1995 CIA Intel on SAIC Model of Anomalous Mental Phenomena

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Decision Augmentation Theory:
Toward a Model
| of .
Anomalous Mental Phenomena

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 P. Spottiswoode
Science Applications International Corporation (Consultant)
Menlo Park, CA

Abstract

Decision Augmentation Theory (DAT) holds that humans integrate information obtained by anoma-
lous cognition into the usual decision process. The result is that, to a statistical degree, such decisions
are biased toward volitional outcomes. We introduce our model and show that the domain over which it
is applicable is within a few standard deviations from chance. We contrast the theory’s experimental
consequences with those of models that treat anomalous effects as due to a force. We derive mathemat-
ical expressions for DAT and for force-like models using two distributions, normal and binomial. DAT
is testable both retrospectively and prospectively, and we provide statistical power curves to assist in the
experimental design of such tests. We show that the experimental consequences of our theory are dif-
ferent from those of force-like models except for one special case.

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Introduction .

We do not have positive definitions of the effects that generally fall under the heading of anomalous
mental phenomena." In the crassest of terms, anomalous mental phenomena are what happens when
nothing else should, at least as nature is currently understood. In the domain of information acquisi-
tion, or anomalous cognition (AC), it is relatively straightforward to design an experimental protocol
(Honorton et al., 1990, Hyman and Honorton, 1986) to assure that no known sensory leakage of in-
formation can occur. In the domain of macroscopic anomalous perturbation (AP), however, it is often
very difficult.

We can divide anomalous perturbation into two categories based on the magnitude of the putative ef-
fect. Macro-AP include phenomena that generally do not require sophisticated statistical analysis to
tease out weak effects from the data. Examples include inelastic deformations in strain gauge experi-
ments, the obvious bending of metal samples, and a host of possible “field phenomena” such as teleki-
nesis, poltergeist, teleportation, and materialization. Conversely, micro-AP covers experimental data
from noisy diodes, radioactive decay and other random sources. These data show small differences
from chance expectation and require statistical analysis.

One of the consequences of the negative definitions of anomalies is that experimenters must assure that
the observables are not due to “known” effects. Traditionally, two techniques have been employed to
guard against such interactions:

(1) Complete physical isolation of the target system.
(2) Counterbalanced control and effort periods.

Isolating physical systems from potential “environmental” effects is difficult, even for engineering spe-
Cialists. It becomes increasingly problematical the more sensitive the AP device. For example Hubbard,
Bentley, Pasturel, and Issacs (1987) monitored a large number of sensors of environmental variables
that could mimic perturbational effects in an extremely isolated piezoelectric strain gauge. Among
these sensors were three-axis accelerometers, calibrated microphones, and electromagnetic and nu-
clear radiation monitors. In addition, the strain gauges were mounted in a government-approved en-
closure to assure no leakage (in or out) of electromagnetic radiation above a given frequency, and the
enclosure itself was levitated on an air suspension table. Finally, the entire setup was locked in a con-
trolled access room which was monitored by motion detectors. The system was so sensitive, for exam-
ple, that it was possible to identify the source of a perturbation of the strain gauge that was due to inno-
cent, gentle knocking on the door of the closed room. The financial and engineering resources to isolate
such systems rapidly become prohibitive.

The second method, which is commonly in use, is to isolate the target system within the constraints of
the available resources, and then construct protocols that include control and effort periods. Thus, we
trade complete isolation for a statistical analysis of the difference between the control and effort peri-
ods. The assumption implicit in this approach is that environmental influences of the target device will
be random and uniformly distributed in both the control and effort conditions, while anomalous effects

* The Cognitive Sciences Laboratory has adopted the term anomalous mental phenomena 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 are neutral with regard to mecha-
nisms. These new terms will be used throughout this paper.

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will tend to occur in the effort periods. Our arguments in favor of an anomaly, then, are based on statis-
* tical inference and we must consider, in detail, the consequences of such analyses.

Background

As the evidence for anomalous mental phenomena becomes more widely accepted (Bem and Honor-
ton, 1994, Utts, 1991, Radin and Nelson, 1989) it is imperative to determine their underlying mecha-
nisms. Clearly, we are not the first to begin thinking of potential models. In the process of amassing
incontrovertible evidence of an anomaly, many theoretical approaches have been examined; in this sec-
tion we outline a few of them. It is beyond the scope of this paper, however, to provide an exhaustive
review of the theoretical models; a good reference to an up-to-date and detailed presentation is Stokes

(1987).

Brief Review of Models

Two fundamentally different types of models of anomalous mental phenomena have been developed:
those that attempt to order and structure the raw observations in experiments (i.e., phenomenological
models), and those that attempt to explain these phenomena in terms of modifications to existing physi-
cal theories (i.e., fundamental models). In the history of the physical sciences, phenomenological mod-
els, such as the Snell’s law of refraction or Ampere’s law for the magnetic field due to a current, have
nearly always preceded fundamental models, such as quantum electrodynamics and Maxwell’s theory.
In producing useful models of anomalies it may well be advantageous to start with phenomenological
models, of which DAT is an example.

Psychologists have contributed interesting phenomenological approaches. Stanford (1974a and 1974b)
proposed PSJ-Mediated Instrumental Response (PMIR). PMIR states that an organism uses anoma-
lous mental phenomena to optimize its environment. For example, in one of Stanford’s classic experi-
ments (Stanford, Zenhausern, Taylor, and Dwyer 1975) subjects were offered a covert opportunity to
stop a boring task prematurely if they exhibited unconscious anomalous perturbation by perturbing a
hidden random number generator. Overall, the experiment was significant in the unconscious tasks; it
was as if the participants were unconsciously scanning the extended environment for any way to provide
a more optimal situation than participating in a boring psychological task!

