# INTUITIVE DATA SORTING

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13
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Final Report- -Objective 1, Task 2 December 1987
INTUITIVE DATA SORTING
By: JESSICA M. UTTS
EDWIN C. MAY
THANE J. FRIVOLD
PETER J. McNELIS, DSW
CONTRACTING OFFICER'S TECHNICAL REPRESENTATIVE
333 Ravenswood Avenue
Menlo Park, California 94025 U.S.A.
(415) 326-6200
Cable: SRI INTL MPK
TWX: 910-373-2046
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Final Report- -Objective 1, Task 2
Covering the Period 1 October 1986 to 30 September 1987
INTUITIVE DATA SORTING
By: JESSICA M. UTTS
EDWIN C. MAY
THANE J. FRIVOLD
PETER J. McNELIS, DSW
CONTRACTING OFFICER'S TECHNICAL REPRESENTATIVE
MURRAY J. BARON, Director
Geoscience and Engineering Center
333 Ravenswood Avenue ? Menlo Park, California 94025 ? U.S.A.
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The Intuitive Data Sorting (IDS) model posits that humans may have an ability to produce
a psychoenergetically mediated sampling bias when designing and conducting experiments that
rely on the use of statistical analyses. If this is true, it would have profound implications, since
much of our current knowledge is based on such studies. The IDS theory would imply that an
experimenter or subject can produce this bias in such a way that the results of the experiment
support the hypothesis of interest, even if it is not true.
Experiments to try to test this theory were conducted at SRI International in FY 1984 and
FY 1986. The FY 1984 experiment involved only two subjects, but the results from each one
supported the IDS model. The FY 1986 experiment never passed the pilot phase, because only
one subject was identified who could consistently exhibit this ability.
The computer program used to test the theory was substantially revised in FY 1987, to
more accurately measure the window of time in which the ability might operate. Unfortunately,
the subject who performed significantly in both previous studies was not available to participate.
The only other subject who had performed significantly in FY 1984 agreed to participate, but
dropped out before the study was completed. Two novices also participated.
Because of the previous results, the unavailability of reliable subjects for this year's study,
and the important implications of the IDS theory, it is recommended that the experiment be
continued at a later date. The new version of the computer program, developed this year, should
be used in future studies.
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TABLE OF CONTENTS
ABSTRACT .............................................................. ii
LIST OF TABLES ......................................................... iv
LIST OF FIGURES ........................................................ iv
I INTRODUCTION AND BACKGROUND ............................ 1
A. The IDS Theory ........................................... 1
B. IDS Experiments at SRI International .......................... 2
II METHOD OF APPROACH ....................................... 5
A. Details of Computer Program ................................. 5
B. Selection and Testing of Subjects .............................. 8
C. Analysis ................................................. 8
III RESULTS AND CONCLUSIONS .................................. 10
REFERENCES ............................................................ 11
APPENDIX ............................................................... A-1
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1. Z-Score as a Function of Clock Ticks from Closest Significant Seed .............. 7
1. IDS Game Computer Screen Display ........................................ 7
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I INTRODUCTION AND BACKGROUND
A. The IDS Theory
Since 1979, SRI has been working on the development of a model that could account for a
large body of experimental data both in the parapsychological literature and in other disciplines.
This model, called Intuitive Data Sorting (IDS), posits that experimental results in many cases
may contain a psychoenergetically mediated sampling bias. * In other words, human
experimenters may have an ability to make decisions regarding their experimental protocol in
such a way that the underlying population is not sampled in a truly random fashion, but rather
with a bias favoring the experimental hypothesis of interest. This bias could enter the experiment
whenever a human decision is made. For example, most studies that rely on statistical analyses
involve some sort of randomization scheme. Decisions about how to conduct the randomization,
what seed number to use, etc., are all under the control of the experimenter. The IDS Theory
suggests that these decisions might be made in such a way that the experimental results are
favorable, not because an effect really exists, but because the data were sampled in a biased way.
