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author: niplav, created: 2023-01-06, modified: 2024-01-17, language: english, status: notes, importance: 3, confidence: log

Modeled after Gwern 2018 I've decided to log my nootropics usage and its effects.

Nootropics

You could put randomized substances in your body and find out what they do by recording the outcomes. That's what I did.

Value tracked Effect size d (λ, p, σ change) Effect size d (λ, p, σ change)
200 mg Caffeine (n=1, m=50) 500 mg L-theanine (n=1, m=50)
Log-score substance prediction1 -0.6 -0.7
Absorption 0.61 (λ=13.3, p=0.00017, -0.072) 0.04 (λ=1.38, p=0.77, -0.07)
Mindfulness 0.58 (λ=11.8, p=0.0007, 0.021) 0.12 (λ=0.72, p=0.89, -0.018)
Productivity 0.58 (λ=28.9, p=1.3-12, 0.11) -0.28 (λ=5.51, p=0.109, 0.03)
Creativity 0.45 (λ=51, p=4.6-27, 0.09) -0.12 (λ=5.05, p=0.14, -0.04)
Happiness 0.27 (λ=10.6, p=0.002, 0.3) 0.16 (λ=3.98, p=0.27, -0.155)
Contentment 0.13 (λ=7.66, p=0.02, 0.47) 0.25 (λ=6.83, p=0.04, -0.04)
Relaxation -0.11 (λ=5, p=0.15, 0.42) 0.12 (λ=1.5, p=0.74, 0.02)
Chastity2 -0.14 (λ=1.9, p=0.64, 0.11) -0.03 (λ=1.15, p=0.8, 0.25)
Subjective length of day Not collected -0.015 (λ=0.35, p=0.95, -0.015)3
Flashcard ease 0.003 (λ≈∞, p≈0, -0.009) -0.072 (λ=∞, p≈0, -0.01)
Flashcard ease factor -0.039 (λ≈∞, p≈0, -32.7) 0.0026 (λ=∞, p≈0, -18.9)
Flashcard new interval 0.011 (λ≈∞, p≈0, -1.88) -0.016 (λ=∞, p≈0, 3.1)
Time per flashcard4 0.006 (λ≈∞, p≈0, 273.4) 0.003 (λ=∞, p≈0, 13.66)

I am especially interested in testing many different substances for their effect on meditation, while avoiding negative side effects. The benefits from high meditational attainments valuable to me, and seem especially likely to benefit from chemical intervention, since the Algernon argument likely doesn't apply: Meditative attainments might've not led to a fitness advantage (even, by opportunity cost, to a fitness disadvantage), and so were likely selected against, but most of us don't care that much about inclusive genetic fitness and more about psychological well-being. Evolutionary dynamics favor being like Ghengis Khan (dozens to hundreds of offspring) over Siddharta Gautama (one son), but I'd rather attain sotāpanna than pillage and murder.

And meditative attainments are costly: they take tens to hundreds to thousands of hours to reach, which would make simple psychopharmacological interventions worthwhile. I also don't buy that they miss the point of meditation—most people already struggle enough, so some help doesn't make it a cakewalk; "reach heaven through fraud". One must be careful not to fall into the trap of taking substances that feel good but lessen sensory clarity (which I believe was the original intent behind the fifth precept, and so I'll exclude e.g. opiates from the substances to test).

Caffeine

I won't dig too deep into the effects of caffeine, as other people have done that already (Examine, Gwern, Wikipedia).

Experiment A: Self-Blinded RCT

Variables tracked (see more here):

The total cost of the experiment is at least 21.5€:

200mg caffeine pills, placebo pills filled with sugar, of each 25. Put each pill with a corresponding piece of paper ("C" for caffeine, "P" for placebo) into an unlabeled envelope. Used seq 1 50 | shuf to number the envelopes, and sorted them accordingly.

Notes on the experiment:

Statistical Method

In general, I'll be working with the likelihood ratio test (encouraged by this article). For this, let $\mathbf{v}_P$ be the distribution of values of a variable for the placebo arm, and $\mathbf{v}_C$ the distribution of values for a variable of the caffeine arm. (I apologise for the $C$ being ambiguous, since it could also refer to the control arm).

