I
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Gs5C
£=.
i
i
Final
Report- -Objective
E,
Tasks 3 and 4
Covering
the Period
October
1985
to 30 September
1986
December
1986
INTUITIVE DATA SORTING: AN INFORMATIONAL
MODEL OF PSYCHOENERGETIC FUNCTIONING
By:
EDWIN
C. MAY
Prepared
for:
PETER
J. McNELIS, DSW
CONTRACTING OFFICER'S TECHNICAL REPRESENTATIVE
SRI
Project
1291
Approved by:
ROBERT
S. LEONARD, Executive
Director
Geoscience and Engineering Center
333
Ravenswood Avenue
(415)
326-6200
*
Cable:
oved For Release 2000/08/07
Menlo
Park,
California
94025
•
U.S.A.
SRI
INTL MPK
• TWX:
910-373-2046
: ClA-RDP96-00787R0005001 40001 -3
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ABSTRACT
We develop a comprehensive model of psychoenergetic functioning called Intuitive Data
Sorting (IDS).
Extending purely philosophical arguments, we derive specific mathematical
predictions for the interpretation of random number generator experiments.
Two experiments
are analyzed:
(1)
a pseudorandom number generator (PRNG) experiment conducted at SRI
International, and
(2)
a random number generator experiment conducted at the Princeton
Engineering Anomalies Research laboratory at Princeton University.
Preliminary results from
the PRNG experiment are in
statistical agreement with the IDS model.
We show, however,
that the Princeton University RNG data were collected under unfavorable conditions to serve
as a test of the IDS model; we recommend an RNG experiment protocol that will allow a
more favorable test.
ii
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TABLE OF CONTENTS
ABSTRACT
LIST OF ILLUSTRATIONS
LIST OF TABLES
I
INTRODUCTION
A.
A Conceptual Thought Experiment
B.
A Practical Thought Experiment
II
BACKGROUND
III
METHOD OF APPROACH
A.
Theoretical Considerations
1.
RA Data Reduction
2.
Mean Chance Expectation
a.
Theoretical Considerations for Overall Mean Chance Expectation
.
,
b.
Theoretical Considerations for an RA Interaction
c.
Theoretical Considerations for the IDS Model
d.
IDS
vs. RA in Perspective
3.
Analysis and Hypothesis Testing
B.
The SRI Pseudorandom Data
1.
Justification for a Pseudorandom Number Generator Experiment
2.
PRNG Experiment Description
C.
The Princeton Engineering Anomalies Research Data
IV
RESULTS AND DISCUSSION
A.
The PRNG Experiment Results
B.
The PEAR Results
1.
Data Reduction and Analysis
2.
Discussion of the PEAR Results
V
CONCLUSIONS AND RECOMMENDATIONS
REFERENCES
iii
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LIST OF ILLUSTRATIONS
1.
Generalized Decision Process
2.
A Conceptual Thought Experiment
3.
A Practical Thought Experiment
4.
One RA Model Compared with MCE
5.
One IDS Model Compared to MCE and One RA Hypothesis
6.
MCE and PK- Data
iv
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LIST OF TABLES
1.
Exact and Approximate Values of ln(|Ap|)
2.
Raw Data for Operator 10—Princeton University Data
3.
MCE
for Each Data Set
4.
Results of the Analysis of the PEAR RNG Data
v
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I INTRODUCTION
Since 1979, SRI International has been constructing a model of psychoenergetic
functioning that may provide an explanation for a broad range of experimental data.*
The
idea
first occurred to us as part of an interpretation of an experiment we conducted that
year. 1 !
In that experiment,
it appeared that individuals were able to make decisions
(psychoenergetically) based upon information propagating backward in time.
This unorthodox
concept, in
its generalized form,
is shown schematically in Figure
1 as one of the inputs to a
decision process.
For example, suppose that you had to decide
if
it were safe to cross a highway.
Clearly,
one input to that decision
is real-time analysis:
There
is a continuous stream of traffic; don’t
cross the road.
Suppose that there was only one car on the road, but
it was at some distance
away.
Experience might suggest that you are unable to run
fast enough to avoid being hit;
don’t cross.
