EPA-600/1-77-038
June 1977
Environmental Health Effects Research Serie<
BEHAVIORAL AND PHYSIOLOGICAL CORRELATES
OF VARYING NOISE ENVIRONMENTS
Office of Health anil Ecological Effects
Office of Research and Development
U.S. Environmental Protection Agency
Washington, D.C. 20468
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
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gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
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The nine series are:
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3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
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This report has been assigned to the ENVIRONMENTAL HEALTH EFFECTS RE-
SEARCH series. This series describes projects and studies relating to the toler-
ances of man for unhealthful substances or conditions. This work is generally
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clude biomedical instrumentation and health research techniques utilizing ani-
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This document is available to the public through the National Technical Informa-
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EPA-600/1-77-038
June 1977
BEHAVIORAL AND PHYSIOLOGICAL CORRELATES OF
VARYING NOISE ENVIRONMENTS
by
Lawrence F. Sharp
John F. Swiney
Mickey R. Dansby
Stephen C. Hyatt
Dale E. Schimmel
Department of Behavioral Sciences and Leadership
United States Air Force Academy, Colorado 80840
Contract No. IAG-D4-0537
Project Officer1
George R. Simon
Health Effects Division
Office of Health and Ecological Effects
Washington, DC 20460
OFFICE OF HEALTH AND ECOLOGICAL EFFECTS
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, DC 20460
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DISCLAIMER
This report has been reviewed by the Office of Health and Ecological Effects,
U.S. Environmental Protection Agency, and approved for publication. Approval
does not signify that the contents necessarily reflect the views and policies
of the U.S. Environmental Protection Agency, nor does mention of trade names
or commercial products constitute endorsement or recommendations for use.
-------
ABSTRACT
Eighty male college juniors and seniors were dichotomized into either High
or Low Anxiety groups. Each subject experienced a household noise profile
under a quiet (50 dBA), intermittent (84 dBA) and continuous (84 dBA)
noise condition, while performing either an easy or difficult pursuit
tracking task. Heart rate, electromyographic potentials, and tracking
error responses were evaluated. Results indicated significant (P<.01) main
effects for task difficulty and noise condition and significant (P<.01)
interaction effects for task difficulty, noise condition and anxiety level
(as measured by the IPAT Self Analysis Form) of subjects. The significant
noise effect occurred for the difficult task condition during the second
tracking period (which includes transfer of training effects) indicating
that factors such as task difficulty, direction of task transfer effects,
duration of noise exposure as well as anxiety level of subjects appear to
be important variables affecting human psychomotor performance in noise
environments below 85 dBA. These findings appear to be consistent with
previous research which suggests that task difficulty is the variable deter-
mining the direction of stress (noise) effects on psychomotor performance and
the nature of the interaction between stress and anxiety level. The present
findings are therefore seen as supporting the concepts of the response
interference hypothesis and the inverted-U function between stress and
performance.
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CONTENTS
Page
Abstract in
List of Figures vi
List of Tables viii
Acknowledgements ix
Sections
I Conclusions 1
II Recommendations 4
III Introduction 5
IV Method 8
V Results 20
VI Discussion 48
VII References 61
VIII Glossary 65
IX Appendices 67
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FIGURES
No
1 Sten Scores of Experimental Sample and National College 9
Sampl e
2 1/3-Octave Band Spectral Analysis of Noise Profile 13
3 Schematic Diagram of EMG Signal Conditioner Circuitry 15
4 Low Anxiety Track Error - Data 1 22
5 High Anxiety Track Error - Data 1 23
6 High Anxiety Track Error - Data 2 26
7 Low Anxiety Track Error - Data 2 27
8 Easy Tracking Task - Data 1 35
9 Difficult Tracking Task - Data 1 35
10 Easy Tracking Task - Data 2 36
11 Difficult Tracking Task - Data 2 36
VI
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TABLES
No.
1 Experimental Design 10
2 Stroop Color Word Chart Arrangement 17
3 Analysis of Variance for the Electromyographic Potential 20
Dependent Variable - First Task
4 Analysis of Variance for the Electromyographic 21
Potential Dependent Variable - Second Task
5 Analysis of Variance for Track Error - First Task 24
6 Track Error Cell Means and Standard Deviations - 24
First Task
7 Track Error Row Means for the First Task - High Anxiety 25
Group
8 Track Error Row Means for the First Task - Low Anxiety 28
Group
9 Analysis of Variance for Track Error - Second Task 29
10 Track Error Cell Means and Standard Deviations - 30
Second Task
11 Track Error Row Means for the Second Task - High Anxiety 31
Group-Task Effect
12 Track Error Row Means for the Second Task - Low Anxiety 31
Group-Task Effect
13 Track Error Column Means for Second Task - High Anxiety 32
Group-Noise Effect
14 Track Error Column Means for Second Tracking Task - Low 33
Anxiety Group-Noise Effect
15 Analysis of Variance for Stroop Color Word Responses 37
16 Stroop Color Word Response Times by Cell Mean 38
17 Analysis of Variance for Heart Rate Responses - Data 1 - 38
First Task
vii
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TABLES (continued)
No. Page
18 Analysis of Variance for Heart Rate Responses - Data 2-39
Second Task
19 Heart Rate Mean Response - First Task 40
20 Heart Rate Mean Response - Second Task 41
21 Analysis of Variance for the Restricted Regression 42
Model for Tracking Error - Data 1
22 Regression Summary Table for the Restricted Model for 43
Tracking Error - Data 1
23 Analysis of Variance for the Full Regression Model for 43
Tracking Error (Data 1)
24 Regression Summary Table for the Full Model for Tracking 43
Error - Data 1
25 Analysis of Variance for the Restricted Regression Model 44
for Stroop Response Time
26 Analysis of Variance for the Restricted Regression Model 45
for EM6 (Data 1)
27 Regression Summary Table for the Restricted Regression 46
Model for EMG (Data 2)
28 Analysis of Variance for the Full Regression Model for 46
EMG - Data 2
29 Regression Summary Table for the Full Regression Model 46
for EMG - Data 2
vm
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ACKNOWLEDGMENTS
This research was funded by the United States Environmental Protection
Agency, Health Effects Division, under EPA-IAG-D4-0537 and EPA-IAG-D5-0537.
The views expressed in this paper represent those of the authors and do
not necessarily reflect the views of the United States Air Force or the
Department of Defense.
In conduct of this research the authors wish to acknowledge the adminis-
trative and technical assistance of Mrs. Berthe Giroux, Mrs. Kathy Rockefeller,
Dr. Karl Kryter, Valentin W. Tirman, Stephen Stitch, Bryon Russell and
Mark 0'Green.
ix
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SECTION I
CONCLUSIONS
The experimental results indicate that the relationship between physio-
logical and motor skill variables when noise is used in combination with
other variables is extremely complex. In this experiment, there were no
significant physiological response differences in electromyographic potential
or heart rate changes as a function of either noise profile, anxiety con-
dition, or task difficulty. Previous studies of physiological response to
noise stress show similarly conflicting findings. There appears to be an
overlap of anxiety and stress in the physiological area and in the awareness
of pressure and tension with the result that the degree of familiarity of
the stimulus, and the tendency of individual differences to cancel each
other out must be taken into consideration before attempting to make signifi-
cant practical predictions concerning the effects of moderate noise levels
on human physiological response.
Anticipation of noxious stimulations also appears to be an important
factor in predicting physiological stress reactions to noxious stimulation.
Research (Speilburger, 1972) shows that most of the autonomic stress reaction
may take place prior to the administration of the noxious stimulation. With
time to appraise the situation, subjects are able to develop self-assuring
coping responses which can lead to lowered physiological activation during
the experimental period. This factor may have contributed to the lack of
practical significance between baseline and experimental condition physio-
logical arousal levels in the present experiment. In summary, human
physiological response to moderate environmental noise is a complexly con-
trolled system and may not be a reliable indicator of arousal level and
stress at noise levels below 85 dBA.
The average performance of each subject on the Stroop Chart reading task
improved as a result of the experimental conditions reflecting the arousing
but not debilitating effect of the experiment. Previous noise research which
dealt with high (115 dBA) sound pressure levels have shown decrements in
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Stroop Chart reading times. Apparently, 84 dBA noise levels are not
debilitative on this task. Instead, the data reflect that merely being in
a controlled environment, or attending, independent of noise condition,
to a demanding task could have produced the improvement in reading times.
In terms of psychomotor performance, task difficulty, anxiety level,
and noise condition all interacted to.determine tracking error with the
result that the poorest tracking performance occurred when high anxiety
subjects were required to switch from the easy to the difficult task during
exposure to intermittent noise. Overall, the best tracking performance oc-
curred when high anxiety subjects performed the easy tracking task, regardless
of noise condition and task transfer direction. Tracking performance on the
difficult task, however, improved significantly when low anxiety subjects
were exposed to noise and also when high anxiety subjects were not exposed
to noise. Apparently, moderate intensity (84 dBA) household noise serves as
a stressor for high anxiety subjects and as a facilitator for low anxiety sub-
jects performing a difficult psychomotor task.
The interaction between noise condition and anxiety level occurred only
during the second five minute tracking/noise exposure period (data 2) when
the task was difficult, which suggests that duration of stress (noise and
tracking) as well as task transfer effects are important variables in moderate
level (below 85 dBA) noise research.
These findings appear to be consistent with previous research which sug-
gests that task difficulty is the variable determining the direction of stress
(noise) effects on psychomotor performance and the nature of the interaction
between stress and anxiety level. The present findings are therefore seen as
supporting the concepts of the response interference hypothesis and the inverted-
U function between stress and performance. Anxiety then, as a personality
variable, when predicting the effects of moderate noise on psychomotor per-
formance, should be evaluated as a probable determinant of moderate noise ef-
fects on human behavior.
In summary, moderate intensity, (84 dBA) household noise, appears to act as
a stressor for high anxiety subjects performing a difficult psychomotor task
and particularly for those who experience noise as: 1) primary overstimulation,
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i.e., feelings of being overwhelmed and bombarded with stimuli; 2) or who
lack an appropriate course of action to resolve undirected arousal, anxiety
and frustration as a result of unavoidable noise exposure; 3) a violation
of expectancies; 4) or where noise serves to interrupt cognitive pro-
cesses resulting in the inability to carry out a cognitive plan when
experiencing interruption or environmental disorganization due to noise ex-
posure. Consequently, it may be that one of the primary effects of moderate
environmental noise is its interrupting, disorganizing quality, which would
be particularly debilitating to those subjects who already experience sub-
stantial internal arousal as the result of elevated trait anxiety levels.
In conclusion, except for that segment of the population that could be
characterized as possessing low trait anxiety and who actually appear to
profit (at least temporarily) from moderate noise stimulation on difficult
psychomotor tasks, or for high and low anxiety subjects on easy psychomotor
tasks, by adding stimulation from the environment (noise) that violates or
precludes the development of expectancies (intermittent noise), it can then
be expected that decrements in psychomotor performance will probably occur
on difficult psychomotor tasks, and as part of the "cost" to the person,
frustration can also be expected to occur with its special relevance for
maladaptive behavior and the normal stresses of everyday living that now
appear to plague our highly industrialized, urban society.
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SECTION II
RECOMMENDATIONS
The results of this study suggest that future research investigating
the physiological effects of stress due to broadband noise below 85 dBA
should consider employing measures other than average heart rate and
electromyographic potentials from which to predict human physiological
response. Additionally, attention should be focused on the degree to
which subjects have achieved physiological baseline prior to rioise
exposure.
The results also suggest that the internal arousal level of subjects
(anxiety) is an important variable although a more heterogeneous subject
population may have resulted in an even larger effect due to the anxiety
variable. In this regard, factors such as task difficulty, direction
of task transfer effects and duration of noise exposure appear to be
important variables requiring careful consideration when predicting the
interaction of previous arousal level (anxiety) and stress (moderate
noise) on psychomotor performance.
