CALIFORNIA STATE UNIVERSITY, NORTHRIDGE
INFORMATION VALUES IN A
\!
~~N-COMPUTER
INTERACTION
A thesis submitted in partial satisfaction of the
requirements for the degree of Master of Arts in
Psychology
by
Michael F. Smith
August, 1974
The thesis of Michael F. Smith is approved:
Committee Chairman
California State University, Northridge
July, 1974
ii
This thesis is dedicated
to the Professional,
without whom
there would be no Amateurs.
iii
TABLE OF CONTENTS
LIST OF FIGURES
v
LIST OF TABLES .
vi
ABSTRACT .
I.
II.
vii
INTRODUCTION .
1
PILOT STUDY
Experimental Method
Apparatus
Objectives
Subjects .
Procedure
Results and Discussion .
7
EXPERIMENTAL STUDY .
Experimental Method .
Objectives
Subjects . . . . .
Experimental Variables
Design and Procedure .
Results .
Discussion .
Task Variables . .
Subjective Information Values .
7
7
13
13
14
21
24
24
24
24
25
25
27
39
39
41
REFERENCES
45
APPENDICES
49
Appendix A--The Autonomous Control
Subsystem Concept (ACS)
Appendix B--SIV Scaling Sheets . .
Appendix C--Computer Analysis Results
iv
50
55
59
LIST OF FIGURES
1.
System Block Diagram
2.
Display Panel .
10
3.
Adaptive Computer Aiding in
Man~Machine Systems
12
4.
Typical Path Form
15
5.
Simulated Data Summary Printout .
18
6.
Task Flow Chart
19
7.
Experimental Design .
26
8.
Total Score Across Trials .
32
9.
Correct Moves Per Error
Across Trials
33
Percent Time in Control
Over Trials .
34
Multiple Correlation Coefficients
for Operator Correct Moves Per
Error and SIV Over Trials
37
ACS Organization .
53
10.
11.
A-1.
8
v
LIST OF TABLES
1.
Analysis of Variance Results
2.
Multiple Correlation
Coefficients Summary
.
.
29
35
3.
Mean SIV and Rankings
38
4.
Subjective Information Values
Index Coefficients
40
vi
ABSTRACT
INFORMATION VALUES IN A
MAN-COMPUTER INTERACTION
by
Michael F. Smith
Master of Arts in Psychology
July, 1974
A man-computer interaction requiring control
decisions is utilized to study subjective information
values.
The values were utilized for training purposes
and for a search for indices of a trained operator.
The subjective information values were obtained
from a direct scaling technique.
Multiple correlations
were used to determine the relationship of three task
performance indices with the subjective information values.
Two indices were found to have a high correlation with the
subjective information values.
Operators trained on the
subjective information values were found to have lower
performance scores than a control group.
Limitations on using the direct scaling technique,
possible uses for subjective information values, and
necessary future research are also presented.
vii
INTRODUCTION
In this day of increasing technology and complex
systems, the information transmitted to the human operator,
and the processing needed can be extremely demanding,
demanding to a point of actually reducing man's output
capabilities by overloading and stressing him.
The computer has been advanced to reduce the
information overloads.
There are more than 70,000 general
purpose computers, not counting the minicomputers,
presently operating (Newman, 1972).
They have been
developed to receive, store, condense, modify, and output
information in time and form that is usable by man.
Recently, the computer has been developed to control autonomously and "intelligently" in parallel with the human
operator.
Man-Computer Interactions.
Research on the hardware
and software aspects of man-computer interactions has been
fairly extensive since the early 1950s.
With the concept
of man-computer "symbiosis," a different aspect of the
interaction is suggested (Licklider, 1960).
Licklider
suggested systems where men and computers could operate
as a team, making decisions and controlling complex
machines.
In the partnership, men would set goals,
formulate hypotheses, determine criteria and perform
1
2
evaluations, while the computers would do all the routine
data processing work.
This concept of the man-computer interaction was
extended by Yntema and Torgerson (1961) to include limited
decision making by the computer element.
It required a
detailed listing of information, and was applicable only to
simple judgments without risk, interaction of variables,
or rapid change of decision environment.
As research on the man-computer interaction
continued, questions arose about the relationship of the
man and computer.
These included the operator's reaction
to allocation of control between himself and the computer
(Thomas
& Pritsker,
1962), the need for exploration of the
human factors aspects of man-computer interactions
(Licklider, 1965; Nickerson, 1969); and, for computer
aiding in decisions, the development of a computer program
to match the decision behavior of the man (Rapoport, 1964,
1967; Shuford, 1965; Miller et al., 1967; Powers, 1969).
One question that has largely been ignored is that
of training the operators to use an intelligent computer
in a symbiotic relationship.
The present study proposes
a method of training that employs subjective information
values, enables trial by trial evaluation of performance,
and provides a basis of decision for the choice of task
factors to be emphasized in training.
In any task, information necessary for desired
performance levels can be analyzed into information factors
3
(i.e., those aspects of the task about which the operator
needs information to perform satisfactorily).
For this
study, an E-defined set of information factors has been
established.
In addition, S-perceived weights must be
empirically derived on the relative perceived importance
of the information factors.
These weights are the
subjective information values (SIV), and are generated by
subjective information evaluation.
Subjective Information Evaluations.
Recent studies
of subjective information evaluations have revealed
several limitations of the purely subjective approach.
Subjective evaluation is characterized by a substantial
degree of random error (Bowman, 1963; Slovic
& Lichten-
stein, 1971).
Information evaluators seem unable to take into
account more than a few value-relevant considerations at a
time, thus ignoring potentially important information
(Wendt, 1969; Slovic
& Lichtenstein,
1971).
Subjects tend
to underestimate information values with more than ten
items of necessary information (Halpin, 1971).
Desirability of an event, and strongly held initial
opinions have been found to bias the Ss' estimates in a
manner which makes the information estimates less accurate.
Rewards for accuracy did not reduce the value bias (Slovic,
1966; Alker
&Hermann,
1971).
Individual differences have a tendency to be quite
large (Slovic, 1966).
The judging of simple information
4
stimuli values increases the Ss' internal consistency, and
rank order correlations between pairs of raters were
higher with task experience (McKendry
& Enderwick,
1967).
Decomposed evaluation procedures have been proposed
as a means for improving subjective evaluation values.
The
essence of this approach is to divide the evaluation process into a set of simpler subtasks, each of which is well
within the judgmental capacities of the evaluator.
hopefully, allows for the assessment of
import~nce
This,
weights
of the information factors.
Several procedures are available for constructing
decomposed evaluation scales (Fishburn, 1965; Raiffa,
1969; Hoepfl
& Huber,
1970; Keeney, 1971), and a number of
validation studies have attested to the feasibility of the
approach (Eckenrode, 1965; Yntema
Peters, 1969; O'Connor, 1972).
& Klem,
1965; Lathrop
&
But, despite the "generally'
favorable results of these validation studies, the assessment of importance weights of the information factors has
still proven to be a problem.
Subjective Information Values.
In a man-computer
interaction, the man must receive, process (attribute
meaning to), and act upon (utilize) information from the
computer.
The results of these three stages is, hopefully,
a response which meets the required man-computer goal.
the desired response is not obtained,_ then it might be
assumed that one or more of the stages is at fault,
assuming that there is motivation to perform correctly.
If
5
An analysis of the stages is as follows:
stage I
(receive information) can be realized by asking the man if
he received the information, or from physiological measurement of the man's sensory channels; the analysis of stage
II (attribute meaning to the information) is performed
through subjective measurement techniques and is influenced
by the man's memory, present perceptions and goals, the
measurement technique itself, and assumption that the
information was received; the analysis of stage III
(utilize the information) is based on the man's environment, his capabilities and characteristics, the nature of
the task, and assumption that the information was received
and processed.
Let it be assumed that the necessary information
has been presented in a form and manner that meets man's
stimulus receiving characteristics.
For man to be able to
utilize the information in a way to produce the desired
response, he must attribute the desired meaning to the
information.
It may be advantageous at this point to con-
centrate not on how man attributes meaning to the information, but what meaning he attributes to the information.
If it were possible to know the information factors
of the task, man's attributed meaning to the factors, and
the desired meaning of the factors, it would allow for the
further analysis of the second and possibly the third
stages of the man in the man-computer interaction.
The
present study advances a method for measuring the factors
6
and the desired meaning of the factors, and. a means for
analyzing training by utilizing subjective information
values.
Experimental Hypotheses.
The experimental study is
testing the following hypotheses:
1.
Subjective information values correlate
positively with performance, and learning the task will
increase the relationship between the information values
and performance.
2.
Training based on correcting subjective informa-
tion values increases performance faster than being
trained without information value correction.
3.
Subjective
informati~n
values have a signifi-
cant correlation between subjects.