As an example of a fundamental model, Walker (1984) proposed a literal interpretation of quantum
mechanics and posited that since superposition of eigenstates holds, even for macrosystems, anoma-
lous mental phenomena might be due to macroscopic examples of quantum effects. These ideas
spawned a class of theories, the so-called observation theories, that were either based upon quantum
formalism conceptually or directly (Stokes, 1987). Jahn and Dunne (1986) have offered a “quantum
metaphor” which illustrates many parallels between these anomalies and known quantum effects. Un-
fortunately, these models either have free parameters with unknown values, or are merely hand waving
metaphors. Some of these models propose questionable extensions to existing theories. For example,
even though Walker’s interpretation of quantum mechanical formalism might suggest wave-like prop-
erties of macrosystems, the physics data to date not only show no indication of such phenomena at room
temperature but provide considerable evidence to suggest that macrosystems lose their quantum coher-
ence above 0.5 Kelvins (Washburn and Webb, 1986) and no longer exhibit quantum wave-like behavior.

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This is not to say that a comprehensive model of anomalous mental phenomena may not eventually
~ require quantum mechanics as part of its explanation, but it is currently premature to consider such
models as more than interesting speculation. The burden of proof is on the theorist to show why sys-
tems, which are normally considered classical (e.g., a human brain), are, indeed, quantum mechanical.
That is, what are the experimental consequences of a quantum mechanical system over a classical one?

Our Decision Augmentation Theory is phenomenological and is a logical and formal extension of Stan-
ford’s elegant PMIR model. In the same manner as early models of the behavior of gases, acoustics, or
optics, DAT tries to subsume a large range of experimental measurements into a coherent lawful
scheme. Hopefully this process will lead the way to the uncovering of deeper mechanisms. In fact DAT
leads to the idea that there may be only one underlying mechanism of all anomalous mental phenome-
na, namely a transfer of information from future to past.

Historical Evolution of Decision Augmentation

May, Humphrey, and Hubbard (1980) conducted a careful random number generator (RNG) experi-
ment which was distinguished by the extreme engineering and methodological care that was taken to
isolate any potentially known physical interactions with the source of randomness (D. Druckman and J.
A. Swets, page 189, 1988). It is beyond the scope of this paper to describe this experiment completely;
however, those specific details which led to the idea of Decision Augmentation are important for the
sake of historical completeness. The authors were satisfied that they had observed a genuine statistical
anomaly and additionally, because they had developed an accurate mathematical model of the random
device, they were assured that the deviations were not due to any known physical interactions. They
concluded, in their report, that some form of anomalous data selection had occurred and named it Psy-
choenergetic Data Selection.

Following a suggestion by Dr. David R. Saunders of MARS Measurement and Associates, we noticed in
1986 that the effect size in binary RNG studies varied on the average as one over the square root of the
number of bits in the sequence. This observation led to the development of the Intuitive Data Sorting
model that appeared to describe the RNG data to that date (May, Radin, Hubbard, Humphrey, and
Utts, 1985). The remainder of this paper describes the next step in the evolution of the theory which is
now named Decision Augmentation Theory.

Decision Augmentation Theory—A General Description

Since the case for AC-mediated information transfer is now well established (Bem and Honorton,
1994) it would be exceptional if we did not integrate this form of information gathering into the decision
process. For example, we routinely use real-time data gathering and historical information to assist in
the decision process. Why, then, should we not include AC in the decision process? DAT holds that AC
information 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 by AC.

This philosophical concept has the advantage of being quite general. To illustrate the point, we describe
how the “cosmos” determines the outcome of a well-designed, hypothetical experiment. To determine
the sequencing of conditions in an RNG experiment, suppose that the entry point into a table of ran-
dom numbers will be chosen by the square root of the barometric pressure as stated in the weather re-

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port that will be published seven days hence in the New York Times. Since humans are notoriously bad at
" predicting or controlling the weather, this entry point might seem independent of a human decision; but
why did we “choose” seven days in advance? Why not six or eight? Why the New York Times and not the
London Times? DAT would suggest that the selection of seven days, the New York Times, the barometric
~ pressure, and square root function were better choices, either individually or collectively, and that other
decisions would not have led to as significant an outcome. Other non-technical decisions may also be
biased by AC in accordance with DAT. When should we schedule a Ganzfeld session; who should be the
experimenter in a series; how should we determine a specific order in a tri-polar protocol? DAT ex-
plains anomalous mental phenomena as a process of judicious sampling from a world of events that are
unperturbed. In contrast, force-like models, hold that some kind of mentally-mediated force perturbs
the world. As we will show below, these two types of models lead to quite different predictions.

It is important to understand the domain in which a model is applicable. For example, Newton’s laws
are sufficient to describe the dynamics of mechanical objects in the domain where the velocities are very
much smaller than the speed of light, and where the quantum wavelength of the object is very small
compared to the physical extent of the object. If these conditions are violated, then different models
must be invoked (e.g., relativity and quantum mechanics, respectively). The domain in which DAT is
applicable is when experimental outcomes are in a statistical regime (i.e., a few standard deviations
from chance). In other words, could the measured effect occur under the null hypothesis? This is not a
sharp-edged requirement but DAT becomes less apropos the more a single measurement deviates from
mean-chance-expectation (MCE). We would not invoke DAT, for example, as an explanation of levita-
tion if one found the authors hovering near the ceiling! The source of the statistical variation is unre-
stricted and may be of classical or quantum origin, because a potential underlying mechanism for DAT
is precognition. By this means, experiment participants become statistical opportunists.

Development of a Formal Model

While DAT may have implications for anomalous mental phenomena in general, we develop the model
in the framework of understanding experimental results. In particular, we consider anomalous per-
turbation versus anomalous cognition in the form of decision augmentation in those experiments whose
outcomes are in the few-sigma, statistical regime.

We define four possible mechanisms for the results in such experiments:

(1) Mean Chance Expectation. The results are at chance. That is, the deviation of the dependent vari-
able meets accepted criteria for MCE. In statistical terms, we have measurements from an unper-
turbed parent distribution with unbiased sampling.

(2) Anomalous Perturbation. Nature is modified by some anomalous interaction. That is, we expect
an interaction of a “force” type. In statistical parlance, we have measurements from a perturbed
parent distribution with unbiased sampling.

