Applications
of
Decision Augmentation Theory
by
Edwin C. May, Ph.D
Science Applications International Corporation
Menlo Park, CA
Jessica M. Utts, Ph.D.
University of California, Davis
Division of Statistics
Davis, CA
and
S. James Spottiswoode (Consultant), Christine L. James
Science Applications International Corporation
Menlo Park, CA
Abstract
Decision Augmentation Theory (DAT) provides an informational mechanism for a class of anomalous
mental phenomena which have hitherto been viewed as a causal, force-like, mechanism.
Under the
proper conditions, DAT’s predictions for micro-anomalous perturbation databases are different from
those of all force-like mechanisms, except for one degenerate case. For large random number genera-
tor (RNG) databases, DAT predicts a zero slope for a least squares fit to the (Z2,n ) scatter diagram,
where/? is the number of bits resulting from a single run and Z is the resultingZ-score. We find a slope of
(L73Az3.19) X 10~ 6 (t = ().543,df= 126, p< 0.295) for the historical binary random number generator
database. In a 2-sequence length analysis of a limited set of RNG data from the Princeton Engineering
Anomalies Research laboratory, we find that a force-like explanation misses the observed data by 8.6-a;
however, the observed data is within
1.1 -o of the DAT prediction. We also apply DAT to one PRNG
study and find that its predicted slope is not significantly different from the expected value. We review
and comment on six published articles that discussed DATs earlier formalism (i.e., Intuitive Data Sort-
ing—IDS). OurDA T analysis of Braud’s hemolysis study confirms his finding in favor of a causal model
over a selection one (i.e. 5jp < 0.023)', so far, this is the only study we have found that supports anomalous
perturbation (AP). We provide six circumstantial arguments, which are based upon experimental out-
comes against the perturbation hypothesis. Our anomalous cognition (AC) research suggests that the
quality ofy4C data is proportional to the total change of Shannon entropy. We demonstrate that the
change of Shannon entropy of a binary sequence from chance is independent of sequence length; thus,
we have provided a fundamental argument in favor of DAT over causal models.
In our conclusion, we
suggest that, except for one degenerate case, the physical RNG database cannot be explained by any causal
model, and that Braud’s contradicting evidence should inspire more AP investigations of biological systems
in a way that would allow a valid DAT analysis.
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1994
Introduction
May, Utts, and Spottiswoode (1994) proposed Decision Augmentation Theory
(DAT)
as a general
model of anomalous mental phenomena (AMP).* DA
holds that anomalous cognition (AC) informa-
tion is included along with the usual inputs that result in a final human decision that favours a “desired”
outcome.
In statistical parlance, DAT says that a slight, systematic bias is introduced into the decision
process byylC.
This concept has the advantage of being quite general. We know of no experiment that is devoid of at
least one human decision; thus, DAT might be the underlying basis forAMP. May et al. (1994) mathe-
matically developed this concept and constructed a retrospective test algorithm than can be applied to
existing databases.
In this paper, we summarize the theoretical predictions of DAT, review the criteria
for valid retrospective tests, and analyze the historical random number generator (RNG) database.
In
addition, we summarize the findings from one prospective test ofDAT (Radin and May, 1985) and com-
ment on the published criticisms of an earlier formulation, which was then called Intuitive Data Sorting.
We conclude with a discussion of RNG results that provide a strong circumstantial argument against a
causal explanation. As part of this review, we show that one biological-AP experiment
is better de-
scribed by an influence model (Braud, 1990).
Review of Decision Augmentation Theory
Since the formal discussion ofDAT is statistical, we will describe the overall context for the development
of the mode] from that perspective. Consider a random variable, X, that can take on continuous values
(e.g., the normal distribution).
Examples of X might be the hit rate in an RNG experiment when the
number of binary bits in the sequence
is large, the swimming velocity of cells, or the mutation rate of
bacteria.
Let Y be the average computed over n values of X, where n
is the number of items that are
collectively subjected to an AMP influence as the result of a single decision—one trial, and let Z be the
appropriate Z-score corresponding to Y.
Often this may be equivalent to a single effort period, but it
also may include repeated efforts. The key point is that, regardless of the effort style, the average value
of the dependent variable is computed over the n values resulting from one decision point.
In the exam-
ples above, n is the sequence length of a single run in an RNG experiment, the number of swimming cells
measured during the trial, or the number of bacteria-containing test tubes present during the trial.