In addition, the study analyzes the effects of risk,
additional visual feedback, type of control mode, and
computer learning level feedback on the SIV and performance
indices.
I .
PILOT STUDY
Experimental Method
Apparatus
The task simulation was part of research in progress
at Perceptronics, Inc., Woodland Hills, California, for
the Office of Naval Research (Contract N00014-72-C-0093).
The long-term goal of the research is to develop human
factors criteria for the application of adaptive, computeraided decision making and control in complex man-machine
systems.
Figure 1 is a block diagram of the experimental
set-up.
The individual components and their functions are:
1.
Task Display.
A large screen oscilloscope with
a single beam (dot) is used as the controlled cursor.
is positionable at any point on the screen, using X-Y
coordinate voltage inputs.
2.
Information Display.
The display contains a
confidence meter, error and takeover lights, a counter
for path task running score, and a risk indicator.
3.
Operator Controls.
The operator has a two-
degree of freedom joystick, and an automatic control
threshold setting
1
.
~ever.
7
It
8
DISPLAY
PANEL
"
JOY
STICK
DISPLAY
OSCILLOSCOPE
r--
1_1
,,..
I
I
ANALOG BUFFER
"
"I'
·I'
..
·r
"4 CONTROL
SELECT
DIGITAL
ANALOG
ANALOGDIGITAL
·I'-
If·
OUTPUT
INPUT
INTERDATA
MODEL 70
Figure 1.
System block diagram
9
4.
Digital Computer.
An Interdata Model 70
computer performs three major functions:
(a) to implement
the autonomous control subsystem; (b) control the task
simulation; and (c) to monitor and record performance
measures.
5.
Data Conversion.
An analog/digital and digital/
analog conversion transfers the data between computer and
physical devices.
6.
Analog Buffer Unit.
It has operational ampli-
fiers for biasing, gain adjustment, and summing and
smoothing of the analog signals.
All controls and displays essential to the operator
are integrated into a single display panel,
An artist's
concept of this panel is provided in Figure 2.
The
components are:
1.
Error Light--signals when an excursion from the
path is made, and results in scoring penalty.
2.
Autonomous Control Subsystem (ACS) Takeover
Light--indicates ACS control of cursor.
3.
Error Return Button--optional automatic return
of cursor to path, results in additional scoring penalty.
4.
Confidence Meter--indicates instantaneous value
of ACS level of confidence.
5.
takeover.
Threshold Setting Lever--sets threshold for ACS
It is attached to the confidence meter and
reads from same scale.
6.
Path Counter--displays cumulative path-foilowing
10
•
2o
3o
Key:
1.
2.
3.
4.
5.
6.
7.
Figure 2.
Error Light
ACS Takeover Light
Error Return Button
Confidence Meter
Threshold Setting
Lever
Path Counter
Override Button
Display panel
11
task score.
7.
Override Button--allows the operator to regain
manual control from the ACS.
Autonomous Control Subsystem (ACS).
The ACS treated
in this experiment exemplifies a class of systems which
extend the idea of computer aiding.
In these systems, the
computer functions not just as an intermediary between man
and machine, but also as an adaptive, "intelligent,"
participant in the control decisions and actions.
Figure 3 illustrates how such a computer system
functions in the man-machine control loop. The major
components of this relationship 1 are defined as follows:
1.
Computer Aiding--is provided by a computer
placed parallel to the human operator in the man-machine
control loop.
The computer aids by making and displaying
control decisions, and by supplying autonomous control
inputs to the machine system.
2.
Adaptive Decision Making and Control--comes
from a trainable "machine learning" algorithm programmed
on the computer.
Various sensors allow the program to
observe operator performance and its results, and to
optimize control decisions accordingly.
The computer
learns complicated control strategies, can be pretrained
for future tasks, and forgets unused actions.
1 For ACS system operation and theoretical basis,
see Appendix A.
.
SYSTEM
DISPLAYS
SYSTEM
CONTROLS
MACHINE
SYSTEM
ENVIRONMENT
OPERATOR
'
DECISION
DISPLAYS
Figure 3.
....
ADAPTIVE
COMPUTER
Adaptive computer aiding in man-machine systems
I-'
N
13
3.
Decision Information--is presented to the
operator continuously by the computer.
The amount and type
can vary with the application, and may include such
factors as the degree of confidence in a computer decision,
the planned action and the probable outcome.
4.
Allocation of Function--between the operator and
the computer is made on the basis of the particular system
and task, the immediate processing load, the decision
information, etc.
The operator retains the capability to
override computer decision, and, in fact, operator overrides help train the adaptive component.
Objectives
The objectives of the pilot study were:
1.
To develop a direct scaling technique for
obtaining subjective information values (SIV).
2. ·To obtain from judges reliable information
values.
3.
To see if more experienced judges' SIV differ
significantly from less experienced, specially trained
judges.
4.
To obtain a correlational measure between
individuals' SIV and their task performance scores.
s.
To obtain standard scores for SIV and
performance.
Subjects
Sixteen male subjects were used in the pilot study.
14
Eight of the subjects had nine hours' experience with the
task, and eight had five hours.
The five-hour group
received special training; the other group did not.
The
special training involved a lecture on how the ACS uses
conditional probabilities, administration of a Scoring
Rule for Probability Assessment test, self-grading of the
test, and a discussion on how to improve their probability
estimations.
Both groups received an hour practice
session, after which they were considered ready for
experimentation.
Procedure
The operator moved a cursor dot over the display
region using a two-dimensional, variable-rate joystick.
Computer-generated boundaries defined an aperiodically
changing path through a virtual lOxlO matrix underlying
the operating space.
The task was to traverse the "safe"
corridor as rapidly as possible while hitting the
boundaries as little as possible.
in Figure 4.
A typical path is shown
The basic task score for the pilot study
was the correct operator moves per error in a fixed time
period.
This measure was utilized to test if a relation-
ship existed between the SIV and performance.
Errors were defined as excursions from the path
into a forbidden element.
When this occurred, a red light
was illuminated, a preset amount was deducted from the
15
Figure 4.
Typical path form
16
task score, and the operator had to return to the path
where he left it.
He could return either by manual control
or by pressing an automatic return button.
The automatic
return, however, resulted in an additional scoring penalty.
Changes in the single, continuously variable maze
were accomplished by replacing short segments of the path
by new segments at random locations.
at random times.
Replacement occurred
In an average run, the operator had
several passes over a specific segment of the maze.
An experimental session was made up of four ten-
minute runs of continuous control.
Each of these runs was
with a different error penalty value, resulting in a
different risk level.
The value of the penalty was
displayed to the operator.
Two control modes were utilized for the task-voluntary and automatic.
In the voluntary mode, control
allocation was made strictly by the operator.
Pressing a
button on the control stick transferred control to the
opposite of what it was in--manual to ACS or ACS to manual.
There was no limit to the number of times the operator
could transfer control in a trial.
In the automatic mode, transfer of control from
the operator to the ACS was automatic, occurring whenever
the ACS confidence to make a move was above a preset
threshold (learning level).
The threshold was determined
by the operator (reflecting his subjective perception of
information value) and could be changed any time during a
17
trial.
The operator could override the ACS control by
pressing a button on the control stick.
Feedback of the instantaneous value of training of
the ACS was provided by a front panel edge meter reading
from 0-10.
The higher the training level, the higher the
reading on the meter.
The adjustable takeover threshold
for automatic control was attached to this meter and read
from the same scale.
The threshold was attached to a
linear potentiometer, so that the set value was available
to the computer at all times.
After each ten-minute trial, a printout was made
of the various performance measures accruing through the
period.
Figure 5 is a simulated sample of this data
summary.
The first two rows in the printout break down the
distribution of control moves into operator and ACS
portions.
The last row gives the total values.
The
columns indicate the separate measures--these are:
DIST--Total number of moves correctly made along
the path.
TIME--Elapsed time in seconds.
SCORE--Total path score achieved by subject in
trial [correct moves - (risk factor x error)
+error returns].
MAXL--Average ACS likelihood of success.
ERRO--Number of moves resulting in error.
Retr--Number of automatic error returns requested
by operator.
OVER--Number of moves in which operator overrode
ACS decision.
18
RISK
1
SUBJECT
JOHN D.
SESSION
1
CONDITION
121
TRIAL
3
DIST
TIME
SCOR
MAXL
ERRO
RETR
OVER
OPR
269
434
102
166
1
0
ACS
TOTAL
224
166
600
216
605
818
8
174
0
1
0
493
Figure 5.
318
0
Simulated data summary printout
These performance measures were not considered
adequate for complete analysis of results.
Two other
derived measures were considered necessary (correct
operator moves per error and correct ACS moves per error).
These measures are fully described in the experimental
study results section.
The only measure utilized in the
pilot study was the correct operator moves per error.
Subjective Information Values.
To determine what
information factors would be needed for the SIV scalings,
a task flow analysis was performed.