In 1969, Schmidt't introduced a type of psychoenergetic experiment in which individuals
were asked to "modify" the statistics of "true" random number generators (RNG), i.e., devices
that generate sequences of numbers based on some fundamental random process such as
radioactive decay or thermal noise. Since publication of that important initial paper, hundreds of
similar studies have been conducted in a variety of laboratories. An analysis of the combined
results showed that the probability that the observed deviations occurred by normal statistical
fluctuations alone was p ~ 3.9 X 10-18 during experimental conditions, and p S 0.78 under
control conditions.2 Clearly, there is a statistical anomaly within these data.
Since 1969, there has been considerable discussion about mechanisms that can explain
these RNG results.1. 3,4 The most frequently proposed explanation is remote action (RA). Under
the RA hypothesis, by definition, a participant "forces" a physical modification in a source of
random signals so as to produce a change in the output statistics. However, with the IDS
hypothesis, we propose that humans can make decisions (by psychoenergetic means) to take
advantage of the natural and unperturbed fluctuations of a system. In the context of an RNG
* This report constitutes the deliverable for Objective I, Task 2.
t References are listed at the end of this report.
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experiment, it appears that individuals can anticipate locally deviant subsequences from within
larger and unperturbed sequences and make decisions based upon that knowledge. Suppose that
an individual is asked to "make" the RNG produce more binary ones than zeros. Rather than
"causing" the device to produce binary ones, we suggest that the participant has simply initiated
the data collection by anticipating when the RNG was going to produce a series of ones as part of
its natural binomial fluctuation. Thus, the participant has capitalized upon natural events, rather
than "causing" anything to occur.
To obtain this information from a system that is changing in time, an individual would
need access to future events. While there are specific examples from physics that could be
interpreted as information propagating backward in time, the idea that it is possible at the
macroscopic level is not generally accepted.
Since the late 1930s, however, the parapsychological research journals have been
reporting evidence that information from the future is available in the present, and that some
individuals have the ability to perceive it. This ability is called precognition. In most cases the
information perceived is above what would be expected by chance guessing, but it is not
extremely accurate. Thus, while there is some evidence that information is available, it appears
to be probabilistic in nature, and not deterministic. In other words, some future events, while
not predetermined, appear to be more likely than chance would dictate, and this information
seems to be available at times.
In summary then, the IDS theory suggests that a subject or experimenter may be able to
obtain information about the probabilities of various future outcomes, and may be able to use
that information to select a biased sample which will produce the experimental results desired.
Because large bodies of research have derived conclusions about cause-and-effect
relationships from statistical hypothesis testing, it is important to determine if our IDS model has
any scientific basis. The first attempt along these lines was to compare the predictions of the IDS
model to those of the RA model for the RNG experiments described above, using a large data
base of such experiments.5
It appeared that the data were described by the IDS model rather than the RA hypothesis.
There were problems, however, because in the test devised to separate the two conditions, the
IDS formalism was derived from the assumption that each string of data resulted from a single
press of a button. However, none of the experiments in the data base were reported in that way.
Instead, all of the data were aggregates over many button presses. While we were able to draw
conclusions based upon averaged data, (e.g., on the average, IDS appears to account for the
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results in the historical data base), the ideal test of IDS must be conducted using data resulting
from single button presses.
B. IDS Experiments at SRI International
1. Design and Rationale
In order to test the IDS model in the laboratory, an experiment was devised using a
pseudorandom number generator (PRNG). Since no evidence has been put forth to support the
idea that putative RA could influence computer hardware, it was assumed that RA could be ruled
out as an explanation of any anomalous results.
The idea of the experiment was to collect sequences of binary digits of varying
lengths, by having a subject push a button to initiate the collection of each sequence. For each
button press, a z-score would be computed to see if the proportion of zeros and ones deviated
significantly from what would be expected by chance. The subject was told only that he/she
should try to achieve a high z-score.
If the IDS theory is correct, subjects should be able to achieve z-scores that on the
average are higher than expected by chance. However, the magnitude of these scores should not
increase as the sequence lengths increase, as they would if RA were in effect. Conversely, the
proportion of ones (or zeros) should approach closer to the expected 50% as the sequence length
increases. This is because we are positing that subjects have an ability to press the button when a
deviant sequence is about to occur. Thus, for example, in a sequence of ten coin flips it would
not be uncommon to observe eight or nine (80 to 90%) heads. But in a sequence of 100 coin
flips it would be extremely unusual to observe 80 or 90 heads. A z-score of 2.9 would result
from ten heads out of ten flips, but the same z-score would be achieved with only 65 heads out
of 100 flips.