Due to subject unavailability, sex differences were not evaluated in
the present study. Future research should attempt to determine if such
differences exist, and the applicability of the present findings should
be experimentally expanded to include a cross section of the general popu-
lation performing psychomotor tasks that are representative of the work
activities experienced by that population.
Finally, in practical terms, even though the independent variables in
the present study resulted in significant mean differences in psychomotor
performance, only a small proportion (19%) of the total variance in
tracking performance has been accounted for. This would indicate that
individual differences as well as other factors have not been fully ex-
plored and, therefore, substantially limit the applicability of these
findings to similar subject populations.
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SECTION III
INTRODUCTION
The 1973 International Congress on noise as a public health problem,
especially the psychological consequences, began with a rather somber
summary. Gulian (1973) noted that most noise research since the 1950s is
generally controversial and no firm conclusions can be drawn. Gulian
attributes the disparate research results to the extraordinary complexity
of factors which, intenvene between noise as the independent variable and
the dependent measures. Even a cursory examination of the literature
during the past five years dramatically substantiates this complexity and
the paucity of variables involved. For example, some researchers have
found no-ise induced: facilitation of learning and cognitive performance
(Fechter 1972) while others observe a decrement (Renshaw, 1973). Others
have found sex (Kumar, 1969 and Elliott, 1971) age (Mathur, 1972) and
social class (Anderson, 1973) differences in response to noise. To further
complicate interpretation of the findings, Harcum and Mont-i (1973) found
that subjects will "cooperate" with the experimenter on noise disturbance
ratings unless this factor is controlled. Although much research seems
to confirm the absence of the main effects for noise alone, some research
is beginning to emerge which shows the interactive nature of noise as an
independent variable. For example, Harris and Schoenberger (1970) demon-
stratedvthat the detrimental effect of noise is additive to that of
vibration when both are presented simultaneously. When combined with noise,
the additional stressor of shock (using rodents) (Campbell, 1968) and
neomycin (Jauhiainen, Kohonen' and Jauhiainen, 1972) produce a synergistic
effect. Indicative of the^problems associated with these research findings
are Grether's (1972).research which, failed.to demonstrate combined effects
of heat, noise and vibration and Kryter's (1970) caution when using rodents
and rabbits as subjects in. noise research.;
The data on threshold shift (temporary or permanent) and hearing loss is
certainly well founded, especially for extreme noise environments, and
-------
with appropiate protective apparatus, damage can be attenuated or avoided.
There seems to be a lack of appreciation however, for the effect of
moderate noise environments and for the effects these environments have
on non-auditory or non-physiological responses. Recent research by
Bull (1973), Edsell (1973) and Glass and Singer (1973) provide good evi-
dence that even "low" (84 dB) noise environments result in important
changes in socially relevant behavior; e.g., tolerance for ambiguity
decreases in a noise environment (Bull, 1973); perception of others
assumes negative dimensions (Edsell, 1973); and frustration tolerance
decreases (Glass and Singer, 1973). These effects obviously represent
the psychological cost the organism pays for exposure to unavoidable
environmental noise. In fact, behavioral responses to noise and behavior-
al differences between subjects may be among the most important indicants
of noise effects and a major source of variation in the various dependent
measures assessed.
Some recent studies of noise and personality have focused on introversion
or extroversion as contributors to psychomotor performance differences
under noise conditions. These studies have been generated by Eysenck's
theory of personality and cortical arousal. In general, extroverts were
found to display greater decrements in psychomotor performance while
experiencing noise stimulation than were introverts. Di Scipio (1971)
showed that white noise facilitates psychomotor response for an optimal
period of time, after which decrements were observed. This effect was
heightened for extroverts. Even though extroverts are more prone to
noise distraction, Elliott (1971) showed that they will tolerate greater
intensities of white noise than will introverts. Results of other studies
on noise and social behavior are diverse. Edsell (1973), indicates that
subjects in a game situation perceived other players as more disagreeable,
disorganized, and threatening under noise as opposed to no-noise conditions,
Jansen & Hoffman (1971) demonstrated that increasing the loudness of a
noise stimulus augmented subjective annoyance, with neurotic personality
tendencies contributing to this effect. Angrier speakers were found to
use more high frequency elements in their speech (Mason 1969).
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Stephens (1970) showed that test anxiety scores correlated positively with
the slope of a loudness judgement function.
The above considerations and the data to be presented in this paper readily
attest to the fact that the main effects of noise, especially moderate
levels (60-90 dB) are elusive, depend to some extent on the psychological
structure of the recipient, and, potentially, can be confounded with a
seemingly endless array of other factors.
The research reported in the paper was conceived and conducted to: (1)
specifically assess a noise profile to which a large proportion of both
urban and suburban dwellers are exposed on a daily basis; (2) examine
these effects on a relatively homogenous population with respect to sex,
age, physical fitness, intellectual ability, psychological structure, and
environmental stress; and (3) provide more adequate control in terms of
research design, of individual differences which could potentially contri-
bute to between group differences in noise responses.
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SECTION IV
METHOD
SUBJECTS
Eighty male junior and senior Air Force Academy cadets provided data
for this study. Subjects were volunteers, solicited from upper
division Behavioral Science and Life Science classes. No inducements
were offered. Eighty subjects initially agreed to participate and
completed "Informed Consent" certificates in accordance with HEW standards.
Participating subjects were administered an anxiety scale to assess their
relative levels of state-trait anxiety. On the basis of this measure,
subjects were dichotomized as above (High Anxiety) or below (Low Anxiety)
the group median score. Each subject was then randomly assigned to one
of the four task sequence groups, which resulted initially in eight cells
of ten subjects each. .Figure 1 displays the frequency distribution of
anxiety measure scores of the experimental sample versus national, male
2
college norms. These distributions differed significantly (x. =25.81,
df_ = 9,-£ < .005) and the group mean,-STEN scores were significantly
different (t_ = -10.88, df = 40, £ < .005).
DESIGN
A 2 x 3 x 4 factoral design was employed. Table 1 shows the variables and
levels involved.
Forty subjects served under each of the two anxiety conditions. Within
each anxiety condition, ten subjects served under each of the four task
sequence conditions. Each cell of ten subjects experienced the three
noise conditions in a counterbalanced, repeated measures sequence.
8
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25
23
21
1U
19
2 17
g
UJ
UJ
0.
15
13
11
9
7
5
3
1
NATIONAL NORMS
EXPERIMENTAL SAMPLE
456
STENSCORES
10
FIGURE!
STEN SCORES OF EXPERIMENTAL SAMPLE AND NATIONAL
COLLEGE SAMPLE.
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TABLE- 1.
EXPERIMENTAL DESIGN
EASY TO
EASY11
EASY TO
DIFFICULT
DIFFICULT
TO EASY
DIFFICULT
TO
DIFFICULT
HI ANXIETY
Qa
SI
THRU
S10
S11
THRU
S20
S21
THRU
S30
S31
THRU
S40
IN
CN
•^
^*
>_. .
•^
>_
>_
••
LO ANXIETY
Q
S41
THRU
S50
S51
THRU
S60
S61
THRU
S70
S71
THRU
S80
IN
CN
>^
>_
aO = QUIET CONDITION (50 dBA MASK)
IN = INTERMITTENT PROFILE
CN
CONTINUOUS PROFILE
bREFERS TO DIFFICULTY LEVEL OF TWO, SUCCESSIVE, 5 MINUTE TRACKING TASKS, ACCOMPLISHED UNDER
EACH NOISE CONDITION.
10
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APPARATUS
The experimental sessions were conducted in the Behavioral Sciences
Laboratory at the United States Air Force Academy. A controlled acoustic
environment was provided through the use of ventilated, Industrial
Acoustics (AIC) audiometric examination booths. Each chamber was
equipped with a Hewlett Packard 1205A dual trace oscilloscope which
displayed a randomly moving, horizontal "target" line as well as the
subject's "controlled" line. A 60 Hz sign-wave was superimposed on the
controlled line to aid in subject differentiation between the two lines.
Total system inputs and outputs are shown by block diagram in appendicies
1 and 2 respectively.
T
A Weston 1242 digital multimeter was located on top of each oscilloscope,
adjusted to display (-10 to +10) volts, and represented real-time in-
tegrated tracking error, which provided immediate feedback to subjects
during the training portion of each experimental session.
Subjects sat facing the oscilloscope at an approximate viewing distance
of 33 cm. The oscilloscope was placed at eye level to minimize parallex
distortion. A Measurement Systems Model 542, 2 axis, gimballed joystick
was installed at the end of the subject's right arm rest. Coil springs,
set at .45 kg maximum deflection force, were used to return the handle to
center. Maximum possible stick deflection was 28° from center in each
direction.
The experimental variable, noise, was introduced via a high-fidelity
(AR2-ax) bookshelf loudspeaker located immediately above and facing the
seated subject. Fifty dBA of background acoustical masking was provided
continuously through a "Sound Shield" random noise generator placed
adjacent to the loudspeaker. A 2-way intercom station was installed in
each chamber as well as the necessary EKG and EMG leads and electrodes.
The audio input was generated by combining signals from a Hewlett-Packard
(HP) 8057A Precision Noise Generator set for "pink" noise, and a HP 3722A
11
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Noise Generator with a selective sequence length of N = 15, clock period
of 100 ms and gaussian noise bandwidth of .5 Hz with the variable output
set for "binary". The generated signal was magnetically recorded on a
Crown Model 824SX 4-track tape recorder, the output of which was then
1/3-octave band shaped in real-time by a Bruel and Kjaer (B&K) Model
125 1/3-octave Graphic Frequency Response Equalizer and amplified by a
Crown IC-150 preamplifier and DC-300A laboratory amplifier. The audio
profile chosen represented 84 dBA of typical suburban household noises
and was generated by magnetically recording a central heating system,
television program, and a canister type vacuum cleaner at operator ear
level in a carpeted and draped living room. A calibrated (+_ 1 dB 20 -
30 KHz) Crown 824SX tape recorder was used with input provided by a B&K
2619 Microphone Preamplifier, B&K 4133 1/2" Condenser Microphone with a
UA 0386 Nose Cone, a B&K 2804 Power Supply and calibrated for absolute
Sound Pressure Level (SPL) by a B&K 4220 Pistonphone. Overall SPL was
measured by a General Radio (GR) 1558-BP Octave Band Noise Analyzer for
both dBA and dBC values. Calibration was performed prior to measurements
with a GR Type 1562 Sound Level Calibrator. The resultant magnetic tape
was then analyzed using a HP 8064A Real-Time Audio Spectrum Analyzer, the
output of which was connected to a HP 7004B X-Y Recorder and automatically
plotted on HP 08064-9010 dB scaled graph paper. Primary spectral energy
content centered between 200 and 300 Hz with a peak at 400 Hz (see Figure
2).
The final audio profile was verified by real-time 1/3 octave band analysis
in the sound chamber at subject ear level. Microphones, equalizers, and
amplifiers, etc. were the same items used in the original recording pro-
cess. Final A-weighted SPL settings were done with a test subject in
place and the chamber door closed.
The random tracking signal input was generated by the HP 3722A low fre-
quency random noise generator with a selective sequence length of N = 4,095,
clock period of 333 ms and gaussian noise band width of .15 Hz.
12
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dB
-10
-15
-20
-25
-30
-35
LEGEND
MICROPHONE: B&K4133
RECORDER: CROWN SX-824
CENTRAL HEATING-VACUUM-T.V.