(3) Decision Augmentation. Nature is unchanged but the measurements are biased. That is, AC in-
formation has “distorted” the sampling. In statistical terms, we have measurements from an unper-
turbed parent distribution with biased sampling.

(4) Combination. Nature is modified and the measurements are biased. That is, both anomalous ef-
fects are present. In statistical parlance, we have conducted biased sampling from a perturbed par-
ent distribution.

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General Considerations and Definitions

~ Since the formal discussion of DAT is statistical, we will describe the overall context for the develop-
ment of the model from that perspective. Consider a random variable, X, that can take on continuous
values (e.g., the normal distribution) or discrete values (e.g., the binomial distribution). Examples of X
might be the hit rate in an RNG experiment, the swimming velocity of single cells, or the mutation rate
of bacteria. Let Y be the average of X computed over n values, where n is the number of items that are
collected as the result of a single decision—one trial. 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 values resulting from one decision
point. In the examples above, n is the sequence length of a single run in an RNG experiment, the num-
ber of swimming cells measured during the trial, or the number of bacteria-containing test tubes present
during the trial. As we will show below, force-like effects require that the Z-score, which is computed
from the Ys, increase as the square root of. In contrast, informational effects will be shown to be inde-
pendent of 2.

Assumptions for DAT -

We assume that the parent distribution of a physical system remains unperturbed; however, the mea-
surements of the physical system are systematically biased by some AC-mediated informational pro-
cess.

Since the deviations seen in experiments in the statistical regime tend to be small in magnitude, it is safe
to assume that the measurement biases will also be small; therefore, we assume small shifts of the mean
and variance of the sampling distribution. Figure 1 shows the distributions for biased and unbiased
measurements.

Unbiased Sample Ne

«— Biased Sample

Z-Scores

Figure 1. Sampling Distribution Under DAT.

The biased sampling distribution shown in Figure 1 is assumed to be normally distributed as:

Z~ Net 02),

where x, and a, are the mean and standard deviation of the sampling distribution.

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Assumptions for an Anomalous Perturbation Model

~ DAT can be contrasted to force-like effects. With a few exceptions reported in the literature of “field”
phenomena, anomalous perturbation appears to be relatively “small.” Thus, we begin with the assump-
tion that a putative anomalous force would give rise to a perturbational interaction, by which we mean
~ that, given an ensemble of entities (e.g., binary bits, cells), an anomalous force would act equally on each
member of the ensemble, on the average. We call this type of interaction micro-AP.

Figure 2 shows a schematic representation of probability density functions for a parent distribution un-
der the micro-AP assumption and an unperturbed parent distribution. In the simplest micro-AP model,
the perturbation induces a change in the mean of the parent distribution but does not effects its vari-
ance. We parameterize the mean shift in terms of a multiplier of the initial standard deviation. Thus,
we define an AP-effect size as:

Egp = (41 = Ho) ;

0
where jt; and up are the means of the perturbed and unperturbed distributions, respectively, and where
00 is the standard deviation of the unperturbed distribution.

Probability Density

Ho Hy = Uo + Epo
Dependent Variable

Figure 2. Parent Distribution for micro-AP.

For the moment, we consider é ap as a parameter which, in principle, could be a function of a variety of
variables (e.g., psychological, physical, environmental, methodological). As we develop DAT for specif-
ic distributions and experiments, we will discuss this functionality of eap

Calculation of E(Z2)

We compute the expected value and variance of Z* for mean chance expectation and under the force-
like and information assumptions. We do this for the normal and binomial distributions. The details of
the calculations can be found in the Appendix; however, we summarize the results in this section. Table
1 shows the results assuming that the parent distribution is normal.

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Table 1.
Normal Parent Distribution
Mechanism
Quantity
MCE 1 micro-AP DAT
E(Z?) 1 1 + €2,n yu? + a2
Var(Z’) 2 2(1 + 2e2pm) | 2(0f + 24202)

Table 2 shows the results assuming that the parent distribution is binomial. In this calculation, po is the
binomial event probability and og = \/po(1—pp).

Table 2.

Binomial Parent Distribution

, - Mechanism
Quantity
MCE micro-AP DAT
E(2?) 1 1+ eip(n- 1) + FE - 2p) wP + 0?
var(z4) | 2 +—4;(1 — 603) 2(1 + 262pn)’ 2(o4 + 24202)
0

* The variance shown assumes pp = 0.5 andn > 1. See the Appendix for other cases.

We wish to emphasize at this point that in the development of the mathematical model, the parameter
gap for micro-AP, and the parameters yz, and o, in DAT may all possibly depend upon n; however, for
the moment, we assume that they are all n-independent. We shall discuss the consequences of this as-
sumption below.

Figure 3 displays these theoretical calculations for the three mechanisms graphically.

large
micro-AP
E(2") stall
DAT
Zo MCE
n

Figure 3. Predictions of MCE, micro-AP and DAT.

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Within the constraints mentioned above, this formulation predicts grossly different outcomes for these
- models and, therefore, is ultimately capable of separating them, even for very small perturbations.

Retrospective Tests

It is possible to apply DAT retrospectively to any body of data that meet 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 decision In terms of their predictions for
experimental results, the crucial distinction between DAT and the micro-AP model is the dependence
of the results upon n; therefore, experiments which are used to test these theories must be those in
which is manipulated and participants are held blind to its values. May, Spottiswoode, Utts and James
(1994) retrospectively apply DAT to as many data sets as possible, and examine the consequences of any
violations of these criteria.

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 (Z4, n) and compute a least square fit to a
straight line. Tables 1 and 2 show that for the micro-AP model, the square of the effect size is the slope of
the resulting fit. A Student’s t-test may be used to test the hypothesis that the effect size is zero, and thus
test for the validity of the micro-AP model If the slope is zero, these same tables show that the intercept
may be interpreted as an AC strength parameter for DAT. A follow-on paper will describe these tech-
niques in detail (May, Spottiswood, and Utts, 1994).