Without defining intuition,
it might
tell you not to cross the highway even though
* This report constitutes deliverable “a”— final report on RA perturbation of device-driven
random sequences—for Objective E, Task
4, Conduct a retrospective test and analysis
of the Intuitive Data Sorting (IDS) model, and the deliverable—final report on RA
activity upon pseudorandom number generators—Objective E, Task
3,
Investigate RA
activity by examining statistical changes of state of pseudorandom number generators.
+
References are listed at the end of this report.
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all other indicators suggest that
it
is safe. We recognize that
all these processes are
intermixed, and that there may be others that we have not yet considered.
Our model proposes that there
is one other source of input to the decision process that
has not yet been considered—information propagating backward in time from the future.
You
have an impression (maybe a visual experience)
that indicates that you are dead on the
highway; don’t cross the road.
While there are specific examples of information propagating backward in time in physics
(e.g., the Dirac equation—a positron traveling forward in time
is mathematically identical to
an electron traveling backward in time), the idea that
it
is possible at the macroscopic level
is
not generally accepted.
It
is beyond the scope of this report to discuss the profound
implications for physics and philosophy
if
it were true.
Since the late
1930s, however, the parapsychological research journals have been
reporting evidence that information from the future
is available in the present—at least
statistically. We are not able,
at this time, to provide a complete analysis of this literature,
which claims evidence for precognition—the parapsychological term for accessing information
from the future.
Rather, we provide a brief discussion of two of the pertinent reports.
In 1983, C. T. Tart reviewed the precognition “forced choice”
literature as part of an
investigation to determine information rates for both real-time and precognition experiments2
In
this context, “forced choice” implies that the subject was aware of the limited number of
possible targets, but was blind to the actual target chosen for any single
trial.
Tart found 32
studies,
of this type, that claimed statistically significant evidence for precognition.
In 1986, Nelson et
al. reported the results of over 400 “free-response” precognition
trials demonstrating strong statistical evidence for information flow from the future. 3 ’ 4
“Free-response” experiments differ from “forced-choice”
in that the target material
is
relatively unbounded
(i.e., while the general nature of the target material might be known by
the subject, the range of target possibilities
is very large and
is unknown to the subject).
Because these reports provide strongly suggestive evidence for macroscopic information
flow from the future, we proceed with the development of the model.
A.
A Conceptual Thought Experiment
Consider an arbitrarily complex experiment as shown in Figure
2.
Further,
suppose that this experiment has one result and that there
is a single decision required before
the experiment
is conducted.
(We recognize that complex experiments produce more than
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one result and require many decisions for their design; therefore, we are considering only the
primary result and decision, and are including the lesser results and decisions as part of the
complexity of the experiment.)
There are no limitations placed upon the complex
experiment.
In principle,
it might be conducted by many researchers at different laboratories
for many years.
To be specific,
let us assume that the single decision involves turning a knob to
one of five settings,
1 through
5.
Also consider that the single result
is a meter reading in
which we assume a high reading
is “good.”
Our model proposes that the following internal
dialogue by the decision maker
is possible:
Information Flow (Backward in Time)
Very
Complex
Experiment
t
f
Single
N.
f
Human
J
Decision
J
f
Single
\
V
Result
M
FIGURE 2
A CONCEPTUAL THOUGHT EXPERIMENT
If I were
to conduct
this experiment with the knob set at
2,
let me
“peek”
into the future and examine the result .
I perceive the meter
showing a low reading and thus I reject that option
.
Suppose I were
to
conduct
this experiment with the knob set at
4.
I perceive the meter
showing a very high reading .
Because / like that result,
I will set the
knob at
4, and conduct the experiment!
For the above dialogue to be sensible, we must be able to “sample” the
future—reject the unwanted ones, and “select”
a preferred one.
When the model
is
formulated mathematically and applied to a specific set of experiments,
it will provide
compelling evidence
(see below) that this unorthodox idea
is possible.
B.
A Practical Thought Experiment
By describing a practical thought experiment, we
will illustrate an important
consequence of the model.
If we are allowed to “sample” the future, then what was
previously thought of as a cause-and-effect relationship might be confused with an
informational relationship involving no causality
at
all.
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Suppose that we wanted to demonstrate that cyclamates cause cancer in laboratory
rats.
