ANALYSIS OF THE SUBJECT-MACHINE RELATIONSHIP

• Document Type: CREST
• Collection: STARGATE
• Document Number (FOIA) /ESDN (CREST): CIA-RDP96-00787R000200150011-4
• Release Decision: RIPPUB
• Original Classification: U
• Document Page Count: 50
• Document Creation Date: November 4, 2016
• Document Release Date: January 16, 2003
• Sequence Number: 11
• Publication Date: January 1, 1975
• Content Type: REPORT

L~ ,, ~~~<
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AIVALY`SI5 OF THE S~lBJECT-r~iACHINE R~LATIONSNIP
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TABLE OF CONTENTS
Overview
Page
I.
Statistical Analysis of the Machine Experimental Data
,
1
Forward-Bakcward State Transition Analysis
l
Experimental Data Randomness Analysis
4
Best Strategy- .
15
iI.
Analysis of S2 Data Responses ? ,
. .
16
Strategy of S2
16
Total Color Choices ; ,
16
State Transition Color Choice
r
19
Hit Analysis .
21
.Learning from Trial to Trial
_
21
Learning Within a Trial
~
20
IIT. Miscellaneous .
30
General .Trial I nformati on ;
Machine States
r 31
Plots of Passes , ,
31
Subject -. Future Machine State Transition ,
40
Distribution of Machine State Colors for Machine 2 .
41
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Page
Figure l.l.a
Distribution of Machine Yellows Over Trials
6
l.l.b
Distribution of Machine Greens Over Trials
7
l.l.c
Distribution of Blues Over Trials.
g
l.l.d
Distribution of Machine Reds- Over Trials
g
Figure 1.2
Figure 1.3
Figure 1.4
Distribution of Machine Colors When Samples are
Taken Five:at a Time
Distribution of Machine Colors When Samples are
Taken. 1.00 at a Time
Distribution of Machine Reds When the Samples
are Taken 100 at a Time
1p
11
12
Figure 1.5
Machine Color Distribution for_Machine 1 and
Machine 2 on a Trial to Trial: Basis
l4
Figure 2.1
S2 Color Choices
17
Figure 2.2
Plot of Number of Hits/.Trial
22
Figure 2.3
Figure 2.4
Frequence Plot of Number of Hits
Cumulative Success Ratio of Subject (both machines
used)
22
23
Figure 2.5
Accumulative Probability of Success on Machine 1
24
Figure 2.6
Accumulative Prababi]ity of Success on Machine 2
25
Figure 2.7
Hits vs Trial Number-for Machine 2
27
Figure 2.8
Total Number_of Nits Within a Trial
~g
Figure 3.1
Selected Parameter Totals Listed by Trial Number
32
Figure 3.2
Color States of Machine 1 During the Experiment
34
Figure 3.3
Color States of Machine 2 During the Experiment
35
Figure 3.4
Figure 3.5
Total Number of Passes of a Trial
Total- Number of Passes Summed of Sample Number
36
37
Figure 3.6
Plot of Number of Nits per Trial and Number of
Passes per Trial
~o
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Page
Figure 3.7
From Subject Choice to Future
State Transitions
Machine State ~
40
Figure 3.8
Distribution of Yellows for Machine 2
42
Figure 3.9
Distribution,of Greens for Machine 2
42
Figure 4.0
Distribution of Blues for Machine 2
43
Figure 4.1
Distribution of Reds for Machine 2
43
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Overview
An apparent phenomenon which defies-the .theory of probability occurs
-.when Subject 2 plays-this experimental game. We significantly exceeds his
prabability of success, .25, by scoring over .29. The question that. this
report addresses is: Is there a statistical. or logical reason why he did
so we71? The methodology used to attack this problem and the resulting
conclusions are summarized below. This summary can also .serve as an outline
to this detailed report.
I. .Statistical-Analysis of the Machine Experimental Data
Pre-experiment data analysis discovered a non-random characteristic
through the examination of forward-backward state transitions (i.e.,
Red-Blue,-Blue-Red). Wowever, the coefficient of correlation between
the forward and backward states of .58 for the experimental data, .49
for Machine 1 data and .48 for Machine 2 data were considered law
enough that this approach was dropped. Pre-experiment state transitions
had a coefficient of correlation of .93.
The experimental data randomness analysis consisted of examining
the distribution of color totals and the distribution of .each color
taken over various combinations and permutations of the data. No evidence
of non-randomness was discovered.
II. Rnaiysis of the Subjects' Data Responses
The subject's responses were analyzed with the emphasis on the
discovery of his strategy or the unveiling of a trend which would give
him a statistical advantage. .The possibilities investigated produces.
no solid reason how he was able to be so successful. Wowever, in one
case there is a strong indication w~ he was able to succeed, It appears
that he vas learning the states of Machine ?_. The details of this are in
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the remainder of the report.
Miscellaneous
Thereport contains a section entitled "Miscellaneous" far the
purpose of displaying detailed data which wasn't directly required by
the above more general analysis. Details such as how many successful
choices in the color red during the 50th trial were there, or what was
the relationship of the number of passes to the number of successes.
The terminology used is as follows: the term "trial" refers. to the
string of machine states and corresponding choices from the time the
subject begins until he makes 25.non-passing choices. A sample.is a
machine -state and/or subject choice (including passes). There are
(25 + # passes/trial) samples-in each trial.
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I. Statistical Analysis of the Machine Experimental Data.
Forward-backward State Transition Analysis
In a previous memorandum (Me~r~o ORD 2240-75, i2 June 1975 to
SG11
the question of randomisers with the. emphasis on state transitions as an indication
of non-randomness-was addressed. The data used in the investigation consisted
of pre-experiment trials, The purpose of this section is to do a similar.
investigation using the actual data which occurred during S2's experiment.