Prospective Tests

A prospective test of DAT could not only test whether anomalous effects occurred, but would also dif-
ferentiate between micro-AP and DAT. In such tests, m should certainly be a double-blind parameter
and take on at least two values. If you wanted to check the prediction of a linear functional relationship
between” and the E(Z“) that is suggested by micro-AP model, the more values of n the better. It is not
possible to separate the micro-AP model from DAT at a single value of n.

In any prospective test, it is helpful to know the number of runs, N, that are necessary to determine with
95% confidence, which of the two models best fits the data. Figure 4 displays the problem graphically.

Exc (2) Ep (Z*)

Probability Density

A ~ 1.645242
E(Z2p) — 1.645 Th

Figure 4. Model Predictions for the Power Calculation.

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Under micro-AP, 95% of the values of Z* will be greater than the point indicated in Figure 4. Even if the
" measured value of Z7is at this point, we would like the lower limit of the 95% confidence interval for this
value to be greater than the predicted value under the DAT model. Or:

E(Z3p) - 1.645 - 1.960742 = Exc (Z).

Solving for N in the equality, we find:

3.605 Om» |
N= leven = os | : ”

Since oap > oc, this value of N will always be the larger estimate than that derived from beginning with
DAT and calculating the confidence intervals in the other direction.

Suppose, from an earlier experiment, one can estimate a single-trial effect size for a specific value of n,
say nz. To determine whether the micro-AP model or DAT is the proper description of the mechanism,
we must conduct another study at an additional value of n, say 72. We use Equation 1 to compute how
many runs we must conduct at nz to assure a separation of mechanism with 95% confidence, and we use
the variances shown in Tables 1 and 2 to conipute oap Figure 5 shows the number of runs for an RNG-
like experiment as a function of effect size for three values of np.

We chose 7 = 100 bits because it is typical of the numbers found in the RNG database and the values of
nz shown are within easy reach of today’s computer-based RNG devices. For example, assuming o, =
1.0 and assuming an effect size of 0.004, a value derived from a publication of PEAR data (Jahn, 1982),
then at; = 100,u, = 0.004 x \/100 = 0.04 and E,ac(Z) = 1.0016. Suppose nz = 10+, then Eap(Z2) =
1.160 and oap = 1.625. Using Equation 1, we find N = 1368 runs, which can be approximately obtained
from Figure 5. That is in this example, 1368 runs are needed to resolve the micro-AP model from DAT
atn2 = 10¢ at the 95% confidence level. Since these runs are easily obtained in most RNG experiments,
an ideal prospective test of DAT, which is based on these calculations, would be to conduct 1500 runs
randomly counterbalanced between n = 10% andn = 10* bits/trial. If the effect size at n = 10* is near
0.004, than we would be able to distinguish between micro-AP and DAT with 95% confidence.

deeb Lt

10°

le]

Required Number of Runs

ie]
fe]

1 tt ait rn A A ra
0.001 0.010 90.100

AC Effect Size at nz = 100 bits

Figure 5. Runs Required for RNG Effect Sizes

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Figure 6 shows similar relationships for effect sizes that are more typical of anomalous perturbation
’ experiments using biological target systems (May and Vilenskaya, 1994).
In this case, we chose ny = 2 because it is easy to use two targets simultaneously. If we assume an effect

size of 0.3 anda, = 1.0, atnz = 10 we compute Eac(Z?) = 1.180, Eap(Z*) = 1.900, cap = 2.366 and N =
~ 140, which can be approximately obtained from Figure 6.

We have included nz = 100 in Figure 6, because this is within reach in cellular experiments although it is
probably not practical for most biological experiments.

10000 £

10008

TTT

+

Required Number of Runs

TOO, q
- m=10 |
E _—_
vL n2 = 100 |

°
fe)

Effect Size at ny = 2 units

Figure 6. Runs Required for Biological Effect Sizes

We chose n; = 2 units for convenience. For example in a plant study, the physiological responses can
easily be averaged over two plants and 2 = 10 is within reason for a second data point. A unit could be a
test tube containing cells or bacteria; the collection of all ten test tubes would simultaneously have to be
the target to meet the constraints of a valid test.

The prospective tests we have described so far are conditional; that is, given an effect size, we provide a
protocol to test if the mechanism for the anomalies is micro-AP or DAT. An unconditional test does not
assume any effect size; all that is necessary is to collect data at a large number of different values of n,
and fit a straight line through the resulting Zs. The mechanism is micro-AP if the slope is non-zero and
may be DAT if the slope is zero.

Stouffer’s Z Tests

One consequence of DAT is that more decision points in an experiment lead to stronger results, because
an operator has more opportunity to exercise AC abilities. We derive a test criteria to determine wheth-
er a force-like interaction or an informational mechanism is a better description of the data.

Consider two experiments of M decisions at nz and N decisions at nz, respectively. Regardless of the
mechanism, the Stouffer’s Z for the first experiment is given by:

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M
in, > by
Z? = a = (nm, Mae,

_ where é7; is the effect size for one decision and where ¢; is the average effect size over the M decisions.
Under the micro-AP assumption that the effect size, e7, is constant regardless of n, Stouffer’s Z in the
second experiment is given by:

n,N

72) =
‘ n,M

ZO,

Under the DAT assumption that the effect size is proportional to 1/,/n, the Stouffer’s Z in the second

experiment becomes:
IN
Ze = /X zo,

As in the other tests of DAT, if data are collected at two values of n, then a test between these Stouffer’s
Z values may yield a difference between the competing mechanisms.