Further, assume that normal double blind protocols are in effect. A laboratory assistant
is asked to administer injections to two separate groups of rats—one a control group, the
other a target group.
By double blind, we mean that the assistant does not know which
is the
control group, nor which injections
will be cyclamates rather than biologically neutral
cyclamate placebos.
Let us assume that the experiment produces a statistically significant
(e.g., p <
0.01) separation between the control and target groups
(i.e., the probability of a target rat
contracting cancer
is significantly larger than for a control rat).
This result
is shown
schematically in the lower half of Figure
3.
The conclusion that
is drawn from this
experiment
is that whatever was in the syringe caused cancer in the rats.
FIGURE 3
A PRACTICAL THOUGHT EXPERIMENT
To illustrate an informational explanation of this same result, we describe a totally
fraudulent way in which this experiment might be conducted.
Suppose that the researcher
is
motivated to cheat, and in doing so
is able to select from a normal population of rats those
that are predisposed toward cancer.
(Because this
is a thought experiment, we can invent a
mechanism by which he/she could do this.)
We assume that this researcher
is sophisticated in
statistical matters and sorts the selected rats into target and control groups in such a way
(by
cross mixing them) to produce the identical results described above.
Furthermore, to
preclude any possible confounding of the fraudulent outcome, the researcher replaces the
contents of the syringes with distilled water.
Then he/she
lets the experiment proceed as
above.
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The
statistically significant result (p < 0.01) would
still be interpreted as before.
Whatever was in the syringe caused cancer in the rats—in this case distilled waterl
Thus,
a
purely informational process (knowledge of the individual rats’ predisposition toward cancer)
is
mistaken as a causal one
(distilled water causes cancer in rats).
The researcher,
in the above example, need not be fraudulent to produce the
identical result. We propose that by statistically “peeking”
into the future he/she
is able select
the predisposed rats.
For example, having picked a
rat, he could sample the future to
determine
if that rat
is likely to contract cancer.
If so, he would add
it to the target group;
if
not,
it would be included in the control group.
Thus,
this researcher has “simply”
psychoenergetically sorted rats
(see Figure
1). We
call this putative
ability Intuitive Data
Sorting (IDS).
Because large bodies of research have concluded some form of cause-and-effect
relationship based upon statistical hypothesis testing,
it
is important to determine
if our model
has any scientific basis.
What follows
is the application of our model to a large body of
psychoenergetic experimental data.
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II BACKGROUND
In 1969, Schmidt5 introduced a type of psychoenergetic experiment in which individuals
were asked to “modify” the
statistics of “true” random number generators (RNG),
i.e.,
devices that generate sequences of numbers based on some fundamental random process such
as radioactive decay or thermal noise.
Since publication of that important initial paper, we
have been able to locate (through 1984) 56 pertinent references in English language journals
and reports describing a total of 332 individual binary RNG experiments. We simulated an
additional 95 nonsignificant experiments to account for a “filedrawer” problem
(i.e.,
experiments that were conducted and not published because they were not significant)
. 6
By
including these simulated studies in the data base, we refrain from using an
artificially inflated
data base because of selected reporting of “successful”
studies.
In the preliminary analysis of
these data, we calculated that the probability that the observed deviations occurred by normal
statistical fluctuations alone was p < 3.9 X
10- 18 during experimental conditions, and p <
0.78 under control conditions.
Clearly, there
is a statistical anomaly within these data.
Since 1969, there has been considerable discussion about mechanisms that can explain
these RNG results. 5
- 7
- 8
(For the purpose of this report, we assume that artifact and incorrect
statistics have been accounted for.
Radin has shown that this assumption
is true to
first
order; 6 a detailed meta-analysis
is now in progress to determine the overall validity of the
RNG data base.)
The most frequently proposed explanations are remote action (RA) and
precognition.
Under an RA hypothesis, by definition, a participant “forces” a physical
modification in a source of random signals so as to produce a change in the output statistics.
Alternatively, under the IDS hypothesis, we propose that humans can make decisions
(by
psychoenergetic means)
to take advantage of the natural and unperturbed fluctuations of a
system.
In the context of an RNG experiment,
it appears that individuals can anticipate
locally deviant subsequences from within larger and unperturbed sequences and make
decisions based upon that knowledge.