-Table 1 presents all possible transition frequencies. All transitions
should have equal probability.
YELLOW` GREEN BLUE RED
YELLOW
204
199
199
216
GREEN
192
218
222
207
BLUE
211
206
228
222
RED
209
206
223
221
Restructuring into a two-by-six table as in Ref 1 produces:
Y/G
Y/B
Y/R
G/B
G/R
B/R
FORWARD
199
199
-216
222
207
222
BACKWARD
192
211
209...-
206
206
223
The conclusion based on pre-experimental data was. that these state-pairs.
show a very strong relationship between forward-.and backward transition
frequencies-{coefficient of correlation =.93). However, computing the. coefficient
of correlation, ps2 actual data = .58, it becomes apparent that the
degree of dependence is slightly reduced. Therefore the dependence of
forward to backward states can no longer be considered as a strong indicator
of non-randomness. ,
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The data used in the above discussion consisted of trials from both
machine 1 and. machine 2. Since non-randomness, made apparent by the state
transitions, clearly existed forpre-experimental data, the investigation of
the experimental data continued to include a search for this trend in the
individual machines.. The transi ions (including identity) are as follows:
Machine 1
YELLOW
YELLOW
96
GREEN
79
BLUE
88
RED
92
GREEN
$5
87
86
88
BLUE
85
82
90
87
RED
91
91
83
92
Machine 2
YELLOW
YELLOW
108
GREEN
120.
BLUE
111
RED
124
GREEN
107
131
136
119
BLUE
126
124.
138
135
RED
118
115
140
129
:Computing the two coefficients
p'machine 1 .4934
s2 data
of correlation,
p`machine 2 - ?4838.
s2 data
it is obvious that the forward and backward transitions are even less dependent
than in the combined case. .Thus-ended the search far nan-randomness through
state transition.
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As a by-product the folloa,~ing table is produced far general information.
BOTH MACHINES
MACHINE 1
MACHIPJE 2
MEAN
SD
MEAN
SD
MEAN
SD
FORWARD
210.8
10.7
86.6
4.27
l24
9.74
BACKWARD
207.8
9.00_
86.2
.3.92
121
11.25
TOTAL DATA POINTS
3483
1446
2037
COEFF OF COV
;
5843
.4934-
.4838
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Experimental Data Randomness Analysis
The machine data used during the S2 experiment his been combined, summarized
and/or permuted in
an attempt to establish evidence o,r randomness or non-
obvious indication
this task would be simplified because
of-non-randomness would have evol d !
v e ~~
,,
it-would have become a closed form i
problem (i.e.,-the solution would be - the data has non-random characteristics).
However, wha~has result d is ha .various forms of the data have bee_n__examined
with all ind_ica_ting that the data is random.
Tables,. plots and commentary are presented in this section to demonstrate
randomness. and in some cases just to provide general information concerning
the machines data.
.The distribution of the colors collectively and for each machine is
as follows:
Machine 1
Yellow
365
Green
353
Blue
356
Red
372
Tatal
1446
Mean
361.5
Machine 2
475
505
538
519
2037
509.25
TOTAL
84.0
858
891
891-
3483
870.75
Machine l was not-used in as many trials as machine 2 (44 trials to 56
for. machine 2), thus the difference in totals. The standard deviation of
binomial distribution with n=3483 and p=1/4 is 25.56-which would imply that
each separate number is reasonably close to the mean.
Accepting the distribution of the totals consider. the distribution of-the
colors throughout the experiment. The popluations used for this .investigation
consisted of the first 25 samples of each trial (100 trials total). This
population is acceptable since the distribution of its totals was reasonable
and since .the performance of S2 was approximately-the same. (success-29.61/)
for this subset,
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The .following three approaches comprise the strategy used to attack
.the question of color distribution..
1. Each _tri_al._~abbreviated to 25 samples) as analyzed__separate interval.
Obviously this .will indicate .any bias. within each trial;,..
.The data (2500 samples) is divided into intervals of five samples
each. This will indicate unusual. repetitions either: within tie
interval or interval-by-.interval..
3. The data. is reformatted into 25 intervals of 100 samples,-.where the-
nth interval consists of the nth sample in each trial.
The results of approach l is shown. in Figures l.l.a, l.l.b, l.l.c, and.
1.l .d.~
The binomial distribution for this strategy (n=25 p=1/4) is mean. 6.25
and the variance 4.69. The plots indicate randomness thro~ac~hout the 700
trials,
The results of approach 2 are similar to approach 1 and are shown in
the four tables in Figure 1.2. The plots indicated randomness but are-not
shown because of monotomy. The binomia l distribution mean is-1.25 and the
variance .94.
The binomial distribution mean and variance for approach 3 is 25 and 18.75
respectively (Figure 1.3). A plot of the data (Figure 1.4) for the "RED"
case because of the concern for the higher variance and ranges. The 13th sample
seems to have an unusually high frequency of "RED" (44~). However in .general
this investigation has not produced a significant non-random characteristic.
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sample size
t00
~~aximum
12
minimum
3
range
~
mean-
6.23
var-fiance
4.239.49949
standard deviation
~.0~9~~3387
mean deviation
1.b314
median
6
made
6
?
l5M
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hi
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Trial Number
Figure l.l.a -Distribution of Machine Yellows Over Trials
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sample size
maximum-
minimum
range
.mean
variance
standard deviation
.mean cleviatiQn
median.
:made
1 5~4
tit
M
M
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60 80 100
Trial Number
Figure l.l.b Distribution of .Machine Greens Over Trials
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7
10Q
12
t2
6.13.
