual, to verify no gross mental deficits; the important measures would be physical activity, so either pedometer or miles on treadmill, and general productivity/mood. The former 2 variables should remain the same or increase, and the latter 2 should increase.

Either prescription or illegal, daily use of testosterone would not be cheap. On the other hand, if I am one of the people for whom testosterone works very well, it would be even more valuable than modafinil, in which case it is well worth even arduous experimenting. Since I am on the fence on whether it would help, this suggests the value of information is high.

l-theanine (Examine.com) is occasionally mentioned on Reddit or Imminst or LessWrong but is rarely a top-level post or article; this is probably because theanine was discovered a very long time ago (>61 years ago), and it’s a pretty straightforward substance. It’s a weak relaxant/anxiolytic (Google Scholar) which is possibly responsible for a few of the health benefits of tea, and whichworkssynergistically with caffeine (and is probably why caffeine delivered through coffee feels different from the same amount consumed in tea - in one study, separate caffeine and theanine were a mixed bag, but the combination beat placebo on all measurements). The half-life in humans seems to be pretty short, with van der Pijl 2010 putting it ~60 minutes. This suggests to me that regular tea consumption over a day is best, or at least that one should lower caffeine use - combining caffeine and theanine into a single-dose pill has the problem of caffeine’s half-life being much longer so the caffeine will be acting after the theanine has been largely eliminated. The problem with getting it via tea is that teas can vary widely in their theanine levels and the variations don’t seem to be consistent either, nor is it clear how to estimate them. (If you take a large dose in theanine like 400mg in water, you can taste the sweetness, but it’s subtle enough I doubt anyone can actually distinguish the theanine levels of tea; incidentally, r-theanine - the useless racemic other version - anecdotally tastes weaker and less sweet than l-theanine.)

On 8 April 2011, I purchased from Smart Powders (20g for $8); as before, some light searching seemed to turn up SP as the best seller given shipping overhead; tt was on sale and I planned to cap it so I got 80g. This may seem like a lot, but I was highly confident that theanine and I would get along since I already drink so much tea and was a tad annoyed at the edge I got with straight caffeine. So far I’m pretty happy with it. My goal was to eliminate the physical & mental “twitchiness” of caffeine, which subjectively it seems to do.

Running low on my theanine in May 2013, I learned SP was now undercut by LiftMode selling 50g for $14, so I got 2 (I was doing a big Amazon order anyway). Like the SP theanine it is a nice fluffy white powder and seems to work as well.

A new all-in-one nootropic mix/company run by some people active on /r/nootropics; they offered me a month’s supply for free to try & review for them. At ~$100 a month (it depends on how many months one buys), it is not cheap (John Backus estimates one could buy the raw ingredients for $25/month) but it provides convenience& is aimed at people uninterested in spending a great deal of time reviewing research papers & anecdotes or capping their own pills (ie. people with lives) and it’s unlikely I could spare the money to subscribe if TruBrain worked well for me - but certainly there was no harm in trying it out.

The ingredients list was sane and similar to what I would have chosen, and doesn’t include any jokers like caffeine:

1. Piracetam: 3g

   Naturally, as piracetam is the standard -racetam to use. It’s always worked well for me. 3g is a reasonable amount for people who aren’t taking any other sources of piracetam.

2. ALCAR: 0.5g

   I didn’t notice any personal benefits from ALCAR, but I didn’t notice any bad effects either and many people claim to benefit.

3. CDP-Choline: 0.25g

   You’ll want choline when using -racetams. 0.25g strikes me as low.

4. EPA/DHA: 1.2g

   Fish oil is a safe bet for supplements. 1.2g is also low, I would prefer double that. (In the batch I got, there was a sort of fruity/gummy taste & aftertaste. Apparently it’s cherry-flavored.)

5. Magnesium glycinate/lycinate: 0.2g

   My magnesium l-threonate correlation from the Noopept self-experiment suggested magnesium would help me, so I don’t mind seeing magnesium here as well.

6. Pramiracetam: 0.3g

   I don’t know very much about pramiracetam.

7. Theanine: 0.2g

   An old favorite of mine.

8. Tyrosine: 0.35g

   Like ALCAR, did nothing subjectively noticeable for me, but nothing bad either.

The packaging is nice, if a little confusing (it’s not entirely clear what packets are supposed to be taken on the initial days as part of the “loading”). The pills are swallowable (one takes a set with breakfast and a second set with lunch). I do not seem to have died or gone into untoward states like seizures, and the first day went swimmingly.

l-Tryptophan is in a sense redundant with taking melatonin, since melatonin is one of the most prominent metabolites of tryptophan. Nevertheless, subjectively I seem to sleep better by taking 1.5mg of melatonin & 0.5g of tryptophan than I do by taking, say, 3mg of melatonin.

One of the other suggested benefits is for boosting serotonin levels; low levels of serotonin are implicated in a number of issues like depression. I’m not yet sure whether tryptophan has helped with motivation or happiness. Trial and error has taught me that it’s a bad idea to take tryptophan in the morning or afternoon, however, even smaller quantities like 0.25g. Like melatonin, the dose-response curve is a U: ~1g is great and induces multiple vivid dreams for me, but ~1.5g leads to an awful night and a headache the next day that was worse, if anything, than melatonin. (One morning I woke up with traces of at least 7 dreams, although I managed to write down only 2. No lucid dreams, though.)

