Effects of low or moderate noise on performance

Journal of Sound and Vibration (1991) 151(3), 429-436

EFFECTS OF LOW OR MODERATE NOISE ON PERFORMANCE T. YOSHIDA Division of Environmental Hygiene, Department of Architectural Hygiene Engineering and Housing, The Institute of Public Health, Tokyo, Japan (Received 11 July 1991) The aims of this study were to confirm the contribution of factors related to performance and to determine dose-effect relationships between noise levels and performance. Experiments including a choice reaction time task and a figure counting task demonstrated that noise at low or moderate levels could affect performance. It was also found that reaction times were shortest at 55 dB(A) and longest at 45 dB(A) and 75 dB(A). As regards the figure counting task, an inverse tendency was observed in the reaction time task. These results suggest a new model for a dose-effect relationshi between noise (x) and performance (y) that yields a type of slow manifold (y = (1/3)x P-4x). The model indicates that broadband noise above 55 dB(A) (x= -2) causes arousal and that broadband noise above 73 dB(A) (x= +2) causes over-arousal, implying that arousal can cause a rise or fall in performance depending upon the type of task in question.

1. INTRODUCTION

It is generally considered that noise can cause adverse effects on performance. However, some researchers who reviewed results in this area showed them to be confusing and inconclusive, despite the many research efforts conducted over a period of 50 years [l-3]. There may be many reasons to explain why no firm conclusions have been reached. Some researchers have attempted to clarify factors related to performance, but these factors are complex [4]. Some have proposed theories of effects of noise on performance, although none are sufficient to interpret the results in this area [l-3]. Koelega et al. [3] showed that many studies had serious methodological flaws and suggested that dose-effect relationships should be clarified, as well as many factors related to performance and noise. Furthermore, levels of noise should be carefully studied [5]. In earlier research efforts, effects of noise higher than 70 dB(A) (noisy condition, N) were compared with those of noise lower than 70 dB(A) (quiet condition, Q) [6]. There seems to be a tacit supposition that noise may have increasing or decreasing adverse effects on performance in proportion to increasing levels of noise, and that noise lower than 70 dB(A) may have no effects. As effects of noise lower than 70 dB(A) have not as yet been clarified, results obtained from such comparisons may not be dependable. Thus effects of noises both lower and higher than 70 dB(A) should be examined for determining any dose-effect relationships between noise and performance. If dose-effect relationships between low or moderate levels of noise and performance were obtained, theories of noise effects on performance would become more clear. Some research has been directed toward the effects of low or moderate levels of noise on performance [ 1, 71. These levels are very important in environments such as schools, offices and the home. The present study was aimed at determining any dose-effect relationships between performance and low or moderate level noise ranging from 45 dB(A) to 80 dB(A) and at 429 0022460X/91/240429+08 $03.00/O

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confirming the contribution of factors related to performance. Performance in this study was judged by a choice reaction time task and a figure counting task. A new model for effects of noise on performance is also discussed.

2. EXPERIMENT 2.1. CONDITIONS AND PROCEDURE Thirteen noise conditions were examined (see Table I), such as continuous broadband noise of 50, 55, 60, 65, 70, 75 and 80 dB(A), road traffic noise of 60, 65, 70, 75 and 80 dB(A), L,,(5 min), and background noise of 42-45 dB(A), L&5 min) which represented control conditions. Frequency characteristics of the noise are shown in Figure 1. Each noise condition was assigned in random order in 13 experimental sessions for each subject during one day. An intra-subject design was employed in this study, as there were too few subjects to allow an inter-subject design. 1

TABLE

Noise conditions Levels

Noise

4245 (dB(A), Leq(Smin)) 50, 55, 60, 70, 75, 80 (dB(A)) 60, 65, 70, 75, 80 (dB(A), L,,(5 min))