5.851616162
x.419011402
1-.9404
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20 40
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Number
of
81ue
Per
Trial
se~nnle size
1 ?0
maximcam
11
minimum
1
range
10
-mean
5.21
variance
5.2i8a80808
standard deviation
2.25431 1.889
.mean d~viatian
1.5194
median
mode-
6
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0 ()()q
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h{() ()
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80 1Op
Trial Number
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Number
of
Red
Per
Trial
1 5M
A+I
M
M
MC)
1O~I OO
tit C)UC)
MO O
sample size
J 00
maximum
J2
minimum
1
ranee
11
mean
6.43
variance
standard deviation.
2.1520720513
mean deviation
1.7J5c3
median
~
mode
d
CI. ~ O t) U O
t)O t) t) U 0 C) Q t)
{)OOO U C) 0O Ot) 0 0 O.
OC)0()
5M OO
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t)t) t) O t) U U
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0
C)
rt
M
QM
0
~t ~~ ~ ~
40 60
Trial fJunber
{)
80
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(~
~~
0
()
 Approv~d9~'ril~e~~ >~'O~~l~lfis~+: CIA-RDP96-007878000200150011-4
saxnpie size 500
maximum. 5
minimum 0
range
5
mean
1.246
Variance
0..9594028056.
standard de~riation
O.97949iO952
mean deviation
O.78484t3
median
1
mode
t
Distribution of Green
same 1 e -size .
500
maximum
minimum
0
range
5
mean
1 ??_26
variance
0.9969178357
standard deviation
7285
84~
0
mean deviation.
512
~
median
mode
1
~
'D'istribution of Blue
__ _
dst~t grp.~-~_
sample-sine
6UU
maximum
4
minimum
U
`~
range
mean
1.24
variance
U.ySl35U/U14
standard deviation
U.S~Iti44299~5
mean- deviation
0. !92 l5~2
median
mode
Distribution of Red
1
i
sample size -
500.
maximum
5
minimum
0
range.-
5
mean
t.2g6
variance
1.O2625ti513
standard deviation
.1.013043194
mean deviation
0.823?_ l 6
median
mode
t
t
Figure l.,~p~~'d4-~-il~~l~~e^~~aas~l~l~~-b~~~~~~a~2ot~~vlpT4P at a Time.
10
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~~~~eia~s~ ~~~~~~~/~~ :
~JA-RDP96-007878000200150011-4
maximum
31~
minimum
J9
range
12
mean
24-. 92
variance
10.57666667 .
standard
deviation
3.252178757
mean deviation
2.6304
median.-
24
mode
24
Green Distribution-
sample size
25
maximum
35
minimum
15
-range
20
mean
24.52
variance-
24.59333333
standard deviation
4.959166597
mean deviation
3.9392.
median-
?25
mode
22 25
Blue Distribution
sample size
25
maximum
34
minimum
19
range
15
mean
24.84
variance
1 4.473 33.333
standard deviation
3.804383437
mean deviation
2.9664
median
25
mode
26
Red Distribution-
..sample size
25
maximum
-
44
.mint
mum
16
range
2g
mean.
25.72
variance
26.71
standard deviation.
5.163171824
mean deviation
3.3664
median
25
mode
25
Figure l.3 Distribution of Machine Colors When Samples-are Taken 100 at a Time
(One From Each Trial) ..
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Number
of
Reds
F45~
M t1
~h
M
M
40~{
?~
h~
M
M
35M
M
M
~ 0
30M p
~( t) O
~h
~~ () C)
M () ()'.)
25~t) U UO , U
~t O ~ _ f)
- M (J
0
15M ~ M M M M ~4
o to 20 ~o
Sample Number
Frigure 1.4 Distribution of Machine "Reds" when the Samples are taken 100 at
a time. (one from each trial)
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Approach 1 has .been repeated for Machine 1 and Machine 2 separately to
check for abnormalities. The binomial distribut-ion mean and variance are
as follows:
Machine 1
Machine 2
44
11
8.25
56
14
10.5
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Machine 1 -
- Machine 2
,
Yel 1 ow
sample size J-
25
sample size
maximum
16
maximum `
minimum
7
minimum
.range
9
range
mean
1i.4
mean
uarance
7..75
variance
standard deviatian
2.783882181
standard deviation
mean deviation
2.224
mean deviation
median
12
median
mode.
12 ~
_
mode
sample size
Gr
25
een
sample size
maximurn
17
maximum'
minimum
4
minimum
range
13
range
mean
10.68
mean
variance
9.726b66667
variance
.standard deviation
3.118760438
standard deviation
mean deviation:
2.35E34
mean deviation
median
L1
median
made _
ii
mode
sample size
_
25
Biue
s~mnle size
maximum
15
''maximum
minimum
3
minimum
range
12
range
mean
10.32
mean .
variance-
7.726bfibb67
standard devi~tian
2,779688232
:
uariance
.
mean deviation
2.3072
standard deviation
median
11
mean deviation
mode
8 12
median
.
.
simple size
maximum
-
25
14
Red
mode
sample sike
maximum
mini
mum
4
minimum
range
15
~
ran
e
-mean
11 .6
g
mean-
variance
standard d~:viation
-mea
10.5
3.240370349
variance
standard deviation
n deviation
ii
2.4
mean deviation
mer
an
1.2
meth tin
made
i2
mode
25
i9
7
12
13.52
7.51
2".7404379? #
2.176
i4
15
25
84
16
.1.3.84
t 2.7233333?
3. 5669781 ~?