Under DAT, we assume that the underlying parent distribution of a physical system remains unper-
turbed', however, the measurements of the physical system are systematically biased by an/IC-mediated
informational process. Since the deviations seen in actual experiments tend to be small in magnitude, it
is safe to assume that the measurement biases are small and that the sampling distribution will remain
normal; therefore, we assume the bias appears as small shifts of the mean and variance of the sampling
distribution as:
Z ~ N[p z,o]\
The Cognitive Sciences Laboratory has adopted the term anomalous mentalphenomena instead of the more widely known psi.
Likewise, we use the terms anomalous cognition and anomalous perturbation for ESP and PK, respectively. We have done so
because we believe that these terms are more naturally descriptive of the observables and arc neutral in that they do not imply
mechanisms. These new terms will be used throughout this paper.
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This notation means that Z is distributed as a normal distribution with a mean of
and a standard
deviation of az . Under the null hypothesis,/^ = 0.0 and oz -
1.0.
Review of a Causal Model
For comparison sake, we summarize a model for AP interactions. We begin with the assumption that a
putative AP force would give rise to a perturhational interaction. What we mean
is that given an en-
semble of entities (e.g., random binary bits), a small force perturbs, on the average, each member of the
ensemble. We call this type of interaction perturbationaMP (PAP).
In the simplest PAP model, the perturbation induces a change in the mean of the parent distribution but
does not effect its variance. We parameterize the mean shift in terms of a multiplier of the initial stan-
dard deviation. Thus:
= Pq ^ £ap
where \ii and \iq are the means of the perturbed and unperturbed distributions, respectively, and where
ao is the standard deviation of the unperturbed distribution. £ap can be considered the AP effect size.
Under the null hypothesis for binary RNG experiments,^/ — ^ = 0,5, ao = 0.5 , and £^p = 0.
The expected value and variance of Z2 under the mean-chance-expectation (MCE), PAP, and DAT as-
sumptions for the normal distribution are shown in Table
. The details of the calculations can be found
in May, Utts, and Spottiswoode (1994).
Table
1.
Normal Parent Distribution
Quantity
Mechanism
MCE
PAP
DAT
E(7J)
1 + elPn
P-) + o]
Var(Z2
)
2(1 + 2E* P n)
2(o? + 2nld*)
Figure 1 displays these theoretical calculations for the three mechanisms graphically.
E(Z2
)
large
i
PAP
i
small
DAT
MCE
Figure 1.
Predictions of MCE, PAP, and DAT.
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This formulation predicts grossly different outcomes for these models and, therefore, is ultimately ca-
pable of separating them, even for very small perturbations.
Retrospective Tests
It is possible to apply DAT retrospectively to any body of data that meets certain constraints.
It is critical
to keep in mind the meaning of n— the number of measures of the dependent variable over which to
compute an average during a single trial following a single decisioa
In terms of their predictions for
experimental results, the crucial distinction between DAT and ih^ PAP model is the dependence of the
results upon n\ therefore, experiments which are used to test these theories ideally should be those in
which experiment participants are blind to n, and where the distribution of n does not contain extreme
outliers.
Aside from these considerations, the application of DAT is straight forward. Having identified the unit
of analysis and n, simply create a scatter diagram of points (
Z
, n) and compute a weighted least square
fit to a straight line. Table
1 shows that for the PAP model, the slope of the resulting fit is the square of
the ylP-effect size. A student's r-test may be used to test the hypothesis that the AP- effect size is zero,
and thus test for the validity of the PAP model.
If the slope
is zero, these same tables show that the
intercept may be interpreted as imAC strength parameter for DAT
In other words, an intercept larger
than one would support the DAT model, while a slope greater then zero would support ho PAP model.
Monte Carlo Verification
The expressions shown in Table
1 are representations which arise from simple algebraic manipulations
of the basic mathematical assumptions of the PAP and DAT models.
To verify that these expressions
give the expected results, we used a published pseudo random number generator (Lewis, 1975) with
well-understood properties to produced data that mimicked the results under three models (i.e., MCE,
PAP and DA T), Our standard implementation of the PRNG allows the integers in the range (0,2 15 -1]
as potential seeds.