Then let $\theta_0=(\mu_0, \sigma_0)=MLE_{\mathcal{N}}(\mathbf{v}_P)$ be the Gaussian maximum likelihood estimator for our placebo values, and $\theta=(\mu, \sigma)=MLE_{\mathcal{N}}(\mathbf{v}_C)$ be the MLE for our caffeine values.

Then the likelihood ratio statistic $\lambda$ is defined as

$$\lambda=2 \log \frac{\mathcal{L}_C(\theta)}{\mathcal{L}_C(\theta_0)}$$

where $\mathcal{L}_C(\theta)$ is the likelihood the caffeine distribution assigns to the parameters $\theta$. This test is useful here because we fix all values of $\theta_0$. See Wasserman 2003 ch. 10.6 for more.

If $\lambda \approx 0$, then the MLE for the placebo arm is very close to the MLE for the caffeine arm, the distributions are similar. If $\lambda>0$, then the MLE for the placebo arm is quite different from the caffeine arm (though there is no statement about which has higher values). $\lambda<0$ is not possible, since that would mean that the MLE of the placebo distribution has a higher likelihood for the caffeine data than the MLE of the caffeine distribution itself—not very likely.

Note that I'm not a statistician, this is my first serious statistical analysis, so please correct me if I'm making some important mistakes. Sorry.

Predictions on the Outcomes of the Experiment

After collecting the data, but before analysing it, I want to make some predictions about the outcome of the experiment, similar to another attempt here.

Moved here.

Analysis

We start by setting everything up and loading the data.

import math
import numpy as np
import pandas as pd
import scipy.stats as scistat

substances=pd.read_csv('../..//data/substances.csv')

meditations=pd.read_csv('../../data/meditations.csv')
meditations['meditation_start']=pd.to_datetime(meditations['meditation_start'], unit='ms', utc=True)
meditations['meditation_end']=pd.to_datetime(meditations['meditation_end'], unit='ms', utc=True)

creativity=pd.read_csv('../../data/creativity.csv')
creativity['datetime']=pd.to_datetime(creativity['datetime'], utc=True)

productivity=pd.read_csv('../../data/productivity.csv')
productivity['datetime']=pd.to_datetime(productivity['datetime'], utc=True)

expa=substances.loc[substances['experiment']=='A'].copy()
expa['datetime']=pd.to_datetime(expa['datetime'], utc=True)

The mood data is a bit special, since it doesn't have timezone info, but that is easily remedied.

mood=pd.read_csv('../../data/mood.csv')
alarms=pd.to_datetime(pd.Series(mood['alarm']), format='mixed')
mood['alarm']=pd.DatetimeIndex(alarms.dt.tz_localize('CET', ambiguous='infer')).tz_convert(tz='UTC')
dates=pd.to_datetime(pd.Series(mood['date']), format='mixed')
mood['date']=pd.DatetimeIndex(dates.dt.tz_localize('CET', ambiguous='infer')).tz_convert(tz='UTC')
Summary Statistics

We can first test how well my predictions fared:

probs=np.array(expa['prediction'])
substances=np.array(expa['substance'])
outcomes=np.array([0 if i=='sugar' else 1 for i in substances])

drumroll

>>> np.mean(list(map(lambda x: math.log(x[0]) if x[1]==1 else math.log(1-x[0]), zip(probs, outcomes))))
-0.5991670759554912

At least this time I was better than chance:

>>> np.mean(list(map(lambda x: math.log(x[0]) if x[1]==1 else math.log(1-x[0]), zip([0.5]*40, outcomes))))
-0.6931471805599453
Meditation

Merging the meditations closest (on the right) to the consumption and selecting the individual variables of interest:

meditations.sort_values("meditation_start", inplace=True)
meditations_a=pd.merge_asof(expa, meditations, left_on='datetime', right_on='meditation_start', direction='forward')
caffeine_concentration=meditations_a.loc[meditations_a['substance']=='caffeine']['concentration_rating']
placebo_concentration=meditations_a.loc[meditations_a['substance']=='sugar']['concentration_rating']
caffeine_mindfulness=meditations_a.loc[meditations_a['substance']=='caffeine']['mindfulness_rating']
placebo_mindfulness=meditations_a.loc[meditations_a['substance']=='sugar']['mindfulness_rating']

So, does it help?