2.7308
13
13
25
25 _
10
15
14si~
8.9433333':
2.9~aO54,Jb42:~
1.984_
14
15
25
21
11
iO
i4.52
i 0.0 i
3. 15323 53404
2,6524
13
11 13
Figure 1 .l~pfll~f@Mg1?leF~~I~e~~~e:~QOU/04/on8 : CIA-RDP96-007878000200150011-4
b for Machine 1 and ~1ac~~il~e 2 on a Trial
Tn Tria7 Racic 7dl
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Best Strate
Based. on the above analysis what is the best strategy to pursue? No
good strategy is available based on .the randomness of the data. The best
possible strategy based- on the above transition matrices is:
1. If the subject can't distinguish between machine then .press blue
- when blue appears, else pass.
2. If the subject-can distinguish them on Machine 1', press yellow
when. yellow occurs, and on Machine 2 press blue when red occurs.
For all its worth, of the existing data the following success would
result - 26%, 26%, and 27%.
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Analysis of S2 Data Responses
The attempt here is to discover a reason for S2's success at responding.
The investigation was unable to give'a definitive reason for his success:
Although no strategies were uncovered there was in-one case a indication that
the subject was learning.
Two major approaches have been taken in this investigation. .They are.
as follows;
1. Strategy of S2 - Was there any trends in the way he guessed?
he respond based on the previous state of the machine?
2. Hit analysis - Did the subjects' hits (correct choices) increase
within a~run; did it increase from run to run (i.e., was he
learning?)
Strategy of S2
For general- information and future reference the first fig;~re (Figure 2.1)
presented i s the actual .choices,, One i tem_ of .curiosity from_ t_hi s i s that
wren h~passes, he tends to do_1_t in__s~i^.n,gs. This characteristic. of course
wasn't pursued because of its insignificance to .this report; however, observations
like that are pointed out throughout the report as possible importance to those
in the field.
Total Color Choices
The distribution of S2's color choice totals are shown below.-
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 Approved For Release 2003/04/18 :CIA-RDP96-007871~OOA200150011-4
~Il;=' j i i:.?;= fl "~ 1;1L 1 =, f11-1>: 91~ [i _; fl ~i?_ . rl
1~;1 la:~; l ~ :1 ._; fl ~; fI;M; [i:_;:~; I;I I,i 1)111 fls;;:' ~:
-,?_,
.^.~ rl Il _; 1 I~i : ; ~i :~: l.b =; 1 ~ I_i >: ~.' 1 I_1:~=. L: i= ~ 1 ~ _~
~< - -
(j'~ =''`' =' j i~i i i;-: 11 -i; i_I j i i =; I~i I,l 1,1~: 1_I: 1 :?
~- ,._ _
:' ~ _, ! 1 11:? 1_t I.i 1 1~I :-: I~l :' Il "~ j? ; 1 1_I >; }vl ~ Il :?
:1: ~ 1;1 >i 11 =; ~, : ~ f I =; i1:~'- i 1 I V (I >:;~' 1_I;_?' 1 fJ :' 1 l 1 =;
fl :~~ _' 1 I:,; 1.? ~; i=i:=; flx_ ~I?~ iJ ~. I~I'_:L il'LL: Ia:-:.`=; 11
i=C'~ 11 _~ 1 I:~; [i;.; 1_I =; 11` ~, ~ I ;=; i_I:=; 11 :_: (1 ~, :_: I i'?
IJ:_: t.i',; I_I:~;;:' 1.1~';~: ~ Ij:J t i 1 I I >' ], :' ! Ic~: j, I.t~ i
w9,f~ 1 11 1 f i _; ~ (I :_: 111: :_; ij:~: i] 1 :? f I~_>; i I ~, :,~
11:_:1:x: i IL;`. :~;1:_;:=: i1 ~:1 (1;=~ Iel 1 ~:1 (I ;;~:_;1
i1~ i r 1 ~:1 1j:~:1 i r,=;1 i ~:_. w ~I :~
~ _,~~~~
'11{ a~~ [i:i!1i?.iniLlall:=;1:~a,1st"i' i' 11i' =;IIG'.:~f t i ._, ,' i';' i-(i
fi:? i1:;1 1 f' l~l I_I 1 ~ i~i .~ C; hi:; ~, >; i_! ~ ' i' i' i' ~ ~~~ ~`.: ~t 1 ~ I ,:
f11:~}1~:j~_Ili~ll:=~~~1~=;1[ii.-~?I_i~1i.?~i-I:-ri ~?i:~!17'J1
-y-,-?~~-?~~-n
[I,~-~ i' :_; ~I:}' I~C~ i' r' r` ?~ ~I:j 1_I 1 :~, fl i' :1 11 ~` ~' i' I_i:' e; I:, i' i t i? i i i .} h1 e' f?1
~1C:.i i' fl_ i~_~1i'y?I11_I.; ?~t'i.?r~i i'3 _: ii ii~IlGi_ifr._:
-1-T,~~e'7 "^-T -7 _~ i - -~r,"~-~-7TT F.?~.~.T?"?., .7.x`'.9-7 J 'l''+ '-5 '' 11 '_' 1 i~ it
[1 {,l e; i "" _~ 11 Y i' I_I >: r i ~... i i i i i 1 ~1 ~' i~ i' i t r' r i f _+ i f i i f i i f ?_? 1 _? ~. l -_ ~. -_ _ i
:j ~ ! =1 i' i~ i' i' i? s' t t i t r i r ~ r f 1 i' 1 IJ i t i i i i x ._-, !_i ~ :.:: i' i' i' i ~ i' _: ~' f1:~i 1 :_: ~ r r i i i e [} >> i_I i i r f _+ i' 11 ~; :?
