Amazing new algorithm makes MMI practical now

Mind Matter Interaction
22

I wondered why there is always statistical convergence in the RNG data stream, which does not allow violating the laws of probability theory. The very existence of the rubberband effect seems counterintuitive, because if Psi creates a bias in one direction, what then creates a bias in the opposite direction? I don’t think statistics are intelligent and correct themselves, or that the bit sequence has physical properties that cause it to regain its original shape.

So far, my main guess is that Psi itself is supersymmetric. That is, when we create Psi by the power of intention, anti-Psi is created simultaneously with it, the impact of which leads to the rubberband effect. Virtual particles behave the same way, by being born in pairs and annihilate.
This raises the question, is it possible to make Psi not only affect the data stream, but also redirect the anti-Psi into a separate data stream, which would be inverted and combined with the first stream?

23

The rubber band effect is anecdotal, meaning to my knowledge it has not been studied enough to confirm its existence. My experience, and several others who have tested MMI effects, suggest there is a real effect. To be sure, the results of MMI measurements will return to an average value without continued mental effort simply by adding more results. However, after a prolonged and concerted effort to obtain results in one direction, the following results without strong effort seem to move surprisingly quickly back to an average value. This is a description of the “rubber band effect” in MMI measurements.

I have tried to separate the direct MMI effect from its reverse effect by recording both the initiated trials and those that would have occurred in between them. I don’t recall seeing any effect in the non-initiated trial data. It is only the trials for which feedback is provided that show MMI effects. Therefore it is user feedback that couples the mental intention to the initiated trial results. Since there was no feedback for the non-initiated or virtual trials, there was no opposite effect in that data.

It is possible that user feedback, which continued after concerted effort stopped, contributes to the rapid return to average. This would be the very time where the counter- or anti-psi results manifest. I have tried reversing the sense of the intended result to the opposite direction during such brief periods of released effort. The results seemed suggestive at times, but I didn’t do any formal data taking or statistical studies.

Another effect I have observed is a type of MMI inertia. That is, results in one direction (for example, to produce more 1s in the output) tend to continue for at least a short time. It seems very difficult to randomly switch between intended directions from one trial to the next. I assume this is a psychological effect: it is hard to immediately release an intensely held intention and focus on the opposite result. This is something that might be overcome by specific training to be able to shift back and forth trial to trial – a practice I never tried.

24

Yesterday I decided to test one theory: if the influence of Psi creates a counteracting rubberband effect, which in real time leads statistics to the theoretically expected results, is it possible to separate the Psi influence from the counteracting anti-psi by the force of intention? For the experiment, the bit stream was conventionally divided into two channels, where the first channel included all the odd bits, and the second channel included all the even ones. Psi was supposed to be focused on the first channel, while the anti-psi bedt was either forced out to the second channel or blurred on both. Since the second channel should contain only anti-psi, after reading all the bits it was subjected to inversion in order to reduce the rubber band effect.

Bits were obtained from two sources, ANU and MED QWR4E004 in groups of 1024 or 64 bits per keystroke. When pressing a key, I tried to focus the intent on getting the Z-score in the series to be as high as possible.

I did not expect any particularly positive results from this experiment, however, after 5 sessions, I noticed that in all data obtained from the MED QWR4E004, the z-score of the bitstream with the anti-psi inversion channel turned out to be higher than the z-score of the raw bitstream.
For ANU and Pseudo, no such regularity was observed.
Nevertheless, there are still few experiments, so it is too early to say anything.

ANU:
Regular: Bits: 53248 Z-score: 1.083399
Splitted: Bits: 53248 Z-score: 0.043336

Regular: Bits: 7936 Z-score: -0.987829
Splitted: Bits: 7936 Z-score: 0.898027

Regular: Bits: 29696 Z-score: 0.487450
Splitted: Bits: 29696 Z-score: 0.116060

Regular: Bits: 2560 Z-score: 0.632456
Splitted: Bits: 2560 Z-score: 1.423025

Regular: Bits: 3072 Z-score: 0.288675
Splitted: Bits: 3072 Z-score: -1.443376

QWR4E004:
Regular: Bits: 53248 Z-score: 0.624038
Splitted: Bits: 53248 Z-score: 1.178738

Regular: Bits: 7936 Z-score: -0.404112
Splitted: Bits: 7936 Z-score: -0.269408

Regular: Bits: 29696 Z-score: -1.172202
Splitted: Bits: 29696 Z-score: 0.916871

Regular: Bits: 2560 Z-score: 0.039528
Splitted: Bits: 2560 Z-score: 0.909155

Regular: Bits: 3072 Z-score: -1.010363
Splitted: Bits: 3072 Z-score: 1.226869

Pseudo:
Regular: Bits: 53248 Z-score: 0.944724
Splitted: Bits: 53248 Z-score: -1.100733

Regular: Bits: 7936 Z-score: 1.234786
Splitted: Bits: 7936 Z-score: 0.695971

Regular: Bits: 29696 Z-score: 1.183808
Splitted: Bits: 29696 Z-score: -0.835629

Regular: Bits: 2560 Z-score: -1.264911
Splitted: Bits: 2560 Z-score: 0.079057

Regular: Bits: 3072 Z-score: 2.670245
Splitted: Bits: 3072 Z-score: 0.866025