Pain Inducing Patterns

Ron Blue rcb1 at LEX.LCCC.EDU
Mon Nov 6 10:51:20 EST 1995


On Fri, 3 Nov 1995, Jagan Narayanan wrote:
> Hi Guys:
>I also downloaded the picture and looked at it. There is nothing unique about
> it. If you change the colors from black and white to red and blue you 
>get the same feeling. If you do concentric circles instead of lines you 
>will have the same feeling. What is the big deal here?? > 
>>>>cut
> > Shouldn't there be a control group for this experiment?  Perhaps 
I asked 14 students if they felt pleasure while looking at the
pain picture.  No one reported pleasure.  Two reported pain.  I said
each time that you are are suppose to feel pleasure.  

The big deal?  The picture is a stationary image.  Assuming that the
brain processes information in oscillations and that random thermal
noise is gaussian from the central limit principle - a stationary signal
will be oscillated into a gaussian pattern.

The following is a guess.  Using the principles in Random Vibrations,
Spectral & Wavelet Analysis by D.E. Newland (c) 1993, I believe the
following would be true.

1.  the wave patterns of white can be viewed as an ON signal that is
linear correlated to vertical positions.  The bandwidths are correlated,
the black is an OFF signal and is correlated.  The black bandwidths are
correlated.  The correlation is +1.  Auto correlation would be 1.  

2.  The black/white patterns are cross correlated.  The correlation is
one.  The frequency or harmonic of white pattern and black pattern are 
perfectly correlated since they were sampled and seeded with the same
wave frequency used for neuroprocessing.  
Fourier equation and inverse fourier equations
would be equal.  Periodic or circular correlation functions would be
1.  The spectral density would be equal.  Overlapping correlational
functions would be equal. 


Black/white, blue/yellow, red/green are opponent process.  This means
that when a blue stimulus had been habituated the opposite color yellow
will be observed in a grey visual field.


ALL FUNCTIONS ARE AT THEIR MAXIMUM VALUE resulting in the FAILURE message
of the neuro system to be activated.  To us this means pain.

Why only one in six or seven?  Why only one in ten with strong pain?

A guess.  I have vision problems.  My perception is frequently distorted.
The correlations for me can not be at their maximum.  Also the
frequency of oscillation generated by randomly looking at the stimulus
should be gaussian.  So only 1/6 would be the values at the maximum range
of a gaussian distribution assuming that the brain is engaging in
wavelet analysis due to opponent process is working on a six standard
deviation formula.

These are pure speculations.  The 1/6 is similar to the reported
hexagons that overlapped in some migraine auras.  This mathematical pattern
I believe is tell us HOW the brain process information.

Assume pain is a stochastic resonance of a zero sum or
flat line resultant over all band widths.  For any observation
data looks chaotic but it is totally deterministic.

Visualize a wave machine made two sheets of flat glass, colored
water, and oil on top of the water.  Also consider multiple wave machines
in front of each other and wave machines perpendicular to each other as
representing a neuro projection area.  

Sorry, back to the simple model.
The total wave length possible is the width of the glass.
The sum of all wave patterns for the oil and water is zero or
a flat line.  At any particular location on the wave machine
(i.e. neuro activity in the pain system) the activity looks chaotic
but can be causally linked to the vibrations on the wave
machine.  Stranglely when you move away from the oscillation
location decreased activity is noticed, and increased in 
activity in noticed in areas that should not be related to
the original location.

If you backup and look at the wave machine
the connect in now obvious.  

You are observing TWO waves.  The top wave is an inversion of the bottom.
The power spectral is zero for the opponent process and the activation
process over the bandwidth!

Ron Blue



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