"~ i' i i t ~I i' i i i' ~ 1 ! i i .' i' i' r i" I_I : i 1 i _+ i' -: ~ i' P i i f :: i I. , [1:r; ~, 1_i. 1 [i:~; ~ (i 11
-,1-.r,..,.fi"-, -,~11r ii'1-?~?-,~i-,-.-,~._,-,-,.,~~-;-.-,~ .-~Ci' ~Il:=:il:~:f, ~t11:="~11
t i i? C r r? i' 1 s -:i _ i .' ! r' i? i i ?_, i i i f i i i i' f, G ._ i . i=
-.-.-.~-. .~-+-< :-a~~-.-.~ ~ -?~^ -.-,-.-,.~, .-, :till :r`'= 11: r: I'I
s i' i ` i ? i' i' i i a i' f -_ , , is 1 1_I :.+ i i i i i i 11 ' !' -. 11 r' r r i' ~ ,` f i' , ._ ~1 1 ?_ .1 _+ ~ - ~'= - ~- '
~~~~~ .~
'111_I~:~ 1_I s' 1 J 1_I ~.:~ IJ i' i' ^ f' t' i' ! P f i i f 1 ._; it ~ _: 1_l t_:-=?~ fi 1 ._, 1_I:='
-,~~-?-,-:.1.11 i1111~'li.ll1 il" =}i!~f1:=..,r.~t._-., ti~",fl'~'-+7-,-,-+-,1,y
11 ~' 1:? i' i' i? ~I ~~ i' ~I i i i I.I :'` i' i? i' i' i-~ ~ _+ I. f/ f i i i i i i i i i r f i i,~ i' i i i i i r s (i Y i i _~ i
,~ rr55 ~' r -. ':; li = "' 11; 9~ _; 11:- _;1 i'- ! 1 ,-+ [I -+-r-r ?-a-~-+-r--+^~~~-ate ii
_L~L. Ji't i'i?1?_ __~i -~L~- _ `1.:_ _u1. ~~r 1'f i'i f' i i i i i i f f i i
:~ ~, I I:_'. ;' >' i 1 f1 ~. a fl 1 :ai f, hI ~' _~ 1 [3 _' ;' I,11 f1 ;' ? _: 1
j ~.: i ~.: i_I ~ :yt ], :~: I! 1 '= ~ [I 1 :~; t~l ~': _; ~' '~ I_I ~ ~, ~. ,, ~1 "j
1:~ii_ti~. ~i:;si'>:~1>~,il~:'~C:ii' i' ~' f i' _;i' i? t-i' i' i' i' (~?fls'~M:~.-='1-1~=~. i_!s' 1
?'1~r -~'J-,
1.yf.~111{'_:~. '.r~~,;`';I.Ic~~-,:_;?_:1;'~1:~:~;~!"i~-','?~;~fi 1_I>~'~~~}~;'Q
1 _~ ~ _~ f 11 '( :e~ i_I ,' f ~ r' ?_~ 1 -_~ :~ i_I _~ ~ ~~ f i' f >~ e' ~ - ~ 1 i. ~ -' ? ~ ~ ~ r' _
^~ 1 - -,-,-+-v-~-~~-?~~-. r,-,...-. f1:~ 1 '_' (I 1 i -+-9-;-~-? q -? Il -"~.z {{ r ^r 4 '1 ~ . .-ti
?ti_i. i' i' i i f i i i !+ .~+ a' i i =1 1 _,1 C. 1 :i i- 1 f f i' f i i 1- i t i t .'+ 1 i" l.. 1 1 i_ r 1-_
fl 1 c= =1 ~' [i i ` _{ i? i~ : 1 f i' i? =: 1 i' ~ i' ?i~., fl 1 i L,' ~ ; !_i i' 97 e' L 1 i' I_I 2 _: I 1
1]_'_ __'_;j 1:'-'(1]~:~: ], 1i-'it], fl-,-,-,it~r-, -,I_f =?--?~fIIS-'[t,
~iGl_ti1 ~? II~SL;::,;1>:~Ia~ll ~:i':7tl~~v~
1:~ ,' ',' i' I_! ~' 1 11:x; 1't: ; ~, I_I? ~' ~ i_I:1 ~. ~ ;_: 11 ( ~, Z 1 iy1>.' ~:~; s' ~.~
1 11>: 1 I,-, 1 I_1.~.=,,.,.-, -, 1 :~: ~1? ;: (1~''~ ~ 11?^"'?_" =' i ! ::-''? it
17 :~ 1_I :? _~ r_I ~' =':1 ,.1;_ ~: i 1 rte' 1 ~ ~. ` 1.~ .? 1
?.~ I_i;',' r (I 1 ~ f ?~` ~. c 1? I_I ! I.,:. 1_I _;.j.~i:' 1 . t_t.~; iJ 11
Fic;ure 2.1 Subject 2 Colar Choices for First Fifty Trials (0-yellow, 1-green,
2-blue, 3-red, 7-pass)
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 031 1037~~~r1"T~~ ~~~~~~~~7~~~ 3~1301~P39330 l0 78000200150011-4
20703123070231703030330.133703. .
30170102031730730300.330.313713-
O1007777373707303777 1737772'1.3377310770777130777373773
20707370731777370077037370002731777707077777073777777377773
0173033032037033032701.370301.3
3037170332.07303073773013023737203
037.37373033730321732333771 73733707371
23707377313 0.33.33370377377370717 3777377373270
131073037377301033333707373137.07700
333 7370773073037 33331 3037 33707077707 33
03303773770333"7337 777077777327777301 027337333
03030372000037737337770"17377 33030332
33032133270323233130121330
1070}101301101313030230123
300703300723730030371 7771 37777033002
..031337373'7030003200030001003
302 3321.310000013713 03 70303 1770
77.2017710077 03072370313731013777377177777303
337~t 7 101 77 11 37 1 30021 7333733030733
3011 7373717.077 }3U } 17303707301373370071
03303203020071027107"37712.12707.03
.03231327320373023770.33 } 1.10077700
3317370'7371071317331331730117207073
27131 3i 07327033277~T3 1 1 17 1 30323303
3000373300033003710303071330
30 12700 1 33330.1 30777 37077.37 33033.77770770
030370;30370.737323.11370710732001773
3773307007200077030037313000300?_
13200?_()00300303770300731723370
30707207020773307033030303777377737377073
070777307037003777777.07731707330307307077770737-.373`
00707737377003073730 777!777073 7 777 7137773770300077773333
130}037132010717301.002720073723.?
