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to allow
for growth and appearance of colonies.
The colonies were then counted manually
using a New Brunswick Scientific Bactronic Colony Counter, Model C110, and the data were
recorded for each dilution.
In
all cases, the
10-s bacterial dilution provided an acceptable
number of nonoverlapping individual colonies to back-calculate the total number of bacteria
in
1 ml of the overnight culture.
The quantitation of the number of mutants
(histidine independent
cells) present
in the overnight cultures was obtained according to the following set of procedures.
First,
a
100-p.l aliquot (approximately
108
cells)
of the undiluted culture was combined with two ml of
molten (43°C) top agar in a 13-x-100-mm
test tube.
Vortexing of the
cells in the top agar
ensured even distribution of the cells when poured on the selective minimal glucose plates.
Each culture was then plated in triplicate, and the plates were incubated at 37°C for 48
hours.
When the incubation process was complete, revertant colonies were counted manually,
and individual plate counts were recorded.
For each of the eleven bacterial cultures, an average CFU for 10 ^1 of the
10-5
dilution was obtained and adjusted in terms of CFU per ml of the
original culture.
The
mutation frequency was then calculated as
mutation frequency/10 ^
ceils = ™mber mutant cells/ml
CFU/ml
When
all of the calculations for a given session were completed, the micro-
biologist provided the monitor with an experimental data sheet detailing the growth rates of
the biological samples and the pertinent experimental parameters involved in the biological
procedures
(see Figure
2).
The most salient column to note on this form
is the
“ Mutants/ 1
6Cells, " which
is the number of ceils per million
cells per test tube that were able
to grow in the absence of histidine.
The most desirable outcome of a session was obtained
if
the subject’s -high-aim"
test tubes exhibited the highest values from this column, and “low-
aim”
test tubes exhibited the lowest values.
After obtaining the form for a given session, the monitor completed a session
eedback sheet
(see Figure
3).
The test tube numbers of the subject’s choices for the IDS
favorable condition
(or the predetermined Numbers
1,
2,
3,
7,
8, and 9 for the IDS
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Experiment
Innoculation Date:
) Time:
7![
GH Broth Batch Date:
Start Tine:
irrvwM
\4o it** f&End
Bacteria Stock
Incubation Date :
Start Tinei fQlln iA. End Tine
:
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Total Tine
: !$• j-
Survival
(YC Plates)
Incubation Start Tine
: /fl \4o JJ£ ft End Tine
i/tf
Total Tine: 4j krf
Tenp
:^
fry
Mutation
(Bio Plates)
Incubation Start Tine
; //! lo \ M. End Tine:
Total Tine ;^#,,! h/* Tenp: ^
<c-
Bio Plate Batch Date
:
9(
—
YC Plate
Batch
Date:
Saline Batch Date:
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BACTERIAL ASSAY FORM SAMPLE
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;
r
*
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unfavorable condition) were written in the boxes below the appropriate low aim or high aim
test-tube icons.
The monitor then consulted the “Mutants/10 6 Cells” column on the form in
order to rank,
relative to each other, the mutation rates of the
six chosen test-tube samples.
To represent the
results graphically, the boxes on the three-test tube icons corresponding to
the three highest mutation rates were colored green, and the three boxes corresponding to the
three lowest mutation rates were colored red.
The monitor also colored the “High Mutation”
box on the key green, and the “Low Mutation” box red, then
filled in the appropriate session
identification number.
As mentioned above, the session feedback sheet was presented to the
subject, whenever possible,
just prior to the start of the subject’s next session.
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I
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Session I.D.
Key
High Mutation
I
I Low Mutation
Low Aim
High Aim
FIGURE
SAMPLE FEEDBACK FORM
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IV RESULTS AND DISCUSSION
Seven subjects participated in six experimental sessions each: three
for the IDS
condition, and three
for the RA condition.
Of the total of 378
test tubes used in these
sessions,
63 tubes corresponded to the high-, low-, and no-aim condition, respectively,
for
the two hypotheses under test.
Thus,
for a given hypothesis, there were 126 tubes to
test
for
differential aim effects.
An estimate of the number of trials necessary for a sensitive
test was
derived from published work, and was calculated on the basis of a significant t-test assuming
a
magnitude of effect on the order of 0.5
o*.
That
is,
it was assumed a priori that for either the
RA or IDS hypothesis,
individuals would be able to produce an effect of 0.5
a.