(78dBA-80 dBC)
50 63 80 100 125 160 200250 315 400 500 800 1.K 1.25K1.6K 2.K 2.5K3.15K4K 5K 6.3K 8K
FREQUENCY-Hz
FIGURE 2. 1/3-OCTAVE BAND SPECTRAL ANALYSIS OF NOISE PROFILE
10K
-------
Subject tracking output and tracking signal input were fed into an EIA
TR-20 analog computer which was pre-programmed to present zero order
(position control) or first order (rate or velocity) control feedback
to the subject and provide an integrated error voltage to drive the
digital multimeter display. The analog computer also provided a real-
time absolute error voltage output which was magnetically recorded on
an Ampex FR-1300 14 channel FM instrumentation recorder.
The EMG (Electromyographic) potentials recorded off the frontal is muscle
group, were filtered, rectified and magnetically recorded on the FM
recorder. The rectifying/filtering circuit was unique and not readily
available in the literature. Figure 3 shows the circuit used.
The electrocardiograph (EKG) signal was processed through a Gould/Brush
Model 4307 Biomedical-Tachometer Coupler and magnetically recorded.
A Datametrics SP-425 time code generator was used to differentiate the
various experimental conditions and to generate a time track on the FM
recorder. The subsequent analog data were processed and digitally analyzed.
14
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RECTIFIER
(FULL WAVE)
RC FILTER NETWORK
IN
OUTPUT
FROM
UNIVERSAL
PREAMPLIFIER
OUT
(D.C. COMPONENT)
FIGURE 3. SCHEMATIC DIAGRAM OF EMG SIGNAL CONDITIONER CIRCUITRY.
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PROCEDURES
Prior to each day's subject runs, complete minimum and maximum calibration
voltages were magnetically recorded. A cardiac simulator was used to
calibrate heart rate.
After reading and signing an informed consent form, each subject was
administered the Institute for Personality Ability Testing "Anxiety
Scale Questionnaire", (self-analysis form) a brief, non-stressful,
clinically validated questionnaire for appraising free anxiety level.
Results of the test were used to divide the subject pool into statisti-
cally significantly different high and low anxiety groups.
Upon entering the experimental room, eye dominance was first assessed,
then each subject was randomly administered one of two versions of a
standard Stroop Chart "Pretest". Table 2 shows each version of the
Stroop Color Word chart. The word in parentheses indicates the color of
the associated word.
Time required to successfully complete the chart was then recorded. If
the subject made an error, he was instructed to correct the error and
continue as quickly as possible.
Normally, two subjects were run simultaneously since two instrumented
sound chambers were available. Subjects were instructed to remove their
upper body clothing. EKG and EMG electrodes were attached and then the
subject seated in the chamber with feet flat on the floor, arm on arm rest,
hand on tracking handle, facing the oscilloscope. Subjects were told to
relax, and cautioned against extraneous movements of body, head, jaws and
random eye movements. Use of the 2-way intercom was explained (it did
not require subject manipulation), duration of the experiment was given,
and subjects informed that all further instructions would be issued over
the speaker located overhead, and that any subsequent questions would be
16
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Table 2. STROOP COLOR WORD CHART ARRANGEMENT9
Stroop A
1 -Yellow
(orange)
3-Green
(blue)
6-Orange
(yellow)
9-Yellow
(green)
11 -Red)
(yellow)
14-Yellow
(orange)
16-Green
(blue)
18-Green
(yellow)
21 -Green
(orange)
2-Black
(green)
4-Blue
(red)
7-Red
(green)
10-Black
(orange)
12 -Green
(blue)
15-Blue
(red)
17-Orange
(blue)
19-Red
(blue)
22-Blue
(red)
5-Blue
(orange)
8-Blue
(orange)
13-Blue
(red)
20-Red
(green)
Stroop B
1 -Green
(blue)
4-Blue
(red)
6-Red
(blue)
9-Green
(orange)
12-Orange
(blue)
14-Black
(orange)
16-Blue
(orange)
19-Orange
(yel 1 ow)
21 -Green
(blue)
2-Red
(green)
5-Yellow
(orange)
7-Black
(green)
3-Blue
(yellow)
8-Blue
(red)
10-Red 11 -Blue
(yellow) (red)
1 3-Green
(blue)
1 5-Yellow
(green)
1 7-Red
(green)
20-Blue
(red)
22-Yellow
(orange)
18-Green
(yellow)
alt is important to note that both charts were identical except for sequence.
The same number of words appeared on each chart, and they appeared in the
same colors, but in different orders. For example, the red-colored word
BLUE appeared four times on each chart: On chart A it was the 4th, 13th,
15th and 22nd word presented, and on Chart B the 4th, 8th, llth and 20th
word presented. '
17
-------
answered via the intercom.
The chamber door was then closed, and after 2-3 minutes, one minute of
physiological data was magnetically recorded (Baseline 1) under the quiet
(50 dBA mask) condition. If subjects were scheduled to be exposed to
either the continuous or intermittent noise condition, then a second one
minute (Baseline 2) of physiological data was recorded with the appropri-
ate noise input.
Two minutes of taped instructions immediately followed. Instructions
included the use of the tracking handle, oscilloscope, digital multimeter
and the tracking task was explained in detail. A one minute period of
questions and answers followed.
Subjects were then exposed to two, 5 minute experimental sessions with a
one minute rest period between sessions. These sessions, Data 1 and Data 2
respectively, consisted of exposing the subject to one of three noise condi-
tions; quiet (50 dBA generated by the Sound Shield white noise generator),
(2) continuous noise (84 dBA), or (3) intermittent noise (84 dBA) of an
identical audio spectrum but randomly cycled, both on and off between one
and nine seconds with a total "on" duration of 50% of the five minute
experimental session. The identical noise condition used for Data 1 was
repeated for Data 2. Each subject was scheduled on a daily basis for three
separate experimental sessions, insuring exposure to all three noise
conditions and minimizing noise adaptation and fatigue effects.
During each experimental session (Data 1 and Data 2), subjects were re-
quired to perform one of two tracking tasks. The task was labeled "easy"
(position feedback) or "difficult" (rate or velocity feedback), and the
serial presentation of the task was counter balanced (Easy, Easy; Easy-
Difficult; Difficult-Easy; and Difficult-Difficult) and remained un-
changed during the three experimental sessions for a given subject.
Subjects practiced the tracking task for four minutes with the integrate
error feedback display on. During the fifth minute, the error display was
18
-------
remotely turned off, and the FM recorder turned on generating the Data 1
and Data 2 information file. Heart rate, EMS level, and absolute tracking
error voltages were recorded at this time. The audio signal and time
code generator signal were also recorded.
At the completion of Data 2, all recording equipment was turned off, and
each subject was immediately administered the Stroop posttest.
To alleviate potential anxiety associated with being exposed to noise and
instrumentated with EKG and EMG electrodes, recorded popular music was
played prior to and immediately after each experimental session.
19
-------
SECTION V
RESULTS
ANALYSIS STRATEGY
Multiple analysis of variance for a design involving two between and one,
within subject factors was used to assess the significance of the main and
interaction effects of the independent variables. This procedure is de-
scribed in Myers (1972) and provides for analysis of a repeated measure-
ments factor. In this research, each subject served under each of the
three noise conditions, hence repeated measurements were made on each sub-
ject, for all dependent variables under a quiet, intermittent, and continu-
ous noise environment.
ANALYSIS OF ELECTROMY06RAPHIC CHANGES
For either Data 1 (First Tracking Task) or Data 2 (Second Tracking Task)
none of the independent variables produced significant changes in general
muscular tension as evidence by changes in electromyographic potentials
taken from the Frontal is Group. Table 3 shows the analysis of variance
for these data for the first tracking task (Data 1).
Table 3. ANALYSIS OF VARIANCE FOR THE ELECTROMYOGRAPHIC POTENTIAL
DEPENDENT VARIABLE - FIRST TASK
Source of variation
Anxiety
Task difficulty
Anx x tsk dif
Error
Noise condition
Anx x noise cond
Task dif x noise cond
Anx x tsk dif x noise
condition
Total
DF
1
3
3
72
2
2
6
144
239
SS
18.2803
33.9461
44.6793
564.5801
5.4246
39.6191
46.5791
1132.7677
1940.0469
MS
18.2803
11.3154
14.8931
7.6200
2.7123
19.8096
7.7632
7.8664
F
2.3989 N.S.
1.4894 N.S.
1.9544 N.S.
.3447 N.S.
2.5182 N.S.
.9868 N.S.
20
-------
Table 4 shows the analysis of variance for EMG for Data 2 (Second Tracking
Task).
Table 4. ANALYSIS OF VARIANCE FOR THE ELECTROMYOGRAPHIC POTENTIAL
DEPENDENT VARIABLE - SECOND TASK
Source of variation
Anxiety
Task difficulty
Anx x task dif
Error
Noise condition
Anx x noise cond
Tsk dif x noise cond
Anx x tsk dif x noise
condition
Error
Total
DF
1
3
3
72
2
2
6
6
144
239
SS
4.9713
39.9109
18.6738
426.0356
14.9123
24.8510
69.3777
54.8425
878.6613
1526.3565
MS
4.9713
13.3036
6.2246
5.9171
7.4562
12.4255
11.5629
9.1404
6.1018
F
.8401 N.S.
2.2483 N.S.
1.0519 N.S.
1.2219 N.S.
2.0363 N.S.
1.8949 N.S.
1.4979 N.S.
ANALYSIS OF TRACKING ERROR RESPONSES
For both the first (Data 1) and second (Data 2) tracking tasks, task diffi-
culty significantly influenced .tracking error performance. Table 5 shows
the analysis of variance for the first tracking task.
Figures 4 and 5, show, for both anxiety groups, that tracking error
responses approximately double (10% to 20%) when the task was the more
difficult rate or velocity tracking.
-------
ro
ro
100
90
80
Z 70
CO
<
60
cc
O
CC
cc
UJ
o 50
uj 40
D
30
20
10
Q= QUIET
I = INTERMITTENT
C = CONTINUOUS
EE
ED
DE
DD
TASK TRANSFER GROUPS
FIGURE 4: LOW ANXIETY TRACK ERROR - DATA 1
-------
ro
CO
100
DC
g
DC
UJ
O
<
QC
80
70
60
50
40
O 30
CO
CO
20
10
QIC
EE
ED
TASK TRANSFER GROUPS
FIGURE 5: HIGH ANXIETY TRACK ERROR - DATA 1
Q= QUIET
I = INTERMITTENT
C = CONTINUOUS
DE
DD
-------
Table 5. ANALYSIS OF VARIANCE FOR TRACK ERROR - FIRST TASK
Source of variation
Anxiety
Task difficulty
Anx x task dif
Error
Noise condition
Anx x noise cond
Tsk dif x noise cond
Anx x tsk dif x noise
condition
Error
Total
Table 6 shows the cell
tracking error.
Table 6. TRACK ERROR
DF
1
3
3
72
2
2
6
6
144
239
means and
CELL MEANS
SS
3.9106
5349.6581 1
295.6206
3443.9221
94.9963
3.3320
82.4734
730.5761
5158.1347
15469.1981
MS
3.9106
783.2194
98.5402
47.8322
47.4982
1.6660
13.7456
121.7627
35.8203
standard deviations for
AND STANDARD
DEVIATIONS
F
.0817 N.S.
37.2807 p <.01
2.0601 N.S.
1.3260 N.S.
.0465 N.S.
.3837 N.S.