31013103}7001300001730073020
03777 7200707'73100 770707373007?_00730700700
30300007100000232113002002
3031301301320130231033003
2301203130120310311303120
30130231031737i3073032300i31
3013013013201302101302303-
13023103230371327303}030130
30103103107732.3031.3073021331
3} 0313031737701 373001:330033777713
3 1 301 0303.10330307377070037717003
0231303320137001372:30201330
0?_ i 7373.10310130370007302 7 777310373
1 370731 07 1 03702373 1:x271 c~331073 703
331 30030170730107()700371 0"/ 3 70071 3
Figure 2.i (Continued) Sw' Co'1d~? choices for Last 50 Trials
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 Approved For Release 2003/04/18: CIA-RDP96-00787RD00200150011-4
Yellow
Green
Blue
Red
Total Times Chosen -
$$1
411
237
971
of Total
35%
]6.5%
9.5%
39%
The first inclination is to-.try and determine how his strategy of choosing
so many yellows and reds benefitted him. Examine the following table:.
Total Number of Hits
Yellow
255
Green
127
Blue
~~
Red
292
% of Total Hits
35%
17%
8%
40%
% of Success in Color
29%
31%
25%
30%
(Hits - Correct Choices)
As can be seen his results with blue are significantly lower than the others.
However, assuming the probability of success to be .25 and using- the binomial
distribution the expected value =69 and the standard deviation = 7. The
inference from this as .that the 60 81ue hits are not a statistical abnormality.
However, it is curious that he dial so much worse on his lowest preference.
State Transition Color Choice
This:investigation consists of examining the states of the machine verses.
the choice on-the next sample of the subject (i.e., if the machine shows "red"
does the subject consistently choose one color on the next turn). Consider
the following table:
SUBS
Machine
S
MACH
Yellow
Yellow
l06
Green
119
Blue
69
Red
314
Pass
210
% Pass
26%
U
B
Green
177
25
69
316
252
30%
J
E
Blue
24}
99
27
198
302
35%
C
T
Red
322
157
65
97
218
25%
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 Approved For Release 2003/04/18 :CIA-RDP96-007878000200150011-4
The. subject obviously avoids repeats (i.e,, he assumes the machine
_i l~l 1 1 1 i i;_% _, ~ ' ~: s_'{ i IL :? it ~_; is
?1 _:,-,1 il:~;l ii:_';Y;1 sit _ =''='t,1:~il~,il._,~
?~' ~~ fl :?'~; 1 ~. 1_i i~ 1 1_i ~ u ~_ ? - ._ '' _% 1 4 1 11~l i_I
:7 I I:~; I~ 4 L 1_!; ':~:;? ' :~; 1 I_i t7i _e 1~I:';:' 1_I ~%,:% 1 C '
=% 1 ti% 1 ~ I_I;~'_; i 1 I~I 1 =; e; i :~; _1 =%i ~' 1 s i ra 1
1;~c11 :=:1. 11 ~~1:=%!a~a1:=%i.i~l:;ilil_~1~~i
I:_% i. ~~. 1 1 L :~; i..' ~ 1 i~ i_ i ~' 1 1 1`I 1 { 1 it :_: i_ I~I : ; 1 i.~. l I 1 i' _~ 1 11:='~; :,! Yi ~I:~; ~,
_ 1 1 1 1:11 tl : ~ ~; I.I [l i~ ~: ! ~ Il 4'c% ? i I=11.11 =; i I 1 '~% ~
1 =; tl i~ c_ :~ ~, 1 } 1 I l i:: ~% ~ _~ i~, c_. 1 r.:: i; 1:~; 9.1'~; I~I ;
! 1 1 i. I"I 1 1 1 fl i::1_l ~: ~';=':_% 1 1 1=%i~ 1 ~ i~ i_l : e~ c 1 1 1.1 i i:=3 i_i i
. ~:y;:_;:~: I_I:~; 1:11-14 [i 1 i i~' 14 ~' 1 11 ~. la =~ ? 4 ~. I~i 1 1 is i. i,' t.
%44 l ]. ;~ 1 i I f11 i:'.1 ~ 1414 l:r 1 I_I 1 f1 ij ~.:=% 1
1 1 1 1 1:~%:~; (I 1 fl 1 (1:_; _; t I_I 1:.; I,i I~I =;1 114.1 i i 1 I_I
fl 1 ~ I_I i;; 1 C::~%-:%i; :r i~ ! l iJ ~ i~ ~ ~. ;~' I i _' "; f~t ;~ : ~ 1 :~; [I
1 I.l _~ 1 1414:,.4 ti ~:?-~ ~ 1:~; 1 1 1 11;x' d=6% 1 iV ~;i'. 1 1 I.1;='~:1 1 1) 1:?:=:1:141.1.1
1= ~%1 ii1 is4i=%1i_I~lil;_~iliil~I~iiilc_;{~IIa~~_~ia4
"~ ~ 1 :_;'_; ], :_;'~; :_; _: ], : _' !_I i I i_::_i c::~ 1_I I_i s= 1 ~; I i ~; ;_ 1_t.'=;
i_ 1 1 ~:.% ~ 1 1 I,I : _: 114 :~::~; (I _' ~. 1 i I (I ;~ I~I !_! ! 111 ! i _ I~l i :_' "~ "_% ~. _:: =; _% ~. ~ v r,"~ i" 11 1 _: i _' _: ~ 1 1 ~~ i~ t.l C: i_
:,%~:~i~ 11;_:x;?'=%:~:;:_;?;:.1 11'4111 c: i_I 1 !14 ~% 1 %1_lis~' .=;~i~ lr fl4',%~,%c I_i 1 ~:1 '% 1 1 1 1 i ~ 1 1 i=c CI i rl_i 14
'~ I~I t 14 1 !_!` CI:~%i=C:4~~~C_ 1 :~4 I~I I_I:=; 11~~411;=%:,%`::~%
1 c 4 ~ ~ fl i l :~; ~% L: 1 ,-_ - ' _' C: 1 i. I_I I_l 1 ~; 1,1: =' _: ~1 i=: i~ ~_% ~% iF e"ti i.