>>> (caffeine_concentration.mean()-placebo_concentration.mean())/meditations['concentration_rating'].std()
0.6119357868347828
>>> (caffeine_mindfulness.mean()-placebo_mindfulness.mean())/meditations['mindfulness_rating'].std()
0.575981762563846

Indeed! Cohen's d here looks pretty good. Taking caffeine also reduces the variance of both variables:

>>> caffeine_concentration.std()-placebo_concentration.std()
-0.0720877290884765
>>> caffeine_mindfulness.std()-placebo_mindfulness.std()
0.02186797288826836
Productivity and Creativity

We repeat the same procedure for the productivity and creativity data:

prod_a=pd.merge_asof(expa, productivity, left_on='datetime', right_on='datetime', direction='forward')
creat_a=pd.merge_asof(expa, creativity, left_on='datetime', right_on='datetime', direction='forward')
caffeine_productivity=prod_a.loc[meditations_a['substance']=='caffeine']['productivity']
placebo_productivity=prod_a.loc[meditations_a['substance']=='sugar']['productivity']
caffeine_creativity=creat_a.loc[meditations_a['substance']=='caffeine']['creativity']
placebo_creativity=creat_a.loc[meditations_a['substance']=='sugar']['creativity']

And the result is…

>>> (caffeine_productivity.mean()-placebo_productivity.mean())/prod_a['productivity'].std()
0.5784143673702401
>>> (caffeine_creativity.mean()-placebo_creativity.mean())/creat_a['creativity'].std()
0.38432393552829164

Again surprisingly good! The creativity values are small enough to be a fluke, but the productivity values seem cool.

In this case, though, caffeine increases variance in the variables (not by very much):

>>> caffeine_productivity.std()-placebo_productivity.std()
0.1139221931098384
>>> caffeine_creativity.std()-placebo_creativity.std()
0.08619686235791152
Mood

Some unimportant pre-processing, in which we filter for mood recordings 0-10 hours after caffeine intake, since pd.merge_asof doesn't do cartesian product:

mood_a=expa.join(mood, how='cross')
mood_a=mood_a.loc[(mood_a['alarm']-mood_a['datetime']<pd.Timedelta('10h'))&(mood_a['alarm']-mood_a['datetime']>pd.Timedelta('0h'))]
caffeine_mood=mood_a.loc[mood_a['substance']=='caffeine']
placebo_mood=mood_a.loc[mood_a['substance']=='sugar']

And now the analysis:

>>> caffeine_mood[['happy', 'content', 'relaxed', 'horny']].describe()
           happy    content    relaxed      horny
count  88.000000  88.000000  88.000000  88.000000
mean   52.193182  51.227273  50.704545  46.568182
std     2.396635   2.911441   3.115254   3.117601
[…]
>>> placebo_mood[['happy', 'content', 'relaxed', 'horny']].describe()
           happy    content    relaxed      horny
count  73.000000  73.000000  73.000000  73.000000
mean   51.575342  50.876712  51.041096  47.000000
std     2.101043   2.437811   2.699992   3.009245
[…]

Which leads to d of ~0.27 for happiness, ~0.13 for contentment, ~-0.11 for relaxation and ~-0.14 for horniness.

Flashcards

Because Anki stores the intervals of learning flashcards (that is, ones that have been answered incorrectly too many times), we have to adjust the numbers to reflect that a negative second is not equal to a day.

flashcards_a=flashcards.loc[(flashcards['id']>expa['datetime'].min()) & (flashcards['id']<expa['datetime'].max()+pd.Timedelta('10h'))]
flashcards_a=expa.join(flashcards_a, how='cross', rsuffix='r')
flashcards_a=flashcards_a.loc[(flashcards_a['idr']-flashcards_a['datetime']<pd.Timedelta('10h'))&(flashcards_a['idr']-flashcards_a['datetime']
>pd.Timedelta('0h'))]
flashcards_a.loc[flashcards_a['ivl']>0,'ivl']=-flashcards_a.loc[flashcards_a['ivl']>0,'ivl']/86400