Discussion

We now address the possible n-dependence of the model parameters. A degenerate case arises if € ap is
proportional to 1/,/n; if that were the case, we could not distinguish between the micro-AP model and
DAT by means of tests on the n dependence of results. If it were the case that in the analysis of the data
from a variety of experiments, participants, and laboratories, the slope of a Z?vs n linear least-squares
fit were zero, then either ¢ap = 0.0 or e,p is proportional to 1/,/n, the accuracy depending upon the
precision of the fit (i.e., errors on the zero slope). An attempt might be made to rescue the micro-AP
hypothesis by explaining the 1/\/n dependence of cap in the degenerate case as a fatigue or some other
time dependence effect. That is, it might be hypothesized that anomalous perturbation abilities would
decline as a function of; however, it seems improbable that a haman-based phenomenon would be so
Widely distributed and constant and give the 1/\/n dependency in differing protocols needed to imitate
DAT. We prefer to resolve the degeneracy by wielding Occam’s razor: if the only type of anomalous
perturbation which fits the data is indistinguishable from AC, and given that we have ample demonstra-
tions of AC by independent means in the laboratory, then we do not need to invent an additional phe-
nomenon called anomalous perturbation. Except for this degeneracy, a zero slope for the fit allows us
to reject all micro-AP models, regardless of their n-dependencies.

DAT is not limited to experiments that capture data from a dynamic system. DAT may also be the mech-
anism in protocols which utilize quasi-static target systems. In a quasi-static target system, a random
process occurs only when a run is initiated; a mechanical dice thrower is an example. Yet, in a series of
unattended runs of such a device there is always a statistical variation in the mean of the dependent
variable that may be due to a variety of factors, such as Brownian motion, temperature, humidity, and
possibly the quantum mechanical uncertainty principle (Walker, 1974). Thus, the results obtained will
ultimately depend upon when the run is initiated. It is also possible that a second-order DAT mecha-
nism arises because of protocol selection; how and who determines the order in tri-polar protocols. In
second order DAT there may be individuals, other than the formal subject, whose decisions effect the

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experimental outcome and are modified by AC. Given the limited possibilities in this case, we might
" expect less of an impact from DAT.

In surveying the range of anomalous mental phenomena, we reject the evidence for experimental mac-
ro-AP because of poor artifact control and accept the evidence for precognition and micro-AP because
~ ofthe large number of studies and the positive results of the meta-analyses. We believe that DAT, there-
fore, might be a general model for anomalous mental phenomena in that it reduces mechanisms for
laboratory phenomena to only one—the anomalous transtemporal acquisition of information.

Acknowledgements

Since 1979, there have been many individuals who have contributed to the development of DAT. We
would first like to thank David Saunders without whose remark this work would not have been. Beverly
Humphrey kept the philosophical integrity intact at times under extreme duress. We are greatly appre-
ciative of ZoltAn Vassy, to whom we owe the Z-score formalism, to George Hansen, Donald McCarthy,
and Scott Hubbard for their constructive criticisms and support.

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References

Bem, D. J. and Honorton, C. (1994). Does psi exist? Replicable evidence for an anomalous process of
information transfer. Psychological Bulletin. 115, No. 1, 4-18.

Druckman, D and Swets, J. A. (Eds.) (1988). Enhancing Human Performance. Issues, Theories, and
Techniques. Washington D.C., Nation Academy Press.

Honorton, C., Berger, R. E., Varvoglis, M. P., Quant, M., Derr, P., Schechter, E. I., and Ferrari, D. C.
(1990) Psi Communication in the Ganzfeld. Journal of Parapsychology, 54, 99-139.

Hubbard, G. S., Bentley, P. P., Pasturel, P. K., and Isaacs, J. (1987). A remote action experiment with a
piezoelectric transducer. Final Report — Objective H, Task 3 and 3a. SRI International Project
1291, Menlo Park, CA.

Hyman, R. and Honorton, C. (1986). A joint communiqué: The psi ganzfeld controversy. Journal of
Parapsychology. 50, 351-364.

Jahn, R. G. (1982). The persistent paradox of psychic phenomena: an engineering perspecitve.
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Jahn R. G. and Dunne, B. J. (1986). On the quantum mechanics of consciousness, with application to
anomalous phenomena. Foundations of Physics. 16, No 8, 721-772. ,

May, E. C., Humphrey, B. S., Hubbard, G. S. (1980). Electronic System Perturbation Techniques. Final
Report. SRI International Menlo Park, CA.

May, E. C., Radin, D. I., Hubbard, G. S., Humphrey, B. S., and Utts, J. (1985) Psi experiments with
random number generators: an informational model. Proceedings of Presented Papers Vol I. The
Parapsychological Association 28th Annual Convention, Tufts University, Medford, MA, 237-266.

May, E. C. and Vilenskaya, L. (1994). Overview of Current Parapsychology Research in the Former
Soviet Union. Subtle Energies. 3, No 3. 45-67.

Radin, D. I. and Nelson, R. D. (1989). Evidence for consciousness-related anomalies in random
physical systems. Foundations of Physics. 19, No. 12, 1499-1514.

Stanford, R. G. (1974a). An experimentally testable model for spontaneous psi events I. Extrasensory
events, Journal of the American Society for Physical Research, 68, 34-57.

Stanford, R. G. (1974b). An experimentally testable model for spontaneous psi events II. Psychokinetic
events. Journal of the American Society for Physical Research, 68, 321-356.

Stanford, R. G., Zenhausern R., Taylor, A., and Dwyer, M. A. (1975). Psychokinesis as psi-mediated
instrumental response. Journal of the American Society for Physical Research, 69, 127-133.

Stokes, D. M. (1987). Theoretical parapsychology. In Advances in Parapsychological Research 5.
McFarland & Company, Inc. Jefferson NC, 77-189.

Utts, J. (1991). Replication and meta-analysis in parapsychology. Statistical Science. 6, No. 4, 363-403.

Walker, E. H. (1974). Foundations of Paraphysical and Parapsychological phenomena. Proceedings of
an International Conference: Quantum Physics and Parapsychology. Oteri, E. Ed. Parapsychology
Foundation, Inc. New York, NY, 1-53.

Walker, E. H. (1984). A review of criticisms of the quantum mechanical theory of psi phenomena.
Journal of Parapsychology. 48, 277-332.