Using Figure 6, the
following information factors were obtained.
1.
Error light on--error light
2.
Return dot to path
3.
From:
a.
Manual control--position and direction of
dot when error occurs
b.
Automatic return--automatic return
Manual Control--risk, plan screen, gridded
19
Start
.
_...
I
Automatic
Return
'-}
~ Dot on Path?
Yes
Move Dot
1
.
Yes
2b
No
Automatic
~
Return?
No
~'
Yes
~
3
Error light
On?
Yes
f-4
Hanual
Control?
Yes
2a
Return
Manually?
~
2.
~
.
....
No
3a
ACS Control?
Return Dot
to Path
~
! Yes
t
4
~lanual
Override?
No
2
1
<.'f.& Error light
Return Dot
to Square
On?
L
o.}'
2a
r-4
Return
Manually?
No
F>i
...:.=..
~
No
2bA utorr:at1c
.
Return?
~es
Yes
Yes
,___
Yes
1
Error light
On?
Move
D~
CorrectS~~
~t
No 3a
Yes. 4 1
Yes 3Keep Hanua 1 ~
ACS Control? ~
'lanual
Override?
Control?
f_.___ _ _ _ _--!. tlo
'~No
Figure 6. Task flow chart
20
screen, and path score
a.
4.
ACS control--confidence meter, risk, control
light, and path score
Override button--confidence meter, risk, and
path score
To obtain the subjective information values, a
multicomponent (a decomposed) evaluation scale was used.
The problem of assessment of importance weights was reduced
because the Ss' assessments were evaluated from the context
of their prior performance and not from an expected
outcome.
The task information factors were presented next to
a continuous scale marked off in units from 0 to 10 or
0 to 20 (see Appendix B).
The Ss were asked to draw a
line from each factor to any appropriate point on the value
scale.
They were permitted to select points between
numbers and to assign more than one factor to a given
position on the scale.
Each S judged the factors four times.
The factors
were judged for two control modes (Voluntary and Automatic)
and each mode was judged on two scales (0-10 or 0-20).
The
factors appeared in a random order for each presentation.
The scales were used for two purposes.
First, to
see if the magnitude of the scale influenced Ss' SIV
response.
Secondly, the scales were used as a consistency
measure for within Ss and between Ss.
The two control
modes were judged separately to ascertain if type of
control influenced Ss' SIV response.
21
Results and Discussion
To see if there was a difference in the SIV of the
more experienced group and the specially trained group, the
mean correlation of each group with the information factors
was calculated.
The correlations of each factor across
the two scales and for the two control modes were combined
for each group.
The mean correlation of the more
experienced group was 0.40 and that of the specially
trained group was 0.51.
A test for difference between
independent correlations was then performed.
The differ-
ence is not significant, which allows for combining the
two groups into one for further SIV analysis.
The correlation of each information factor across
the two scales for each control mode was then calculated.
The mean correlation for the Voluntary control mode was
0.77 and the.Automatic mode was 0,67,
A test for differ-
ences between dependent correlations was then used to see
if there was a significant difference in SIV.
The
difference is not significant.
To see if the magnitude of the scale influenced the
consistency of the subjects' SIV responses, the mean
correlations of the informations factors for the two
scales were calculated.
The mean correlation of the 0-20
scale was 0.65, and of the 0-10 scale was 0.72.
A test
for difference between dependent correlations was done;
the difference is not significant.
22
It appears that SIV are neither affected by the
control nor by the difference in magnitude (0-20 to 0-10)
of the scales.
The 0-10 scale tends to have a slightly
higher correlation than the larger scale, which is to be
expected with the decreased range.
Between subject consistency was tested by each S's
correlation with each of hhe other subjects.
The range was
0.21 to 0.57 with a mean of 0.45 which is significant at
the 0.05 level.
The significance of the consistency
between Ss reflects the ability of the Ss to have reasonably
similar responses for their SIV scalings.
This indicates
that the direct scaling method used was appropriate for
the task.
To obtain a correlational measure between the SIV
and performance, a multiple correlation coefficient was
used.
Multiple correlation was used to eliminate the
problems which arise when a large number of different
SIV are combined into a single information value,
The multiple correlation coefficient for the SIV
and operator moves per error is 0.96 which is significant
at the 0.05 level.
This allows for the use of SIV to
determine information utilization in the task.
To be able to use SIV for training purposes, SIV
standards are needed.
The 0-20 scale was chosen to
obtain the standards, since it allows for greater flexibility in responses and thus gives more freedom for
training.
The standards are the means of the SIV obtained
23
from the two 0-20 scales.
The question of the standards validity is critical.
The standards are assumed to have face validity, although
the assumption of validity based on a small sample is poor
at best.
As limited time and resources constrained the
use of a larger sample, the information gained from the
use of the standards in the experimental study must be
qualified.
The results of the pilot study indicate that a
direct scaling technique can be used for obtaining SIV,
but the SIV are subject to random error.
The Ss, as a
whole, are fairly consistent with each other on SIV, but
some large individual differences can be expected.
II.
EXPERIMENTAL STUDY
Experimental Method
Objectives
The objectives of the experimental study were:
1.
To determine the effects that risk, additional
visual feedback, type of control mode, and ACS learning
feedback have on the S's SIV and performance.
2.
To determine the relationship between SIV and
performance during learning of the task.
3.
To determine the effect on performance of
training the Ss on incorrect SIV information factors.
Subjects
Sixteen male Ss were used in the experimental
study.
The Ss ranged in age from 18 to 32 years with a
mean age of 24 years.
The majority of the Ss were college
undergraduate or graduate students.
Each S received
monetary compensation, the amount being determined by how
well he performed (i.e., on total score).
pensation received was $3.75,
The Ss had little or no
experience working with a computer.
24
The mean com-
25
Experimental Variables
The experimental variables were:
1.
Training--based on correcting SIV information
factors, or no training.
2.
Control Mode--Voluntary or Automatic.
3.
Risk--determined by amount of error penalty
( -1 or 0) .
4.
Visual Feedback--a plain (no markings) CRT
screen or a gridded screen (grid squares correspond to
maze squares).
5.
ACS Feedback--a meter that displays the
learning of the ACS and control light, or the control
light alone.
Design and Procedure
The 16 Ss were divided into two groups of eight
each.
Both groups were indoctrinated to the information
factors of the task, and were given a 30-minute practice
session with the task.
of the variables.
Figure 7.
Each S received all combinations
The experimental design is shown in
The nontrained and trained groups received the
same six-factor, repeated measures on four factors mixed
design.
An experimental meeting was composed of two
sessions.
Each session had two ten-minute trials of
continuous control.
Two of the trials were run with risk
(error penalty) and two were run without risk.
The value
.
.
---------
---~
26
Voluntary
Ul
Automatic
+J
u
.....
Q)
,.0
::s
.-\4
s
Ul
·r-1
s
GS
GS
~
M
U)
NM
M
NM
M
NM
M
NM
A
R
NR
1
2
4
3
5
6
8
7
B
R
NR
7
8
2
1
3
4
6
5
c
R
NR
5
6
8
7
1
2
4
3
D
R
NR
3
4
6
5
7
8
2
1
E
R
NR
2
1
3
4
6
5
7
8
F
R
NR
8
7
1
2
4
3
5
6
G
R
NR
6
5
7
8
2
1
3
4
H
R
NR
4
3
5
6
8
7
1
2
Key:
S--Screen
GS--Gridded Screen
M--Confidence Meter
NM--No Confidence Meter
R--Risk
NR--No Risk
A-H--Individual Ss
1-8--Presentation Order
Figure 7.
Experimental design
27
of the risk was displayed to the operator during each
trial.
At the end of the two sessions, S filled out an SIV
sheet containing the information factors.
For the train-
ing group, the SIV of an individual was compared with the
standards (the mean SIV derived from the pilot study).
Any SIV which was more than plus or minus 1 from the
standard was considered incorrect.
Immediately before
his next experimental meeting, he received an indoctrination on the factors to which he assigned incorrect SIV.
The indoctrination consisted of a discussion of the
information factor, how it was presented in the task and
several possible uses.
few minutes of:
The nontrained group received a
How are you?
How have you been? etc.
Thus, both groups received individual treatment before
each experimental meeting.
The performance measures were
automatically printed after each trial.
Results
Performance Indices.
The task simulation is of
such complexity that several different performance indices
are possible.
The choice of performance measures for the
experiment was based on the following rationalei
1.
Total Score.
The total score is a reflection
of how well the operator utilizes himself and the ACS
under the varying conditions in the task simulation
28
(total score
=
operator moves + ACS moves - risk level
[operator errors + ACS errors]).
2.
Correct Operator Moves Per Error.
This
operator score removes the influence of error penalty and
minimizes the influence of the random new path insertions.
3.
Correct ACS Moves Per Error.