t_li::i.i~: Ij 1,1 r::_;1_I~ l~i~:~~%~ 1 C: [l i=' =; ~, ~:=%f~: 1~I~-: I.I ]. (_i i_li`'s=:%i '. 1 i' 1.1 j,'
4 i::1 : 1 ~. 1,11_1 :a; I~i I~I !, E' _: ~.:_~ .t ~ ~ ~ 1?1:_;11 :-; I_I i?? ~ i_ ~~% i.l ~' i~ i~ i`' P I : =; ~. 1 1 r:11`_'y~111J~~':':ar.~;^~1
c1 I:r::111~:iii~s:.1 f ii:=;a:Ij11~=I:iI_I:~;:=;r:!~':=1:?11~~::::cc11 ill:I~?1:~
1 ~=:1: 1 Ij;~ ~ ':~::=;;~ :,;1 Ij:~;1 Ij 1 ~i i ! .1 1 1 r:I I:I . ~ ? .ia r CI;=` ~ i 1 ~ 1:~:
C r_f 114: _: I I 1 ~ ~ 1 i i 1 1 1 ~; L' I! 1 ~ i:_' 1 ~ :` ?,1 :~ r1 ~q~l 1;;1 i. r_I i ~ 1 i_1 ! 1 I:1:_~ r.: ~ ~_: _~ 1 ~ e~ I:I :: f-1: =; Ij 1 : a _; !j ~! L
L L 1 ?.~' 11 L I_i L ? ~' 1 :~' ~' +J V J L: 1f ~' ~, ~ L 1 ?.~~ fl t L, , i I I I_-:' 1 L
1 L -J r_I fi U 1111111 11.,: 11 1 1 11 ~ 11 -. i. c ~! ~ c 1~ It,:; ~, I:I . ,,
1 1 ~~ 1 ~' _.~~ ri 1:_;:~:;:_:~;~:?;.' 1 n 1 i ~L 1 _,:=;1 li ~: i 1 1 ~:1 !:+ 1 :1 ri ~:
L:~1~~:?~at_I~;i11:_i711i11: ?''='=:1cl~~tl~__:11~?~1-'- '~??'=i?-'--1=.__`'t1
may-,-,.- _CL 'L_.'.-~ Jl..' ?JLL~ Jl. ..
'~"~'.:: ~= Il ~ :' i?I C: ~' +_i ~ ' Ij l:l =; ': ` 1 1:I tl. 1 ~ 4 +.1 1 c: ~ 1;.1 1 ` I i 1 ..1 ~ 1 =':~; fl:;::? Ij 1, _' [I._; L_ 1.11 c 1 '_;:_; fl;.R
1:~Ij:=;-''~1:11 Ij1._;i1-~-, r,1 -, _:?-~ f'I-.1 -,.1-,.~t11 il1?'
+:r.?~:=;:~;:_:;~ 1 1 r_r r:I 1 i,+ x:11:11 I:I 1 c 1 -s; ':~; 1j 1!:!:~: l i
11:,.iIljl~i4::~~_111 iii:ll r:l:~;i1C+1~:~'111~1~~
30M
M (]
M O
~( () () ()
M`
254 a c)()
~t c]
~ oc]
()
r]
(] () (] c]
(] 0 () U
M (] (]
M
aat ()
~M
~t Q U
1 5~~
~++I
M
1 U~~()
~i
M
~S
M
5M
M
tJl
M
MO
01~f r~ ~A ~h
0 2Q
(]
U (](]
(] (](] (]U(]
~( ?~ ~ (](](]()U(]()U(]
4o ba a(a
Sample Number
Figure 3.5 Total number of passes summed over sample number
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 Approved For Release 2003/04/18: CIA-RDP96-007878000200150011-4
Number of passes.-and the number of hits vs. the-trail number
on one plot. Investigation of the hits/passes relationship
was dropped when the coefficient of correlation between the
two was computed at -,114-
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38
 Approved For Release 2003/04/18 :CIA-RDP96-007878000200150011-4
5 Qp,{
t~4
~~
lu(
?tt ()
?:(
30~~( ()
O
~S
2C)t~ C) C)C)() U () ()
M C) Q U
~ () C) C) C)() () C) U
?~ P () P P P() C) U () ~ P P
10~~ PPP P P P P PP PP C) P PPPC)P P C)
t~4PP PP P PP P PP PP PC)PP P()PPPPP PC)C) PPPPP C)PC)
~tPPP P P PP P P PPPP PPP () P P C) P PP P
~i C) C)OP t)Ot) O P C)PP C) C) UC)
~ P P P () () () C) C)
-0MOOOOOOOO i~ ~fi 1~S f) A~ ~~S ~4 {)C) Sri
p 2C) 40 6Q X30 1 Q(?