Washburn S. and Webb, R. A. (1986). Effects of dissipation and temperature on macroscopic quantum
tunneling in Josephson junctions. In New Techniques and Ideas in Quantum Measurement Theory.
Greenburger, D. M. Ed. New York Academy of Sciences, New York, NY, 66-77.

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Appendix

Mathematical Derivations for the Decision Augmentation Theory

In this appendix we develop the formalism for the Decision Augmentation Theory (DAT). We consider
cases for mean chance expectation, force-like interactions, and informational processes under two as-
sumptions—normality and Bernoulli sampling. For each of these three models, we compute the ex-
pected values of Z and Z?, and the variance of Z o

Mean Chance Expectation -

Normal Distribution

We begin by considering a random variable, X, whose probability density function is normal, (i.e., N(uo,
a0)"). After many unbiased measures from this distribution, it is possible to obtain reasonable ap-
proximations to uy and Go7in the usual way. Suppose 7 unbiased measures are used to compute a new
variable, Y, given by:

-15
Y= nD Xn
j=l

Then Yis distributed as N(ug, 0,2), where o,2 = 092/n. If Z is defined as

zo eT

n

then Z is distributed as N(0, 1) and E(Z) is given by:

EN .(Z) = “FE ze~°dz = 0. (1)
Since Var(Z) = 1 = E(Z?) — E*(Z), then
EXxce(Z") = E |? edz = 1. (2)

The Var(Z?) = E(Z4) — EZ?) = E(Z*) — 1. But

* We wish to thank Zoltan Vassy for originally suggesting the Z? formalism.
+ Throughout this appendix, this notation means:
-05[5

N(j,0?) = ee

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EN AZ‘) = | zte de = 3.

1
Tn
So

Varvcx(Z*) = 2. (3)

Bernoulli Sampling
Let the probability of observing a one under Bernoulli sampling be given by pp. After n samples, the

discrete Z-score is given by:

k — npo

Z= ,
Ooin

where

Oy = yPo(1 — Po);

and k is the number of observed ones (0 <* <n). The expected value of Z is given by:

Exce(Z =

— npo)B,(n, po), (4)

in

where

B,(n, po) = (i) psa — Po)" *.

The first term in Equation 4 is the E(k) which, for the binomial distribution, is npg. Thus

7x4

= aT Sk — Mpy)B,(n, Po) = 0. (5)

k=0

The expected value of Z? is given by:
EXcs(Z’) = Var(Z) + E(Z),

_ Var(k - MPo)
= —" + 0,
na?

no
Z*) = — = 1,
Excel ) no? ( 6 )

As in the normal case, the Var(Z2) = E(Z4) - E2(Z2) = E(Z4) - 1. But*

* Johnson, N. L., and S. Kotz, Discrete Distributions, John Wiley & Sons, New York, p. 51, (1969).

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- Eke(Z*) = ey > (k — npo)‘Bul.Po)
Okm0

=3+ PAG ~ 607).
So,

Varher(Z*) = 2+ soa (1 ~ 603) = 2- Fs @o = 05). (7)

Force-Like Interactions
Normal Distribution

Under the perturbation assumption described in the text, we let the mean of the perturbed distribution
be given by 0+ p00, where ép is an anomalous-perturbation strength parameter, and in the general
case may be a function of n and time. The parent distribution for the random variable, X, becomes
N(tot €ap%, 092). As in the mean-chance-expectation case, the average of n independent values of X,
is Y~ N(up+ £4790, G,7). Let

Y = Uy + apy + Ay,
where

Ay = y — (Uo + Espo).
For a mean of m samples, the Z-score is given by

Y= Ho _ Fap% t Ay
Z= a = —F = Egy in +6.

where € is distributed as N(0, 1) and is given by Ay /a,. Then the expected value of Z is given by
EN(Z) = Egp(ap {ii + 0) = ap lt + EO) = tap (8)

and the expected value of Z? is given by
E%p(Z?) = Ezp([€ap Vm + EJ) = nee + E(G?) + eq Yn EC)
= 1+ e2n, (9)
since E(t) = 0 and E(t?) = 1.

In general, Z? is distributed as a non-central X? with 1 degree of freedom and non-centrality parameter
NEqp 7, X?(1, n€qp”). Thus, the variance of Z7is given by"

VarN,(Z?) = 2(1 + 2ne?,). (10)

Bernoulli Sampling

As before, let the probability of observing a one under mean chance expectation be given by pg, and the
discrete Z-score be given by:

* Johnson, N. L., and S. Kotz, Continuous Univariate Distributions—2, John Wiley & Sons, New York, p. 134, (1970).

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_ k= "Do

Ooyn ”
where k is the number of observed ones (0 <k <n). Under the perturbation assumption, we let the
mean of the distribution of the single-bit probability be given by py = pp + &ap 0p, where égp is an anoma-
lous-perturbation strength parameter. The expected value of Z is given by:

Z

EX A(Z) = aa — npo)B,(n,P1),

where

Bn, py) = (2) pia — p,)*.

The expected value of Z becomes

k=0Q

= Papin aha. (11)

Since ég, = E(Z)/Vn, 80 fap is also the binomial effect size. The expected value of Z? is given by:

E3,(Z*) = Var(Z) + E*(Z),

= Var ~ Po), 52

no? ,
= ne Pd) 5 gay,
Expanding in terms of pz = po + fap,
ER AZ?) = 1 + e3,(n — 1) + Z2(1 — 2p). (12)

If pp = 0.5 (i.e., a binary case) and n> 1, then Equation 12 reduces to the E(Z?) in the normal case,
Equation 9.

We begin the calculation of Var(Z2) by using the equation for the jth moment of a binomial distribution

m, = Fg + pe | saw
Since Var(Z?) = E(Z*) — E7(Z*), we must evaluate E(Z*). Or,

BAZ) = ig D (k ~ npo)'Balts Pr).
k=0

Expanding n ~209 —4(k — npo)4, using the appropriate moments, and subtracting E7(Z?), yields
Var(Z”) = Cg + Cyn t+ C_yn). (13)

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Where

&
Cy = 2 — 3662, + 10e4, + 822 GE(L = Ypo)(1 — 2€%,) + 6,
0

C, = 4e2,(1 — €3,) + ace (1 — 2po), and

(l= 7%)

3
C_, = 48 - 6[e2, - 3]? + 121 — 2p.) +
0

+ a — 2p,)(12p3 — 12p, + 1).