This score
reflects the operator's ability to utilize the ACS's
path learning.
4.
Percent Operator Time in Control.
The percent
control time of the operator reflects the control
strategy displayed under the experimental variables.
Comparison of percent time in control with the other
performance measures gives an indication of control
all6cation quality.
An analysis of variance was performed on each of
the performance indices.
The summary of the mean results
is presented in Table 1; the complete tables are presented
in Appendix C.
actions.
There were no significant two-way inter-
One three-way interaction was significant;
treatments x grid x ACS feedback.
No interpretation of
this interaction has become apparent; one significant
three-way interaction can be expected by chance with this
experimental design.
Control Mode.
Voluntary allocation of control
resulted in better performance for the operator, but a
lower total score.
The highly significant performance of
the ACS under automatic allocation was superior to the
29
TABLE 1
ANALYSIS OF VARIANCE RESULTS
Performance Indices
Variables
Op. Mov.
Per
Error
ACS Mov.
Per
Error
Total
Score
% Op.
Time in
Control
SIV
Control
Vol.
Auto.
1.91
11.77
416
59.3
11.59
1.64
17.95**
434
55.2
11.25
Screen
Grid
No Grid
2.14**
15.17
454**
58.1
11.34
1. 41
14.54
396
56.3
11.51
1.75
15.19
426
56.8
--
1. 79
14.53
425
57.6
--
0
1.77
13.99
488 **
57.1
-1
1.78
15.73**
362
57.3
---
1.56
12.60
406
56.0
11.91
1.99
17.11*
444
58.4
10.94
ACS
-
Meter
No Meter
Risk
-
Training
Yes
No
*p<0.05
** p<O.Ol
30
operator's performance, resulting in a higher total score.
The tendency toward better operator control for the
voluntary mode is reflected by the slightly greater percent
of time the operator was in control, and slightly better
operator moves per error.
Visual Feedback,
The gridded screen allowed the
operator more feedback as to the location of the invisible
path.
The result is the highly significant operator moves
per error and total score indices.
The operator tended to
have more time in control and tended to allocate control
more efficiently to the ACS with the grid present.
ACS Feedback.
Knowledge of the ACS's learning
level appears to have had no effect on any of the performance indices.
Possibly the rapidly changing
characteristics of the feedback system caused the operator
to make minimal use of the information.
The noneffective-
ness of the meter may also be caused by operator
overconfidence in the path running ability of the ACS,
causing him to ignore the meter.
Risk.
The influences of error penalty affected
the ACS moves per error and total scores.
The effects of
risk on total score is expected because of the error
penalty deduction.
The risk factor had no effect on
operator moves per error or percent operator control time.
When high risk was imposed on the task, the operator
tended to be more careful in his allocation of control
to the ACS, resulting in a better performance score.
31
Training.
Training the operator on SIV had negative
performance effects compared to the nontrained group.
The
nontrained group had greater percent control time, better
total score, more efficient operator moves per error, and
significantly better ACS moves per error.
that training the operators on
It appears
srv in some way hinders
their task performance learning.
Learning.
Figure 8 shows the control and trained
groups' mean total scores over trials.
The trained
group started with lower scores and finished with higher
scores than the control group.
Figure 9 displays the mean correct moves per error
scores for both groups.
The ACS component score shows
the greatest variation across trials, while the operator
component remains fairly stable.
The trained group had a
steady increase in their ACS score, while the control
group had a decrease and then an increase.
The control
group's score was better than the trained group for all
four trials.
The percent control time for operators is shown in
Figure 10.
Both groups start at the same value, but the
trained group finishes with eight percent less time in
control than the nontrained group.
Multiple Correlation Coefficients.
the relationship between the
To determine
srv and performance,
multiple correlation was utilized.
Table 2 shows the
correlation results of the SIV and three performance
32
500
Trained
, ,_.
400
, ""
, "" ""
- -- -
--
Control
,
(!)
H
0
u
300
U)
...-i
c1j
.f-)
0
E-<
200
100
1
2
3
Trials
Figure 8.
Total score across trials
4
33
20
Control: ACS
- - - __,.,.
- - -
.--
Trained: ACS
,,
5
Control: Oper.
---------------------------- Trained:
--1
2
3
4
Trials
Figure 9.
Correct moves per error across trials
Oper.
34
100
~
80
0
!-1
.j..)
~
0
u
~
•r-l
60
....
Cl)
s
•r-l
E-<
--
-
Control
... -. ...
.... Trained
.j..)
~
Cl)
u
!-1
40
Cl)
0..
20
1
3
2
4
Trials
Figure 10.
Percent time in control over trials
35
TABLE 2
MULTIPLE CORRELATION COEFFICIENT SUMMARY
Independent Variables
(SIV)
Error Light
Gridded Screen
Position & Direction
of Dot
Confidence Meter
Automatic Error Return
Position of Dot
No Risk (0)
Override Button
High Risk (-1)
Plain Screen
Path Score
*p<O.OS
**
p<O.Ol
Dependent Variables
(Performance)
Correct O_p. Mov./Er
Correct ACS Mov./Er
Total Score
N =
32
16
o. 7 3**
0.81
**
0.70
0.85
0.43
0.83
.
----------
36
indices for the experimental group, and the combined pilot
study and experimental group.
None of the experimental
coefficients are significant.
In the. combined group, both
the correct moves per error indices are highly significant.
Figure 11 displays the coefficients of the correct
operator moves per error and SIV over the experimental
trials.
The coefficients are nearly monotonic over
trials, with the fourth trial coefficient significant at
the 0.05 level.
Learning the task increases the multiple
correlation over trials.
How much of this is due to the
training condition is difficult to resolve because of the
small sample sizes of the groups.
Subjective Information Values.
The mean SIV for
the experimental groups was unaffected by the control
allocation mode, visual feedback, and training.
The mean
SIV were also stable across trials for both groups.
The mean ordering and values of the two groups'
SIV are displayed in Table 3.
The mean ranking and
values are fairly consistent for the two groups.
To check between subject consistency for the two
groups, Pearson product-moment correlations were done.
The mean consistency for the control group was 0.37 (not
significant) and for the trained group 0.52 (significant
at 0.10 level).
The tendency of the trained group to be
more consistent among themselves than are the nontrained
subjects indicates a reduction of variability.
This is
the purpose of the training, and indicates that the
37
U)
.f-)
~
1.0
(])
•M
u
"M
4-l
4-l
(])
0
~
0.75
~
0
·M
.f-)
Clj
r-f
(])
~
~
0
u
0.50
(])
r-f
p,.
•M
.f-)
r-f
;:I
~
0.25
1
2
3
Trials
Figute 11.
Multiple correlation coefficients
for operator correct moves per
error and SIV over trials
4
38
TABLE 3
MEAN SIV AND RANKINGS
Trained Group
Control Group
Value
Component
1. Error Light
2. Gridded Screen
3. Position & Direction of Dot
4. Confidence Meter
5. Position of Dot
6. No Risk (0)
7. Auto. Error Return
8. Override Button
9. Path Score
10. High Risk ( -1)
11. Plain Screen
19.0
15.6
15.1
11.6
11.6
10.5
10.3
8.3
7.3
6. 7
4. 5
Mean= 10.94
Value
Component
1. Error Light
2. Position & Direction of Dot
3. Gridded Screen
4. Position of Dot
5. No Risk (0)
6. Confidence Meter
7. Override Button
8. Auto. Error Return
9. Path Score
10. High Risk ( -1)
11. Plain Screen
18.9
16.1
15.4
13.1
11.7
11.2
10.0
9.5
8.4
6.8
5. 3
Mean= 11.45
39
training had some effect.
SIV Comparisons.
The training level of the subjects,
as determined from a significant multiple correlation,
indicates that the group as a whole was trained at the end
of the fourth trial.
To determine the comparisons of the
subjects' SIV and the SIV standards, correlations were
calculated from the fourth trial results.
Table 4 displays the correlations for both groups.
As determined from the table, 25 percent of th_e control
group and 63 percent of the trained group were significantly correlated with the SIV standards.
Discussion
Task Variables
Operator control decisions in the task simulation
are influenced by the amount and type of information
available.
The operator will use the ACS for control, but
when given additional manual control information prefers
to utilize himself.
He has the tendency to allocate
control to the ACS less efficiently under voluntary
control than under automatic control, and more efficiently
to the ACS under high risk.
The ACS is most efficient
when it takes control automatically even though the
operator sets the confidence level of computer takeover.
Autonomous control capabilities of the task
simulation in a situation where the operator serves as an
..
----~---~
~
.....--~-
-- --
40
TABLE 4
SUBJECTIVE INFORMATION VALUES INDEX COEFFICIENTS
Trained
Control
Subject
Coefficient
Subject
Coefficient
1.
0.37
9.
0.42
2.
0.86 *
10.
0.75 *
3.
0.54
11.