We then again separate into placebo and caffeine:

placebo_flashcards=flashcards_a.loc[flashcards_a['substance']==placebo]
substance_flashcards=flashcards_a.loc[flashcards_a['substance']==substance]
Likelihood Ratios

We assume (at first) that the data is distributed normally. Then we can define a function for the gaussian likelihood of a distribution given some parameters:

def normal_likelihood(data, mu, std):
    return np.product(scistat.norm.pdf(data, loc=mu, scale=std))

And now we can compute the likelihood ratio $\frac{\mathcal{L}{θ}}{\mathcal{L}{θ_0}}$ for the null hypothesis $θ_0=\text{MLE}(\mathbf{v}_P)$ for the placebo data $\mathbf{v}_P$, and also the result of the likelihood ratio test:

def placebo_likelihood(active, placebo):
    placebo_mle_lh=normal_likelihood(active, placebo.mean(), placebo.std())
    active_mle_lh=normal_likelihood(active, active.mean(), active.std())
    return active_mle_lh/placebo_mle_lh

def likelihood_ratio_test(lr):
    return 2*np.log(lr)

And this gives us surprisingly large values:

>>> placebo_likelihood_ratio(caffeine_concentration, placebo_concentration)
776.6147119766716
>>> likelihood_ratio_test(placebo_likelihood_ratio(caffeine_concentration, placebo_concentration))
13.309888722406932
>> placebo_likelihood_ratio(caffeine_mindfulness, placebo_mindfulness)
363.3984201164464
>>> likelihood_ratio_test(placebo_likelihood_ratio(caffeine_mindfulness, placebo_mindfulness))
11.790999616893938
>>> placebo_likelihood_ratio(caffeine_productivity, placebo_productivity)
1884090.6347491818
>>> likelihood_ratio_test(placebo_likelihood_ratio(caffeine_productivity, placebo_productivity))
28.8979116811553
>>> placebo_likelihood_ratio(caffeine_creativity, placebo_creativity)
14009015.173307568
>>> likelihood_ratio_test(placebo_likelihood_ratio(caffeine_creativity, placebo_creativity))
32.910423242578126

And, if one is interested in p-values, those correspond to (with 2 degrees of freedom each):

def llrt_pval(lmbda, df=2):
    return scistat.chi2.cdf(df, lmbda)

>>> llrt_pval([13.309888722406932,11.790999616893938, 28.8979116811553, 32.910423242578126])
array([1.66559304e-04, 7.23739116e-04 ,1.34836408e-12, 5.17222209e-15])

I find these results surprisingly strong, and am still kind of mystified why. Surely caffeine isn't that reliable!

And, the same, for mood:

>>> placebo_likelihood_ratio(caffeine_mood['happy'], placebo_mood['happy'])
204.81283712162838
>>> likelihood_ratio_test(placebo_likelihood_ratio(caffeine_mood['happy'], placebo_mood['happy']))
10.644193144917832
>>> placebo_likelihood_ratio(caffeine_mood['content'], placebo_mood['content'])
46.08310645632934
>>> likelihood_ratio_test(placebo_likelihood_ratio(caffeine_mood['content'], placebo_mood['content']))
7.6608928570645105
>>> placebo_likelihood_ratio(caffeine_mood['relaxed'], placebo_mood['relaxed'])
12.229945616108525
>>> likelihood_ratio_test(placebo_likelihood_ratio(caffeine_mood['relaxed'], placebo_mood['relaxed']))
5.007775005855661
>>> placebo_likelihood_ratio(caffeine_mood['horny'], placebo_mood['horny'])
2.670139324155222
>>> likelihood_ratio_test(placebo_likelihood_ratio(caffeine_mood['horny'], placebo_mood['horny']))
1.9642613047646074

And the p-values of those are:

>>> llrt_pval([10.644193144917832, 7.6608928570645105, 5.007775005855661, 1.9642613047646074])
array([0.0020736 , 0.02462515, 0.15015613, 0.63984027])

Conclusion

Caffeine appears helpful for everything except relaxation (and it makes me hornier, which I'm neutral about). I'd call this experiment a success and will be running more in the future, while in the meantime taking caffeine before morning meditations.