For the sequence lengths 100, 500, 1000, and 5000 , we computed Z-scores for all
possible seeds with an effect size of 0.0 to simulate MCE and an effect size of 0.03 to simulate PAP. We
used the fact that if the effect size varies as 1 / >/
n
, DAT and PAP are degenerate to simulate DAT For
this case we used effect sizes of 0.030, 0.0134, 0.0095
, and 0.0042 for the above sequence lengths, respec-
tively.
Figures 2a-c show the results of 100 trials, which were chosen randomly from the appropriate
Z-score data sets, at each of the sequence lengths for each of the models.
In each Figure, MCE is indi-
cated by a horizontal solid line a Z 2 =
1.
The slope of a least squares fit computed under the MCE simulation was -(2.81±2.49) x 10~~ 6
, which
corresponded to a/?-value of 0.812 when tested against zero, and the intercept was 1.007±0.005, which
corresponds to a /7-value of 0.131 when tested against one.
Under the PAP model, an estimate of the
effect size using the expression in Table
1 was eap — 0.0288^:0.002, which
is in good agreement with
0.03, the value that was used to create the data.
Similarly, under DAT the slope was — f,2.443^57.10
)
x
10~s
, which corresponded
to a /7-value of 0.515 when
tested
against zero, and the intercept was
l.OSOdcO.OOl, which corresponds to a/?-value of 2.4 x 1()~4 when tested against one.
Thus, we are able to say that the Monte Carlo simulations confirm the simple formulation shown in
Thble 1.
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Figure 2.
Z2 vs n for Monte Carlo Simulations of MCE, PAP, and DAT
Historical Binary RNG Database
Radin and Nelson (1989) analyzed the complete literature
(i.e., over 800 individual studies) of con-
sciousness-related anomalies in random physical systems. They eloquently demonstrated that a robust
statistical anomaly exists in that database. Although they analyzed this data from a number of perspec-
tives, they report an average Z / y/n
effect size of approximately 3 X 10-4
, regardless of the analysis
type. Radin and Nelson did not report p-values, but they quote a mean Z of 0.645 and a standard devi-
ation of 1.601 for 597 studies. We compute a single-mean r-score of 9.844, df - 596 (p < 37 X 10~ 23
).
We returned to the original publications of all the binary RNG studies from those listed by Radin and
Nelson and identified 128 studies in which we could compute, or were given, the average Z-score, the
number of runs, N, and the sequence length, n , which ranged from 16 to 10,000. For each of these stud-
ies we computed:
Z1 = ri +
£
(1)
Since we were unable to determine the standard deviations of the Z-scores from the literature, we as-
sumed that^ = 1.0 for each study.
In addition, if aim directions were reported, we inverted the Z-score
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for the negative aim since the DAT analysis is fundamentally two-tailed. We see from Table 1 that under
MCE the expected variance is 2.0 so that the estimated standard deviations for the Z2 from Equation 1
is y/WN.
Figure 3 shows a portion of the 128 data points (
f n). MCE is shown as a solid line (i.e., Z2 = /), and
the expected best-fit lines for two assumed AP effect sizes of 0,01 and 0,003, respectively, are shown as
short dashed lines. We calculated a weighted (i.e., using N/2.0 as the weights) least squares fit to an a +
b*n straight line for the 128 data points and display it as a long-dashed line. For clarity, we have offset
and limited the Z2 axis and have not shown the error bars for the individual points, but the weights and
all the data were used in the least squares fit. We found an intercept of a = 1.036^:0.004 . The 1-stan-
dard error for the intercept is small and is shown in Figure 3 in the center of the sequence range. The
r-score for the intercept being different from 1.0
(i.e., t -9.1, df - 126, p <
4.8 x 10~ 20
)
is in good
agreement with that derived from Radin and Nelson's analysis. Since we set standard deviations for all
theZ’s equal to one; and since Radin and Nelson showed that the overall standard deviation was 1.6, we
would expect that our analysis would be more conservative than theirs.
The important result, however, was that the slope of the best-fit line was/? =
(l.73±3.19) x 10~6
(t = 0.543, df = 126, p < 0.295), which is not significantly different from zero. The l-o standard error for
the slope encompasses zero. Even though a very small AP effect size might fit the data at large sequence
lengths, it is clear in Figure 3 what happens at small sequence lengths; an e^p = 0.003
,
suggests a linear
fit that is significantly below the actual fit.
I
Sequence Length (n)
Figure 3.
Binary RNG Database: Slope and Intercept for Best Fit Line
The sequence lengths from this database are not symmetric nor are they distributed and do contain
ourliers (i.e., median = 64, average = 566).