0.62 *
4.
-0.01
12.
0.58 *
5.
0.29
13.
0.42
6.
0.29
14.
0.62 *
7.
0.82 *
15.
0.53
8.
0.52
16.
0.77 *
*p<0.05
41
overseer with control override capabilitie£ would be
determined by the operator's perceived risk involvement
and the type of visual control feedback available to the
operator.
Subjective Information Values
SlY-Performance Correlations.
The SIV are positive-
ly correlated with the performance indices.
This indicates
that utilizing a direct scaling technique to measure the
SIV is appropriate.
Considering the high significant
multiple correlation coefficients obtained, it appears
that there is a strong relationship between subjects'
performance and their SIV.
This relationship becomes
stronger as the subjects learn the task.
This would allow
for the multiple correlation coefficient to be used as a
task learning indicator for training operators.
The SlY-performance relationship indicates that
utilizing a multicomponent scaling method, where the
information factors are integral parts of the operator's
task, allows the operator to assess importance weights of
the information factors.
The control of random error
(individual variability) in using the scaling technique is
reduced through the operator's learning of the task.
This random error is an inherent part of subjective
evaluations and must be considered in any subjective
evaluation analysis.
The dependent variable selected for the SIV-
42
performance relationship determines the amount of correlation that could be expected.
Either of the correct moves
per error indices could be used as the performance measure.
The total score measure had a positive, but not significant correlation with the SIV.
The reason for this is not
immediately assessable. Possibly a multiple correlation
comparison of SIV-performance for each experimental group
might have revealed a reason.
Because of the large number
of independent variables and small number of Ss, the
comparison is not possible,
Performance Measurement Problems.
The task is
sufficiently complex that the dependent measures utilized
in this study are not the only possibilities.
A major
weakness of the error measurement utilized was the failure
to consider the magnitude of errors made.
In a control
decision task simulation, the magnitude of errors is very
pertinent.
Any· future work using the simulation should
take this into consideration.
Between Subject Variability.
The SIV-trained
subjects were more consistent among themselves on their
SIV than was the control group; however, the performance
scores for the control group were higher.
that using the SIV for training purposes in
hinders a high task performance score.
It appears
som~
way
Again, a multiple
correlation comparison of SIV-performance for each
experimental group might have revealed a reason for the
difference.
43
SIV Standards.
One cause of the trained group's
lower scores could be the standards used.
These standards
were based upon a sample whose performance scores were, as
it turned out, lower than either the trained or control
groups.
The trained group scores were very close to the
standards group but lower than the control group.
The
trained group may have been trained into the scores of
the standards and thus had lower scores than the control
group.
An indication of this is that two-thirds of the
trained group had a significant correlation with the
standards as compared to one-fourth of the control group.
In order to determine that the trained group was
trained to the standards, rather than trained to inferior
performance, the study would have to be reproduced using
appropriately high performance standards.
The trained and
nontrained groups must contain enough subjects each to
allow for separate multiple correlation analyses.
If the
trained group could obtain these higher standards in
comparison with a control group, some light could be shed
upon the problem.
The disadvantages of using SIV are the complexity
of performing multiple correlations, the need for sample
sizes larger than the number of variables used in the
correlation, and the absolute necessity of obtaining
appropriate standards,
Any future research of SIV must
take these difficulties into consideration, and must,
44
in particular, include more extensive empirical derivations
of the SIV standards.
REFERENCES
45
46
Alker, H.A., &Hermann, M. G. The effect of task and
personality on conservation in processing information. Journal of Personality and Social Psychology,
1971, 19(1), 31-41.
Bowman, E.H. Consistency and optimality in managerial
decision making. Management Science, 1963, ~'
310-21.
Eckenrode, R.T. Weighting multiple criteria.
Science, 1965, ~(3), 180-92.
Management
Fishburn, P.C. Independence in utility theory with whole
product sets. Operations Research, 1965, 13, 28-45.
Freedy, A. The application of a theoretical learning model
to remote manipulation control. Ph.D. dissertation,
University of California, Los Angeles, 1969.
Freedy, A.; Hull, F.; Lucaccini, L.; & Lyman, J. A computer
based learning system for remote manipulator
control. IEEE Transactions on Systems, Man and
Cybernetics, SMC-1, October 1971, 356-63.
Halpin, S.M.; Streufert, S.; Steffey, J.; & Lanham, N.
Information load, proportion of relevance, and
relevance perception. Psychonomic Science, 1971,
~(6), 404-06.
Huber, G.P.; Daneshgar, R.; & Ford, D.L. An empirical
comparison of five utility models for predicting
job preferences. Organizational Behavior and Human
Performance, 1971, ~' 267-82.
Keeney, R.L. Utility independence and preference for
multi-attributed consequences. Operations Research,
1971, 19, 875-93.
Lathrop, R.G., & Peters, B.E. Subjective cue weighting
and decisions in a familiar task. Proceedings,
77th Annual Convention, American Psychological
Assoc1ation, 1969.
Licklider, J.R.C. Man-computer symbiosis. IRE Transactions on Human Factors in Electron1cs, HFE-1,
March 1960, 4-11.
Licklider, J.R.C. Man-computer partnership. International
Science and Technology, 1965, 41, 18-26.
47
McKendry, J.M., & Enderwick, T.P. Judgmentally derived
information utility estimates. Ergonomics, 1967,
10, 187-94.
Miller, L.W.; Kaplan, R.J.; & Edwards, W. JUDGE: A
value-judgment-based tactical command system.
Organizational Behavior and Human Performance, 1967,
~(4), 329-74.
Newman, J. (Ed) The Computer: How It's Changing Our Lives.
Washington, D.C.: Books by U.S. News and World
Report, A Division of U.S. News and World Report,
Inc., 1972.
Nickerson, R.S. Man-computer interaction: A challenge
for human factors research. Ergonomics, 1969,
.!1_(4)' 501-17.
O'Connor, M.F. The application of multi-attributed scaling
techniques to the development of indices of water
quality. Ph.D. dissertation, University of
Michigan, 1972.
Powers, J.R. The investigation of human decision-making by
means of man-computer interaction. International
Symposium on Man-Machine Systems, Cambridge, England,
September 1969 (IEEE Conference Record No.
69C58-MMS).
Raiffa, H. Preferences for multi-attribute alternatives.
The Rand Corporation, RM-5868-DOT/RC, April 1969.
Rapoport, A. Sequential decision-making in a computercontrolled task. Journal of Mathematical Psychology, 1964, ~(2), 351-74.
Rapoport, A. Dynamic programming models for multistage
decision-making tasks. Journal of Mathematical
Psychology, 1967, 4(1), 48-71.
Shuford, E.H. A computer-based system for aiding decision
making. Proceeding of the 2nd Congress of Information System Sciences, Spartan Books, 1965, 157-68.
Slovic, P. Value as a determiner of subjective _probability.
IEEE Transactions on Human Factors in Electronics,
HFE-7, 1966, ~' 22-28.
Slovic, P., & Lichtenstein, S. Comparison of Bayesian and
regression approaches to the study of information
processing in judgments. Organizational Behavior
and Human Performance, 1971, ~' 649-744.
48
Thomas, R.E., & Pritsker, A.A.B. Decision programming: A
model of man-machine control. IRE Transactions on
Human Factors in Electronics, HFE-1, March 1962,
25-28.
Wendt, D. Value on information for decisions. Journal of
Mathematical Psychology, 1969, ~' 430-43.
Yntema, D.B., & Klem, L. Telling a computer how to
evaluate multi-dimensional situations. IEEE Transactions on Human Factors in Electronics, HFE-6,
1965, 3-13.
Yntema, D.B., & Torgerson, W.S. Man-computer cooperation
in decisions requiring common sense. IRE Transactions on Human Factors in Electronics, HFE-2,
1961, 20-26.
...
APPENDICES
Appendix A--The Autonomous Control Subsystem
(ACS)
Appendix B--SIV Scaling Sheets
Appendix C--Computer Analysis Results
49
50
Appendix A
The Autonomous Control Subsystem (ACS)
ACS System Operation.
Man-machine system control
with the ACS involves two main control loops:
an external
loop, which incorporates the operator and his means of
feedback; and an internal loop, which contains only the
autonomous control subsystem.
The M/M system responds to
either the operator or the autonomous subsystem.
Initially, the ACS acts as a passive observer,
trying to "understand" how the operator controls the system,
and developing an "awareness" of the operating environment.
In this phase, the ACS defines the relationship of the
operator's response to the external world.
After the acquisition of this passive experience,
the internal loop begins to participate in the control of
the M/M system.
In this second phase, the operator acts
mainly as an instructor, letting the internal loop control
the system whenever possible, and correcting its decisions
when they are wrong.
In time, for certain classes of
tasks, the function of the operator may be reduced to that
of an initiator and inhibitor, who provides occasional
start and override commands.