This suggests that a test of the IDS model could be achieved by comparing z-scores
across sequence lengths. Or, equivalently, the test could examine the proportion of zeros and
ones as a function of sequence length. This proportion should steadily approach 50% as the
sequence length collected at one button press increases.
This formulation can be turned into a linear model by transforming to logs. Let I opI
represent the absolute difference between the proportion of ones and 0.50. Then, by
algebraically manipulating the formula for a z-score, we obtain:
In I A p I = In I z I- In 2 - 0.5 in n.
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This suggests that if the IDS assumption is correct, and the magnitude of z remains constant
across sequence lengths, then a plot of In IApI versus In n should result in a line with a slope of
-0.5 and an intercept of { E (In 1zl) - In 2 }. If the magnitude of z changes with the sequence
length, then the slope of the line will no longer be -0.5, because the term E (In Izi) will be a
function of n. In fact, the relationship between In JOpI and In n may no longer be linear. On
the other hand, if mean chance expectation (MCE) results, then the slope will still be -0.5, and
the intercept can be computed using the appropriate integrals. Finally, if IDS is operational, then
the E (In 1zl) will be larger than it would be under MCE, while retaining a slope of -0.5.
2. Experimental Results
The first experiment done at SRI International using this framework was reported in
1985 by Radin and May.6 Two subjects (I.D. 105 and I.D. 531) who had been successful at
similar tasks participated. Both individuals showed independently significant evidence for IDS by
producing lines with intercepts significantly greater than predicted by MCE (p < 0.005 for each
participant), but slopes which were not significantly different from -0.5.
During the FY 1986 program, we planned to conduct the experiment in two phases: a
screening and an experiment phase.? For the pilot phase, we asked 20 individuals to contribute
100 trials each with sequence lengths varying from 101 to 100001. All but four of them
completed this task. For availability reasons, the four participants contributed varying numbers
of trials (less than 100). We had decided to select seven individuals from within the pilot group
to participate in a formal PRNG IDS experiment. The criterion for being included in the formal
group was that the participant had to produce a significant increase above MCE of the variance
of the z-score distribution over 100 trials (the MCE variance = 1.0).
Of the 16 participants who finished the 100 trial series, only Subject 531, met the
above requirement (variance = 1.37, p < 0.008). The second best performer, however,
produced a variance of 1.21 (p < 0.07). Judging from the 1984 study, we would not expect to
see a significant intercept with only 100 trials, and none were observed.
While it was particularly interesting that Subject 531 maintained his/her consistent
performance, we felt that we should continue the screening until we were able to select seven
participants who scored significantly during this pilot phase. Thus, a formal experiment was not
conducted in FY 1986.
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II METHOD OF APPROACH
The experiment was modified for FY 1987, to examine a conclusion that had been made
on the basis of incorrect reasoning in the 1985 report.5 In that report, it was concluded that
humans would have to time the moment of the button press to within 20 milliseconds (ms) to
achieve the observed results. The conclusion was based on comparing z-scores collected at the
moment of the button press with those that would have been collected if the button had been
pressed from one to five "clock ticks" (20 ms per clock tick) sooner or later. It was shown that
of the eleven sets of z-scores and sequences lengths generated this way, only the set at the actual
time of the button press would have produced the observed significant results. Thus, if the
subject had always been off consistently by as little as 20 ms in one direction or the other, the
results would have been at chance.
This is an interesting observation, but it does not necessarily imply that the subjects must
be accurate to within 20 ms to achieve significant results. The reason for this is that the z-score
at each "clock tick" is based on the seed generated at that point. Therefore, pushing the button
at adjacent clock ticks will not result in highly overlapping binary sequences. A completely
separate binary sequence will be generated from each seed. So the IDS task requires the subject
to anticipate the time at which a seed is present that will produce a . good binary sequence, and
not the time at which a rapidly moving binary sequence is about to produce an overabundance of
zeros or ones. Thus, z-scores generated by adjacent clock ticks should not be correlated. It was
therefore impossible to tell what would have happened if the subject had consistently pushed the
button at almost the right time.