Figure 4 shows that the lower half of the data, however, is
symmetric and nearly uniformly distributed (i.e., median = 35, average = 34).
Since the criteria for a
valid retrospective test is that n should be uniform, we analyzed the two median halves independently.
The intercept for the weighted best-fit line for the uniform lower half is a = 1.022Ai0.006 (t — 3.63, df =
62, p <2.9 X 10~ 4
), and the slope is h = (~0.034±3.70) x 10~ 4 (t = -0.010, df= 62, p< 0.504). The
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fits for the upper half yield a =
1.064zk0.005
(t =
13.47, df =
62, p <
7.2 x
and
=
(-4.52±2.38
)
x 10~6 (t = -L903, df = 62,p< 0.969), for the intercept and slope, respectively.
Since the best retrospective test for DAT is one in which the distribution of n contains no outliers, the
statistically zero slope for the fit to the lower half of the data
is inconsistent with a simple HP model.
Although the same conclusion could be reached from the fits to the database in its entirety (i.e., Figure
3), we suggest caution in that this
fit could possibly be distorted by the distribution of the sequence
lengths. That is, a few points at large sequence lengths can easily influence the slope. Since the slope for
the upper half of the data is statistically slightly negative,
it is problematical to assign an imaginary HP
effect size to these data. More likely, the results are distorted by a few outliers in the upper half of the
data.
Figure 4.
Historical Database: Distribution of Sequence Lengths < 64.
From these analyses,
it appears that Z 2 does not linearly depend upon the sequence length; however,
since the scatter is so large, even a linear model is not a good fit (i.e.,A2 = 171.2, df - 125, p < 0.0038 ),
whereX2 is a goodness-of-fit measure in general given by:
; = 1
J
where the oj are the errors associated with data pointyy,^
is the falue of the fitted function at point/, and
v is the number of data points.
A “good” fit to a set of data should lead to a non-significantX2
. The fit is not improved by using higher
order polynomials (i.e.,A2 = 170.8, df - 124; X2 = 174.1, df - 123; for quadraticsand cubics, respective-
ly).
If, however, the HP effect size was any monotonic function of n other than the degenerate case
where theHP effect size is exactly proportional to
/ \/n,
it would manifest as a non-zero slope in the
regression analysis.
Within the limits of this retrospective analysis, we conclude for RNG experiments that we must reject all
causal models ofHP which propose a shift of the mean of the parent distribution.
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Princeton Engineering Anomalies Research Laboratory RNG Data
The historical database we analyzed does not include the extensive RNG data from the Princeton Engi-
neering Anomalies Research (PEAR) laboratory since their total number of bits exceeds the total
amount in the entire historical database. For example, in a recent report Nelson, Dobyns, Dunne, and
Jahn (1991) analyze 5.6 x 10 6 trials all at n = 200.
In this section, we apply DAT retrospectively to their
published work where they have examined other sequence lengths; however, even in these cases, they
report over five times as much data as in the historical database.
Jahn (1982) reported an initial RNG data set with a single operator at n = 200 and 2,000.
Data were
collected both in the automatic mode (i.e., a single button press produced 50 trials at n) and the manual
mode (i.e., a single button press produced one trial at n). From a DAT perspective, data were actually
collect at four values of n
(i.e., 200, 2000, 200x50 = 10,000, and 2000x50 = 100,000). Unfortunately
data from these two modes were grouped together and reported only at 200 and 2,000 bit/trial.
It would
seem, therefore, we would be unable to apply DAT to these data. Jahn, however, reports that the differ-
ent modes “...give little indication of importance of such factors in the overall performance.”
This qual-
itative statement suggests that the PAP model
is indeed not a good description for these data, because,
under PAP, we would expect stronger effects at the longer sequence lengths.
Nelson, Jahn, and Dunne (1986) describe an extensive RNG and pseudorandom RNG (PRNG) data-
base in the manual mode only (i.e., over 7 X
trials); however, whereas Jahn provide the mean and
standard deviations for the hits, Nelson et al. report only the means. We are unable to apply DAT to
these data, because any assumption about the standard deviations would be highly amplified by the
massive data set.
As part of a cooperative agreement in 1987 between PEAR and the Cognitive Sciences Program at SRI
International, we analyzed a set of RNG data from a single operator/ Since they supplied the raw data
for each button press, we were able to analyze this data at two extreme values of n. We combined the
individual trial Z-scores for the high and low aims by inverting the sign of the low-aim scores, because
our analysis is two-tailed, in that we examine Z2
.
