*author: niplav, created: 2023-01-06, modified: 2023-09-16, language: english, status: notes, importance: 3, confidence: log*

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

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 prediction^{1} |
-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) |

Chastity^{2} |
-0.14 (λ=1.9, p=0.64, 0.11) | -0.03 (λ=1.15, p=0.8, 0.25) |

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 flashcard^{3} |
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
Dschingis 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).

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

Variables tracked (see more here):

**Arm Prediction**: I tried to predict whether the substance I'd taken was placebo or caffeine.- Meditation: 45 minutes of ānāpānasati, started 0-60 minutes after taking the dose, tracking two variables.
**Mindfulness**: How aware I was of what was going on in my head, modulo my ability to influence it.**Absorption**(often called concentration): How "still" my mind was, how easily I was swept away by my thoughts.

**Productivity**and**creativity**, recorded at the end of the day.- Mood: Tracking 4 different variables at random points during the day, namely
**Happiness/Sadness****Contentment/Discontentment****Relaxation/Stress****Horniness/Chastity**: Chastity being simply the opposite of horniness in this case.

**Flashcard performance**: Did my daily flashcards for ~20 minutes, started 0-60 minutes after finishing meditation. More explanation here**Ease**: How easy I remembered the card (1: not at all, 4: baked into the memory).**New ease factor**: How much the card will be pushed into the future if I answer it correctly next time.**New interval**: How far the card has been pushed into the future.**Time**: How long I spent on the card.

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

- Time: The Clearer Thinking tool for the value of my time returns 15€/hour, which gives a time cost of 18.75€ for preparing the experiment.
- Time for filling: 35 minutes
- Time for preparing envelopes: 40 minutes

- Cost of caffeine pills:
`$\frac{0.0825€}{\text{200mg caffeine pill}} \cdot \text{ 200mg caffeine pills}=2.0625€$`

- Cost of empty capsules:
`$\frac{0.03€}{\text{capsule}} \cdot 25 \text{ capsules}=0.75€$`

- Cost of sugar: Negligible.

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:

- 3rd dose: Out of fear that the placebo pills have some sugar stuck outside of them, which could de-blind the dose, I take a bit (~10 g) of sugar with each pill.
- 7th dose: Increase time between consumption and starting to meditate to ~45 minutes, after finding out that the onset of action is 45 minutes-1 hour.
- 14th dose: Noticed that during meditation, sharpness/clarity of attention is ~high, and relaxing after becoming mindful is easy, but attention strays just as easily.
- 49th dose: Took the pill, meditated, lay down during meditation and fell asleep. Likely
_{10%}placebo.

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.

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.

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')
```

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
```

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
```

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
```

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.

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]
```

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])
```

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.

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
```

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.

- Take 20-25g (or 0.3g/kg) for 5-7 days (Loading)
- Then take 5g daily for 3-4 weeks (Maintenance)
- Take a week or two off creatine, and then repeat (Wash-out)

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 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*

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

- Time for preparation: 93 minutes
- Cost of l-theanine pills:
`$\frac{~0.25€}{\text{500mg L-theanine pill}} \cdot 25 \text{ 500mg L-theanine pills}=6.25€$`

- Cost of empty capsules:
`$0.75€$`

Notes during consumption:

- 1st dose: Made a mistake while filling the envelopes, accidentally deblinded myself.
- 19th dose: Took L-Theanine & did my routine, then took a nap and woke up 3 hours later.
- 43rd dose: Woke up with "brain fog", meditation was dull & all over the place. Maybe because I'd been drying laundry in my room during the night? Also took nicotine later the day to kickstart some work on a project that needed to be finished.

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.

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?

See my report on my melatonin consumption.

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.

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*