3.3992 p <.01
the first task
- FIRST TASK
Hi anxiety
Q
EE
ED
DE
DD
X"
X 12.65
s.d.10.00
X" 6.69
s.d. 4.97
123.04
s.d. 15. 47
X" 20.34
s.d. 15. 56
15.68
IN
In. 76
s.d. 9.40
X" 7.65
s.d. 6.10
121.16
s.d. 15. 08
I 24.25
s.d. 17. 61
16.18
CN
X" 12.93
s.d. 9.96
X" 9.47
s.d. 6.02
X" 33.29
s.d. 13. 60
X" 25.08
s.d. 17. 48
20.19
X
12.45
7.94
25.83
23.19
Lo anxiety
EE
ED
DE
DD
X"
X 12.01
s.d. 9.38
X" 10.25
s.d. 8.04
120.12
s.d. 15. 01
118.45
s.d. 11. 68
15.21
X 12.25
s.d. 9.78
X" 10.95
s.d. 9.05
124.73
s.d. 17. 62
X" 17.65
s.d. 11. 94
16.40
X" 22.76
s.d. 16. 35
JTlO.31
s.d. 7.77
124.64
s.d. 16. 51
I 15.34
s.d. 11. 34
18.26
24
15.67
10.50
23.16
17.15
-------
Neuman-Kuhls analysis of row means for both anxiety groups showed that,
within each anxiety group, the difficult tracking task resulted in signifi-
cantly higher error scores than the easy tracking task. Table 7 shows the
Neuman-Kuhls analysis for the High Anxiety Group.
Table 7.
TRACK ERROR ROW MEANS FOR THE FIRST TASK - HIGH ANXIETY GROUP
Noise Condition
Task
E
E
D
D
Q
12.65
6.69
~ 23.04
20.34
IN
11.76
7.65
21.16 ,
24.15
CN
12.93
9.47
33.29
25.08
7
12.45
7.94
25.83
23.19
T2
Tl
T4
T3
Ti
4.51(2.10)*
15.28(2.49)*
10.77(2.10)*
17.89(2.73)*
13.38(2.49)*
2.61(2.10)*
q2 = 2.10; q3 = 2.49; q4 = 2.73, £ <.05
/
Table 8 shows the row means and Neuman-Kuhls analysis for the low anxiety
group.
In the high anxiety group, both groups which performed the easy task first
had significantly smaller track errors than either of the difficult task
groups. Subject differences are apparently operating however, in that the
two groups performing the easy task differed significantly. Additionally,
the two difficult groups differed significantly from each other. Similar
25
-------
ro
100
90
80
2 70
oc
o
£60
LU
Q 50
£ 40
D
I
30
20
1.0
QIC
EE
ED
TASK TRANSFER GROUPS
FIGURE 6: HIGH ANXIETY TRACK ERROR - DATA 2
Q= QUIET
I = INTERMITTENT
C = CONTINUOUS
DE
DD
-------
ro
100 _
90
80
Z 70
EC
O
£60
< 50
DC
LU 40
D
§30
CQ
20
10
EE
ED
TASK TRANSFER GROUPS
FIGURE 7: LOW ANXIETY TRACK ERROR - DATA 2
Q= QUIET
I = INTERMITTENT
C = CONTINUOUS
DE
DD
-------
results hold for the low anxiety groups, with the exception that one of the
easy task groups (T«) did not have significantly poorer performance than
one of the difficult task groups (T3).
Table 8. TRACK ERROR ROW MEANS FOR THE FIRST TASK- LOW ANXIETY GROUP
Noise Condition
Task
E
E
0
D
Q
12.01
10.25
20.12
18.45
IN
12.25
10.95
24.73
17.65
CN
22.76
10.31
24.64
15.34
X
15.67
10.50
23.16
17.15
T2
Tl
T4
T3
'4
5.17(2.10)*
6.65(2.49)*
1.48(2.10)
12.66(2.73)*
7.49(2.49)*
6.01(2.10)*
q2 = 2.10; q3 = 2.49; q,= 2.73, £ <.05
Table 9 shows the analysis of variance for the second tracking task.
(Data 2). These data now include the effects of transfer of training.
Again, significant main effects are present for task difficulty. Addition-
ally, these data now show significant main effects for noise condition.
Figures 6 and 7 show, that for both high and low anxiety groups, the second
task (Data 2) tracking error responses increased when the transfer was from
an easy to a difficult tracking task.
28
-------
Table 9. ANALYSIS OF VARIANCE FOR TRACK ERROR - SECOND TASK
Source of Variation
Anxiety
Task difficulty
Anx x task dif
Error
Noise cond
Anx x noise cond
Task dif x noise cond
Anx x task dif x noise
condition
Error
Total
DF
1
3
3
72
2
2
6
6
144
239
SS
158.9432
7806.7954
261.7668
7035.2170
244.7820
56.8519
507.0714
194.5361
3186.5429
19222.5994
MS
158.9432
2602.2651
87.2556
97.7113
122.3910
28.4259
84.5129
32.4227
22.1287
F
1.6266 N.S.
26.6321 p_<.01
.8929 N.S.
5.5308 p_<.01
1.2845 N.S.
3.8191 p_ <.01
1.4651 N.S.
The Data 2, analysis of variance indicated significant main effects for
noise condition on tracking error, as well as for task difficulty.
Table 10 shows the cell means and standard deviations for the second task
(Data 2) tracking error responses.
Neuman-Kuhls analysis of row means for both anxiety groups showed that
within each anxiety group, the difficult tracking task resulted in signifi-
cantly higher error scores than the easy tracking task. Table 11 shows the
row means and Neuman Kuhls analysis for the high anxiety group.
Table 12 shows the row means and Neuman-Kuhls analysis for the low anxiety
group (Data 2).
In the high anxiety group (second task), the difficult task (T4) preceded by
a difficult task resulted in significantly higher track error scores than
29
-------
either the difficult task (T~) preceded by an easy task or an easy task (T-j)
preceded by an easy task. This finding also applied for the difficult task
(T2) followed by an easy task. Regardless of prior (first) task, as long as
the second task was difficult, error scores were significantly higher than
when the second task was easy. For the low anxiety group, the difficult
task (T.) preceded by an easy task resulted in significantly higher track
error scores than either the difficult task (To) preceded by a difficult
task or the easy task (T2) preceded by an easy task or the easy task following
a difficult task (T^).
Table 10. TRACK ERROR CELL MEANS AND STANDARD DEVIATIONS - SECOND TASK
Hi anxiety
Q
EE
ED
DE
DD
X"
X 9.99
s.d. 8.68
115.43
s.d. 12. 07
X" 11.43
s.d. 9.36
JT22.12
s.d. 16. 52
14.74
IN
X" 9.78
s.d. 8.56
140.29
s.d. 8.92
X" 12.96
s.d. 9.45
X" 27.54
s.d. 17. 18
22.64
CN
X" 11.06
s.d. 9.39
JM3.27
s.d. 10. 24
X" 12.44
s.d. 8.06
JT35.97
s.d. 14. 48
18.19
X
10.28
23.00
12.28
28.54
Lo anxiety
EE
ED
DE
DD
X"
X 9.99
s.d. 8.57
JT37.79
s.d. 12. 42
X" 11.59
s.d. 9.83
X" 15.59
s.d. 11. 54
18.74
X 11.11
s.d. 9.24
X"27.74
s.d. 17. 82
X" 16.86
s.d. 12. 29
X" 14.49
s.d. 11. 12
17.55
X 20.25
s.d. 14. 06
JT21.75
s.d. 15. 71
JM1.20
s.d. 9.16
I 18.78
s.d. 11. 89
18.00
13.78
29.09
13.22
16.29
30
-------
Table 11. TRACK ERROR ROW MEANS FOR THE SECOND TASK - HIGH ANXIETY GROUP-
TASK EFFECT
Noise condition
Task
E
D
E
D
Q
9.99
15.43
11.43
22.16
IN
9.78
40.29
12.96
27.54
CN
11.06
13.27
12.44
35.97
X
10.28
23.00
12.28
28.54
Tl
T3
T2
T4
Tl
T2
T3
T4
Table 12.
q2 = 5.11;
TRACK ERROR
TASK EFFECT
2.0(5.11)
q3 = 6.05; q4 =
ROW MEANS FOR THE
12.72(6.05)*
10.72(5.11)*
6.62 £ <.05
SECOND TASK -
18.26(6.62)*
16.26(6.05)*
5.54(5.11)*
LOW ANXIETY GROUP-
Noise Condition
Task
E
D
E
D
Q
9.99
37.79
11.59
15.59
IN
11.11
27.74
16.86
14.49
CN
20.25
21.75
11.20
18.78
X
13.78
29.09
13.22
16.29
T2
T4
Tl
T3
'1
'2 '3
56(5 11 } ? f)7(fi 05)
o c-ifti TH
'4
15 87(6 12)*
15 31(6 05)*
12.80(5.11)*
~ 5.11; q~ = 6.05; q. = 6.62 £<
.05
31
-------
Tables 13 and 14 show the noise group cell and column means for each anxiety
group, along with the Neuman-Kuhls analysis.
Table 13. TRACK ERROR COLUMN MEANS FOR SECOND TASK - HIGH ANXIETY GROUP -
NOISE EFFECT
Task
E
D
E
D
X"
Tl
T2
T3
q2 = 2.10;
Noise
Q
9.99
15.43
11.43
22.16
14.74
Tl
Tl
q3 = 2.53, £<
condition
IN
9.78
40.29
12.96
27.54
22.64
T3
T2
3.45(2.10)*
.05
CN
11.06
13.27
12.44
35.97
18.19
T2
T3
7.90(2.53)*
4.45(2.10)*
Neuman-Kuhls analysis of column means revealed that only the high anxiety
group (second task) displayed significant decrements in tracking accuracy
as a result of noise condition. In the high anxiety group, the IN condition
*
produced the greatest decrement, followed by the CN condition. In the
low anxiety group, the Q condition produced the largest mean decrement in
tracking error, however, this result was clearly not significant.
32
-------
Table 14. TRACK ERROR COLUMN MEANS FOR SECOND TRACKING TASK LOW ANXIETY
GROUP - NOISE EFFECT
Noise condition
Task
E
D
E
D
JT
Tl
Tl
T3
Q IN CN
9.99 11.11 20.25
37.79 27.74 21.75
11.59 16.86 11.20
15.59 14.49 18.78
18.74 17.55 18.00
T3 Tl T2
T2 T3
.45(2.10) 1.19(2.53)
.74(2.10)
q2 = 2.10; q3 = 2.529, £ <.05
33
-------
Figures 8 and 9 show, for the easy and difficult task respectively,
the significant main effect of task difficulty and the significant inter-
*
action effect of anxiety, task difficulty and noise condition on tracking
error for the first task (Data 1). Figures 10 and 11 (which include
transfer of training effects) show, for the easy and difficult task
respectively, the significant main effects of task difficulty and noise
condition, as well as the significant task difficulty by noise condition
interaction effect on tracking error for the second task (Data 2). By
inspection, the significant main effect of noise condition occurred during
the difficult tracking task (Figure 11).
ANALYSIS OF STROOP RESPONSES
7 ' ' >
Table 15 displays the analysis of variance for the Stroop color-word
responses. There was a significant main effect for noise condition.
Table 16 shows the cell and column means for the Stroop color-word responses.
These data are collapsed over anxiety groups.
In the Neuman-Kuhls analysis, these data were collapsed across anxiety groups
with the resulting column means:
Q = 1.24 sees.
IN = 1.33 sees.
CN = .87 sees.
34
-------
O
a:
UJ
a,
ffl
34 r-
30
28
26
24
22
20
18
16
14
12
10
8
g
uj
O
HIGH ANXIETY
LOW ANXIETY
QUIET
INTERMITTENT CONTINUOUS
NOISE CONDITION
FIGURES: EASY TRACKING TASK - DATA 1
34
32
30
28
26
24
22
20
18
12
10
< 8
I
QUIET
INTERMITTENT CONTINUOUS
NOIES CONDITION
FIGURE 9: DIFFICULT TRACKING TASK - DATA 1
35
-------
34 _
32
30
28
cc
g 26
£ 24
X 22
^ 20
£E 18
uj 16
3 14
12
10
8
HIGH ANXIETY
LOW ANXIETY
I
QUIET
INTERMITTENT
CONTINUOUS
NOISE CONDITION
FIGURE 10. EASY TRACKING TASK-DATA 2
34
S5 32
Z 30
cc 28
g 26
£ 24
* 22
< 20
cc 1R
I- 1°
^ 16
3 14
8 1n -
< 10
8
_L
QUIET INTERMITTENT
NOISE CONDITION
FIGURE 11. DIFFICULT TRACKING TASK - DATA 2
CONTINUOUS
36
-------
Table 15. ANALYSIS OF VARIANCE FOR STROOP COLOR WORD RESPONSES
Source of variation
Anxiety
Task difficulty
Anx X tsk dif
Error
Noise cond
Anx X noise cond
Tsk dif x noise cond
Anx x tsk dif x noise
condition
Error
Total
DF
1
3
3
72
2
2
6
6
144
239
SS
11.2509
3.7769
6.2205
285.8412
30.5040
6.2864
51.6820
43.3612
624.0214
1054.7181
MS
11.2509
1.2590
2.0735
3.9700
15.2520
3.1432
8.6137
7.2269
4.3334
F
2.8339 (N.S.)