Suppose that an individual
is asked to “make” the
RNG produce more binary ones
(Is) than zeros
(0s).
Rather than “causing” the device to
produce binary ones, we suggest that the participant has simply initiated the run by
anticipating when the RNG was going to produce a series of ones as part of
its natural
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binomial fluctuation.
Thus, the participant has capitalized upon natural events, rather than
“causing” anything to occur.
In our final report to a client in FY 1984, we applied the IDS formalism to the data
base of RNG experiments described above. 9 We found that the data were described by the
IDS model rather than the RA hypothesis.
There were problems, however, with the
comparison of the IDS model to the previously published RNG data base.
The IDS formalism
is derived from the assumption that the sequence length,
[n] ,
results from a single press of a
button.
None of the experiments in the data base were reported in that way.
All of the data
were aggregates over many button presses.
While we were able to draw conclusions based
upon averaged data,
(e.g., on the average, IDS appears to account for the results in the
historical data base)
, the ideal test of IDS must be conducted using data resulting from single
button presses.
There are two questions that should be addressed when conducting single button press
experiments:
•
Is IDS possible under conditions that preclude any RA?
If so, are there
any sequence length dependencies in the ability?
•
Can IDS provide an interpretation of RNG data?
We have addressed both issues as separate tasks for FY 1986.
The
first task was to
examine the IDS model,
at SRI under a condition that precludes RA—using pseudorandom
number generators.
To know
if IDS
is possible, in principle, we must conduct experiments
that have as few confounding factors as possible.
The second was to examine the IDS model
using “true” RNGs.
The only data of this type that was available was collected by the
Princeton Engineering Anomalies Research (PEAR) Laboratory,
in the school of Engineering
at Princeton University. 10 We therefore
let a subcontract to this laboratory to provide us with
their single button press RNG data.
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III METHOD OF APPROACH
Our method of approaching the data generated at SRI and Princeton proceeds in three
phases:
•
Theoretical considerations—a derivation of mean chance expectation for
the various models under study.
•
The SRI data—details of the pseudorandom data collection and display.
•
The PEAR data—details of the Princeton “true” RNG data collection.
A.
Theoretical Considerations
The data analysis proceeds in three steps, data reduction, definition of mean chance
expectation, and analysis and hypothesis testing.
1.
RA Data Reduction
For each button press, the raw data consist of a sequence length
[n] and the
number of “hits”
[h]
(e.g., binary
Is). We transform these data into logarithmic form for
analysis by computing the following quantities for each data point:
ln|A P
| =ln|(£ -0.5)|
ln(n)
and
If the linear correlation coefficient for
all pairs of data points, ln(n) and ln|Ap|,
is
significant, then a straight line may be
fit to the data, and thus, the raw data are reduced to
two coefficients—the slope and the intercept.
&
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2.
Mean Chance Expectation
In the absence of all forms of psychoenergetic functioning, we must determine,
theoretically, the mean chance expectation (MCE)
for the type of data described above.
a.
Theoretical Considerations for Overall Mean Chance Expectation
We define the sequence length,
[n], as the number of samples collected from
an RNG as a result of a single button press.
If we consider
all possible values of sequence
lengths,
it
is convenient to use the binomial
statistic in
its exact form for n < 200, and use the
normal approximation for larger sequence lengths. We use the observed fractional hit rate, p
(hits/trials), minus the expected fractional hit rate, p0 (equal to 0.5
for binary RNGs),
as the
dependent variable, Ap = p — po, and
[n]
as the independent variable.
For the “continuous” region where n > 200, Ap
is normally distributed about
a mean of zero with a standard deviation of
cr0 given by
P
Q
Ko
o
where q0 =
(1 - p0).
For convenience, we shall examine the
statistical properties of
[n] and
Ap in logarithmic form—ln(n) and ln(Ap),
respectively, and, without loss of generality,
consider only the absolute value of Ap,
|Ap|.
The expected value of ln|Ap|
is given by
-0.5
J
In
| Ap
|
e
V °°
d (Ap)
In
| Ap
|
I
e
° 3
d (Ap)
Following the usual definition of a z-score,
let
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Substituting into Equation
(1), we find that
In
I Ap
|
=
In a0 + In
| z
|
And because
In
| Ap
|
=
0.5 In P0 q0
+
In
| z
|
-
0.5
In n
•
(2)
Equation
(2)
is a simple way to express the null hypothesis of no
psychoenergetic functioning for the RNG data.