Generalising the Code

After finishing the coding for this experiment, I decided it'd be easier if for the future I could call a single function to analyze all my data for me. The result can be found here, the function is analyze(experiment, substance, placebo).

To analyze this specific experiment, I simply call caffeine_results=analyze('A', 'caffeine', 'sugar') and get this nice DataFrame:

    absorption  mindfulness  productivity    creativity      happy   content   relaxed     horny      ease    factor       ivl        time
d     0.611936     0.575982  5.784144e-01  4.544300e-01   0.270813  0.129624 -0.114858 -0.140795 -0.009327 -0.068599 -0.046602    0.021811
λ    13.309889    11.791000  2.889791e+01  5.103201e+01  10.644193  7.660893  5.007775  1.964261       inf       inf       inf         inf
p     0.000167     0.000724  1.348364e-12  4.609786e-27   0.002074  0.024625  0.150156  0.639840  0.000000  0.000000  0.000000    0.000000
dσ   -0.072088     0.021868  1.139222e-01  9.659407e-02   0.295592  0.473630  0.415262  0.108356 -0.011060 -0.679137 -0.567543  697.624130

Discussions

Creatine

Examine. I follow the loading procedure detailed here:

Creatine is a supplement that is known for having a 'loading' phase followed by a 'maintenance' phase. A typical creatine cycle has three parts to it.

First dose was taken on 2023-01-06.

I'm especially interested in the effects of creatine on my cognition (it might increase IQ in vegetarians (or it might not?), and I'm a lacto-vegetarian), my exercising performance and my meditation ability.

L-Theanine

L-Theanine is synergistic with caffeine in regards to attention switching[318] and alertness[319][320] and reduces susceptibility to distractions (focus).[320][321] However, alertness seems to be relatively subjective and may not be a reliable increase between these two compounds,[318] and increases in mood are either present or absent.[322][318][323] This may be due to theanine being a relatively subpar nootropic in and of itself pertaining to the above parameters, but augmenting caffeine's effects; some studies do note that theanine does not affect the above parameters in and of itself.[324] Due to this, any insensitivity or habituation to caffeine would reduce the effects of the combination as L-theanine may work through caffeine.

L-Theanine does not appear to be synergistic with caffeine in regards to attention to a prolonged and monotonous task.[325]

—Kamal Patel, “Caffeine”, 2023

See again Examine, Wikipedia and Gwern.

Sitiprapaporn et al. 2018 test the effect of an unspecified quantity of L-theanine via Oolong tea on meditation on 10 university students (non-randomized, it seems). Data collected via EEG and indicates statistically significantly more alpha waves during meditation (although it is unclear how long the meditation was).

This paper is bad. The english is so horrendous it feels like I'm having a stroke while I'm reading it, but that would be fine if they were good at reporting methods, which they are not (missing amounts of L-theanine and duration of meditation, they also mention reading earlier in the article, which I assumed was the control activity, but it doesn't come up again?). Also they report differences between scores, not effect sizes, and some figures are screenshotted images from a Windows Vista clustering application.

Examine agrees on the cognitive effects of l-theanine (if not on meditation specifically):

L-Theanine supplementation in the standard dosages (50-250mg) has been repeatedly noted to increase α-waves in otherwise healthy persons. This may only occur in persons with somewhat higher baseline anxiety[25][26] or under periods of stress (positive[14] and negative[27] results), but has been noted to occur during closed eye rest[5] as well as during visuospatial tasks[16] around 30-45 minutes after ingestion.[5][4] It appears that only the α-1 wave (8-10Hz) is affected, with no influence on α-2 wave (11-13Hz).[4]

Bill Willis, “Theanine”, 2022

Although I'm confused about the increased α-waves in "otherwise healthy patients"‽

Additionally, it notes that memory was slightly increased:

One study using a supplement called LGNC-07 (360mg of green tea extract and 60mg theanine; thrice daily dosing for 16 weeks) in persons with mild cognitive impairment based on MMSE scores, supplementation was associated with improved delayed recognition and immediate recall scores with no effect on verbal and visuospatial memory (Rey-Kim test).[17]

Bill Willis, “Theanine”, 2022

Experiment B: Self-Blinded RCT

This time I explicitely divided my meditation into a concentration part (first 15 minutes) and a mindfulness part (last 30 minutes).