Background noise Broadband noise Road traffic noise

Eight male students participated as subjects. All had normal hearing and vision, and had no previous experience of the tasks used in this experiment. No information about the effects of noise on performance was given to the subjects. They were paid for their participation. For the reaction time task, the subjects were required during 70 trials for each noise condition to tap one of three keys connected to a yellow, green or red lamp, respectively, as rapidly and accurately as possible after one was presented at roughly 4 s intervals without prior warning. The reaction time was measured with an accuracy of a millisecond. The figure counting task had a duration of about 5 min and was conducted following a short rest after the reaction time task, although the order of the tasks was reversed for half of the subjects. In this task, the subjects were required at their own speed to search for and count numbers of one kind among four kinds of symbols randomly distributed in

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figures presented on a screen. The total time for counting was measured to an accuracy of 1 s and the numbers of symbols found were also recorded. These paired tasks were conducted with 10 minute rest intervals from lo:00 to 16:30 during one day. The subjects were given two rest periods of 1 hour at lunch time and afternoon tea, respectively. Average, standard deviation and coefficient of variation values were employed as measures of the reaction time task. Total time for counting symbols, omitted numbers of symbols and effective time (total time/numbers found) were employed as measures of the figure counting task. 2.2. DATA ANALYSIS Data were analyzed by a multiple regression method for dummy variables (Hayashi’s quantification method, Type 1 [S]), expressed in the form A = 1 C W) i k

x($)7

(1)

where d(jk) = 1 if data fall into a k category, or d(jk) = 0 if data fall into another category other than k in each predictor variable (ai factor). The variable denoted as A is a criterion variable, and x(jk) is a predictor variable in the k category ofj factor. Three factors of subjects (Sub), experimental sessions (Ses) and noise levels (N) were chosen as predictor variables in preliminary analyses. All analyses were conducted respectively for each measure of both tasks. To confirm the usefulness of each factor, a doubly adjusted multiple correlation coefficient (R’) was used as a criterion, which is defined in the equation [9]. R;==R;-

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(1 -R;),

wherep is apth predictor variable employed in the analyses and RP is a multiple correlation coefficient. If R; > R;_, , the pth predictor variable that corresponds to selection at F> 2 in the F-test [lo] is statistically useful. The first predictor variable was (Sub). The second predictor variable was (Ses) and the third was (N).

3. RESULTS 3.1. CONTRIBUTION OF FACTORS Changes in the doubly adjusted multiple correlation coefficients in relation to numbers of the predictor variables are shown in Table 2 for the reaction time task and in Table 3 for the figure counting task. These tables indicate that all factors are statistically useful in all cases of the analyses. 2

TABLE

Changes in the doubly tijusted

multiple

correlations for the reaction time task

Cases of factors employed Measures

Sub

Sub, Ses

Sub, Ses, N

Average time Standard deviation Coefficients of variation

0.866 0.629 0.513

0.904 0.660 0.533

0.912 0,732 0.638

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TABLE 3 Changes

in the doubly adjusted multiple correlations for counting task

the jigure

Cases of factors employed r-

Measures Time for counting Omitted numbers Effective time

Sub

Sub, Ses

Sub, Ses, i

0.730 0.592 0.654

0.802 0.629 0.703

0.820 0.668 0.736

Correlations among (Sub), (Ses) and (iv) and partial correlations (p.c.) of each factor are shown in Tables 4 and 5 for each measure of both tasks as well as multiple correlations (r). TABLE 4 Correlations among factors and partial correlations

(p.c.) for each measure of the reaction time task (r = multiple correlation) (a) Average time (r=O*917) Factors Ses

N D.C.

Sub

Ses

-0.030 0.029

-0.182

0.910

0.571

N

0.329

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Sub

Ses

Ses N

-0.055 0.043

-0.193

p.c.

0.696

0.371

N

0447

(c) Coefficients of variation (r = O-655) Factors

Sub

Ses

Ses N

-0.056 0.036

-0.195

p.c.