.3171 (N.S.)
.5209 (N.S.)
3.5196*
.7253 (N.S.)
1.9877
1.6677 (N.S.)
£ < .05
In every cell, subjects read the color-words faster, after being exposed
to the experimental condition. These ranged from a low of .11 seconds
faster to a maximum of 2.79 seconds faster.
ANALYSIS OF HEART RATE'RESPONSES
Heart rates changed significantly as a function of task difficulty and noise
condition (Data 1- First Task) and Task Difficulty alone (Data 2- Second task),
Tables 17 and 18 show the analyses of variance for both first and second
tasks (Data 1 and Data 2) respectively.
37
-------
Table 16. STROOP COLOR-WORD RESPONSE TIMES BY CELL MEAN
High Anxiety Low anxiety
^*k Q IN CN Q IN
EE 1.37 1.71 .24 1.15 1.10
ED 1.38 2.62 .67 .80 .28
DE .70 .95 2.22 .71 1.24
DD 2.79 1.68 .96 1.07 1.10
X" 1.56 1.74 1.02 .93 .93
T,-Q T0-IN T,-CN
1 2 3
T] — .37 .46
T2 — .09 -
T3
CN X
.11 .94
1.02 1.12
1.15 .83
.64 1.37
.73
q2 = .64 q3 = .76 £< .05 (N.S.)
Table 17. ANALYSIS OF VARIANCE FOR HEART RATE RESPONSES-DATA 1-FIRST TASK
Source of variation
Anxiety
Task difficulty
Anx x tsk dif
Error
Noise cond
Anx x noise cond
Tsk dif x noise cond
Anx x tsk dif x noise
condition
Error
Total
DF
1
3
3
72
2
2
6
6
144
239
SS
11.2873
1004.5748
73.0760
7871.6596
223.3829
123.3906
154.6267
156.1343
5263.0169
14813.7616
MS
11.2873
334.8583
24.3587
109.3286
111.6915
61.6953
25.7711
26.0224
36.5487
F
.1032 (N.S.)
3.0628*
.2228 (N.S.)
3.0559*
1.6880 (N.S.)
.7051 (N.S.)
.7119 (N.S.)
*p <.05
38
-------
TABLE 18. ANALYSIS OF VARIANCE FOR HEART RATE RESPONSES-DATA 2-SECOND TASK
Source of variation
Anxiety
Task difficulty
Anx x tsk dif
Error
Noise cond
Anx x noise cond
Tsk dif x noise cond
Anx x tsk dif x noise
cond
Error
Total
DF
1
3
3
72
2
2
6
6
144
239
SS
91.1439
1141.0576
261.6626
8754.8715
187.8425
81.9342
169.4013
108.1718
5069.4002
15875.5860
MS
91.1439
380.3525
87.2209
121.5924
93.9212
40.9671
28.2335
18.0286
35.2041
F
.7495 (N.S.)
3.1280*
.7173
2.6679 (N.S.)
1.1637 (N.S.)
.8019 (N.S.)
.5121 (N.S.)
*p <.05
Table 19 shows the cell, column and row means for the Data 1 (First Task)
heart rate responses. Since there was no main or interaction effects for
the anxiety measure, these data are collapsed across the anxiety variable.
Neuman-Kuhls1 analysis of column means showed that although the F test was
significant, the differences between noise condition, heart rate means was
not large enough to produce a significant multiple comparison. Row mean
analysis indicated that mean heart rate for one of the Difficult Task
groups (T,) was significantly lower (57.54 BPM) than any of the other task
groups. This again apparently reflects the operation of uncontrolled
individual differences. There is no a priori reason to expect T^ versus
TO differences since both groups performed the same task.
39
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TABLE 19. HEART RATE MEAN RESPONSE - FIRST TASK
Noise condition
Task Q
E 59.53
E 75.14
D 63.52
D 56.24
IN
63.17
62.17
67.25
60.11
CN
71.38
63.10
68.58
56.29
X
64.69
66.80
66.45
57.54
(T2)
-------
Table 20 shows the heart rate means for the second tracking task. Since
there were no main or interaction effects for either anxiety or noise
condition, these data are collapsed across both variables.
TABLE 20. HEART RATE MEAN RESPONSE - SECOND TASK
E
X 63.84
T2
Tl
Tl -
T2
T3
T4
D
70.01
T4
T2
4.70
E D
68.41 59.14
T3 Tl
T3 T4
9.27* 10.87*
4.57 6.17
1.6
— — — —
q2 = 7.00; q3 = 8.42; q4 = 9.26; £ <.05
The T, group mean heart rate was significantly lower than either the Tg
or T, group. These data indicate significantly lowered heart rates for
a difficult task group preceeded by a difficult task or an .easy task.
Again, regardless of prior task, as long as the second task was difficult,
heart rates tended to be lower.
REGRESSION ANALYSES
Since track error, heart rate, and Stroop chart reading times reflected
the effects of either task difficulty or noise conditions, it was deemed
appropriate to assess the contribution of these independent variables to
the dependent variables in terms of the proportion of variance accounted
for.
41
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ANALYSIS STRATEGY
Multiple stepwise regression analysis was used to evaluate the contri-
bution of the independent variables to the dependent variables. The
major independent variables were: Anxiety Level, Noise Condition, and
Tracking Task Difficulty. These variables were manipulated either by
selection and assignment of subjects (Anxiety level) or directly as a
function of the experimental conditions (Noise Condition and Tracking
Task Difficulty). Two additional variables, Eye Dominance and Time,of Day,
known to be correlated with motor skills and tracking task performance
were controlled statistically by adding them to a second regression analy-
ses along with the three major independent variables. The restricted
model includes only the major independent variables. The full model in-
cludes the former plus those variables whose effects are to be controlled
statistically. An analysis of regression comparison of the two models
yields the same results as the traditional analysis of covariance (Roscoe,
(
1975). The coefficients of determination for each model are compared
using an £ test. If the F_ is significant, the covariates contribute a
significant proportion of variance to the dependent variable. Using this
technique six regression analyses were performed for Data 1 (First Tracking
Task); a full and a restricted model for each of the three dependent variables,
TRACK ERROR DATA 1 (RESTRICTED MODEL)
Table 21 reflects the summary data for the restricted model regression
analysis. The three major independent variables account for a signifi-
cant (£= 18.4727, df_= 3,236, £<.01) but relatively small (R2 = .19)
proportion of the Tracking Error variance. Since Data 1 represents the
first of each subjects two tracking tasks, the contribution of task
p
difficulty alone to tracking error (R = .19) represents the difference
between the easy and the difficult tracking task.
Table 21. ANALYSIS OF VARIANCE FOR THE RESTRICTED REGRESSION MODEL FOR
TRACKING ERROR - (DATA 1)
R
R2
S.E.
.43598
.19008
12.02125
SV
Regression
Residuals
Total
DF
3
236
239
SS
5290.70
22543.62
27834.32
MS
1763.56
95.52
F
18.4627*
42
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Table 22. REGRESSION SUMMARY TABLE FOR THE RESTRICTED MODEL FOR TRACKING
ERROR - (DATA 1)
Variable
Task difficulty
Noise condition
Anxiety
Constant
R
.40506
.43585
.43598
R2
.16407
.18997
.19008
R2 Change
.16407
.02589
.00011
r
.40506
.16754
.06731
b
4.06
2.61
.06
.37
TRACK ERROR-DATA 1 (FULL MODEL)
Table 23 shows the regression analysis of variance for tracking error for
the full regression model which includes time of day and eye dominance.
Table 23. ANALYSIS OF VARIANCE FOR THE FULL REGRESSION MODEL FOR TRACKING
ERROR - (DATA 1)
R
R2
S.E.
.45537
.20736
11.93057
SV
Regression
Residuals
Total
DF
4
235
239
SS
5771.86
22062.46
27834.32
MS
1442.96
93.88
F
15.3703*
Table 24 shows the summary data for the full model.
Table 24. REGRESSION SUMMARY TABLE FOR THE FULL MODEL FOR TRACKING ERROR
(DATA 1)
Variable9
Task difficulty
Noise condition
Time
Eye dominance
Constant
R
.40506
.43585 '
.45213
.45537
R2
.16407
.18997
.20442
.20736
2
R Change
.16407
.02589
.01445
.00295
r
.40506
.16754
-.12766
-.10133
b
4.52090
2.59801
-.83190
-.99007
5.45925
aThe contribution of anxiety to residual reduction was less than the
"F to enter" the equation, therefore anxiety does not appear.
43
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As described, the restricted and full regression models were compared
using an F_ test. The full model was not significantly different from the
restricted model. (£ = .8537 d_f = 2,238) indicating that neither eye
dominance nor time of day contributes significantly to the reduction of
residual variance.
EMG - DATA 1 - RESTRICTED AND FULL MODELS
The independent variables in either the restricted or full model did not
contribute significantly to electromyographic changes. The restricted
model multiple R was .14960 which was not significant (£ = 1.8024, df_ =
3,236, 2<.05). In the full model, the multiple R was .16824 which was
not significant (£ = 1.7112, df= 4,235, £ <.05)
HEART RATE- DATA 1 - RESTRICTED AND FULL MODELS
Although task difficulty and noise condition produced significant changes
in heart rate means as evidenced by the analysis of variance, the inde-
pendent variables in both the full and restricted models were not signi-
ficantly related to heart rate changes. For the restricted model, the
multiple R with heart rate was .08489 (£ = .8602, df = 2,237- £< .05).
In the full model, the multiple R was .14953 (£ = 1.799, df = 3,236, £<.05.)
STROOP RESPONSES - RESTRICTED MODEL
The independent variables in the restricted model did not account for a
significant proportion of the variance in reading time. Table 25 shows
the regression analysis of variance.
Table 25. ANALYSIS OF VARIANCE FOR THE RESTRICTED REGRESSION MODEL FOR
STROOP RESPONSE TIME
R =
R2 =
S.E.
.13171
.02301
= 2.43239
SV
Regression
Residuals
Total
DF
3
211
214
SS
45.67
1276.24
1321.91
MS
15.2243-
6.048
F
2.517(N.S.)
The multiple R (.13171) was not significant (£ = 2.68, df = 3,211, £ <.05)
44
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DATA 2 - REGRESSION ANALYSIS
The second tracking task of each experimental session comprised the Data 2
information. Independent variables in the restricted model were not signi-
ficantly related to tracking error nor did inclusion of eye dominance or
time of day improve the relationship (£_ = .6762, df = 2,237 p_ > .05,
F_ = 1.1117, df = 5,234 £ > .05, respectively). Multiple analysis of variance
of these same data however, reflected significant main effects for task se-
quence on tracking error and noise condition on tracking error. It is quite
evident from these data that a significant difference in tracking error was
present for the groups differing in task difficulty of the second tasks.