For FY 1987, the experiment was modified so that if the subject pushed the button at a
clock tick which was close to one which would produce a significant z-score, then the resulting
z-score would be close to significance. In this way, if IDS is operating, and if timing is exact to
within 40 ms, a histogram of z-scores for the experiment should produce a spike in the
significant range. On the other hand, if timing is approximate, the z-score histogram should
show a gradual increase in frequency as significance is approached.
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To achieve this goal, "seed catalogs" for each sequence length were created by computing
the actual z-score corresponding to each of the 215 - 1 = 32,676 possible seeds. From these,
four catalogs were computed. The first catalog contained all seeds that would produce a
significant z-score, and thus contained 5% of the seeds. The second catalog contained the next
10%, the third catalog contained the next 20%, and finally, the fourth catalog contained seeds
that would produce z-scores in the middle 65% of the distribution.
Each trial in the experiment would proceed as follows:
(1) The subject presses the button and generates a seed. Seeds are in numerical
order (but the corresponding z-scores are not), so a button press one tick
(about 20 ms) sooner or later would result in a seed of one more or less than
the one generated.
(2) A random integer increment between 0 and 39, generated previously, is added
to the seed. Call this sum S. The purpose of this step is to eliminate an artifact
based on the subject knowing how long it takes the clock to cycle through 20
seeds. As will be seen momentarily, this knowledge coupled with exquisite
timing would allow a subject to continue to generate significant z-scores once
the first one had been generated.
(3) An integer from 0 to 19 is derived, using the formula:
I = integer [ (S mod 40) / 2.0 ].
(4) I is used to choose a seed catalog using the scheme shown in Table 1. Note
that the magnitude of the z-score depends on the absolute distance of I from
zero; this is equivalent to the number of clock ticks away from a "significant
seed" the button was pressed. Thus, partial credit is given for being close in
timing.
(5) A z-score is randomly chosen from the selected seed catalog. This ensures
that all z-scores will be chosen with the correct probabilities, since those closer
to the mean will occur more often in the seed catalog.
(6) The subject is shown feedback in the form of two "thermometers" displaying
the z-score and corresponding p-value. Also, a running tally is shown
representing the test of whether or not the variance of the z-scores generated
so far exceeds the expected value of 1.0, with a diagonal line representing the
10% significance level. Finally, a histogram of the absolute values of the
z-scores generated in the current set of 50 trials is displayed. The subject
therefore has a choice of feedback information on which to focus. See Figure
1 for an example of the computer display following the button press for trial
(7)
The z-score, I, and information such as date and time are stored for future
analysis.
(8) The next trial begins. The screen is cleared at the end of each set of 50 trials.
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Z-SCORE AS A FUNCTION OF CLOCK TICKS FROM CLOSEST SIGNIFICANT SEED
I
Distance from I = 0
Seed Catalog
Percent of z-scores
0
0 ticks
1
extreme 5%
1, 19
1 tick
2
next 10%
2, 3, 17, 18
2 or 3 ticks
3
next 20%
4 to 16
4 or more ticks
4
middle 65%
IZI
Trial 43
L = Select R = Quit
FIGURE 1 IDS GAME COMPUTER SCREEN DISPLAY
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B. Selection and Testing of Subjects
Since this experiment had been tested on a limited basis only, there was no available pool
of subjects who had been successful in the past. In FY 1984, both subjects who were tested
(Subjects 105 and 531) produced significant results, but in FY 1986 the only significant result
was produced by Subject 531, even though 16 subjects were tested.
Subject 531 was unavailable to participate in the FY 1987 experiment. Two novices
(Subjects 235 and 908) and Subject 105 were recruited. Each participant was asked to
contribute 600 trials (button presses). The two novices finished the experiment, but Subject 105
dropped out after completing only 500 trials. In accordance with SRI's human use guidelines,
subjects have the right to discontinue participating in an experiment at any time. This subject
was also contributing to remote viewing experiments during the same period.