Notes during consumption:

Ran the experiment from 2023-06-22 to 2023-09-28, sometimes with pauses inbetween samples.

I use the same statistical techniques as in the caffeine experiment, and start, as usual, with my predictions about the content of the pill:

>>> substances=pd.read_csv('../../data/substances.csv')
>>> experiment='B'
>>> substance='l-theanine'
>>> placebo='sugar'
>>> expa=substances.loc[substances['experiment']==experiment].copy()
>>> expa['datetime']=pd.to_datetime(expa['datetime'], utc=True)
>>> probs=np.array(expa['prediction'])
>>> substances=np.array(expa['substance'])
>>> outcomes=np.array([0 if i=='sugar' else 1 for i in substances])
>>> np.mean(list(map(lambda x: math.log(x[0]) if x[1]==1 else math.log(1-x[0]), zip(probs, outcomes))))
-0.705282842369643

This is not great. In fact, it's slightly worse than chance (which would be about -0.693). Not a great sign for L-theanine, and, in fact, it gets worse. I use the generalized and compacted code from the last experiments to get the other results, and they don't point a rosy picture for L-theanine:

>>> analyze('B', 'l-theanine', 'sugar')
    absorption  mindfulness  productivity  creativity     happy   content   relaxed     horny      ease     factor       ivl       time
d     0.040887     0.124170     -0.278448   -0.116001  0.164261  0.254040  0.119069 -0.031665 -0.072098   0.002561 -0.015955   0.003073
λ     1.378294     0.720780      5.517769    5.049838  3.983760  6.833004  1.496601  1.148131       inf        inf       inf        inf
p     0.765758     0.894798      0.109735    0.146420  0.266491  0.045270  0.740705  0.813279  0.000000   0.000000  0.000000   0.000000
dσ   -0.067847    -0.017736      0.039855   -0.043241 -0.155797 -0.046668  0.019655  0.251454 -0.016542 -18.901846  3.108518  13.660820

It worsens productivity and creativity (though not quite statistically significantly, but it's on the way there), but at least it improves my mood somewhat (though those results, besides contentment, might as well be due to random chance). No clear effect sizes with the flashcards either.

Conclusion

So a hard pass on L-theanine, I think. My current best guess is that as a night owl in the morning I'm still quite tired, and lack energy, with l-theanine just making me more sleepy than I already am. But then again, under Bonferroni-correction none of the p-values are statistically significant, so it looks like l-theanine just doesn't do anything. Maybe it's better when combined with caffeine?

Discussions

Melatonin

After being bullied into it by Gwern 2019 and reading more about dosage & administration in Scott Alexander 2018, I decided to tackle my irregular sleeping rhythm and my late bedtimes by taking Melatonin.

Getting enough high-quality sleep had been quite a problem for most of my life, I just could not find the willpower to actually go to bed early on most days. Most other advice relied on exactly bringing up this willpower (just read before going to bed/just stay away from screens/just do sports in the morning/just spend more time outside/just masturbate (actually counter-productive in my case!)); Gwern's framing as an enforcement mechanism appealed to me, and the cost-benefit analysis seemed sound.

I first tried buying Melatonin at a pharmacy, only to find out that it is prescription only in my country. A friend told me he had bought his from Ebay as a food supplement (laws have interesting loopholes), I ordered 100 3mg pills for ~30€ and they arrived, together with around 10g of protein powder.

Effects

I experimented around with administration time & dosage, finding out that 1/8th (≈0.375g) of a pill, administered at ~20:00, was usually sufficient to make me sleepy enough at 23:00 to actually go to bed (though the pills are kind of hard to cut well). I also realized that it was not necessary to take Melatonin every evening, once a good rhythm had been established, a dosage every 2 or 3 days was usually enough to keep the habit of going to bed early.

In the last couple of weeks I've felt like 1/8th of a pill is not enough, perhaps this is adaption to the substance (though I remember reading that adaption is negligible). Alternatively, the placebo effect might be wearing off.