The student of analysis of variance vis-a-vis regression analysis will recog-
nize the apparent conflict between regression analysis and analysis of
variance across tracking tasks. For Data 1, subjects were relatively consis-
tent in the tracking error performance across noise conditions as evidenced
by the significant multiple R and group means were different across task
difficulty. For Data 2, although, groups differed on their mean tracking
performance as a function of task difficulty as evidenced by the signifi-
cant main effects for task sequence, there was obvious inconsistency in indi-
vidual performance across noise conditions as indicated by the absence of a
significant multiple R.
EMG - DATA 2 - RESTRICTED AND FULL MODELS
Variables in the restricted model were significantly related to EMG changes
(£ = 4.5935, df_ = 3,236, p_ < .01). Table 26 shows the analysis of variance
for regression and Table 27 displays the summary data.
Table 26. ANALYSIS OF VARIANCE FOR THE RESTRICTED REGRESSION MODEL FOR EMG-
(DATA 2)
SV DF SS MS F
R .23816 Regression 3 64.09 21.36 4.5935
R2 .05672 Residual 236 1065.89 4.51
S.E. 2.66571
45
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Table 27. REGRESSION SUMMARY TABLE FOR THE RESTRICTED REGRESSION MODEL FOR
EMG - (DATA 2)
Variable
Task difficulty
Noise condition
Anxiety
Constant
R
.19092
.23513
.23816
R2
.03645
.05529
.05672'
2
R change
.03645
.01884
.00143
r
.19092
-.13385
.03646
b
.46393
-.45992
-.05028
37.72333
The variables involved in the full model were significantly related to £MG
(F = 3.0905, df = 5,234, £ <.01).
Table 28. ANALYSIS OF VARIANCE FOR THE FULL REGRESSION MODEL FOR EMG-DATA 2
SV
DF
SS
MS
R = .24102 Regression 5 65.642 13.1284 3.0905*
R2 = .05809 Residuals 234 1064.348 4.548
S.E.= 2.68170 Total
Table 29. REGRESSION SUMMARY TABLE FOR THE FULL REGRESSION MODEL FOR EMG
DATA 2
Variable Multiple R
Task difficulty
Noise
Anxiety
Eye dominance
Time
.19092
.23513
.23816
.24080
.24102
R Square
.03645
.05529
.05672
.05798
.05809
Simple R
.13385
.13385
.03646
.02583
.00258
Beta
.19779
.13387
.04014
.03562
.01040
46
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The multiple R (.24102) was significant (F_ = 3.09, df = 5,234, p_ < .01
and the variables now account for 5.8% of criterion variance composed
of 5.67% in the restricted model. Comparison of the two models indicates
that addition of time of day and eye dominance does not account for a
significantly increased proportion of criterion variance. (f_ = 1.7618,
df = 2,234; £ >.05)
HEART RATE - DATA 2 - RESTRICTED AND FULL MODELS.
The variables in both the restricted and full models were not significantly
related to the heart rate criterion. For the restricted model, R^= .07332;
£= .4251, df = 3,236, £ >.05 (N.S.). For the full model, R = .19283; F_ =
2.26, df = 5,234, £ >.05 (N.S.).
47
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SECTION VI
DISCUSSION
Data analysis indicate the complexity of the relationships between
physiological and motor skills variables when noise is used in combination
with other variables. There were no significant mean differences between.
groups in electromyographic potential changes as a function of either
noise profile, anxiety condition or task difficulty. Tracking error
responses reflected significant changes as a function of some but not
all the variables. For example, in both tracking tasks, errors increased
when the task was the more difficult rate plus velocity tracking. This
is not an altogether unexpected result. This increase in error was
evident, independent of the anxiety and noise conditions. Within the
anxiety groups however, there were some anomalous results. In the high
anxiety subjects, the two groups which performed the easy task first,
differed significantly but not greatly from each other, as did the two
groups performing the difficult task first. This is clearly an indication
of subject differences operating. Similar significant, but slight dif-
ferences exist in the 40 low anxiety subjects. For the first tracking task,
there were no main effects for either noise condition or anxiety level of
subjects.
Tracking error data from the second task, which includes transfer of
training effects, again shows significant main effects of task difficulty
and also noise condition (high anxiety only). Again, track errors in-
creased for the difficult task. In the high anxiety group, regardless of
prior (first) task, as long as the second task was difficult, error scores
were significantly higher than when the second task was easy.
For the second task, both intermittent (IN) and continuous (CN) noise
profiles resulted in significant decrements in track error performance
compared to a quiet (Q) condition. In high anxiety subjects, IN produced
48
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the greatest decrement; however, within the low anxiety group, the IN
profile did not produce differential effects compared to CN or Q. High
anxiety subjects are apparently more susceptible to the effects of dif-
fering noise profiles than are low anxiety subjects.
The poorest tracking performance occurred when high anxiety subjects were
required to switch from the easy to the difficult task during exposure to
intermittent noise. The best tracking performance was demonstrated by
high anxiety subjects also, who performed the less difficult task under
the quiet (no noise) condition. These results are similar to those
found by Goodstein, Speilburger, Williams, and Dahlstrom, 1955 (Speil-
burger, 1966) where performance of the high anxiety subjects was inferior
to that of the low anxiety subjects for the more difficult tasks and
superior on the less difficult tasks. The results, according to the
authors, would be predicted from Drive Theory. An extension of Drive
Theory might be called the "response interference hypothesis", which
states that task-irrelevant responses, which in some situations may
interfere with efficient performance, are more easily elicited in high
than in low anxiety subjects (Spence, 1956; Taylor, 1956; Taylor, 1959).
According to Child (1954), "high anxiety subjects tend to react emotionally
to many experimental situations, even those in which stress stimulation
is not explicity employed" (Speilburger, 1966).
Previous investigations concerning stress effects on complex task perfor-
mance between anxiety groups have "demonstrated all varieties of relation-
ships, suggesting that these stress conditions are complex in their effects
and interact with a number of variables to determine performance" (Farber,
1955; Lazarus, Deese, & Osier, 1952; Speilburger, 1966). One relatively
consistent finding has been that with certain complex speed tasks, "the
performance of high anxiety subjects will decline earlier on the stress
continuum than low anxiety subjects, and at any given point, be more pro-
nounced". I. G. Sarason and Palola (1960) found that with simpler speed
49
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tasks, the high anxiety subjects improved their performance under stress
(Speilburger, 1966). This finding is consistent with the results of the
present experiment in which high anxiety subjects performed the more simple
tracking task with lower average error scores than the low anxiety subjects
regardless of noise condition. Additionally, I. G. Sarason and Palola
(1960) suggest that "task difficulty was the variable determining the
direction of the effect of stress on performance and hence the nature of
the interaction between stress and anxiety level" (Speilburger, 1966).
This relationship was also evident in the present experiment since there
was a significant interaction between stress, (noise condition) anxiety
and task difficulty. By visual inspection, the significant anxiety,
stress (noise condition), and task difficulty interaction only occurred
during the more difficult tracking task.
STROOP RESPONSES
The average performance of subjects in each cell improved as a result of
the experimental conditions. Stroop Chart reading times were lower after
the experimental session regardless of the independent variable combina-
tion. Intermittent noise produced the largest significant reduction in
reading time. However, the quiet condition produced a significantly
larger reduction than did the continuous conditions. It is not clear
that the noise profile alone, or its mode of presentation are directly
responsible for the reduction. It is conceivable that merely being in
a controlled environment, or attending to a demanding tracking,task
produced the reading time reductions. It is therefore inappropriate,
as a result of these data, to infer noise facilitated performance. The
arousal value of attending to a demanding task, independent ..of noise
stimulation, is indicated by the data in Table 16. When both the first
and second tasks were the more difficult rate or velocity tracking, the
largest facilitation of Stroop Chart reading occurred (2.79 - cell;
1.37 - group average).
50
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HEART RATE RESPONSES
Heart rates changed by less than one full beat per minute as a func-
tion of the differing noise environments. This result has no partic-
ular practical significance. Across task difficulty groups there was
a significant decrease in heart rate as a function of performing the
more difficult rate plus velocity tracking in one group performing
this task. This result is seen as spurious in that a similar group
of subjects performing the same task did not show a like decrease
(Table 19).
Results of previous studies of heart rate changes following the pre-
sentation of a noxious stimulus (loud tone) show similarly conflict-
ing findings. Epstein (Speilburger, 1972) concludes that "the find-
ings on heart rate are, to say the least, surprising." Epstein found
marked accelerative and decelerative reactions in different individuals
which cancel each other out. "It is apparent that heart rate is a
complexly controlled system, and that strong stimulation does not
always produce acceleration." Similar results are also reported by
Eason et. al. (1964). Interestingly, a possible explanation for the
lack of significant heart rate changes as a function of differing
noise environments, particularly the intermittent noise condition
may coincide with Epstein's conclusion that "it is only after the
stimulus is familiar within the experimental context, such as after
it has been presented a number of times in the count-up, that its
presentation by surprise is as apt to produce decelerative as
accelerative reactions." Epstein further states that a "possible
explanation of the differential response to a familiar and unfamiliar
strong stimulus presented by surprise is that in the former case some
degree of habituation has already taken place, and the stimulus, there-
fore, is less threatening, and less apt to evoke orienting reflexes, with
corresponding heart-rate deceleration. An unfamiliar strong stimulus, on
the other hand, is apt to evoke defensive reflexes, with corresponding
heart-rate acceleration."
51
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Finally, in considering the inconsistencies of human physiological
response to stress, Cattel & Nesselroade (Speilburger, 1972) state that
there is an overlap of anxiety and stress in the physiological area,
and in the awareness of pressure and tension and "it is evident that
a person can be stressing himself most when he is calm, concentrated,
and successfully working hard."
Because subjects were run at different times on experimental days and
because of the different visual orientation of the subjects to the
display depending on experimental booth, these two factors were
statistically controlled by adding them to a regression equation with
the major independent variables. In effect, these former factors were
considered as covariates. Coefficients of determination for each
regression model were compared by an F Test. Addition of the time of
day and eye dominance variables did not significantly reduce residual
variance for any of the dependent measures for either the first or second
task. Further, when considering only the major independent variables
(anxiety,, noise condition and task difficulty), significant but relatively
small proportions of variance were accounted for. For example, in Data 1,
the major independent variables accounted for only 19% of the variance
in track error, 2% of the variance in Stroop response time, 19% of the
variance in EMG changes and 6% of the variance in heart rate. In practical
terms therefore, the independent variables, though they may have resulted
in significant mean differences, do not contribute much to our under-
standing of the differences. Instead, the data suggest that no simple
empirical or theoretical statement about the influence of noxious stimu-
lation (noise) can be made and that in predicting its effect on performance,
account must be taken of such variables as the nature of the task materials,
the nature and direction of task transfer effects, the manner in which the
noxious stimulation or its anticipation is introduced into the situation,
the instructions subjects are given about its significance, the trait
anxiety level of the subjects, and the number and intensity of the noxious
stimulation. Previously, such variables had not been systematically in-
vestigated and the interactions that exist among them have been little
understood, if at all.
52
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ANXIETY AS A MODERATOR OF NOISE EFFECTS ON HUMAN PSYCHOMOTOR PERFORMANCE
Previous findings by Elbiet and DiScipio (1971), that extroverts
were found to display greater decrements in psychomotor performance
while experiencing noise stimulation than introverts, were not fully
supported by the results of this study. Instead, it appears that
the psychomotor performance of those assigned to the high anxiety
group was effected to a greater extent by the noise environment
than was the performance of the low anxiety subjects. The relation-
ship between A-STAIT, A-TRAIT anxiety and introversion is described
in the MMPI Handbook (Dahlstrom et. al, 1973) High "0" scale males
(introverts), display many of the personality traits of high anxiety
subjects with marked insecurities and worries. Anxiety, as measured
by the IPAT STAI-A scale, simiarly involves feelings of tension,
nervousness, worry and apprehension with high scores reflecting
states of intense apprehension and fearfulness approaching panic
and low scores reflecting feelings of calmness and serenity.