The version of the computer program used for this experiment generated three different
sequence lengths, 101, 3279, and 100001. (The log of the middle number is halfway between
the logs of the other two.) These were randomly assigned such that each one would occur 200
times if the entire 600 trials were completed.
C. Analysis
Since this was the first time this study was conducted with this format, the analysis
procedure was designed to be exploratory rather than to give a strict confirmation of the theory.
Thus, several features of the data were examined for each subject.
The results of the analysis for one participant (Subject 908) are shown in the Appendix.
The key features of the analysis, and the results for the appended example, include the following:
(1)
(2)
(3)
(4)
(5)
Characteristics of the overall set of z-scores produced for all button presses,
and a chi-square test of whether or not this set had a larger variance than the
1.0 expected by chance. In the example, chi-square = 619.0780, p = 0.278.
The least squares fit to the pairs (in IApl, in n) over all trials.
The exact slope and intercept expected by chance for these sequence lengths.
The expected and observed averages for IApI at each of the sequence lengths.
Tests to see if the slope, or intercept, or both for the least squares line deviated
from MCE. In the example shown, the p-values for these three tests are
0.913, 0.6565, and 0.9004, respectively. The IDS hypothesis predicts that the
intercept will differ from chance, but the slope will not.
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(6) A frequency distribution for the z-scores, for the total set, and for each
sequence length individually.
(7) The characteristics of the distribution of z-scores produced at each individual
sequence length, and a chi-square test for whether or not each of the variances
deviates significantly from the expected variance of 1.0. In the example, none
of them did.
(8) The values necessary to plot the average In jOpl and one error bar in either
direction on log paper, for each sequence length.
(9) A histogram of the number of times the button was pressed i clock ticks away
from the significant seed, where i ranges from -9 (360 ms too early) to +10
(400 ms too late). A significant seed occurred on the average, about once
every 800 ms. This range was chosen to cover normal human response times.
Also, a chi-square test was made of the hypothesis that the probabilities of the
twenty positions were each 1/20. In the example, chi-square = 27.07, df = 19,
p = 0.10. If IDS were operating, and if the ability also enabled one to push
the button at close to the right time, the histogram should peak at bin 0, and
steadily decrease as distance from bin 0 increases. This pattern was not
evident in the example shown.
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None of the subjects in the experiment showed results significantly different from chance
on any of the measures tested. The two novices in the experiment never deviated from chance
during the course of the study. However, the performance of Subject 105 steadily declined as
the experiment progressed. After the first 200 trials had been collected, the p-values for his/her
slope and intercept tests, respectively, were 0.313 and 0.002. Further, the histogram of clock
ticks showed a significant proportion falling at three ticks before and after the significant seed,
representing a displacement of about 180 ms in either direction. These results are reported here
only because this individual had performed well in the past. Other than that we ascribe no
further meaning to this "related" result.
As mentioned, this subject was concurrently participating in remote viewing experiments.
Although he/she had performed exceedingly well in the past, none of the results of the 1987
experiments showed significant psi ability. This subject also dropped out of 'the remote viewing
studies before completion.
If the IDS theory correctly portrays a human ability, the implications would be profound.
Parapsychology laboratories continue to produce significant results in RNG studies, and IDS is
currently the most plausible alternative to RA. For these reasons, and because of the lack of
reliable subjects for the FY 1987 experiment, a compelling argument can be made for continuing
this research at a later date.
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REFERENCES
1. Schmidt, H., "Precognition of a Quantum Process," Journal of Parapsychology, Vol.
33, pp. 99-108, 1969.
2. Radin, D. I., May, E. C., and Thomson, M. J., "Psi Experiments with Random
Number Generators: Meta-Analysis Part 1," Proceedings of the Presented Papers of
the 28th Annual Parapsychological Association Convention, pp. 199-234, Tufts
University, Medford, Massachusetts, August, 1985.
3. Schmidt, H., "A PK Test with Electronic Equipment," Journal of Parapsychology, Vol.
34, pp. 175-181, 1970.