While I haven't experiencde more vivid dreams from Melatonin (which I'd consider an advantage), sometimes my sleep on Melatonin is very light, bordering on dozing, and I also sometimes experience sleep paralysis while on melatonin. This is in stark contrast with my normal sleep on melatonin, which I'd guess is deeper than my normal sleep.

Reducing Sleep Duration

One large (potential) advantage of Melatonin would be a reduction in the amount of time slept. 2½ months after getting a wearable tracker, I decided to analyze my data on this. I'll spare you the details of data conversion (and will just say that it's kind of annoying that pandas merge doesn't implement the antijoin) and cut straight to the chase (of which the code can be found here):

>>> melatonin_sleep['minutes_asleep'].mean()
395.5652173913044
>>> non_melatonin_sleep['minutes_asleep'].mean()
387.2142857142857
>>> non_melatonin_sleep['minutes_asleep'].var()
17452.53506493507
>>> melatonin_sleep['minutes_asleep'].var()
5158.620553359683
>>> len(non_melatonin_sleep)
56
>>> len(melatonin_sleep)
23

It doesn't look like Melatonin has a large effect on sleep durations, at least with the current (meagre) sample sizes).

Maybe it helps if we filter out sleep that starts later than 6:00 in the morning (which excludes naps)?

>>> non_nap_melatonin_sleep=melatonin_sleep.loc[(melatonin_sleep['start_time'].dt.hour<6) & (melatonin_sleep['start_time'].dt.hour<18)]
>>> non_nap_melatonin_sleep['minutes_asleep'].mean()
395.5652173913044
>>> non_nap_non_melatonin_sleep=non_melatonin_sleep.loc[(non_melatonin_sleep['start_time'].dt.hour<6) & (non_melatonin_sleep['start_time'].dt.hour<18)]
>>> non_nap_non_melatonin_sleep['minutes_asleep'].mean()
419.29545454545456
>>> len(non_nap_melatonin_sleep)
23
>>> len(non_nap_non_melatonin_sleep)
44
>>> lr=likelihood_ratio_test(placebo_likelihood_ratio(non_nap_melatonin_sleep['minutes_asleep'], non_nap_non_melatonin_sleep['minutes_asleep']))
6.363562898136653
>>> llrt_pval(lr)
0.06284859113951252

Here it looks like there is a medium-sized advantage to taking melatonin, with ~25 minutes shorter sleep (at the edge of 'statistical significance').

While Melatonin has been very useful at enforcing bedtimes, the advantage of sleeping less has been moderate, and potentially just caused by noise.

Takeaway

I am very glad that I've bought & tried Melatonin; it has to a large degree fixed a significant problem in my life. I am now happier in the morning when I wake up, less tired during the course of the day, and don't have to feel guilty at 04:00 because I stayed up too late.

At my current usage, my stash will last me $95 \hbox{ pills } \cdot 8\frac{\hbox{dosages}}{\hbox{pill}} \cdot 2\frac{\hbox{days}}{\hbox{dosage}}=1520 \hbox{ days}$: more than 4 years! Even if the future effects are just half as good as the past effects, this investment was completely worth it.

Nicotine

I started taking nicotine (in the form of nicotine chewing gum with 2mg of active ingredient) in high-pressure situations (e.g. I'm procrastinating on an important task and have anxiety around it, or during exams). So far, it seems especially useful to break me out of an akratic rut.

See Also

Appendix A: Predictions on Self-Blinded RCTs

Predicting the outcomes of personal experiments give a useful way to train ones own calibration, I take it a step further and record the predictions for the world to observe my idiocy. The probabilities link to PredictionBook/Fatebook.