The finding that level of anxiety is a variable that differentially
effects psychomotor performance (track error) is both interesting
and significant in terms of the possible consequences regarding the
effects of moderate noise on human behavior. Watson (1930) postu-
lated only two innate fear stimuli: loss of support and loud noises.
It would seem inappropriate to classify 84dBA as a loud noise. The
question that then arises is, what is it about moderate noise as a
stimulus that interacts with and degrades the psychomotor performance
of subjects who demonstrate an elevated (but not pathological) level
of anxiety? That is, what possible mechanisms are operating that
would allow us to understand the relationship between anxiety, noise,
and psychomotor performance. We believe that fright (as an explanation)
is a class of fear-related emotions which are relatively stimulus
bound, moreover, fright reactions may differ among themselves depending
53
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on-their objects (Speilburger, 1972). It does not appear likely
that there is, in this experiment, the necessary appropriate stimuli
to warrant the development of the emotion of fear. Substantial
efforts were made to reduce fear related stimuli (i.e., pleasant
background music was played prior to the start of the experiment,
instructions were low keyed, relaxed and designed to reduce the impact
of upper body disrobing and EKG electrode application, and fellow
students normally assisted during the experiment.
Lazarus & Averill (Speilburger, 1972) state that "there can be little
doubt that theorizing with respect to anxiety is still in the
elementary stages, somewhat like the concept of air in 18th century
chemistry." However, there is a significant body of literature on
the subject of the causes and consequences of anxiety (Speilburger,
1972) which classifies anxiety into three general categories in
terms of etiology. They are (1) Primary Overstimulation (2) Response
Unavailability and (3) Cognitive Incongruity.
According to the neobehavioristic learning theorists, pain is the
unconditioned stimulus for fear and anxiety. Organisms have an
upper limit to stimulation. Overstimulation is associated with
feelings of being overwhelmed and bombarded with stimuli, corresponding
to the statement, "Stop it, I can't stand it anymore." Epstein
(Speilburger, 1972) states that "organisms are energy systems that
are responsive to energy inputs and must maintain their levels of
excitation, no less than their other internal states, within
homeostatic limits in order to survive. Small increments in arousal
cause the individual to attend to his environment and to register the
stimulus associated with the increment. Large increments in arousal
cause a reduction in receptivity to and registration of stimulation
and are experienced as unpleasant. It appears that arousal is con-
trolled through inhibition, which is intimately associated with the
54
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establishment of expectances. Through the process of inhibition,
stimuli such as moderately loud noises that were initially attended
to only because of their energetic properties become registered
or "learned1, and thereafter can be responded to in terms of their
cue properties." It may be that for low anxiety subjects, the
introduction of moderate noise as an environmental stimulus serves
as a general facilitator of performance because it causes the
organism to attend to its environment and raises its general activation
level. Evidence for this can be seen in the consistent decrease in
the time needed to successfully respond to the post-experimental
STROOP color-word chart. In every cell, subjects read the color-
words faster after being exposed to the experimental condition.
This finding is contrary to other studies involving high sound pressure
levels (Sommer & Harris, 1972) and indicates the arousing effect of
the experimental condition alone. Several studies (Scott, 1966; Malmo,
1957) show that performance increases as a function of activation level
up to a point and then declines as general arousal is further increased.
This concept would be in agreement with Di Scipio's (1971) finding that
white noise facilitates psychomotor response for an optimal period of
time, after which decrements were observed. However, with subjects dis-
playing higher than normal anxiety with concomitant internal feelings of
tension, nervousness, worry and apprehension, further increases in external
sources of stimulation, such as moderate noise, may well be debilitating
to some degree due to the requirement that the organism cope with
heightened levels of general arousal, particularly since "increases in
arousal are produced by any stimulation, internal or external" (Epstein, 1967)
i
According to Lazarus (1966), anxiety is viewed as a state in which the
individual experiences diffuse arousal but is unable to direct that
arousal into purposive action. Moreover, "it is the arousal and the
defenses against it, and not the anxiety, and the defenses against it,
55
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that are responsible for the primary symptoms of the behavior dis-
orders. In this respect, it has been established that experimental
neurosis can be evoked by conditions that are arousing, but not
frightening, such as difficult discriminations and the disconfirmation
of established expectancies."
The notion that anxiety is more noxious to an organism than fear appears
to have adaptive value. Epstein notes that "one of the most common
distinctions made between anxiety and fear is that in fear the source
of the threat is known and in anxiety it is unknown." Epstein further
states that fear is an avoidance motive. If there were no restraints,
internal or external, fear would support the action of flight. As
mentioned previously, anxiety can be defined as unresolved fear, or
alternately, as a state of undirected arousal following the perception
of threat. Given a crisis, it is important that the organism rapidly
assess the situation and take immediate action. In the course of
evolution, man, as an animal, has also developed the ability to, when
danger is perceived, generate a heightened state of arousal that
provides nonspecific preparation for flight or fight. "Thus, it is
adaptive for the state of diffuse arousal to be an acutely unpleasant
one, and for it to become more so in time, thereby providing the animal
(man) with a powerful incentive to resolve indecision and to select
a course of action."
Thus, it would appear that noise need not be so loud as to generate
fear to be arousing or anxiety provoking. Diffuse arousal, resulting
from moderately noxious stimulation (noise) coupled with heightened
states of trait anxiety should, according to Epstein, be perceived by
the organism as acutely Unpleasant, thereby providing the input to
select a course of action. However, in the case of environmental
noise, particularity intermittent, uncontrollable noise, there may be
no appropriate course of action available to the organism, resulting in
unresolved and undirected arousal, anxiety and frustration.
56
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Epstein (Speilburger, 1972) concludes by noting that the relationship
between frustration and anxiety is evident. "Frustration has been
accorded special significance in psychological theorizing because
of its relevance for maladaptive behavior and the normal stresses of
everyday living. The consequences of heightened states of diffuse
arousal have been observed to include restlessness and tension,
aggression, apathy, withdrawal, disorganized behavior, regression
and escape." All of which appear to be common symptoms of the
"urban din" we must live in.
An additional mechanism by which environmental noise may interact with
the second basic source of anxiety can be described as Response Un-
availability. In this experiment, as in real life, the subject
could not control the noise. It was just there. Mandler (1961)
states that "in a state of arousal, the organism who has no behavior
available to him, who continues to seek situationally or cognitively
appropriate behavior is 'helpless' and also may consider himself, in
terms of the common language, as being in a state of anxiety."
Mandler further states that "any such arousal - peripheral or central,
environmentally or behaviorally induced - will lead to a feeling of
helplessness when no cognitive or behavioral sequence appropriate
to the situation is available, or when no substitute sequence or
escape from the field provides a means of terminating the state
of arousal."
This raises the question of what would happen if escape from a source
of threat (noxious stimulation) were blocked. Studies by Seligman and
his colleagues (Speilburger, 1972) indicate that "a state of helplessness
produced by unavoidable noxious stimulation is extremely debilitating
and tends to be self-maintaining." This factor may also apply in this
experiment, particularly for those subjects who displayed higher than
normal states of anxiety and who were locked in a sealed acoustic chamber
and subjected to unpleasant stimulation over which they had no control.
57
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A third basic parameter that affects arousal level is Cognitive
Incongruity which is determined by the consistency of one's expecta-
tions and the ability to establish an adequate cognitive model of events
(Epstein, 1967). In the present experiment, subjects were not able
to predict the onset or duration of the intermittent noise. (Event
Certainty, Temporal Uncertainty). There is considerable evidence in the
research literature indicating that in a normal subject population the
violation of expectancies beyond a certain point induces anxiety. "It
would appear that an accurate expectation with regard to the occurrence
of noxious stimulation is generally sought" (Epstein, 1967; Speilburger,
1972). In the case of intermittent noise, the violation of expectancies
is clearly evident, since the experimental results indicate that high
anxiety subjects were more affected by the intermittent noise condition
than by the continuous noise condition, whereas the low anxiety subjects
were not significantly affected by noise conditions at all. Again,
demonstrating the need to specify anxiety level when predicting the
effects of moderate noise on human behavior.
Interestingly, the concept of Cognitive Incongruity may also be a
factor in the apparently anomalous finding that low anxiety subjects
(2nd task) displayed the greatest decrement in track error performance
when transferring from the easy to difficult task under the quiet (no
noise) condition. Under both anxiety conditions, the transfer from the
easy to the difficult task resulted in the greatest track error per-
formance decrement. This finding appears consistent with the concept
of Cognitive Incongruity in that expectations concerning the difficulty
of the tracking task were most severely violated under this condition.
A possible explanation for the low anxiety subject's poor performance
under these conditions (quiet) may be linked to overall activation
levels, i.e., insufficient internal and external stimulation to perform
well under these conditions. This would be consistent with Scott's
(1966) findings concerning activation level and performance previously
mentioned. Several other studies have also suggested an inverted U-shaped
58
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function as describing the relationship between motivation and perfor-
mance (Malmo, 1957, 1958, & 1959) generally by appealing to the notion
that "difficult" tasks elicit more emotionality than "easy" ones,
particularly in high anxiety subjects. Such studies have generally con-
firmed the notion that "subjects who were low in both anxiety score and
experimentally manipulated stress and those who were high in both were
poorer in performance than members of the other two groups" (Speilburger,
1966).
•Normally, anticipation is of great importance for research on anxiety
(and stress in general), due to the emerging cognitive appraisal by
the person of the significance of the event. Breznitz (Speilburger,
1972) speaks of the "incubation of threat", observing in his research
and that the longer subjects had to wait, the greater the stress, as
measured by heart rate, just prior to shock. Breznitz found that most
of the stress reaction (as measured autonomically) took place during
the. anticipatory period, with little further increment during the noxious.
stimulus period itself. Furthermore, a dissertation by Folkins (1970)
showed that degree of stress varied significantly as a function of antici-
pation as time increases from 5-30 seconds, with a further rise up to 1
minute, with a drop in disturbance from 3-5 minutes - though it rose
slightly at 20 minutes. In the present experiment, baseline autonomic
data was taken during the 2nd minute period, followed by 2 minutes of
instructions, with the onset of the noise following during the 4 to 9
minute period, thereby elevating the baseline response and depressing
the experimental condition response. It appears, according to Folkins,
that with 3-5 minutes to appraise the situation, "subjects are better able
to develop self-assuring coping responses, and therefore display less
stress." These findings may well have contributed to the lack of
practical significance between baseline and experimental condition phys-
iological arousal levels in the present experiment.
59
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Finally, interruption of cognitive processes appears to generate a
state of heightened arousal (Mandler, 1961). Dr. Gordon Davis,
University of California Medical School at Davis, provides a
"clinical" description of interruption (due to noise) in its purest
form (Speilburger, 1972). The case study of a 9 year old boy is de-
scribed - when noise or activity going on at home reaches a certain
point - an explosion of behavior results - "he may go out and ride
his bicycle to get away from it," apparently as the result of a
deficient ability to carry out a cognitive plan when experiencing
interruption or environmental disorganization. It may be that one
of the primary effects of moderate environmental noise is its
interrupting, disorganizing quality, which would be particularly
debilitating to those subjects who already experience substantial
internal arousal as the result of elevated trait anxiety levels.
In sum, "human beings are motivated to structure their world and to
find ways of dealing with it largely because of the characteristics
of their anxiety system. At low levels of anxiety, the process is
a constructive one, leading to expanded awareness and increasing
control of nature. At high levels, it produces defensive retrenchment,
including delusional interpretations of events (any explanation is
better than none), and compulsive rituals for dealing with them
(any action is better than none)" (Epstein, 1967; Speilburger, 1972).