0.579

0.296

N

0.437

In Tables 4 and 5 it is indicated that the factor of subjects (Sub) contributes most to these tasks in all cases. The factor of noise (IV) contributes third or second to each measure of both tasks. Correlations among the factors of (Sub), (Ses) and (N) are rather small (n.s., p=O*Ol) in both Tables 4 and 5, except in the case of omitted numbers (see Figure 3(b)). Nevertheless, all partial correlations and multiple correlations indicate high statistical significance in both Tables 4 and 5. This means that noise at low or moderate levels can affect performance in the reaction time task and the figure counting task, and that cross-effects between noise and other factors are rather small. 3.2. DOSE-EFFECT RELATIONSHIP BETWEEN NOISE AND PERFORMANCE The dose-effect relationship between noise and performance is shown in Figures 2 and 3 for each measure of both tasks. The upper sides of each ordinate in these figures means the direction of adverse effects of noise on performance.

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TABLE 5 Correlations among factors and partial correlation

(p.c.) for each measure of the figure counting task (r = multiple correlation)

(a) Total time (r=0*832) Factors Ses N

p.c.

Sub -0.124 -0.118 0.812

Ses

N

0.042 0.541

0,374

(b) Omitted numbers (r = O-692) Factors

Sub

Ses

Ses N

-0.265 0.062

-0.110

p.c.

0.654

0.396

N

0.326

(c) Effective time (r=O.754) Factors

Sub

Ses N

0.023 0.130

p.c.

0.676

Ses

N

-0.152 0.429

0.374

As regards the reaction time task during broadband noise and background noise (see Figures 2(a), 2(b) and 2(c)), the dose-effect relationship shows some different tendencies among the measures. The average reaction time is shortest at 75 dB(A) and longest at 50 dB(A). The standard deviation is smallest at 55 dB(A) or 80 dB(A) and largest at the background noise and 70 dB(A). Coefficients of variation are largest at 60 dB(A) or 80 dB(A) and smallest at 75 dB(A). However, the effects of the traffic noise show a different tendency, especially above 75 dB(A). As for the figure counting task in broadband noise and background noise (see Figures 3(a), 3(b) and 3(c)), the total time for counting is shortest at 75 dB(A) and longest at 65 dB(A) or 80 dB(A). Omitted numbers are largest at 55 dB(A) or 80 dB(A) and smallest at 70 dB(A). The effective time is longest at 60 dB(A) or 80 dB(A) and is shortest at 75 dB(A). Some discrepancies are found between the effects of the broadband noise and the traffic noise.

4. DISCUSSION

In this study, an intra-subject design was employed because the number of subjects was too small for inter-subject design. Nevertheless, the results showed statistical significance for all variables of (Sub), (Ses) and (IV) in all analyses. This demonstrates that low or moderate level noise can affect performance on a reaction time task or figure counting task, although effects on performance of the (Sub) factor were very large. Some discrepancies are found among the measures in each task. The ordinary measure may be the average reaction time for the reaction time task and omitted numbers for the figure counting task. However, the standard deviation and the total time for counting are also affected by noise. Thus, this suggests that coefficients of variation or effective time

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Figure 2. Dose-eITect relationship between noise and performance of the reaction time task (a) Average reaction time; (b) standard deviation of reaction time; (c) coeflicients of variation. 0, P=broadband noise; A, T-trallic noise; 0, B=background noise.

may be a more adequate measure for each of these tasks, although each measure expresses a different quality of performance. In Figure 2(c) and Figure 3(c), the dose-effect relationship seems to form an N shape or an inverse N shape in the broadband noise for each task. However, the dose-effect relationship for traffic noise seems to show some shifts of relationship from those for the broadband noise. The dose-effect relationship obtained above cannot be explained by the traditional arousal theory [ 1,2], that noise can promote performance under moderate levels of noise and that over-arousal occurs above these levels. The results shown in Figure 2(c) and Figure 3(c) can be interpreted such that the arousal effects of noise may occur above levels higher than 50 dB(A). Arousal effects may play a role in the increase of performance for the reaction time task or decrease of performance for the figure counting task, whereas over-arousal effects of noise may occur above levels of 70 or 75 dB(A) and over-arousal effects appear, respectively, in the decrease in performance for the reaction time task or increase in performance for the figure counting task. Thus arousal by noise does not always indicate a benefit and/or that noise can only make the brain more active [5,7]. Increases or decreases in performance occur in relation to different types of tasks. Such discrepancies in the effects of noise between types of tasks have of course been found in the earlier studies [5, 71.