In such an arrangement, the decision load associated
with controlling the system is substantially reduced,
giving significant advantages in completion time,
efficiency of manipulator use, and operator satisfaction.
51
The ACS is designed with the ability to discard
what it has accumulated and accumulate new tasks in place
of old, unused ones.
This feature provides the adaptive
capability to change behavior in response to changes in the
operator's control policies and in the environment.
It
makes the ACS a powerful tool, applicable to a large
variety of M/M systems.
Theoretical Basis.
The theoretical basis for the
ACS is the maximum likelihood decision principle.
Its
structural organization is a conditional probability matrix,
relating future states of the M/M system device to its past
and present states.
Spatial movement of a M/M system is nonrandom for
practical tasks.
That is, patterns of movements in the
past lead to predictable movements in the future. 1 In the
ACS, prediction is based on the likelihood of occurrence
of a particular position in space, or of a movement path,
computed from the conditional probability matrix.
Maximum likelihood was chosen for the ACS over
various other possible classification system because it has
several significant advantages.
These include:
1.
Training is rapid and relatively simple.
2.
Decision strategy can be changed while the
system is active.
1 A complete description of the system, including
mathematical development of the ACS model, is available in
Freedy (1969) and Freedy et al. (1971).
52
3.
Classification categories are not restricted
to disjoint sets.
As the ACS acquires more and more information about
previously observed states, the current state, and the
next observed state, of the M/M system it controls, the
conditional probability matrix is sharpened, and the ACS
achieves its control decisions with a higher level of
confidence.
Figure A-1 illustrates the system organization,
and shows how ACS experience leads to "reward"- or "punishment" of the probability (P) matrix.
In one sense, the ACS is a redundancy machine, able
to extract redundant aspects of a task even if they are
hidden in a scatter of apparently random motions.
In most
systems operations, the element of redundancy is quite
high.
Movements from place to place tend to be repeated,
certain paths are followed, certain areas avoided.
This
makes the ACS a shrewd predictor of M/M system action.
Redundancy is highest in repetitive operations, and
the ACS learns extremely fast under these conditions.
But
it is important to understand that the ACS organization
allows it to comprehend much more subtle task factors after
long-term operation.
For example, as the ACS builds up a
depth of experience with a certain general class of tasks,
learning of specific subtasks occurs much more rapidly
than it did initially.
Furthermore, the ACS is able to
move among the separate subtasks without losing its
adaptation to each.
53
READ INPUT PATTERN
APPLY MAXIUM LIKELIHOOD
DECISION SYSTEM
PROCEDURE (SELECT ai)
YES
. NO
DISPLAY
"LACK OF
CONFIDENCE"
GENERATE
OUTPUT
VECTOR
OBSERVE
OPERATOR
CONTROL
UPDATE P
MATRIX
REWARD
P MATRIX
Figure A-1.
PUNISH
P MATRIX
ACS organization
54
Likewise, the ACS is able to recognize patterns of
movement and of implicit movement "rules," and adapt to
changes in such patterns.
An operator placing objects
sequentially in locations A, B, C, D . . . will find that
the ACS learns to predice
E, F, and so on, even
though it has not yet observed that particular movement.
Through a built-in function termed "List Control,"
the ACS is also able to recognize repetitive sequences (or
lists) of M/M movements, add these lists to the probability
matrix decision space, and retain them as long as they are
needed.
Thus, the ACS can at times handle a complete
subtask as a single decision.
There are marked advantages
to this approach in practical machine control.
The power of this approach is in the ability of the
ACS to operate with limited amount of input data.
By
using long-term experience, it is able to operate autonomously with a minimum amount of sensory cues.
It is
difficult to gauge the limit of ACS capability when implemented on more powerful, specialized computing systems,
and after extensive training with practical machine tasks.
55
Appendix· B
SIV Scaling Sheets
\
INFORMATION VALUES
----------------------------------- DATE~-------------
Draw a line from each of the words below to"the location on
the scale that indicates the value to you, to enable you to
perform the task in the Voluntary Control Hode.
No screen ••••.••••••••••••
Confidence meter •••••••••
20 {maximu.-n value)
Automatic error return •••
19
18
Low risk {-1/2) ••••••••••
17
Path score meter •••••••••
16
15
Gridded screen •••••••••••
14
High risk {-2) •••••••••••
Position and direction
of dot when error occurs.
No risk •••••••••••••••••
13
12
11
10
9
8
Position of dot
when error occurs ••••••••
Easy maze ••••••••••••••••
7
6
5
4
Plain screeri ••••••••••• ~.
OVerride button ••••••••••
Error light ••••••••••••••
Hard maze ••.••••••••••••••
3
2
1
0 {no value)
56
INFORMATION VALUES
NAME~----------------------------------
DATE._______________
Draw a line from each of the words below to the location on
the scale that indicates the value to you, to_enable you to
perform the task in the Automatic Control Mode.
10 (maximum value)
Plain screen •••••••••••••
Confidence meter ••••••.•••
9
High risk (-2) •••••••••••
Easy Maze
8
e • e •
Cl
•••
II
••••
II
••
Automatic error return •••
7
Low risk
(-1/2) ••••••••••
Position and direction
of dot when error occurs.
6
5
No risk (0) •.... ~~ ... ~ ...•
Hard maze ••••.... o•••••••
4
OVerride button ••••••••••
3
Path score meter •••••••••
Position of dot
when error occurs .••••••
Error light •••••••••••••
2
1
Gridded screen ••••••••••
0 {no value)
57
INFORMATION VALUES
NAME
----------------------------------- DATE --------------
Draw a line from each of the words below to the location on
the scale that indicates the value to you, to ~nable you to
perform the task in the Voluntary Control Mode.
No risk (0)
fil
•••••••••••
e.
Easy maze • ...••••••••••••
No screen ..•..••..•.••.
10 (maximum value)
9
o.
Confidence meter •••••••••
8
Path score meter •••••••••
Override button ••••••.•••
1
~igh
risk (-2) •••••••••••
7
6
Low risk (-1/2) ••••••••••
Hard maze .••••.••••.•••••
5
Gridded screen ••••••• ~ •••
Plain screen ..••••.•...•
4
a
Automatic error return •••
3
Position of dot
when error occurs ••••••••
2
Error light ••••••••••••••
Position and direction
of dot when error occurs.
1
0 (no value)
58
INFORMATION VALUES
NMffi ----------------------------------
DATE~------------
Draw a line from each of the words below to tne location on
the scale that indicates the value to you, to enable you to
perform the task in the Automatic control Mode.
Hard maze •••..
No screen •••.
Geo•••••••a.
e•••••••••••
Override button ••••••••••
High risk (-2) •••••••••••
Position of dot
when error occurs ••••••••
Path score meter •••••••••
Plain screen . ............ .
20 {maximum value)
19
18
17
16
15
14
13
12
11
10
Confidence meter··········
Low risk {-1/2} ••••••••••.
Gridded screen •••••••••••
9
8
7
6
5
]i!rror light ••••••••••••••
4
No risk (0) .. ~ .•••••.•••.
3
2
Automatic error return •••
Position and direction
of dot when error occurs.
1
0 (no value)
'
'
59
Appendix C
Computer Analysis Results
Analysis of
Variance Coding
Key
T = Training
s = Subjects
R = Risk
c = Control Mode
G = Grid Feedback
M = Confidence Meter
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SAMPLE SIZE
siv --.1\cs·Moves
Per
Error
(Pilot·+ Experimental-Groups)
32
NO. OF
V~RIA9LES 12·-------No~··oF VARIABLES DELHEa··-o ... IFO~ VARIABLF.:S OELETEDt SEE tlELOWI --------------DEPENOE~T VARIAqlE IS NOW NO. t2
.
COEFFIC!!NT OF
MULTIPL! GJ~~.
SUM OF
SUM OF
OETE.M!NATIO~
o.~~·a·
CDEFFICIE~T
0.70~1
S1U\~FS ~TTRIOUTA~L~
~1UA~CS
0~
TO RfG~~SSION.
OF.VIATION FROM RCGRESSION
VARIA~~€ JF ESTI~ATE
ER~JR 0~ f.5TIMATE
1f,.O:,J3()7
STu.
.INTE~CE~T
I~
VALUE!
~.07d~lt
12.1tO'Iq~
A,ALYSIS
S1U~C£
DUE TO
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VA~IANCE
LI~EAR
OF VARIATION
A10UT
11
~~GRESSION•••
~0
TOTAL•••
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--
VARIA!ll.<:
MEAN
Ni).
1
r,
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10
1t
COMPo
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s. ::.~ ;)q'\
REG.
COF.FF.
STD.E~RH
OF ~~~~.C::JE
•
lo7&n
CO)If'UTEl
T VALIJ•
PARfi AL
COR~. COEo
-0.~21137
SUM
OF SQ.