Because
of
the large standard deviation
(0,46 mutation
cells per million) observed in 187 baseline control
tubes
(cf.
Figure
4),
it was hypothesized that three sessions per subject in both the RA and
IDS conditions would be sufficient to demonstrate a significant effect.
Due
to large “batch”
variations
(i.e., variation between sets of nine experimental
test tubes), the normalization technique used by Nash was adopted.
The measured mutation
rate for each test tube was divided by the set-of-nine average and multiplied by the global
average mutation rated for the 378 tubes in the experiment.
Or,
normalized mutation rate
=
mutation rate
X
avera 8e
set average
Table
1 shows the normalized mutation rates observed during this experiment,
summed across
all subjects
for the RA and the IDS conditions,
respectively.
Following the
analysis done by Nash,
pairs of t-tests between the various aims and conditions have been
calculated.
During the RA condition, there were no significant differences among the aims.
When subjects were allowed to choose their test tubes, however, the low-aim condition was
significantly lower than the no-aim condition
(p < 0.05).
While the low-high-aim difference
was
in the correct direction, the difference was not significant.
There were no significant
differences
for various aims observed in the RA condition.
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a Approved For Release 2000/08/10 : CIA-RDP96-00789R003800290001-9 = (1.58 + 0.04) x 107° = (0.456 + 0.032) x 1076 ~ 15% ERRORS i) e = Ww = us cc ~ n ft uu = LL 1e) faa Lu ca =. - 2 ° 0 1 2 3 MUTATION PROBABILITY X 10-6 FIGURE 4 DISTRIBUTION OF MUTATION PROBABILITY FOR 187 BASELINE TEST TUBES 25 Approved For Release 2000/08/10 : CIA-RDP96-00789R003800290001-9
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There was no compelling evidence, however, that subjects are able to interact causally with
this particular biological system.
Tables 2 through
display the data contained in Table
t
in a subject-by-subject
format.
One subject (310) produced strong effects in the IDS condition:
his low-aim
condition was significantly lower than his high-aim condition
(p < 0.003), and his low-aim
condition was also significantly lower than the no-aim condition
(p < 0.018).
(U)
In addition to the various t-tests, a multiway analysis of variance (ANOVA) was
conducted as a second form of analysis
(post hoc).
Aim
(low, no, and high) and condition
(IDS and RA) were used as the two “main effects”
for the analysis.
When the ANOVA
examines one “main effect,"
it sums
all the data
in the other “main effects.”
For example,
to examine the IDS and RA condition, the ANOVA sums across
all aims.
Likewise, to
examine an aim effect, the ANOVA sums across the IDS and RA conditions.
No significance
was anticipated in these two dimensions, and none was observed.
Significance was observed,
however,
in the interaction term between the IDS and the RA condition
(p < 0.05), which
may indicate that there
is some difference between the IDS and RA conditions when
examined
as a function of aim.
This does not imply that IDS or RA
is “more significant.”
It
should be noted that the interpretation of the ANOVA interaction term has been traditionally
difficult, and Rosenthal has suggested that ANOVA with more than one “main effect” should
not be used in the social/psychological sciences.*
The analysis has been included here merely
for the sake of completeness.
In summary,
this experiment has produced a relatively weak, but statistically
significant effect, which most readily supports the conclusion that subjects are able to acquire
information, psychoenergetically, about the mutation rates of Salmonella, but are unable to
cause physical perturbations in these bacteria.
To reiterate the criteria
set forth in the
Introduction,
a physical system
will not be considered a candidate intrusion detector unless
it
registers energetic effects directly
(as a result of intentional perturbation),
or indirectly
(as a
result of concomitant acquisition of information).
To
first order, therefore,
it must be
concluded on the basis of this one experiment, that the Salmonella bacterium does not appear
to be
a promising intrusion detector.
R. Rosenthal and R. Rosnow,
Essentials of Behavioral Research,
p. 254 (McGraw Hill
Book Co., New York,
1984).
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Table 2
NORMALIZED MUTATION RATES
X
(Subject 164)
Condition
Aim
Low
No
High
Remote Action (RA)
Remote Action (RA)
Remote Action (RA)
Mean
3.00
3.69
3.06
3.25
2.76
2.90
3.09
2.92
3.54
2.59
3.09
3.07
t (Low < No)
= -1.430
n.s.