Taking the tryptophan is fairly difficult. The powder as supplied by Bulk Nutrition is extraordinarily dry and fine; it seems to be positively hydrophobic. The first time I tried to swallow a teaspoon, I nearly coughed it out - the power had seemed to explode in my mouth and go down my lungs. Thenceforth I made sure to have a mouth of water first. After a while, I took a different tack: I mixed in as much Hericium as would fit in the container. The mushroom powder is wetter and chunkier than the tryptophan, and seems to reduce the problem. Combining the mix with chunks of melatonin inside a pill works even better.

Tyrosine (Examine.com) is an amino acid; people on the Imminst.org forums (as well as Wikipedia) suggest that it helps with energy and coping with stress. I ordered 4oz (bought from Smart Powders) to try it out, and I began taking 1g with my usual caffeine+piracetam+choline mix. It does not dissolve easily in hot water, and is very chalky and not especially tasty. I have not noticed any particular effects from it.

Bought 5,000 IU soft-gels of Vitamin D-3 (Examine.com; FDA adverse events) because I was feeling very apathetic in January 2011 and not getting much done, even slacking on regular habits like Mnemosyne spaced repetition review or dual n-back or my Wikipedia watchlist. Introspecting, I was reminded of depression & dysthymia& seasonal affective disorder.

There are a number of treatments for the last. I already use melatonin. I sort of have light therapy from a full-spectrum fluorescent desk lamp. But I get very little sunlight; the surprising thing would be if I didn’t have a vitamin D deficiency. And vitamin D deficiencies have been linked with all sorts of interesting things like near-sightedness, with time outdoors inversely correlating with myopia and not reading or near-work time. (It has been claimed that caffeine interferes with vitamin D absorption and so people like me especially need to take vitamin D, on top of the deficits caused by our vampiric habits, but I don’t think this is true.) Unfortunately, there’s not very good evidence that vitamin D supplementation helps with mood/SAD/depression: there’s ~6 small RCTs with some findings of benefits, with their respective meta-analysis turning in a positive but currently non-statistically-significant result. Better confirmed is reducing all-cause mortality in elderly people (see, in order of increasing comprehensiveness: Evidence Syntheses 2013, Chung et al 2009, Autier & Gandini 2007, Bolland et al 2014).

Given the involvement with circadian rhythms and the synthesis involving direct sunlight, it is likely a bad idea to take vitamin D close to bedtime, and there have been anecdotes that it damages sleep quality; I investigated this with my Zeo and concluded it seemed to be true for me.

The soft gels are very small; one needs to be a bit careful - Vitamin D is fat-soluble and overdose starts in the range of 70,000 IU, so it would take at least 14 pills, and it’s unclear where problems start with chronic use. Vitamin D, like many supplements, followsaU-shapedresponsecurve (see also Melamed et al 2008 and Durup et al 2012) - too much can be quite as bad as too little. Too little, though, is likely very bad. The previously cited studies with high acute doses worked out to <1,000 IU a day, so they may reassure us about the risks of a large acute dose but not tell us much about smaller chronic doses; the mortality increases due to too-high blood levels begin at ~140nmol/l and reading anecdotes online suggest that 5k IU daily doses tend to put people well below that (around 70-100nmol/l). I probably should get a blood test to be sure, but I have something of a needle phobia.

I have elsewhere remarked on the apparent lack of benefit to taking multivitamins and the possible harm; so one might well wonder about a specific vitamin like vitamin D. However, a ‘multivitamin’ is not ‘vitamin D’, so it’s no surprise that they might do different things. If a multivitamin had no vitamin D in it, or if it had vitamin D in different doses, or if it had substances which interacted with vitamin D (such as calcium), or if it had substances which had negative effects which outweigh the positive (such as vitamin A?), we could well expect differing results. In this case, all of those are true to varying extents. Some multivitamins I’ve had contained no vitamin D. The last multivitamin I was taking both contains vitamins used in the negative trials and also some calcium; the listed vitamin D dosage was a trivial ~400IU, while I take >10x as much now (5000IU).

10,000 IU is highly likely to be enough, and is similar to what one might get from an hour on the beach; so 5000 IU should be satisfactory.

• Bacopa - The research looks interesting and it’s cheap; but I am suspicious of anything Ayurvedic because of heavy metals, and Bacopa monnieri is a hyperaccumulator of heavy metals.
• Naringenin - Timothy Ferriss in excerpt from Four-hour Body claims it extends effectiveness of caffeine (how to buy - capsule? grape fruit concentrate?)
• Memantine - linked with depression alleviation
• Ginseng (cf. the Cochrane Review)

I’m testing it for its memory enhancing effects so I’m up to 20IU 3x per day. The dose used for healthy humans was 40IU 4x per day so I’m gradually working up to it. At my 3x per day dose it lasts all day.

need nasal spray device; can reuse Semax/Selank Ceretropic spray? –>