AOOt:O
1F..9'i&!IJ
P~OP. VA~.
CJ~~
o.o?.o;7o
0,1AO'!l
-a.t2bq~
•O.il'i77'1
-o. ot2n
0.05123
0.02407
0.31')?2
0.07038
11j.C.Q5A3
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0.02517
1.111250
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5. ~f,&lol)
0.17')2~
O.OD21t5
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0.1&1~1
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O.O.l'Hii
-0.01231
1.1>192•
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0.1'>2ld
o. 131 3l
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o.tn"t
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o.or.u7
o.tzst7
t~.g~~7·
t l.r.1·1~C
1'1.110 1'•"
0.00019
0.03001
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0. 15 ~ !'.4
a. '?f>t7 .1
lt1.1731J
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0.3~74&
411.7~3~!t
0.073'lS
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0.00041,
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11.17500
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12
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VAI.UE
-0.022118
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SQUARES
2'1.73':107
1">.'>3367
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1\oQ'l.l]lj
12.r;H2'i
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c;
HU~TIPLE
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2
THE
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St:UAP.ES
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DEVIATIO~
327.12'175
3J2.HJitD
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lt. 61 3'•
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o••!'JJH
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1.21?7'1
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s~c;n~
1.~'1352
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-0.02886
VARIAnLES OELETEJ •••
0\
0\
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SELE~Ttl)l
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SIV-Operator Moves Per Error
1"' 0
SAMPLE SIZE
JZ
NO. lF VAR.IA!!LES 1Z ·--·-NO, ::IF VARIABLES DELETED
OEPE~~E~T VARIA3LS IS NOW NO. 12
COEFFI~IcNT OF
:o~R.
MULTIPLE
O~TERMINATION
CO~FFICIENT
SUM OF
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S)'J~CE
CFOR VARIABLES DELETED, SEE .. BELOIII
0.~083
0,7600
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&.S09'!J
4·,t•HZ7
REG~CSSION
o.zoq~&
OF ESTIMATE
~R~J~
0
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0
0.4~77~
1.41760
ANALYSIS OF VARIANCF FQ~ THE MULTIPLE.
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OF VARIATION
O.F.
SUM OF
MEAN
SQU~RES
OUE TO ~FG~ESSTO~ ••••••••••••
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1
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12.5.!12'5
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0.00231
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7
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tl.7tH5
0,01727
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10
11
11.37500
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<;.~377'1
12
t.<t3JOO
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... 19127
10.70UO
11
10.€>:?500
r,. O·l.!7S
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s
(Pilot + Experimental Groups)
0.59181
VALUE
2.6?.40
Oo20'!S&
ST:l.E~RO=t
~:OG.COE.
OF
O.OZJZJ
o.ot7r.7
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T VALUo
O.ZBIOf.
1.<;30'51\
o.ats?.3
a,
l~.Jv,
o.t ... oaa
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SUM OF SQ •
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0.0&272
0,4o8S~&
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a. H'H1
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0.'5851;'!
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o.osr.13
Q,03351t
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1.~01<;3
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0.02171
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_SAMDLE SIZE
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.
_
.
..
. . --··-----------·--·---------~--------~0. OF VARIARLES 12
NO. OF VARIABLES DELETED 0 fFO~ VARIABLES OELETEO,.SEE OELDWI
OEPEND~NT VARIA~LE IS NOW NO. 12
.
OF
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0.~129
0.7829
TO REGRESSION
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8.43700
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LI~FAR
S~J~CE
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13.76499
11
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•
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.. ,.,M..--·-·· ~ . · - · - ............,_ ...
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~-!~~-~..,-~_-•_(I.!_J.4~~-~~_!!.1.~-~·-~~.I.~.--....!l.!~J~·~}l ___ _!!j •'•l~A~----·--0•1511.~--IJ • ~~51~ .. - - - - - - - ·
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____ l l _____ ,i•: • Y'· ~·.! -------2 ·~ ~ 1 J':'---~'!.t l '< 1't<:!.---'lo 0~1 .13 _____ o 3.3'?21 ...---"'~ • '1!>00 7. ---- ........ 0 • '~'•'·''•'I ....... 0.34~2'> ...... _ ................................ -· ................ ..
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~
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1 • n •.
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-------------------~-·----·--· ..... -·--· ---··-..... ---· . -. ------·
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--..:a
0
C
-SELEcrro-~ -No:---·~::-·o--------·sxv.;;oper:·-Moves/Erroi·-·-·(exper.--trial·J)
sAMPLE S!ZF.
16
"10. OF V~RIA9LES -1Z
OEPE~DE"'T
VA~IA~LE
OF
~0. OF VARIA!lLES
IS "'OW "10.
COEFFIC!f':~T
CO~R.
~ETEPH!...,ATI'JN
~.~&n~
MULTIPLE
COEFFICif"'T
D.,tbl
SUH OF
SUM OF
ATTRIPUTARLE TO R~GqESSION
OF DEVIAT!O"' F~OH REGRESSION
S~UA~ES
~1UARF.S
VARIANC~
OF
ESTI~ATF
'1FA"'
S'.lUARF.S
~~G~C~SION••••••••••••
OEVIATIO~ A~OUT ~EGRESSIO"'oo•
1t
12'.\!1'!37
t.t7'+1t9
~
6.~~763
l.llH9f>
TOTAL•••
17
l'l.3B7?0
srn.
_yAR!A'3LE ____ Mn"'
---NO.
1
?.
3
.
c;
-----~
.
\-
r..~r.711l
2.006~7
DUE TO
'
··----......
9ol!l7'<:i00
7.
7. 0" ?'i 0 ----·
~.t'iUh7
t..A?"tiDt'
&.'i23'lq
... ~711?.~
7.11U7'+
lt.?.7201
2. !)f"\t;c; fl
--
1'•·f,~'jQQ
!I
1qo01~00
q ·-- .. ... ·-· 1?. 3 ,., 0 0
q.<.H50
10
1&:.. 2r, 'l 0 0 ' ···•·
11 ..
~
1Z
-- ---
t.~~~oo
~47!i0
S,H73<J
1'+o'+H<;O
tO.~HSO
r:;.'l'i~1~
lt,%711'+
..
t;. '!<17 an
t.
COfF~'.
o,on<J~s
STJ.ERR'lR
OF
RI':G.C~f'.
0.07·1~·~
COHPUTFO
T VALU':'
0.12117
o. ?2<'71
F.
VALUE
0. 7(.'6,.
0.060'<7
-o. or,ao7
-0.0&821\
-o.oJ~tt2
-~.r,s<Jolt
-0.0\11\'l
o.u~o1
-0.09901',
o.o~Ol'l
o.tOllll
-o •.~t2'l7
-0.04q'+7
q,?.35'll
fl. r,r.t '.i2
... 0.036&9
-0.02133
o. znr.r,
-o.ur.Qr
o.tznr;
n... Bsc;e
t.h7Z':i7
D.20'l'i3
•0 ·"'lq?1
-1. ·l'lJn•.
Q,OF>'l'l8
-o. O'l749
•0.1371')
0.1372'1
-t.Jb31tl\
0.12~31 --· _0.24'>r,t- ·-- O.'oitOL'l
'
SUH I)F' SQ.
AO:'l£:0
PARTIAL
-- CI)R~.
CO"•
0.173(.-J
Oo31235
1),10321
t3&~7
C_OHP, C-4<;:;1( 0"1 FH!AL CO!::FF,
VARIA9LES
~FG.
Of':VIH!nN
'!.1~7'i0
6
7
BELOill.
1Zo91937
ANALYSIS o~ VARIANCE FOR THE MULTIPLE
LINEAR RFGRr.SSION
SJURCE OF VARIATIO"l
O.F.
SUM OF ...
SQ!JARES
-------
(F'J~ ·vARIARLES OELETF.Dt SE~
1.27160
INTERCEPT lA VALUE!
1,
·o-···
1of'>t~'J6
STD. (RqOR aF ESTIHATF.
_
DELETED
tz
----------------------------------------------------- ·---· -----
o.uo~a
-- -··
.. •0.2Sit~O
-o.:;Ttr;z.
-o.r.&l?'l
0.~~716
....•.
P~OI>o VAR~---------· ___
CU'I.
0.'!?.608
o. 0'+"787
O.lt23S2
0.13021
Q,'l01llt
0.7731'+
0.02185
o,nor,r2
·o.1&3~7
~.on~,.,.
'-
o.n~G'+9
o.~J<JIIe
~.791'>1
o.o~o~J
Q .6.!'>'17
o.~~za~
.....1277
3,31731'
O.lt203'l
0.17111
0.22~"'"
o.nt7t
··-~
......
~------.'
--. ····' ---- -- . ·-····
__ .,_ ·--
•...
__ ... -·
.,.
0.12SH
OELETEO··~--
-....]