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This leads to a new model for a dose-effect relationship between noise and performance, which is an N shaped or an inverse N shaped relationship. Such a dose-effect relationship thus seems to yield a type of slow manifold [lo], as shown in Figure 4. In this figure, y

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Figure 4. Estimated curve of the model and data for broadband noise and background noise. 0, Reaction time ; A, figure counting.

T. YOSHIDA

436

and x indicate an index for performance and an index for levels of noise, respectively. Noise effects shown an N shaped curve from XI to x2. Noise above xA causes arousal and noise above xs causes over-arousal. Upper and lower limits would also be needed, because people can only conduct their tasks within some margin of performance. An attempt was made for this model in the cases of the broadband noise and the background noise, using the data of coefficients of variation of the reaction time and the data of effective time of the figure counting task. An equation was then obtained, v=(l/3)x3--

TX,

(T=4, a task parameter),

(4)

where x= (L- 64)/4.5 for the levels of noise (L), and y= -206(P, - 0.188) for the coefficients of variation (P,) in reaction time, or y = (23/4)(P2 - 2.86) for the effective time (P2) of counting figures. In this equation, the signs of each task are switched as plus for the figure counting task and minus for the reaction time task. The results for the broadband noise and the background noise are also shown in Figure 4. The estimated curve from Equation (4) shows a similar tendency to the data for the broadband noise and the background noise (r=0.676 (n = 16)), but some discrepancy is found between them in Figure 4. This model indicates that noise above LA =55 dB(A) (xA = -2) causes arousal and that noise above LB = 73 dB(A) (xs = +2) causes over-arousal for the broadband noise and the background noise. Further research is needed for more complex models including parameters for types of noise or frequency characteristics as well as types of tasks. This is important for the doseeffect relationship between noise and performance. Some researchers have detected a problem related to subjects’ personalities, which were also assessed in this study. Although this factor showed smaller effects than the factor of (Sub) that was employed in this study, such a consideration might also be important. Further research is needed to clarify this problem. ACKNOWLEDGMENTS

The author wishes to acknowledge Y. Osada, M. Yasuoka and S. Yoshizawa for their encouragement in this study. REFERENCES 1. K. D. KRYTER 1985 The Effects of Noise on Man. New York: Academic Press. 2. M. LOEB 1986 Noise and Human Eficiency. Chichester: John Wiley. 3. H. S. KOELEGA,J.-A. BRDJKMANand H. BERGMAN1986 Human Factors 28,465-481. Noise and vigilance, an evaluative review. 4. V. J. GAWRON 1982 Human Factors 24, 225-2430. Performance effects of noise intensity, psychological set, and task type and complexity. 5. T. YOSHIDA1986 Journal of the Acoustical Society of Japan (E) 7,315-323. Effects of noise and vibration on choice reaction time task and paired associates learning task. 6. G. R. J. HOCKEY 1978 in Handbook of Noise Assessment (D. N. May, editor), 335-372. New York: Van Nostrand Reinhold. Effects of noise on human work efficiency. 7. T. YOSHIDAand N. SAKATA1987 Journal ofthe Acoustical Society ofJapan (E) 8, l-l 1. Effects of noise and vibration on learning task and subjective ratings of disturbance. 8. Y. ANDO and H. HATTORI1973 Journal of Sound and Vibration 27, 101-l 10. Statistical studies on the effects of intense noise during human fetal life. 9. T. OKUNO, H. KUME, T. HAGA and T. YOSHIZAWA 1971 Multivariate Analysis. Tokyo: Nikkagiren-Shupansha (in Japanese). 10. R. THOM, E. C. ZEEMAN, S. USHIK~ and T. SAWA 1977 Form and Structure. Tokyo: Misuzu Shobo (in Japanese).