Thus, for organisms that already possess heightened internal states
of arousal (high trait anxiety), by adding stimulation from the environ-
ment (noise) that violates or precludes the development of expectancies,
(intermittent noise) it can then be expected that decrements in psycho-
motor performance should result, and as part of the "cost" to the
organism, frustration can also be expected to occur with all of the
negative ramifications that now appear to plague our highly industrial-
ized, urban society.
60
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SECTION VII
REFERENCES
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Attraction for Similar and Dissimilar Others," Journal of Social Psychology,
88, No. 1, pp 151-152 (1973).
3. Campbell, B. A., "Interaction of Aversive Stimuli: Summation or Inhi-
bition?," Journal of Experimental Psychology, 78, No. 2, pp 181-190
(1968).
4. Child, I. L., "Personality," Annual Review of Psychology, No. 5, pp 149-
170 (1954).
5. Dahlstrom, W. G., Welsh, G. S., and Dahlstrom, L. E., "On MMPI Handbook,
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6. Deese, J., Lazarus, R. S., and Keenan, 0., "Anxiety, Anxiety Reduction
and Stress in Learning," Journal of Experimental Psychology, 46, pp 55-60
(1953).
7. DiScipio, W. J. "Psychomotor Performance as a Function of White Noise
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8. Dyer, F. N., "The Stroop Phenomenon and It's Use in the Study of Perceptual,
Cognitive, and Response Processes," Memory and Cognition, 1, No. 2, pp
106-120 (1973).
9. Eason, R. G., Harter, M. R., and Storm, W. F., "Activation and Behavior.
I: Relationship Between-Physiological 'Indicants' of Activation and Per-
formance During Investigation of Nonsense Syllables Using Differing
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110 (1964).
10. Edsell, R. D., "The Effect of Noise on Some Interrelationships Between
Social Interaction and Anxiety," (Doctoral dissertation, Drexel University)
Ann Arbor, Mich: University Microfilms. No. 734040 (1973).
11. Elliott, C. D., "Noise Tolerance and Extraversion in Children," British
Journal of Psychology, 62, No. 3, pp 375-380 (1971).
61
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12. English, H. B., and English, A. V., "A Comprehensive Dictionary of Psycho-
logical Terms, David McKay Co., New York (1958).
13. Epstein, S., "Toward a Unified Theory of Anxiety," In B. A. Mather (Ed.),
Progress in Experimental Personality Research, Academic Press, New York,
Vol 4, pp 1-89 (1967).
14. Epstein, S., "The Nature of Anxiety with Emphasis Upon It's Relationship*to
Expectancy," In C. D. Spiel burger (Ed.), Anxiety, Current Trends in Theory
and Research Vol II, Academic Press, New York, pp 291-337 (1972).
15. Farber, I. E., "The Role of Motivation In Verbal Learning and Performance,"
Psychological Bulletin, No. 52, pp 311-327 (1955).
16. Fechter, J. V., "The Effects of Noise on Human Learning," (Doctoral dis-
sertation, University of South Dakota) Ann Arobr, Mich: University Micro-
films, No. 72-32728 (1972).
17. Folkins, C. H., "Temporal Factors and the Cognitive Mediators of Stress
Reactions," Journal of Personality and Social Psychology, No. 14, pp 173-
184 (1970).
18. Glass, D. C., and Singer, J. E., "Experimental Studies of Uncontrollable
and Predictable Noise," Representative Research in Social Psychology, 4,
No. 1, pp 165-183 (1973).
19. Goodstein, L. D., Spielburger, C. D., Williams, J. E., and Dahlstrom, W. G.,
"The Effects of Serial Position and Design Difficulty on Recall of the
Bender-Gestalt Test Designs," Journal of Consultive Psychology, No. 19,
pp 230-234 (1959).
20. Grether, W. F., "Two Experiments on the Effects of Combined Heat, Noise,
and Vibration Stress," Aerospace Medical Research Laboratory, Wright-
Patterson AFB, Ohio. No. AMRL-TR-71-113 (1972).
21. Gulian, E., "The Factor 'Difficulty of the Task1 and It's Influence Upon
Performance Level Under Noise Conditions," Revista de Psihologie, 18, No.
3, pp 323-330 (1972).
22. Gulian, E., "Psychological Consequences of Exposure to Noise, Facts and
Explanations," Proceedings of the International Congress on Noise as a
Health Problem, U. S. Environmental Protection Agency, Washington, D. C.,
No. 550/9-73-008.
23. Harcum, E. R. and Monti, P. M., "Cognitions and Placebos in Behavioral
Research in Ambient Noise," Perception & Motor Skills, 37, No. 1, pp 75-
79 (1973).
62
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24. Harris, C. S., and Schoenberger, R. W., "Combined Effects of Noise and
Vibration on Psychomotor Performance," US.AF AMRL Technical Report, No. 70-
14, pp 24 (1970).
25. Jansen, G., and Hoffman, H., "Noise-Induced Changes in Fine Motoricity
and Noise Induced Feelings of Annoyance Which Depend Upon Definite Person-
ality Dimensions," Army Foreign Science and Technology Center, Charlottes-
ville, VA., No. FSTC-HT-23-236-71 (1971).
26. Jauhiainen, T., Kohonen, A., and Jauhiainen, M., "Combined Effect of Noise
and Neomycin in the Cochlea," Acta Oto-Laryngologica, 73, No. 5, pp 387-
390 (1972).
27. Kryter, K. D., "The Effects of Noise on Man," Academic Press, New York (1970).
28. Kumar, P., -and Mathur, C. N., "Sex and Noise Destractibility," Indian Journal
of Applied Psychology, No. 6, pp 13-14 (1969).
29. Lazarus, R. S., Psychological Stress and the Coping Response, Vol 3, McGraw-
Hill, New York, pp 96-102 (1966).
30. Lazarus, R. S., and Averill, J. R., "Emotion and Cognition: With Special
Reference to Anxiety," In C. D. Spielburger (Ed.), Anxiety, Current Trends
in Theory and Research, Vol II, Academic Press, New York, pp 242-283 (1972).
31. Lazarus, R. S., Deese, J., and Osier, S. F., "The Effects of Psychological
Stress Upon Performance," Psychological Bulletin, No. 49, pp 293-317 (1952).
32. Malhur, C. N., "Age as a Factor in Noise Distractibility," Manas, 19, No. 1,
pp 31-33 (1972).
33. Mai mo, R. B., "Anxiety and Behavioral Arousal," Psychological Review, No. 64,
pp 276-287 (1957).
34. Malmo, R. B., "Measurement of Drive: An Unsolved Problem in Psychology,"
In M. R. Jones (Ed.), Nebraska Symposium on Motivation, Lincoln, Nebraska,
University of Nebraska Press, pp 229-265 (1958).
35. Malmo, R. B., "Activation: A Neuropsychological Dimension," Psychological
Review, No. 66, pp 367-386, (1959).
36. Mandler, G., "Helplessness: Theory and Research In Anxiety," In C. D. Spiel-
burger (Ed.), Anxiety. Current Trends in Theory and Research, Vol II, Academic
Press, New York, pp 360-378 (1972).
37. Mason, R. K., "The Influence of Noise on Emotional States," Journal of Psycho-
mat ic Research, 13, No. 3, pp 275-282 (1969).
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38. Myers, 0. L., Fundamentals of Experimental Design. Allyn and Bacon, Boston
(1966).
39. Renshaw, F- M., "The Combined Effects of Heat and Noise in Work Performance,"
Dissertation Abstracts International, 33, No. 8-B, pp 3699 (1973).
40. Roscoe, J. T., Fundamental Research Statistics for the Behavioral Sciences,
Holt, Rinehart and Winston, Inc. (1969).
41. Sarason, I. G., and Palola, E. G., "The Relationship of Test and General
Anxiety, Difficulty of Task, and Experimental Instructions to Performance,"
Journal of Experimental Psychology, No. 59, pp 185-191 (1960).
42. Scott, W. E., JR., "Activation Theory and Task Design," Organizational Be-
havior and Human Performance, No. 1, pp 3-30 (1966).
43. Spielburger, C. D., Anxiety and Behavior, Academic Press, New York (1966).
44. Spielburger, C. D., Anxiety, Current Trends In Theory and Research, Vol II,
Academic Press, New York (1972).
45. Spence, J. T., and Spence, K. W., "The Motivational Components of Manifest
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Behavior, Academic Press, New York (1966).
46. Spence, K. W., Behavior Theory and Conditioning, Yale University Press, New
Haven, Conn. (1956).
47. Stephans, S. D., "Personality and the Slope of Loudness Function," Quarterly
Journal of Experimental Psychology, 22, No. 1, pp 9-13 (1970).
48. Taylor, J. A., "Drive Theory and Manifest Anxiety," Psychological Bulletin,
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49. Taylor, J. A., "Manifest Anxiety, Response Interference and Repression,"
Experimental Foundations of Clinical Psychology Symposium, University
of Virginia Medical School, April (1959).
50. Watson, J. B., Behaviorism (2nd ed.), Norton & Company, New York (1930).
64
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SECTION VIII
GLOSSARY
Activation Level - The preparation or the tendency toward action. The
level of activation of a whole system is the degree of tension.
Anxiety - A feeling of threat, especially of a fearsome threat, without the
person's being able to say what he thinks threatens.
Anxiety, Free - A chronic state of anxiety which attaches to almost any
situation or activity of the individual.
Anxiety, Trait - A chronic state of anxiety which remains unattached and
constant over all situations and activity of the individual.
A-STATE - State anxiety may be conceptualized as a transitory emotional
state or condition that varies in intensity and fluctuates over time.
A-TRAIT - Trait Anxiety refers to relatively stable individual differences
in anxiety proveness.
Eye Dominance - A tendency to fixate objects with one eye rather than with
both and to depend primarily upon the impressions of that one eye, though
the nbn-preferred eye is not blind.
Extravert - A person who tends strongly to the attitude of extraversion.
Extraversion has three aspects to include outward oriented interests, ease
of social adjustment and open behavior.
Homeostasis - The maintenance of consistency of relationships or equilibrium
in the bodily processes, whether physical or psychological. Any departure
from the equilibrium sets in motion activities that tend to restore it.
Inhibition - A mental state in which the range and amount of behavior is cur-
tailed, beginning or continuing a course of action is difficult, and there
is a peculiar hesitancy as if restrained.
Introvert - A person who tentis strongly to the attitude of introversion. In-
troversion has three aspects to include inward oriented interests, difficulty
of social adjustment and secretive behavior.
IPAT - Institute for Personality Assessment and Testing.
Neurosis - A mental disorder ill-defined in character but milder than psychosis,
Neurosis are usually characterized as disfunction within the individual, as
opposed to between the individual and his environment.
65
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STAI-A - State-Trait Anxiety Inventory - Scale A - consists of twenty
statements that ask people to describe how they generally feel.
Stroop Chart - A collection of stimulus words describing colors which are
printed in a visual color other than the word. This maximizes the inter-
ference between the written and reproduction colors.
66
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SECTION IX
APPENDICES
Page
A. System Inputs - Block Diagram 68
B. System Outputs - Block Diagram 69
67
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INPUTS
BOOTH 1
BOOTH 2
oc
*
85
i~r
Q O
TR-20
(ANALOG COMPUTER)
I
REAL TIME SHAPED NOISE
INTEGRATED
ERROR
POWER AMP
RANDOM
SIGNAL
GEN.
I
FREQ. SHAPER
I
TAPE
68
-------
EMG
DC
Mv
SIGNAL
INT/REC
CIRCUIT
I
UNIVERSAL
PRE-AMP
OUTPUTS
EKG
TRACKING
VOLTAGE
BIO-TACK
DC VOLTAGE
chir2 ch, 1
TR-20
(ANALOG COMPUTER)
ABSOLUTE ERROR
TAPE
REC.
TCG
NOISE CONDITION |
chl 3 chl 4 chll4
FM RECORDER
69
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TECHNICAL REPORT DATA
fflease read /nLtntcti
------- |