If many RNG runs of varying sequence lengths
are conducted in the absence of
all psychoenergetic phenomena, the natural logarithm of the
sequence lengths and their associated logs of |Ap|’s are linearly related—having a slope of
-0.5, and an intercept determined by p0 , qo and the average value of ln|zj—a known
constant.
Given the
“ unboundedness’’ of
|z|
(i.e., 0 <
|z| ^ oo),
it may surprise some
readers that there
is a linear relationship between the expected value of ln|Ap| and ln(n).
To
demonstrate this linear relationship from a different perspective, we calculate the expected
linear correlation coefficient,
r, under the null hypothesis: 9 *
r
-0.5774 X(S X-S M )
19.7392 +
(S x- S M )
(3 )
Sx and Sm are the logarithms of the maximum and minimum sequence lengths, respectively.
Equations
(2) and
(3) form the basic set of relationships that describe,
in
detail, the expected results under the null hypothesis.
A significant note must be added at this point.
Equation
(2) represents the
MCE under a normal approximation.
As we
will show,
for any actual experimental case, the
*The authors wish to acknowledge and thank Dr.
J. Utts for deriving this relationship.
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difference between normality and the exact binomial calculation becomes important in
determining the expected slope of the MCE line.
Table
1 shows a comparison between the
normal approximation and the exact binomial calculation for the expected value of ln|Ap|
as
a function of selected sequence lengths.
Table
EXACT AND APPROXIMATE VALUES OF ln|Ap|
Sequence
Length
Exact
Binomial
Normal
Approx.
Percent
Error
-3.7880
-3.9774
4.76
-5.0504
-5.1288
1.53
-5.8922
-5.9335 warn
100000
-7.0689
-7.0848 mEm
For any given experiment the MGE line should be computed in the following
way;
•
Compute the exact value for ln|Ap|
for each sequence used in the
experiment.
•
Fit the above result with a weighted straight line.
The weighting
factor for each sequence length
is the number of trials,
for that
sequence length, that were conducted in the experiment under
study.
•
Use the slope and intercept from the above
for the MCE values.
b.
Theoretical Considerations for an RA Interaction
Because in the most general case of RA a subject could “perturb
the RNG
device in any way, the Gaussian in Equation
(1) must be replaced by an arbitrary function,
f(Ap,n).
Or,
co
/in
| Ap|
f(Ap,n) d(Ap)
~ 0
In
| Ap
|
=
CO
J'f(Ap,n) d(Ap)
~ 0
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To evaluate this general relationship, we must assume some specific form for f(Ap,n).
From
this point on in the development of the model, we will assume that RA induces a minor
perturbation in the physical system.
Thus, we assume that f(Ap,n) remains Gaussian, but RA
shifts
its mean—slightly.
We consider a
class of RA models in which RA perturbs
(to a small degree)
a binomial distribution by shifting
its mean.
The theoretical task
is to evaluate the expected
value for the ln|Ap| using Equation
(1).
Define
AP
=
P
- P,
AP,
=
P a
- Po
AP a
=
P
- Pa
= AP - AP S
.
As before, p0
is the mean of an unperturbed distribution, and now pa is the mean of a shifted
distribution.
Apa is the difference between the observed fractional hitting rate, p, and the
shifted mean, pa, and Ap s is the
shift in the means of the two distributions.
We have derived
the expected value of ln|Ap| elsewhere, 9 but we present a simplified version of
it here.
For this calculation, we assume that the RNG
is binary
(i.e., po = 0.5).
Under these conditions, the expected value of ln|Ap|
is given by
In
| Ap
|
=
0.5 ln(0.25)
+
In [zf(Ap
g ) + z„]
-
0.5 ln(n)
(4 )
where
and
A p,
f(Ap
s )
=
yj
l - 4Ap
s
This latter term arises because the variance of a binomial distribution depends
upon the mean of that distribution.
If the
shift of the distribution
is small
(i.e., Ap s
<=* 0),
then f(Ap s) ^
1.
For any RA model under consideration, we must define how the mean of
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