Under the condition that ¢,, < J (a frequent occurrence in many experiments), we ignore any terms of
higher order than Eap*. Then the variance reduces to

Var(Z') = 2 — 366%, + sz — 2p.) + ait E+ del +

Ql — 7) a Tip)

1
A] ~ 6+ a6, «

a — 2po)(12p5 — 12p_ + o|

We notice that when ¢ = 0, the variance reduces to the mean-chance-expectation case for Bernoulli
sampling. When n >> 1, e < 1, and pp = 0.5, the variance reduces to that derived under the normal
distribution assumption. Or,

Var5,(Z?) = 2(1 + 2ne2,). (14)
information Process

Normal Distribution

The primary assumption in this case is that the parent distribution remains unchanged, (i.e., N(ui, 092).
It further assumes that because of an anomalous-cognition-mediated bias the sampling distribution is
distorted leading to a Z-distribution as N(iac, Oac2). In the most general case, ac and Gag may be func-
tions of n and time.

The expected value of Z is given by (by definition)

EXc(Z) = Mac: (15)
The expected value of Z? is given by definition as

EN (Z2) = u2, + 0%, (16)
The Var(Z”) can be calculated by noticing that

So the Var(Z?) is given by

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var(Z2) = a1 + ott)
Onc Oe
Varii(Z") = 262. + Qui. Oi)» ( 17)

Bernoulli Sampling

As in the normal case, the primary assumption is that the parent distribution remains unchanged, and
that because of a psi-mediated bias the sampling distribution is distorted leading to a discrete Z-dis-
tribution characterized by #a,(n) and 0,,2(n). Thus, by definition, the expected values of Z and Z? are
given by

(18)

EX A(Z?) = w2, + 0%,

For any value of n, estimates of these parameters are calculated from N data points as

The Var(Z*) for the discrete case is identical to the continuous case. Therefore

Var’ (Z*) = 2(04, + 2u2,02.). (19)

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Applications of Decision Augmentation Theory 14 May 1995

Applications
of
Decision Augmentation Theory
by

Edwin C. May, Ph.D
S. James Spottiswoode. (Consultant)
Science Applications International Corporation
Menlo Park, CA
Jessica M. Utts, Ph.D.
University of California, Davis
Division of Statistics
Davis, CA

Christine L. James

Science Applications International Corporation

Abstract

Decision Augmentation Theory (DAT) provides an informational mechanism for a class of anomalous
mental phenomena which have hitherto been viewed as being caused by a force-like mechanism. Under
specifiable conditions, DAT’s predictions for statistical anomalous perturbation databases are differ-
ent from those of all force-like mechanisms. For large random number generator databases, DAT pre-
dicts a zero slope for a least squares fit to the (Z2,n) scatter diagram, where n is the number of bits result-
ing from a single run and Z is the resulting Z-score. We find a slope of (1.7343.19) x 10~9 (t = 0.543, df
= 126, p = 0.295) for the historical binary random number generator database which strongly suggests
that some informational mechanism is responsible for the anomaly. 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 8.6-0; however, the observed data are within 1.1-o of
the DAT prediction. We also apply DAT to one pseudorandom number generator study and find that its
predicted slope is not significantly different from the expected value for an informational mechanism.
We review and comment on six published articles that discussed DAT’s earlier formalism (i.e., Intuitive
Data Sorting). We found two studies that support a force-like mechanism. Our analysis of Braud’s 1990
hemolysis study confirms his finding in favor of an influence model over a selection one (p = 0.023), and
Braud and Schlitz (1989) demonstrated a force-like interaction in their remote staring experiment (p =
0.020). We provide six circumstantial arguments against an influence hypothesis. Our anomalous
cognition research suggests that the quality of the 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 in-
dependent of sequence length; thus, we suggest that a fundamental argument supports DAT over influ-
ence models. In our conclusion, we suggest that, except for one special case, the physical random num-
ber generator database cannot be explained by any influence model, and that contradicting evidence
from two experiments on biological systems should inspire more investigations in a way that would al-
low valid DAT analyses.

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Introduction

May, Utts, and Spottiswoode (1994) proposed Decision Augmentation Theory as a general model of
anomalous mental phenomena.” DAT holds that anomalous cognition information is included along
_ with the usual inputs that result in a final human decision that favours a “desired” outcome. In statisti-
cal parlance, DAT says that a slight, systematic bias is introduced into the decision process by anoma-
lous cognition.

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 for anomalous mental phenomena.
May et al. (1994) mathematically 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 genera-
tor (RNG) database. In addition, we summarize the findings from one prospective test of DAT and
comment on the published criticisms of an earlier formulation, which was then called Intuitive Data
Sorting. We conclude with a discussion of the RNG results that provide a strong circumstantial argu-
ment against a force-like explanation. As part of this review, we show that one biological-AP experi-
ment is better described by an influence model.

Review of Decision Augmentation Theory

Since the formal discussion of DAT is statistical, we will describe the overall context for the develop-
ment of the model from that perspective. Consider a random variable, X, that can take on continuous
values (e.g., the normal distribution) or discrete values (e.g., the binomial distribution). Examples of X
might be the hit rate in an RNG experiment, the swimming velocity of single cells, or the mutation rate
of bacteria. Let Y be the average of X computed over 7 values, where n is the number of items that are
collected as the result of a single decision—one trial. 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 examples above, n is the sequence length of a single run in an RNG experiment, the num-
ber of swimming cells measured during the trial, or the number of bacteria-containing test tubes present
during the trial. As we will show below, force-like effects require that the Z-score, which is computed
from the Ys, increase as the square root of n. In contrast, informational effects will be shown to be inde-
pendent of 7.