Question Caffeine probability Caffeine outcome L-Theanine probability L-Theanine outcome
Prediction of Arm
My prediction about the content of the pill is more accurate than random guesses 80% Yes 65% No
My prediction about the content of the pill has a log score of more than -0.5 60% No 40% No
Meditation
On interventional days, my average mindfulness during meditation was higher than days with placebo 60% Yes 45% Yes
On interventional days, my average absorption during meditation was higher than days with placebo 40% No 55% Yes
On interventional days, the variance of values for mindfulness during meditation was lower than on placebo days 55% No 60% No
On interventional days, the variance of values for absorption during meditation was lower than on placebo days 35% Yes 65% No
$\lambda<1$ for the mindfulness values 20% No 7% Yes
$\lambda<1$ for the absorption values 25% No 5% No
$\lambda<4$ for the mindfulness values 82% No 15% Yes
$\lambda<4$ for the absorption values 88% No 20% Yes
$\lambda<10$ for the mindfulness values 65% Yes
$\lambda<10$ for the absorption values 60% Yes
Mood
On interventional days, my average happiness during the day was higher than days with placebo 65% Yes 55% Yes
On interventional days, my average contentment during the day was higher than days with placebo 45% Yes 60% Yes
On interventional days, my average relaxation during the day was higher than days with placebo 35% No 65% Yes
On interventional days, my average chastity during the day was higher than days with placebo 50% No 50% No
On interventional days, the variance of values for happiness during the day was lower than on placebo days 55% No 60% Yes
On interventional days, the variance of values for contentment during the day was lower than on placebo days 30% No 65% Yes
On interventional days, the variance of values for relaxation during the day was lower than on placebo days 30% No 65% No
On interventional days, the variance of values for chastity during the day was lower than on placebo days 50% No 50% No
$\lambda<1$ for the happiness values 45% No 8% No
$\lambda<1$ for the contentment values 40% No 5% No
$\lambda<1$ for the relaxation values 37% No 5% No
$\lambda<1$ for the chastity values 60% No 10% No
$\lambda<4$ for the happiness values 85% No 18% No
$\lambda<4$ for the contentment values 90% No 12% No
$\lambda<4$ for the relaxation values 90% No 12% Yes
$\lambda<4$ for the chastity values 95% Yes 20% Yes
$\lambda<10$ for the happiness values 75% Yes
$\lambda<10$ for the contentment values 70% Yes
$\lambda<10$ for the relaxation values 70% Yes
$\lambda<10$ for the chastity values 85% Yes
Productivity and Creativity
On interventional days, my average productivity during the day was higher than days with placebo 52% Yes 65% No
On interventional days, my average creativity during the day was higher than days with placebo 55% Yes 55% No
On interventional days, the variance of values for productivity during the day was lower than on placebo days 40% No 70% No
On interventional days, the variance of values for creativity during the day was lower than on placebo days 65% No 50% Yes
$\lambda<1$ for the productivity values 40% No 7% No
$\lambda<1$ for the creativity values 45% No 9% No
$\lambda<4$ for the productivity values 75% No 20% No
$\lambda<4$ for the creativity values 80% No 25% No
$\lambda<10$ for the productivity values 60% Yes
$\lambda<10$ for the creativity values 70% Yes

I also recorded my predictions about the content of the pill on PredictionBook (Caffeine: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50) and Fatebook (L-theanine: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50).

I continue to be worse than chance in my predictions on the outcomes of my own experiments:

>>> import math
>>> import numpy as np
>>> probs=np.array([0.8, 0.6, 0.6, 0.4, 0.55, 0.35, 0.2, 0.25, 0.82, 0.88, 0.65, 0.45, 0.35, 0.5, 0.55, 0.3, 0.3, 0.5, 0.45, 0.4, 0.37, 0.6, 0.85, 0.9, 0.9, 0.95, 0.52, 0.55, 0.4, 0.65, 0.4, 0.45, 0.75, 0.8])
>>> outcomes=np.array([1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0])
>>> np.mean(list(map(lambda x: math.log(x[0]) if x[1]==1 else math.log(1-x[0]), zip(probs, outcomes))))
-0.8610697622640346
>>> np.mean(list(map(lambda x: math.log(x[0]) if x[1]==1 else math.log(1-x[0]), zip([0.5]*50, outcomes))))
-0.6931471805599452

Die Welt gibt dir viel falsche Zeichen,
dem tückischen Geist zu vergleichen,
Du bist, alle Zeichen verachtend,
zu dem ohne Zeichen gegangen.

—Dschelāladdīn Rūmī, “Am Ende bist du entschwunden”, 1256


  1. Higher is better. 

  2. Whether higher or lower values are better here is not clear. 

  3. Only 21 datapoints. 

  4. The value of higher or lower values here is not clear: Do we want to spend more time per flashcard, or are we content with fast but sloppy performance?