Given that the datasets at n = 200 and 100,000 were independently significant (Stouffer’sZ of 3.37 and
2.45, respectively), and given the wide separation between the sequence lengths, we used DAT as a ret-
rospective test on these two data points.
Because we are examining only two values of n, we do not compute a best-fit slope. Instead, as outlined
in May, Utts, and Spottiswoode (1994), we compare the PAP prediction to the actual data at a single
value of n.
At n = 200, 5918 trials yielded Z = 0.044 ± 1.030 and Z 2 = 1.063 ± 0.019. We compute
<xproposedAP
effect size Z / y/200 = 3.10 x 10~ 3
. With this effect size, we computed what would be expected under
the PAP model at n = 100,000. Using the theoretical expressions in Table 1, we computed Y2 = 1.961 ±
0.099. The 1-sigma error is derived from the theoretical variance divided by the actual number of trials
(597) at n = 100,000. The obsen>ed values were Z =
0. 100 ± 0.997 and Z2 = 1.002 ± 0.050. A r-test
between the observed and expect values ofY2 gives t = 8.643, df= 1192. Considering this t as equivalent
to a Z, the data at n = 100,000 fails to meet what would be expected under the causal PAP model by
* We thank R. Jahn, B, Dunne, and R. Nelson for providing this raw data for our analysis in 1987.
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8.6-a. Suppose, however, that the effect size observed at n- 100,000 (3.18 X 10~ 4
) better represents
theAP effect size. We computed the predicted value ofY2 - 1.00002 zt 0.018 for n -200. Using ar-test
for the difference between the observed value and this predicted one gives t = 2.398, df— 11,834. The
PAP model fails in this direction by more than 2.4-o. DAT predicts that 7? would be statistically equiva-
lent at the two sequence lengths, and we find that to be the case
(it — 1.14, df — 6513
, p < 0.127).
Jahn (1982) indicates in their 1 982 PEAR RNG data that “Traced back to the elemental binary samples,
these values imply directed inversion from chance behavior of about one or one and a half bits in every
one thousand...
Assuming 1.5 excess bits/1000, we calculate an AP effect size of 0. 003 , which is consis-
tent with the observed value in their n = 200 data set. Thus, we are forced to conclude that this data set
from PEAR is inconsistent with the simple PAP model, and that Jahn’s statement is not a good descrip-
tion of the anomaly.
We urge caution in interpreting these calculations. As is often the case in a retrospective analysis, some
of the required criteria for a meaningful test are violated. These data were not collected when the oper-
ators were blind to the sequence length. Secondly, these data represent only a fraction of PEARLS RNG
database.
A Prospective Test of DAT
In developing a methodology for future tests, Radin and May (1986) worked with two operators who
had previously demonstrated strong AP ability in RNG studies. They used a pseudorandom number
generator (PRNG), which was based on a shift-register algorithm by Kendell and has been shown to
meet the general criteria for “randomness” (Lewis, 1 975), to create the binary sequences so that timing
considerations could be examined.
The operators were blind to which of nine different sequences (i.e., n = 101, 201, 401, 701, 1001, 2001,
4001, 7001, 10001 bits)" were used in any given trial, and the program was such that the trials lasted for a
fixed time period and feedback was presented only after the trial was complete. Thus, the criteria for a
valid test of DA T had been met, except that the source of the binary bits was a PRNG.
We re-analyzed the combined data from this experiment with the current Z-score formalism of DAT.
For the 200 individual runs (i.e. 10 at each of the sequence lengths for each of the two participants) we
found the best fit line to yield a slope = 4.3 xW~8 d= 1.6xl0~ 6 (t = 0.028, df = 8, p < 0.489) and an
intercept = 1.16 ± 0.06 (t = 2.89, df = 8, p < 0.01). The slope easily encompasses zero and is not
significantly different from zero, the significance level
is consistent with what Radin and May reported
earlier.
Since the PRNG seeds and bit streams were saved for each trial, it was possible to determine if the ex-
periment sequences exactly matched the ones produced by the shift register algorithm; they did. Since
our UNIX-based Sun Microsystems workstations were synchronized to the system clock, any momen-
tary interruption of the clock would “crash” the machine, but no such crashes occurred. Therefore, we
believe no casual interaction occurred.