1-'
c
NOo
SELECTIO~
sxv-oper :-"Moves/:Error ------CEi'cper:--·tdaf -4 , - - - - - · - · - - - - - - · - - - - - - - - - - - - - - - - -
1- 0
_SAH"'LE Stzf.
.. __11' ______ ---·-.
NO. 0~ V~RIA9LES 12
NO. OF VARIABLES DELETED
OEPE~DENT V~~IABLE IS NOW NO. 12
COEFFICIENT D~ n[TERHtNAT!~N
MULTIPLE CORR. COEfFICIENT
STO.
OF
INTERCE T
0
ESTIHAT~
I~
VALUFI
VARIA~LES
.DELETEO;-sEF. BELOWi------·--------- ·-- ---·--------
----- ·- --· - ------
12.43'H2
1o3SU7
0.31807
OF fSTIMATF.
E~RQ~
IFOR
0.9019
0.9497
SUH OF S1UARFS ATTPtRUTA~L~ TO Rr.~RfSS!ON
SUH 0~ S1UARFS OF OfVIATION FRO~ REGRESSION
VARI~N~E
0
o.s~tr,4·
..... 3&34
ANALYSIS OF VARIANCE FOR THE HULTIPLF.
LINFA~
S~JRCE
DUE TO
J-
11
12.43512
1.13047
4
t •.1522 7
13.78739
0.3Jll07.
H.EAN
9.50000
7.41750
·--- 12.75000
4
7.8\ZSD
srn.
15
OEV I ATION
RFG.
COEFI'".
.;.r;dZI\1
Oo0420'i
0.0~71;3
o. 03302
o.04Y4'l
•O.Aio940
-0.122'31
o.or.~37
tll.%?'i0
2.'14321
-o.o5?Y7
ts.~7'300
- ··-- -· 12.<;'=>250
t.
~?.7~7
... .,'l297
10
9.At?.~D
11
15.91750
12--- ·------· 1.~~···l7
5.?0517
q
__ (;O~i> •.. C~':CK
0~
0~030:t2
C0'1PUTF.D
T VALUC'
-0.05477
-0.001'>06
-o.tntr.s
7
~E"G.COE.
7.t3209
"'· 51377
1135~
..... ?~00
OF
S.GS~'i:!
r,.:>4gO~
tt. f>~t;~O
ST'l.ER~OR
t.38701
1o44?31
r,.
c;
- - - - & - - - -·-·
e
MEAN
SQUAQF.S
SUH 0~
SC.UA!?£:3
REGRESSION •••
TOTAL •••
A~OUT
VARUBLC::
--NO.
1
2
RFGR~SSION
O.F.
~:GRF.~StON••••••••••••
OEVI~TIO~
-i
OF VAPIATION
r;.
"147~1
o. %44~
o. or.~t 34
-1.57'3!11
F
VALliE
3."3419
PARTIAL
CO!Ho COE.
n.'>&'l'l7
0. 'ill~ 94
-o.ac.us
-0.39091
-o.:.t'l65
o... r.~J!i
1.DF.02r,
..,Q.717'H
-0.1371>11
-0.0031>5
0.05121
o. 07 H?.
0.19201)·
-0.01'30~
0.01011
-0.05420
-o. o7J':l~
o. 335?.7
-O.OC'I'iO
0.28F.72
o.n'id'l4
0.070'l5
-1.00433
•1.0~213
-0.~1>210
SUH OF SQ.
AOOFD
PRO!'. V.AR.
· cul1.
-··
~.1'i033
o.nt090
t.l5&7&
n. o9~41
o.ooon2
o.usa4
o.oool4
'i.?.illllO
1.431\ll3
0.2~231
o.ooe~o
- ·- -·----- · ··-
....
O. BltJl
n.tn4Jit
0.0?.04~
o.t&uta
0.0111\2
0.14191
n.s~t307
·-n.~~i171l
z.7219n
0.3'>715
0.03919
0.1'1742
0.02(>&3
0. qr, 117 3
FI"'AL. CO!:FF •.
-o.o73'l'•
__VARIA!lL~<; O!::LETEn •••
. "'-l
N
/
,..,
....
SELECTION NO,
_s~:wcE_srz~ _______
NO. OF VA~IA~LES
DEPE~OENT
' SIV-Oper. Moves/Error
1- 0
tr,
NO, OF VARIA8LES nELETEO
IS NOV NO, 12
COEFF'ICIE,,T- uF .. UET!::~<MINiTION
~ULT!PLE COR~. COtF'F'!CIENT
su~: l'F
~U"' OF
VA'I!4
~f,).
'-..._
.
meanii)--------------·
_ -----------------··------ ___ ···------··---- ____ ···-·-· .. _··--
1~
VA~lA~LE
{Exper.
Oeb629
0.!1142
··-·- -·-
0
-----
. ··-·---··"
S~d~RES AT T..; II:IUTAt-lLE TO REGI'lESS ION
SQ.J4QF.:S OF 1lEII!ATJO~ FROM RFGRESS!Ofo,l
~CE •JF ESTI~!•ITE
t::RRQ>< OF EST!,·'IITE
Y'<H.«CEi>T
(~ VALt)E)
4olR<;FIZ
?..1?872
~.53?.18
0.7?.'151
I • R3328
AN~LYSJS
OF V4R!ANCE FOR THE MULTIPLE
--·
_ ·-.LINEAl-! . HE.GRESS!ON - ··----·---------·----·-·-SUURCE OF VAW!AT!O~
O.F,
SUM OF
MEAN
S~UARFS
___t:\'.!E TO
>I~G'l!'SSI0'-1. • • • , , • , , • , , .••.. 11.
DtVIATION
A~UUT
Rt~WESSION,..
TOTAL•••
l
4
15
1
~--·
__CO~'P• ..<:o:<EC'\ (.1'~- F l'~~I. •. COt.fF,
ABLES
(lt::L~ H.O
...
F
SQU~RES
VALUE
4.18"i'32 -·----· 0.3805, -···-· 0.7150
i"e\2A7?.
Oe<;3?l~
~o314S4
_liAR I 6'lLE______ ,MEAN_ ---·-·· STU, ·------- Rf.G 0 ..
STO.EflROR ...
r-.~n.
OF.VTATION
COtFFe
OF RF..f'ioCOEe
•• :>~1!3,
0 • ., 7"i (/, .
10·00000
-n.ot5:>1
_____ 21
5.)4093
o.u.,to~
o.o~::tl3
--···-· ... 6odl?5('.
13old7!)1l
Oo0!\31'0
n.n'IQO~
3
"'ol :>591'
llo3fS~o
4
s. 7t:l3b0
OoOli307
n,n717"
_ _ 5. _____ .. "io4.l75·!_ ___ 3•:lVS93 ·---· •0 oOU"'OS
0 elBJ'H ....
,.,
4.~'11,7<;
lloJISO·.l
o.onnl
n.o6?7:>
7
1~·315vv
2o -ltl~OR
Oo0>\717
0.)3777
R
o.tb4111:\
)~o'f.l7'>u ·--- lo5b~24 .... •0•04241
--- 'l
ll•7!JOOO
5.?3450
•Ootl593T
n, o97Sl
9.h~~0')
10
3e>J3Qb5
o.ooS!b
Oo!3h57
_ .ll-----l6o000UU ..• ---"•9'1333 -·- •Oo0!l'l!l3 ----0.17'-lB'l ·-·);1
I • T T!->-~ ~
Oot--'+882
__ VA~ l
-----------------
!FOR VARIARLES DELETED• SEE AELOWI
C0"1PUTEO ... PARTIAL ..
CORR, cnE,
T 1/AL.UF.:
~~.21);>1)~
•n.I007~
1.!4~94
- -· n.411Al?.
lle:IO"ihO
n.1249n
-n,nt'-44
n.Jfir;?n
llo:'\0?.?'>
(1,1',4)'ll
(1,2!'>177
-o .03?Afl
n.33.,nn.
Oo63'c17
-o.2':i70">
-o.6o~77
!IUM Of" 513 0
ADorn
0.1024<1
1.n17;>J
o.n<'l:V•
o.~~·~~
0.44?."i7
·~el?74R
o.o17711
-o.?CJ!!'l
n,O)RA'l
•0.1<1-1~37
-o.:>~767
.. -
-
PROP, Vt\Do .
cu~.
n.nl'-~"
o.t""""
Ooi!03311
·-·······
•r
•
· - · - ·..
,_
0.0700<1
n,nt7fl4
0.)?41 ..
1.11 .. 71<;
OeiiiCJI',Q
Oo?.640<;
Oo0ll4?.7
o.t:?74c;
- ..
o.oo?.i!R
Oell?~;>
o.on3~7
0·""~1 :>
... .....
o.ooo,,
o,o;>otR
----•OoO!!~OJ
o, •--··- .... _ .... ____,__ .
-....:J
t.N'