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 AC-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:

* The Cognitive Sciences Laboratory has adopted the term anomalous mental phenomena instead of the more widely knownpsi.
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 are neutral in that they do not imply
mechanisms. These new terms will be used throughout this paper.

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ications of Decision Augmentation Theory 14 May 1995

where pt, and 0, are the mean and standard deviation of the sampling distribution. Under the null hy-
- pothesis, 4, = 0.0 and o, = 1.0.

Review of an Influence Model

~ For comparison’s sake, we summarize a class of influence models. We begin with the assumption that a
putative anomalous force would give rise to a perturbational interaction by which we mean that given an
ensemble of entities (e.g., random binary bits), an anomalous force would act equally on each member
of the ensemble, on the average. We call this type of interaction micro-AP.

In the simplest micro-AP model, the perturbation induces a change in the mean of the parent distribu-
tion but does not effect its variance. We parameterize the mean shift in terms of a multiplier of the
initial standard deviation. Thus:

Hy = Ug + Exp Op,
where py and 1g are the means of the perturbed and unperturbed distributions, respectively, and where
Oo is the standard deviation of the unperturbed distribution. ¢,p can be considered the AP effect size.
Under the null hypothesis for binary RNG experiments, uw; = ug = 0.5, 09 = 0.5, and eap = 0.

The expected value and the variance of Z? for mean chance expectation and under the force-like and
information assumptions for the normal distribution are shown in Table 1. The details of the calcula-
tions can be found in May, Utts, and Spottiswoode (1994).

Table 1.
Normal Parent Distribution
. Mechanisms
Quantity
MCE Micro-AP DAT
=
E(Z’) 1 1 + e2,n “2 + o?
Var(Z’) 2 2(1 + 2¢2,n) | 2(ot + 24207)

Figure 1 graphically displays these theoretical calculations for the three mechanisms.

E(Z?)

——

large

4

micro-AP
1

small

eee

DAT

MCE

n

Figure 1. Predictions of MCE, micro-AP 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 effects. The important differences are in the slope and
intercept values. MCE gives a slope of zero and an intercept of one. Dat predicts a slope of zero, but an
intercept greater than one, and Micro-AP predicts an intercept of one, but a slope greater than zero.

Monte Carlo Verification

The expressions shown in Table 1 are representations which arise from simple algebraic manipulations
of the basic mathematical assumptions of the 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 produce data that mimicked the results under three models (i.e., MCE, micro-AP and
DAT). Our standard implementation of the pseudo-RNG allows the integers in the range (0,2!5—1] as
potential seeds. For the sequence lengths 100, 500, 1000, and 5000, we computed Z-scores for all pos-
sible seeds with an effect size of 0.0 to simulate MCE and an effect size of 0.03 to simulate micro-AP. To
simulate DAT, we used the fact that in the special case where the effect size varies as 7 /,/n, micro-AP
and DAT are equivalent. For this case we used effect sizes of 0.030, 0.0134, 0.0095, and 0.0042 for the
above sequence lengths, respectively. 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 mod-
els. In each Figure, MCE is indicated by a horizontal solid line at Z? = 1.

The slope of a least squares fit computed under the MCE simulation was —(2.812.49) x 10—°, which
corresponded to a p-value of 0.812 when tested against zero, and the intercept was 1.007-L0.005, which
corresponds to a p-value of 0.131 when tested against one. Under the micro-AP model, an estimate of
the effect size using the expression in Table 1 was eap = 0.0288-L0.002, which is in good agreement with
0.03, the value that was used to create the data. Similarly, under DAT the slope was —(2.44-+57.10) x
10-8, which corresponded to a p-value of 0.515 when tested against zero, and the intercept was
1.050-40.001, which corresponds to a p-value of 2.4 x 10~* when tested against one.

Thus, we are able to say that the Monte Carlo simulations confirm the simple formulation shown in
Table 1.

Retrospective Tests

It is possible to apply DAT retrospectively to any body of data that meet 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 decision. In terms of their predictions for
experimental results, the crucial distinction between DAT and the micro-AP 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 ton, 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 7, simply create a scatter diagram of points (Z2, n) and compute a weighted least square
fit to a straight line. Table 1 shows that for the micro-AP model, the slope of the resulting fit is the square
of the AP-effect size. A student’s t-test may be used to test the hypothesis that the AP-effect size is zero,
and thus test for the validity of the micro-AP model If the slope is zero, these same tables show that the

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Bplications of Decision Augmentation Theory 14 May 1995

and thus test for the validity of the micro-AP model If the slope is zero, these same tables show that the
’ intercept may be interpreted as a strength parameter for DAT. In other words, an intercept larger than
one would support the DAT model, while a slope greater than zero would support the micro-AP model.

If the DAT strength is presumed to be constant (i.e., 4, and 0, are constant) then an additional test is

possible. That is, in two experiments involving N at nz and M at nz decisions, respectively, DAT predicts
that Stouffer’s Z’s of these experiments should be in the ratio of \/N/M and \/N/M x./n2inj; for AP.

2.0 ss See 2.007 a aoe

1.2 4 1.2 4
4 | a 44
zm ry rt
O.8F 4 Oar 4
ot 4 o.4b :
o.oL pe fog a a oolrit iti 1 alia 1 1.
Qo 41200 2400 3600 4800 6006 0 1200 2400 3600 4800 6000
(a) (b)
6.0
micro-AP ‘
”
4.8b a 4
“4
3.67 S
A
v
a“

2at ra
5

0.0 L at ion yanron area
Q 1200 2400 3600 4800 6000

(c)
Figure 2. Z?ys n for Monte Carlo Simulations of MCE, micro-AP 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 demonstrated that a robust statistical
anomaly exists in that database. Although they analyzed this data from a number of perspectives, they
report an average Z/\/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 deviation of 1.601
for 597 studies. We compute a single-mean t-score of 9.844, df = 596 (p = 3.7 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

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