To explore the timing aspects of the experiment Radin and May reran each run with PRNG seeds rang-
ing from -5 to +5 clock ticks (i.e., 20 ms/tick) from the actual seed used in the run. We plot the resulting
* The original IDS analysis required the sequence lengths to odd because of the logarithmic formalism.
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run effect sizes, which we computed from the experimental F-ratios (Rosenthal, 1991), for operator 531
in Figure 5. The estimated 1-standard errors are the same for each seed shift and equal 0.057.
Figure 5.
Seed Timing for Operator 531 (298 Runs).
Radin and May erroneously concluded that the significant differences between zero and adjacent seed
positions was meaningful, and that the DAT ability was effective within 20 milliseconds.
In fact, the
situation shown in Figure 5 is expected.
Differing from true random number generators in which slight
changes in timing produce essentially the same sequence, PRNGs produced totally different sequences
as a function of single digit seed changes. Thus, it would be surprising if the seed-shift display produced
anything but a spike at seed shift zero. We will return to this point in our analysis of some of the pub-
lished remarks on our theory.
From this prospective test of DAT, we conclude that for PRNGs
it
is possible to select a proper entry
point into a bit stream to produce significant deviations from MCE that are independent of sequence
length.
The Literature: Review and Comment
We have identified six published articles that have commented upon the Intuitive Data Sorting (IDS)
theory, the earlier name for DAT.
In this section, we chronologically summarize and comment on each
report.
Walker - September 1987
In his first of two criticisms of IDS, Walker (1987) suggested that his Monte Carlo simulations did not fit
the predictions of the IDS model. He generated a single deviant set of 100 bits (i.e., Z = 2.33, p < 0.01),
and he inserted this same sequence as the first 100 bits of 400 otherwise randomly generated sequences
ranging from 100 to 10^ bits in length. Walker’s analysis of these sequences did not yield a least square’s
slope of —0.5 as predicted under the IDS formalism. Walker concluded that the IDS theory was incor-
rect. Walker s sequences, however, are not the type that are generated inAP experiments or the type for
which the DAT/IDS model is valid.
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May et ai. (1985) were explicit about the character of the sequences that fit the IDS model Specifically,
Walker quotes May et al “ Using psi-acquired information, individuals are able to select locally deviant
subsequencesfrom a large random sequence” (Italics are used in the original May paper.) The very next
sentence on page 249 of the reference says, “Such an ability,
if mediated by precognition, would allow
individuals (subjects or experimenters) to initiate a collection unit of continuous samples (this has been
reported as a trial, a block, a run, etc.) in such a way as to optimize the final result. (Italics added here for
emphasis.) Walker continued, “Indeed, the only way the subject can produce results that agree with the
data is to wait for an extra-chance run that matches the experimental run length.”
In the final analysis,
Walker actually supported our contention that individuals select deviant subsequences. Both from our
text and the formalism in our 1985 paper,
it is clear that what we meant by a “large random sequence,”
was large compared to the trial length, n.
In his second criticism of IDS in the same paper, Walker proposed that individuals would have to exhibit
a physiologically impossible control over timing (e.g., when to press a button). As evidence apparently
in favor of such an exquisite timing ability, he referred to the data presented by Radin and May (1986)
that we have discussed above. (Please see Figure 5.)
Walker suggested that Radin and May's result,
therefore, supported his quantum mechanical observer theory.
It is beyond the scope of this paper to
critique Walker's quantum mechanical models, but we would hope they do not depend upon his analysis
of Radin and May's results. The enhanced hitting at zero seed and the suppressed values ± one 20 ms
clock tick that we show in Figure 5 is the expected result based upon the well-understood properties of
PRNG's and does not represent the precision of the operator’s reaction time.
We must consider how it is possible with normal human reactions to obtain significant scores, which can
only happen in 20 ms windows.
In typical visual reaction time measurements, Woodworth and Schlos-
berg (1960) found a standard deviation of 30 ms.
If we assume these human reactions are typical of
those forA C performance and are normally distributed, we compute the maximum probability of being
within a 20 ms window, which is centered about the mean, of 23.5%. For the worst case, the operators
must “hit” significant seeds less often than 23.5% of the time. Radin and May do not report the number
of significant runs, so we provide a worst-case estimate. Given that they quote ap-value of 0. 005 for 500
trials, we find that 39 trials must be independently significant. That is, the accumulated binomial proba-
bility is 0.005 for 39 hits in 500 trials with an event probability of 0.05 . This corresponds to a hitting rate
(i.e., 39/500) of only 7.8%, a value well within the capability of human reaction times. We recognize that
it is not a requirement to hit only on significant seeds; however, all other seeds leading to positive Z-
scores are less restrictive then the case we have presented.