4. Tart, C. T., "Laboratory PK: Frequency of Manifestation and Resemblance to
Precognition," Research in Parapsychology 1982, W. G. Roll, J. Beloff, and R. A.
White (Eds.), Scarecrow Press, Metuchen, New Jersey, pp. 101-102, 1982.
5. May, E. C., Radin, D. I., Hubbard, G. S., and Humphrey, B. S., "Psi Experiments
with Random Number Generators: An Informational Model," Final Report, Project
8067, SRI International, Menlo Park, California, October, 1985.
6. Radin, D. I., and May, E. C., "Testing the Intuitive Data Sorting Model with
Pseudorandom Number Generators: A Proposed Method," Proceedings of the
Presented Papers of the 29th Annual Parapsychological Association Convention, pp.
539-554, Sonoma State University, Rohnert Park, California, August, 1986.
7. May, E. C., "Intuitive Data Sorting: An Informational Model of Psychoenergetic
Functioning," Final Report, Project 1291, SRI International, Menlo Park, California,
December, 1986.
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Sequence Order: 3179, 101, 100001,
(1) Overall Trials = 600
Overall Z-mean
Overall Z-variance
Overall Z-max
Overall Z-min
Overall Chi-sqr
Overall z(Chi)
Overall p-value
= 1.0335
= 2.9264
_ -3.6816
= 619.0780
= 0.5898
(df = 599
= 0.277678 (1-tailed)
(2) LINEAR LEAST SQUARES FIT TO THE DATA
y = a+b(x-xbar): a = -5.3578 +/- 0.0452
b = -0.5097 +/- 0.3929
(3) MCE-LINE: a = -5.3377
b = -0.5080
ybar = -5.3578
xbar = 8.0641
(4) MCE-LINE (n,deltaj): (101 , 2.77e-02)
(3178 , 4.81e-03)
(100001, 8.34e-04)
DATA-LINE (n,delta p): (101 , 2.73e-02)
(3178 , 4.71e-03)
(100001, 8.12e-04)
(5)
Slope (-0.508): F = 0.0119 (dfl = 1; df2 = 598
Slope : p = 0.913
Intcp (-5.338): F = 0.1980 (dfl = 1; df2 = 598
Intcp : p = 0.6565
Joint : F = 0.1050 (df1 = 2; df2 = 598 )
Joint : p = 0.9004
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(6) TOTAL Z-SCORE DISTRIBUTIONS
Z-Cent Total
101
3179
100001
-3.3
2
1
0
1
-3.0
0
0
0
0
-2.7
1
0
0
1
-2.4
5
2
2
1
-2.1
12
3
4
5
-1.8
21
5
7
9
-1.5
22
8
5
9
-1.2
35
16
12
7
-0.9
53
10
33
10
-0.6
67
29
12
26
-0.3
55
16
13
26
0.0
81
27
29
25
0.3
61
18
25
18
0.6
56
23
16
17
0.9
47
13
20
14
1.2
37
16
9
12
1.5
22
6
8
8
1.8
8
2
1
5
2.1
7
3
2
2
2.4
6
2
1
3
2.7
1
0
0
1
3.0
1
0
1
0
3.3
0
0
0
0
(7) Z-SCORE by SEQUENCE LENGTH
SQL Trials Mean Var Max
101 200 -0.065 0.996 2.289
3179 200 -0.102 0.969 2.926
100001 200 -0.062 1.144 2.577
Min
Chi* *2
Z_Chi p_value
-3.682
198.292
-0.010
0.5042
-2.323
192.837
-0.286
0.6127
-3.165
227.749
1.418
0.07816
(8) Lengths . 101 3179 100001
E + 1 sig 3.017e-02 4.796e-03 9.124e-04
E(lnjdyI) : 2.830e-02 4.395e-03 8.410e-04
E - 1 sig 2.654e-02 4.027e-03 7.75le-04
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200220001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200220001-3
(9)
IDS Game Reaction-Time Histogram:
Chi* `2 = 27.06666667
Chi* *2/df = 1.42456140 (df = 19)
BIN COUNT
-9
35 *k**