Statistics*
t
(No < High)
=
0 .484
n.s.
t (Low < High) = -0.475
n.s.
Condition
Aim
Low
No
High
Intuitive Data Sorting (IDS)
Intuitive Data Sorting (IDS)
Intuitive Data Sorting (IDS)
Mean
2.99
2.79
3.57
3.11
2.89
3.54
2.68
3.04
3.26
2.93
2.83
3.01
'
t (Low < No)
= -0.310
n.s.
Statistics*
t (No < High)
=
-0.112
n.s.
t (Low < High) = -0.475
n.s.
* Degrees of freedom
. =
16.
Given the weak
statistical nature of the effect and the potential operational
importance
of intrusion detection,
replication
is recommended for a variety of compelling
reasons.
First, there
are
a number of proposed methodological changes to this experiment
(as
discussed in Chapter V)
that would in
all likelihood enhance the robustness of the
effect.
From this perspective, the experiment might legitimately be considered a pilot study.
Second,
this
is the
first experiment of
its kind that has used Salmonella as the target biological system;
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Table
NORMALIZED MUTATION RATES X
(Subject 240)
Aim
Condition
Low
No
High
Remote Action (RA)
2.91
3.45
2.87
Remote Action (RA)
3.48
3.09
2.68
Remote Action (RA)
3.45
2.58
3.16
Mean
3.28
3.04
2.90
t (Low < No)
= -0.809
n.s.
Statistics*
t
qsj0 < High)
==
-0.543
n.s.
t (Low < High) = -1.443
n.s.
Aim
Condition
Low
No
High
Intuitive Data Sorting (IDS)
2.59
3.23
3.40
Intuitive Data Sorting (IDS)
3.40
2.87
2.98
Intuitive Data Sorting (IDS)
2.98
3.20
2.92
Mean
2.99
3.10
3.10
t (Low < No)
=
0.345
n.s.
Statistics*
t (No < High)
=
-0.004
n.s.
t (Low < High)
=
0.317
n.s.
* Degrees of freedom =16
thus,
at least one attempt at replication would be appropriate, before any definitive statements
could be made regarding the efficacy of Salmonella, as an intrusion detector.
Finally, the
statistically significant result in the IDS condition—the informational
ability to obtain a desired
outcome using intuition—demonstrates the existence of a psychoenergetic
ability that has
numerous operational applications; replications could be designed to further explore and
enhance
this potentially useful psi talent.
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L
'
*
.
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I
Table 4
NORMALIZED MUTATION RATES X
(Subject 310)
Aim
Condition
Low
No
High
Remote Action (RA)
2.73
2.84
3.74
Remote Action (RA)
3.21
2.91
2.99
Remote Action (RA)
2.53
3.16
2.99
Mean
2.82
2.97
3.32
t (Low < No)
=
0.394
n.s.
Statistics*
t (No < High)
=
0.976
n.s.
t (Low < High) =
1.485
n.s.
Aim
Condition
Low
No
High
Intuitive Data Sorting (IDS)
2.71
3.36
3.04
Intuitive Data Sorting (IDS)
2.65
2.99
3.47
Intuitive Data Sorting (IDS)
2.64
3.06
3.38
Mean
2.67
3.14
3.29
t (Low < No)
=
2.298
0.018 t
Statistics*
t (No < High)
=
0.677
n.s.
t (Low < High) =
3.294
0.003*
* Degrees of freedom =16.
^ p-value.
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Table
NORMALIZED MUTATION RATES X
(Subject 385)
-6
Condition
Aim
Low
No
High
Remote Action (RA)
3.21
3.28
2.81
Remote Action (RA)
3.57
2.62
2.89
Remote Action (RA)
3.08
2.92
3.28
Mean
3.27
2.95
2.99
Statistics
^
t (Low < No)
t (No < High)
t (Low < High)
-0.948
n.s.
0.144
n.s.
-1.012
n.s.
Condition
Aim
Low
No
High
Intuitive Data Sorting (IDS)
2.75
3.02
3.26
Intuitive Data Sorting (IDS)
2.86
2.97
3.30
Intuitive Data Sorting (IDS)
3.21
3.29
2.68
Mean
2.94
3.09
3.08
Statistics*
t (Low < No)
=
-0.310
n.s,
t (No < High)
=
-0.052
n.s.
t (Low < High) =
0.487
n.s,
Degrees of freedom =
16.
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