The zero-center “spike” in Figure 5 misled Walker and others into thinking that exceptional timing was
required to produce the observed deviations.
As we have shown this is not the case, and, therefore,
Walker's second criticism of the IDS theory is not valid.
Bierman - 1988
Bierman (1988) attempted to test the IDS model with a gifted subject.
His experimental design ap-
peared to meet most of the criteria for a valid test of the model; however, Bierman found no evidence
forAMP and stated that no conclusions could be drawn from his work. We encourage Bierman to con-
tinue with this design and to be specific with what he would expect to see if DAT were the correct mecha-
nism compared to if it were not.
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Applications of Decision Augmentation Theory
V5 25 August 1994
Braud and Schlitz - 1989
Braud and Schlitz (1989) conducted an electrodermal PK experiment specifically to test the IDS model
They argued that if the mechanism of the effect were informational, then allowing participants more
opportunities to select locally deviant values of the dependent variable should yield stronger effects. In
their experiment, 12 electrodermal sampling epochs were either initiated individually by a press of a
button, or all 12 were determined as a result of the first button press. Braud and Schlitz hypothesized
that under IDS, they would expect to see a larger overall effect in the former condition. They found that
the single button press data yielded a significant result; whereas the multiple press data scored at chance
(ts
'mgie[31] = 2.14\
tmuiti[31] = -0.53). They concluded, therefore, that their data were more consistent
with an influence mechanism than with an informational one.
In both button-press conditions, the dependent variable was averaged over all 12 epochs; therefore, the
formalism discussed in this paper cannot be applied because the data should have been averaged over at
least two different values. The idea of multiple decision points, however, is still valid. As stated in their
paper, the timing of the epochs was such that 20 seconds of the 30 second epoch was independent of the
button-press condition and could not, therefore be subjected to a D/IT-like selection. To examine the
consequence of this overlap, we computed the effect size for the single button case as 0.359 (Rosenthal,
1991, Page 19, Equation 2.1 6). Since data for the 20 seconds is the same in each condition, the operator
can only makeHC-mediated decisions for the first 10 seconds of the data.
If we assume that on the
average the remaining 20 seconds meets mean chance expectation and the effect size is constant’ then
we would expect an effect size of (0.359 + 0 + 0)13 = 0.119* The measured effect size was 0.095, which
is consistent with this prediction.
Braud and Schlitz’s idea was good and provides a possible way to use DAT effectively. Because of the
epoch timing and the consistency of the effect sizes, however, we believe they have interpreted their
results in favor of causal mechanism prematurely. Aside from the timing issues, their protocol compli-
cates the application of DAT further.
To observe an enhanced effect because of multiple decision
points, Z-scores should be computed for each decision and combined as a Stouffer’s Z where the de-
nominator is the square root of the number of decision points.
In their protocol, they only combine the
dependent variable.
Vassy - 1 990
Vassy (1990) used a similar timing argument to refute the IDS model as did Walker (1987). Vassy gener-
ated PRNG single bits at a rate of one each 8.7 ms. He argued that if IDS were operating, that a subject
would be more likely to identify bursts of ones rather than single ones given the time between consecu-
tive bits.
While he found significant evidence for the primary task of “selecting” individual bits, he
found no evidence that these hits were imbedded in excess clusters of ones.
We compute that the maximum probability of a hit within an 8. 7 ms window centered on the mean of the
normal reaction curve with a standard deviation of 30 ms (Woodworth and Schlosberg, 1960) is 11.5%.
Vassy quotes an overall Z-score for 100 runs of 2.39. From this, we compute a mean Z of 0.239 for each
run of 36 bits. To obtain this result requires an excess hitting of 0.717 bits, which corresponds to an ex-
* Asa function ofn,DATpredicts a lf\/n dependency in the effect size; however, at a fixcd«, as in this case, thecffcct size should
be constant.
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cess hitting rate of 2%. Given that 77. 5% is the maximum one can expect with normal human reaction
times, Vassy’s results easily allow for individual bit selection, and, thus, cannot be used to reject the DAT
model on the basis of timing.