Q -passes per trial
P -hits per trial
Figure 3.6 Plot of number of hits per trial and number of passes
per. trial
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39
 .Approved For Release 2003/04/18 :CIA-RDP96-00787800020015001.1-4
IV. Tables of state transitions which reflect the influence of the subject
on the machine: For color choices of the subject the table shows the number
of colors the machine has on the next sample. For example on the first table,
when the subject picked yellow, on the next sample 197 times-the machine
state was yellow.
MACHINE STATES ON FOLLOWTNG SAf~PLE
Ye11 ow
Green
,_
B1 ue ~
Red
Yellow
88
77
87
95
Green
38
46
39
47
Machine 1
Blue
27
28
24
24
Red
120
105
99
11.2
Pass
84
83
98
81
Yetiow
109
124
128
141
Green
58
47
58
66
Machine 2
Blue
25
32
42
30
Red
1.21
125
136
102
Pass
146
162.
161
168
Yellow
l97
201
215
236
Green
96
93
97
113
Both
Machines
Blue
52
60
66
54
Red
241
230
235
214
Pass
230
245
259
249
Figure 3.7 State Transitions from Subject Choice to Future Machine State
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 Approved For Release 2003/04/18.: CIA-RDP96-007878000200150011=4
V. Because of the possibility that the subject was learning the state of machine
2 the distribution of the colors are plotted in Figures 3.8, 3.9, 4.0, and
4.1. The only states used are those in which the subject didn't pass.
Therefore there is a total of 25 for each trial.
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 Approved for Release 2003/04/18 :CIA-RDP96-007878000200150011-4
t o. aM c? c~
M
M n
M
M c? c?c? r? c? c? c? c?
7.5 i~f
M C) O(?f) (? ~ U t? 0 O i?
M
I~f) (? (? (? Qt? O
Number M
of 5. oMC? c? c? t? c?c?c? o c? c?c?
Yellow M
M f) (? t) (? t) ~)
M
M f) O t) t?
2.5M
M (7 f
M
M
hh ._
0.O M M hit M M hi M
0 20 40 6D
Trial
Figure 3.8 Distribution pf Yellow for. Machine 2
t2.5M
;~ t?
M
M _
M
tO.C}M U
M
Mt) U (? t)
M
M O (? f) (? O ~?C)
7.5M
M
}~~) p i?(~ C) (?(? 0O
M
5. aM o c? c?c? c? c? oc? c? r? c?
M
M Q (? t) (?O ')c) c) t?
~+(
~,{ O O
2.5M
M (? U
M
M
ti~
~? . 0,~ M M
a ~~
M }~ ~ .
4p b0
Approved For Release 2003/04/18TrGf~A'-RDP96-007878000200150011-4
Figure 3.9 Distribution of Green for Machine 2
42
 12. ~~~
~{
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~ E)
N{
1 C . OtyS c) t) t)
M
~ c)c) c) t)
~#
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43
 Test
Description !
Scoring
'
i
I
53 I
S4 I
S5 I
S6
Halstead Category
Nonverbal test requiring abstraction of conceptual relation-
7
14
33
26
6
28
Test
ships. Score: Total errors.
Tactual Performance
Requires placement of 10 geometrically shaped blocks in
Test
their correct locations on a formboard while blindfolded.
16.4
11.8
7.7
7.7
11.4
6.9
Separate RT, LT, and bimanual trials.
Score: Total time (min.).
Speech Perception
Discrimination of non-word speech sounds.
4
2
0
2
5
3
Test
Score: Total errors.
~
'
Seashore Rhythm Test
Discrimination of nonverbal rhythms. Score: Number correct. I
27
25
28
29
26
29
Finger Tapping Test
Measure of finger oscillation rate for 10-sec. period, bath
RTiLT
RT/LT
RT/LT
RT/LT
RT/LT
RT/LT
RT and LT hand trials. Score: No. taps/10 sec.
53/50
53/44
48/47
54/53
47/47
48/43
Trail Making Test
Requires connecting numbered circles in order from 1 to 25.
40
16
18
19
30
27
(Part A)
Paper and pencil task. Score: Total times (sec)
Trail Making Test
Requires connecting alphabetic and numbered circles by
56
50
55
50
54
53
(Part B)
alternating 1-~A}2}B, etc. Score: Total time (sec)
Knox Cube Test
Measure of attention span and immediate visual memory.
13
14
13
16
17
17
Score: Number correct.
Raven Progressive
Nonverbal intelligence test involving spatial matrices.
39
53
49
55
60
54
Matrices
Score:. Number correct.
Verbal Concept
Requires abstraction of verbal conceptual relationships.
22
24
27
23
21
24
Attainment Test
Score: Number correct.
~
I
Buschke Memory Test
Requires learning a 20-word list in a maximum of 12 trials with
Total
14/20
:
17/20
18/20
14/20
20/20
20/20
repetition of words omitted after each trial. Score: Max. no.
List:
words correctly remembered; List: no. words consistently remembered
8/20
14/20
11/20
16/20
15/20
16/20
(8 trials)(? trials
Grooved Pegboard Test
Requires insertion of 25 pegs in their holes in a pegboard. Both
RT/LT
RTfLT
RT/LT
RT/LT
RT/LT
RT/LT
RT and LT hand trials. Score: Total time (sec).
76/74
69/70
58/67
59(67
48/50
7 2/7D
Spatial Relations
Requires mental rotation and identification of fi~?res
_
_
_
60
52
Subtest of the PMA
rotated in 2 dimensions. Score: no. correct - no). errors.
`
Gottschaldt Hidden
~,,
----
-
v.good
outst.
r
outst.
Figures Test