Braud - 1990
In a cooperative effort with SRI International, Braud (1990) conducted a biological AP study with hu-
man red blood cells as the target system. The study was designed, in part, as a prospective test ofDAT, so
all conditions for a valid test were satisfied. Braud found that a significant number of individuals were
independently able to “slow” the rate of hemolysis (i.e., the destruction of red blood cells in saline solu-
tion) in what he called the “protect” mode.
Using data from the nine significant participants, Braud
found support in favor of PAP over DAT Figure 6 shows the results of our re-analysis of all of Braud’s
raw data using our more modern formalism of DAT
ior
CM
<N
c
cd
<u
O
Effort Data
Control Data
x
Predicted AP
Predicted DAT
oh
— 2
l
Number of Test Tubes
Figure 6.
DAT Analysis of Hemolysis Data.
The solid line indicates the theoretical expected value for MCE. The squares are the mean values ofZ 2
for the control data, and the error bars indicate the
1 -standard error for the 32 trials in the study. We
notice that the control data with eight test tubes is significantly below MCE (r = -2.79, df = 62, p <
0.996). Compared to the MCE line, the effort data is significant (t = 4.04, df = 31, p < 7.6 x 10~5
) for
eight test tubes and nearly so for n = 2
(t = 2.06, df = 31, p < 0.051). The x
at n = 8 indicates the
calculated value of the mean of Z 2 assuming that the effect size at n — 2 was entirely because of AP;
similarly, the x atn = 2 indicates the calculated value assuming that the effect size, which was observed
at n = 8, was totally due to AP. These AP predictions are not significantly different from the observed
data (f = 0.156, p < 0.431 , df - 62 and
t = 0.906, p <
0. 184, df = 62,
at n = 2 and 8 , respectively).
Whereas DAT predicts no differences between the data at the end points for n
, we find a significant
difference (/ = 2.033
, p < 0.023, df = 62). That
is, to a statistical degree the data at n = 8, cannot be
explained by selection alone. Thus, we concur with Brand’s original conclusion; these results indicate a
possible causal relationship between mental intent and biological consequences.
It is difficult to conclude from our analysis of a single study with only 32 trials that /IP is part of nature;
nonetheless, this result is very important.
It is the first data we have encountered that supports the PAP
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hypothesis, which is in direct opposition to the substantial support against PAP resulting from the analy-
sis of the RNG data sets.
In addition, May and Vilenskaya (1993) and Vilenskaya and May (1994) re-
port that the preponderance oiAMP research in the Former Soviet Union is the study ofAP on biologi-
cal systems. Their operators, as do ours, report their internal experiences as being a causal relationship
between them and their biological targets.
Dobyns - 1993
Dobyns (1993) presents a method for comparing what he calls the “influence” and “selection” models,
corresponding to what we have been calling DAT and PAP He uses data from 490 “tripolar sets” of
experimental runs at PEAR. For each set, there was a high aim, a baseline and a low aim condition.
The three values produced were then sorted into which one was actually highest, in the middle, and
lowest for each set. The data were then summarized into a 3 X 3 matrix, where the rows represented the
three intentions, and the columns represented the actual ordering.
If every attempt had been success-
ful, the diagonal of the matrix would consist of the number of tripolar sets, namely 490. We present the
data portion of Dobyns' Table from page 264 of the reference as our Table 2:
Table 2.
Scoring Data From Dobyns (1993)
Actual
Intention
High
Middle
Low
Total
High
180
167
143
490
Baseline
159
156
175
490
Low
^
151
167
172
490
Total
490
490
490
Dobyns computes an aggregate likelihood ratio of his predictions for the DAT and PAP models and con-
cludes in favor the the influence model with a ratio of 28.9 to one.
However, there are serious problems with the methods used in Dobyns
5 paper.
In this paper we simply
outline only two of the difficulties. To fully explain them would require a level of technical discussion
not suitable for a short summary such as this.
One problem is in the calculation of the likelihood ratio function using Equation 6, which we reproduce
from page 265 of the reference:
B{p/q) = P?P?P?
wherep and q are the predicted rank frequencies for each aim under the influence and selection models,
respectively, and the n are the observed frequencies for each aim. We agree that this relationship cor-
rectly gives the likelihood ratio for comparing the two models for one row of Table 2. However, immedi-
ately following that equation, Dobyns writes, “The aggregate likelihood of the hypothesis over all three
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